text:
min and modelling reduced insulin sensitivity
   after the non-linearity, compared to <29\% for modelling reduced insulin
   sensitivity before the non-linearity. Scaling the insulin effect (after
   the non-linearity) is a suitable assumption in this model structure.
   These results are preliminary and subject to further and more extensive
   validation of the model's capability to predict the longer term (>2 h)
   blood glucose excursion in critically ill patients. (C) 2009 Elsevier
   Ireland Ltd. All rights reserved.}},
DOI = {{10.1016/j.cmpb.2009.06.004}},
ISSN = {{0169-2607}},
ResearcherID-Numbers = {{Chase, James/B-1584-2012}},
ORCID-Numbers = {{Chase, James/0000-0001-9989-4849}},
Unique-ID = {{ISI:000275897600002}},
}

@article{ ISI:000273764600001,
Author = {Wang, Youqing and Dassau, Eyal and Doyle, III, Francis J.},
Title = {{Closed-Loop Control of Artificial Pancreatic beta-Cell in Type 1
   Diabetes Mellitus Using Model Predictive Iterative Learning Control}},
Journal = {{IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}},
Year = {{2010}},
Volume = {{57}},
Number = {{2}},
Pages = {{211-219}},
Month = {{FEB}},
Abstract = {{A novel combination of iterative learning control (ILC) and model
   predictive control (MPC), referred to here as model predictive iterative
   learning control (MPILC), is proposed for glycemic control in type 1
   diabetes mellitus. MPILC exploits two key factors: frequent glucose
   readings made possible by continuous glucose monitoring technology; and
   the repetitive nature of glucose-meal-insulin dynamics with a 24-h
   cycle. The proposed algorithm can learn from an individual's lifestyle,
   allowing the control performance to be improved from day to day. After
   less than 10 days, the blood glucose concentrations can be kept within a
   range of 90-170 mg/dL. Generally, control performance under MPILC is
   better than that under MPC. The proposed methodology is robust to random
   variations in meal timings within +/-60 min or meal amounts within
   +/-75\% of the nominal value, which validates MPILC's superior
   robustness compared to run-to-run control. Moreover, to further improve
   the algorithm's robustness, an automatic scheme for setpoint update that
   ensures safe convergence is proposed. Furthermore, the proposed method
   does not require user intervention; hence, the algorithm should be of
   particular interest for glycemic control in children and adolescents.}},
DOI = {{10.1109/TBME.2009.2024409}},
ISSN = {{0018-9294}},
Unique-ID = {{ISI:000273764600001}},
}

@article{ ISI:000273772000006,
Author = {Zakharov, P. and Talary, M. S. and Kolm, I. and Caduff, A.},
Title = {{Full-field optical coherence tomography for the rapid estimation of
   epidermal thickness: study of patients with diabetes mellitus type 1}},
Journal = {{PHYSIOLOGICAL MEASUREMENT}},
Year = {{2010}},
Volume = {{31}},
Number = {{2}},
Pages = {{193-205}},
Month = {{FEB}},
Abstract = {{Changes in morphology of the skin are an important factor that can
   affect non-invasive measurements performed through this organ, in
   particular for glucose monitoring in e. g. patients with diabetes
   mellitus. A characterization technique for non-contact in vivo profiling
   of the superficial skin layers can be beneficial for evaluation of the
   performance of such measurement systems. We applied a full-field optical
   coherence tomography (OCT) system followed by the fully automatic
   processing for this task. With the developed procedure, non-invasive
   quantification of the skin morphology can be performed within a few
   minutes. The dorsal skin of the upper arm of 22 patients with Type 1
   Diabetes Mellitus was investigated with an OCT system and with a
   commercially available dermatological laser scanning confocal microscope
   (CM) as a reference method. The estimates of epidermal thickness from
   OCT were compared with the results of expert-assisted analysis of
   confocal images. The highest correlation with the CM measurements has
   been obtained for the distance from the entrance peak to the first
   minimum of the OCT reflection profile ((R) over bar (2) = 0.657, p <
   0.0001). In this specific patient group, we have observed a
   statistically significant correlation of the subjects' body mass index
   with the distance from the entrance peak to the dermal reflection peak
   in the OCT profile (p = 0.010). Furthermore, the same OCT parameter is
   negatively correlated with age with marginal statistical significance (p
   = 0.062). At the same time, no relation of diabetes-related parameters
   (duration of disease and concentration of glycated haemoglobin) to the
   skin morphology observed with the OCT and CM was found.}},
DOI = {{10.1088/0967-3334/31/2/006}},
ISSN = {{0967-3334}},
ResearcherID-Numbers = {{Zakharov, Pavel/A-3537-2009}},
Unique-ID = {{ISI:000273772000006}},
}

@inproceedings{ ISI:000287219100002,
Author = {Karahoca, Adem and Karahoca, Dilek and Kara, Ali},
Book-Group-Author = {{IEEE}},
Title = {{Diagnosis of Diabetes by using Adaptive Neuro Fuzzy Inference Systems}},
Booktitle = {{2009 FIFTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, COMPUTING WITH
   WORDS AND PERCEPTIONS IN SYSTEM ANALYSIS, DECISION AND CONTROL}},
Year = {{2010}},
Pages = {{5-8}},
Note = {{5th International Conference on Soft Computing, Computing with Words and
   Perceptions in System Analysis, Decision and Control, Famagusta, CYPRUS,
   SEP 02-04, 2009}},
Abstract = {{Most of discoveries indicate that the best way to overcome diabetes is
   to prevent the risks of diabetes before becoming a diabetic. With this
   opinion, we would like to find a way to estimate diabetes risk,
   according to some variables such as age, total cholesterol, gender or
   shape of the body. Due to having fuzzy input and output (glucose rate)
   values and because of that dependent variable have more than 2 values
   (unlike binary logic), ANFIS and Multinomial Logistic Regression should
   be executed for comparison. Then the results were benchmarked. As a
   result, in case of that there is a system which contains fuzzy inputs
   and output, ANFIS gives better results than Multinomial Logistic
   Regression for diabetes diagnosis.}},
ISBN = {{978-1-4244-3429-9}},
Unique-ID = {{ISI:000287219100002}},
}

@inproceedings{ ISI:000287964001180,
Author = {Stahl, F. and Johansson, R. and Renard, Eric},
Book-Group-Author = {{IEEE}},
Title = {{Post-prandial Plasma Glucose Prediction in Type I Diabetes Based on
   Impulse Response Models}},
Booktitle = {{2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2010}},
Pages = {{1324-1327}},
Note = {{32nd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBC 10), Buenos Aires,
   ARGENTINA, AUG 30-SEP 04, 2010}},
Organization = {{IEEE Engn Med \& Biol Soc (EMBS)}},
Abstract = {{In this paper the impact of different meals and rapid insulin were
   estimated as Finite Impulse Response Models from a data set of 18
   patients. Based on these models short-term individualized predictors
   were tested for 20 and 60 minute prediction. The predictors were
   evaluated using Clarke Grid Analysis and had on average more than 94 \%
   and 75 \% in the A zone and less than 1 \% and 3 \% in the errorous
   C/D/E zones, which in comparison to other published results is
   competitive.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4124-2}},
ResearcherID-Numbers = {{Johansson, Rolf/C-1845-2013}},
Unique-ID = {{ISI:000287964001180}},
}

@inproceedings{ ISI:000287964005166,
Author = {Poulsen, Jens Ulrik and Avogaro, Angelo and Chauchard, Fabien and
   Cobelli, Claudio and Johansson, Rolf and Nita, Lucian and Pogose, Mike
   and del Re, Luigi and Renard, Eric and Sampath, Sivananthan and Saudek,
   Frantisek and Skillen, Michael and Soendergaard, Jacob},
Book-Group-Author = {{IEEE}},
Title = {{A diabetes management system empowering patients to reach optimised
   glucose control: from monitor to advisor}},
Booktitle = {{2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2010}},
Pages = {{5270-5271}},
Note = {{32nd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBC 10), Buenos Aires,
   ARGENTINA, AUG 30-SEP 04, 2010}},
Organization = {{IEEE Engn Med \& Biol Soc (EMBS)}},
Abstract = {{The DIAdvisor (TM) is an EC/FP7 funded project aiming at the development
   of a Blood Glucose prediction device which uses easily available
   information to optimise the therapy of patients with diabetes.
   The project will achieve its objectives by developing a unique system
   for patients with diabetes to help them manage their disease
   effectively, optimising safety and giving an improved quality of life.
   One main outcome of DIAdvisor will be to reduce the negative effects of
   long-term hyperglycaemia with no increased occurrence of hypoglycaemia.
   A comprehensive clinical database has been established and utilised for
   systems development. In-silico testing shows promising results.
   In parallel a device platform has been developed for running these
   systems. During early 2010 a first prototype has been exposed to
   extensive in-vitro validation and is now undergoing clinical testing.}},
DOI = {{10.1109/IEMBS.2010.5626313}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4124-2}},
Unique-ID = {{ISI:000287964005166}},
}

@inproceedings{ ISI:000287964007029,
Author = {Skevofilakas, Marios and Zarkogianni, Konstantia and Karamanos, Basil G.
   and Nikita, Konstantina S.},
Book-Group-Author = {{IEEE}},
Title = {{A hybrid Decision Support System for the Risk Assessment of retinopathy
   development as a long term complication of Type 1 Diabetes Mellitus}},
Booktitle = {{2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2010}},
Pages = {{6713-6716}},
Note = {{32nd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBC 10), Buenos Aires,
   ARGENTINA, AUG 30-SEP 04, 2010}},
Organization = {{IEEE Engn Med \& Biol Soc (EMBS)}},
Abstract = {{The aim of the present study is to design and develop a Decision Support
   System (DSS) closely coupled with an Electronic Medical Record (EMR),
   able to predict the risk of a Type 1 Diabetes Mellitus (T1DM) patient to
   develop retinopathy. The proposed system is able to store a wealth of
   information regarding the clinical state of the T1DM patient and
   continuously provide the health experts with predictions regarding the
   possible future complications that he may present. The DSS is a hybrid
   infrastructure combining a Feedforward Neural Network (FNN), a
   Classification and Regression Tree (CART) and a Rule Induction C5.0
   classifier, with an improved Hybrid Wavelet Neural Network (iHWNN). A
   voting mechanism is utilized to merge the results from the four
   classification models. The proposed DSS has been trained and evaluated
   using data from 55 T1DM patients, acquired by the Athens Hippokration
   Hospital in close collaboration with the EURODIAB research team. The DSS
   has shown an excellent performance resulting in an accuracy of 98\%.
   Care has been taken to design and implement a consistent and
   continuously evolving Information Technology (IT) system by utilizing
   technologies such as smart agents periodically triggered to retrain the
   DSS with new cases added in the data repository.}},
DOI = {{10.1109/IEMBS.2010.5626245}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4124-2}},
Unique-ID = {{ISI:000287964007029}},
}

@inproceedings{ ISI:000307744700018,
Author = {Chemlal, Salim and Colberg, Sheri and Satin-Smith, Marta and Gyuricsko,
   Eric and Hubbard, Tom and Scerbo, Mark W. and McKenzie, Frederic D.},
Editor = {{Herold, KE and Bentley, WE and Vossoughi, J}},
Title = {{HealthiManage: An Individualized Prediction Algorithm for Type 2
   Diabetes Chronic Disease Control}},
Booktitle = {{26TH SOUTHERN BIOMEDICAL ENGINEERING CONFERENCE: SBEC 2010}},
Series = {{IFMBE Proceedings}},
Year = {{2010}},
Volume = {{32}},
Pages = {{67-70}},
Note = {{26th Southern Biomedical Engineering Conference, SBEC 2010, Univ
   Maryland, College Park, MD, APR 30-MAY 02, 2010}},
Organization = {{Natl Sci Fdn; Biomedical Engn Soc; Natl Canc Inst}},
Abstract = {{This paper describes a prediction algorithm for blood glucose in Type 2
   diabetes. An iPhone application was developed that allows patients to
   record their daily blood glucose levels and provide them with relevant
   feedback using the prediction algorithm to help control their blood
   glucose levels. Several methods using theoretical functions were tested
   to select the most accurate prediction method. The prediction is
   adjusted with each glucose reading input by the patient taking into
   consideration the time of the glucose reading and the time after the
   patient's last meal, as well as any physical activity. The
   individualized prediction algorithm was tested and verified with real
   patient data and also validated using a non-parametric regression
   method. The accuracy of prediction results varied from different
   approaches and was adequate for most of the methods tested. The
   predicted results merged closer to the patients' actual glucose readings
   after each additional input reading. The findings of the research were
   encouraging and the predictive system provided what we believe to be
   helpful feedback to control, improve, and take proactive measures to
   regulate blood glucose levels.}},
ISSN = {{1680-0737}},
ISBN = {{978-3-642-14997-9}},
Unique-ID = {{ISI:000307744700018}},
}

@inproceedings{ ISI:000279621600012,
Author = {Zhou, Fuchao and Yang, Hen-I and Alamo, Jose M. Reyes and Wong, Johnny
   S. and Chang, Carl K.},
Editor = {{Lee, Y and Bien, ZZ and Mokhtari, M and Kim, JT and Kim, J and Lee, H and Khalil, I}},
Title = {{Mobile Personal Health Care System for Patients with Diabetes}},
Booktitle = {{AGING FRIENDLY TECHNOLOGY FOR HEALTH AND INDEPENDENCE}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2010}},
Volume = {{6159}},
Pages = {{94-101}},
Note = {{8th International Conference on Smart Homes and Health Telematics,
   Seoul, SOUTH KOREA, JUN 22-24, 2010}},
Organization = {{Korean Gerontol Soc; Kyung Hee Univ, Ctr Sustainable Healthy Bldg;
   Yonsei Univ, Inst Millennium Environm Design \& Res; Electr Times; Seoul
   Welfare Fdn; Inst TELECOM; Ulsan Natl Inst Sci \& Technol; Natl Rehabil
   Ctr}},
Abstract = {{In this paper, we propose a personal diabetes monitoring system which
   integrates wearable sensors, 3G mobile phone, smart home technologies
   and Google Health to facilitate the management of chronic disease -
   diabetes. Our system utilizes wearable sensors and 3G cellular phone to
   automatically collect physical signs, such as blood glucose level and
   blood pressure. It allows users, especially seniors with diabetes, to
   conveniently record daily test results and track long term health
   condition changes regardless of their locations. It does so without
   having to ask users to manually input them into the system. Our system
   also utilizes Google Health to manage Personal Health Records (PHRs),
   which not only bridges the gaps between patients and different health
   care providers but enabling accesses to patients' PHRs anywhere and
   anytime by taking advantage of the universal accessibility of Google
   Health.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-13777-8}},
ResearcherID-Numbers = {{Meyer, Michelle/B-4019-2012}},
Unique-ID = {{ISI:000279621600012}},
}

@inproceedings{ ISI:000284032900087,
Author = {Valdez, Andre Calero and Ziefle, Martina and Alagoez, Firat and
   Holzinger, Andreas},
Editor = {{Miesenberger, K and Klaus, J and Zagler, W and Karshmer, A}},
Title = {{Mental Models of Menu Structures in Diabetes Assistants}},
Booktitle = {{COMPUTERS HELPING PEOPLE WITH SPECIAL NEEDS, PROCEEDINGS, PT 2}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2010}},
Volume = {{6180}},
Number = {{II}},
Pages = {{584-591}},
Note = {{12th International Conference, ICCHP 2010, Vienna, AUSTRIA, JUL 14-16,
   2010}},
Abstract = {{Demographic change in regard to an aging population with an increasing
   amount of diabetes patients will put a strain on health care rentability
   in all modern societies. Electronic living assistants for diabetes
   patients might help lift the burden on taxpayers, if they are usable for
   the heterogeneous user group. Research has shown that correct mental
   models of device menu structures might help users in handling electronic
   devices. This exploratory study investigates construction and
   facilitation of spatial mental models for a menu structure of a diabetes
   living assistant and relates them to performance in usage of a device.
   Furthemore impact of age, domain knowledge and technical expertise on
   complexity and quality of the mental model are evaluated. Results
   indicate that even having a simplified spatial representation of the
   menu structure increases navigation performance. Interestingly not the
   overall correctness of the model was important for task success but
   rather the amount of route knowledge within the model.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-14099-0}},
ResearcherID-Numbers = {{Holzinger, Andreas/E-9530-2010
   Calero Valdez, Andre/E-8359-2012}},
ORCID-Numbers = {{Holzinger, Andreas/0000-0002-6786-5194
   Calero Valdez, Andre/0000-0002-6214-1461}},
Unique-ID = {{ISI:000284032900087}},
}

@inproceedings{ ISI:000280516800006,
Author = {Guo, Dongmin and Zhang, David and Li, Naimin and Zhang, Lei and Yang,
   Jianhua},
Editor = {{Zhang, D and Sonka, M}},
Title = {{Diabetes Identification and Classification by Means of a Breath Analysis
   System}},
Booktitle = {{MEDICAL BIOMETRICS, PROCEEDINGS}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2010}},
Volume = {{6165}},
Pages = {{52-63}},
Note = {{2nd International Conference on Medical Biometrics, Hong Kong, PEOPLES R
   CHINA, JUN 28-30, 2010}},
Organization = {{Int Assoc Pattern Recognit; IEEE Computat Intelligence Soc; Natl Nat Sci
   Fdn China; Springer}},
Abstract = {{This article proposes a breath analysis system that makes use of
   chemical sensors to detect acetone in human breath, and hence detect the
   diabetes and measure the blood glucose levels of diabetics. We captured
   the breath samples from healthy persons and patients known to be
   afflicted with diabetes and conducted experiments on disease
   identification and simultaneous blood glucose measurement. SVM
   classifier was used to identify diabetes from healthy samples and three
   models were built to fit the curves that can represent the blood glucose
   levels. The results show that the system is not only able to distinguish
   between breath samples from patients with diabetes and healthy subjects,
   but also to represent the fluctuation of blood sugar of diabetics and
   therefore to be an evaluation tool for monitoring the blood glucose of
   diabetes.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-13922-2}},
Unique-ID = {{ISI:000280516800006}},
}

@article{ ISI:000281995800009,
Author = {Chandra, A. and Agarwal, R. L. and Bajaj, S. and Agarwal, A.},
Title = {{Clinicoradiological study of tuberculosis in diabetes mellitus and its
   treatment outcome under DOTS}},
Journal = {{NATIONAL ACADEMY SCIENCE LETTERS-INDIA}},
Year = {{2010}},
Volume = {{33}},
Number = {{7-8}},
Pages = {{255-258}},
Abstract = {{Diabetes mellitus has been reported to modify the presenting features of
   pulmonary tuberculosis and its treatment outcome. In all 56 patients of
   pulmonary tuberculosis, 19 with diabetes mellitus visiting DOTS centre
   under RNTCP were taken in study at Department of Pulmonary Medicine,
   M.L.N. Medical college, Allahabad for assessment of comparison of
   outcome of treatment.}},
ISSN = {{0250-541X}},
Unique-ID = {{ISI:000281995800009}},
}

@inproceedings{ ISI:000289185200035,
Author = {Baskaran, Abhirami and Karthikeyan, Dhivya and Swamy, Anusha T.},
Editor = {{Deb, K and Bhattacharya, A and Chakraborti, N and Chakroborty, P and Das, S and Dutta, J and Gupta, SK and Jain, A and Aggarwal, V and Branke, J and Louis, SJ and Tan, KC}},
Title = {{Modeling and Automation of Diagnosis and Treatment of Diabetes}},
Booktitle = {{SIMULATED EVOLUTION AND LEARNING}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2010}},
Volume = {{6457}},
Pages = {{339-348}},
Note = {{8th International Conference of Simulated Evolution and Learning, Indian
   Inst Technol Kanpur, Kanpur, INDIA, DEC 01-04, 2010}},
Organization = {{Golden Jubilee Comm; IIT Kanpur; Dept Sci \& Technol New Delhi; Council
   Sci \& Ind Res (CSIR)}},
Abstract = {{The present. work aims at designing and implementing an automated
   decision making system for the treatment of diabetes. The automated
   medical tool has been equipped to handle the decisions regarding the
   care plan of the patient and also helps in diagnosis. It takes in
   essential parameters like glucose, cholesterol, blood pressure and
   devises a care plan for the patient. Fuzzy logic was used to implement
   the medical decision support system. A knowledge base for diabetes
   containing the essential concepts, treatment algorithms was created. The
   fuzzy logic based system used the knowledge base for constructing the
   collection of rules. The essential parameters from the patient database
   were provided as input and the decisions like the type of diabetes, diet
   plans, medication etc were recorded. The tool also takes the decisions
   and the parameters that led to the decisions to build an optimal care
   pathway for the patient.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-17297-7}},
Unique-ID = {{ISI:000289185200035}},
}

@inproceedings{ ISI:000283095800014,
Author = {Hadar, Eran and Hod, Moshe},
Editor = {{Creatsas, G and Mastorakos, G}},
Title = {{Establishing consensus criteria for the diagnosis of diabetes in
   pregnancy following the HAPO study}},
Booktitle = {{WOMEN'S HEALTH AND DISEASE}},
Series = {{Annals of the New York Academy of Sciences}},
Year = {{2010}},
Volume = {{1205}},
Pages = {{88-93}},
Note = {{7th Athens Conference on Women's Health and Disease: Gynecologic,
   Endocrine, and Reproductive Issues, Athens, GREECE, SEP 11-13, 2008}},
Abstract = {{The current diagnostic criteria for gestational diabetes mellitus are
   controversial because they lack correlation to maternal and perinatal
   outcome. The results of the hyperglycemia and adverse pregnancy outcome
   (HAPO) study demonstrate a linear association between increasing levels
   of fasting, 1- and 2-h plasma glucose post a 75 g oral glucose tolerance
   test to several significant outcome endpoints, such as birth weight
   above the 90th percentile, cord blood serum C-peptide level above the
   90th percentile, primary cesarean delivery, clinical neonatal
   hypoglycemia, premature delivery, shoulder dystocia or birth injury,
   intensive neonatal care admission, hyperbilirubinemia, and preeclampsia.
   A consensus report by the IADPSG, based on a vigorous assessment of the
   HAPO results and other studies, recommended an endorsement of
   risk-based, internationally accepted criteria for the diagnosis and
   classification of diabetes in pregnancy. This review follows the steps
   from defining the problem to the endpoint of achieving a worldwide
   policy change.}},
DOI = {{10.1111/j.1749-6632.2010.05671.x}},
ISSN = {{0077-8923}},
ISBN = {{978-1-57331-768-9}},
Unique-ID = {{ISI:000283095800014}},
}

@article{ ISI:000272004600019,
Author = {Ke, Yazi D. and Delerue, Fabien and Gladbach, Amadeus and Goetz, Juergen
   and Ittner, Lars M.},
Title = {{Experimental Diabetes Mellitus Exacerbates Tau Pathology in a Transgenic
   Mouse Model of Alzheimer's Disease}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{11}},
Month = {{NOV 19}},
Abstract = {{Diabetes mellitus (DM) is characterized by hyperglycemia caused by a
   lack of insulin, insulin resistance, or both. There is increasing
   evidence that insulin also plays a role in Alzheimer's disease (AD) as
   it is involved in the metabolism of beta-amyloid (A beta) and tau, two
   proteins that form A beta plaques and neurofibrillary tangles (NFTs),
   respectively, the hallmark lesions in AD. Here, we examined the effects
   of experimental DM on a pre-existing tau pathology in the pR5 transgenic
   mouse strain that is characterized by NFTs. pR5 mice express P301L
   mutant human tau that is associated with dementia. Experimental DM was
   induced by administration of streptozotocin (STZ), which causes insulin
   deficiency. We determined phosphorylation of tau, using
   immunohistochemistry and Western blotting. Solubility of tau was
   determined upon extraction with sarkosyl and formic acid, and Gallyas
   silver staining was employed to reveal NFTs. Insulin depletion by STZ
   administration in six months-old non-transgenic mice causes increased
   tau phosphorylation, without its deposition or NFT formation. In
   contrast, in pR5 mice this results in massive deposition of
   hyperphosphorylated, insoluble tau. Furthermore, they develop a
   pronounced tau-histopathology, including NFTs at this early age, while
   the pathology in sham-treated pR5 mice is moderate. Whereas experimental
   DM did not result in deposition of hyperphosphorylated tau in
   non-transgenic mice, a predisposition to develop a tau pathology in
   young pR5 mice was both sufficient and necessary to exacerbate tau
   deposition and NFT formation. Hence, DM can accelerate onset and
   increase severity of disease in individuals with a predisposition to
   developing tau pathology.}},
DOI = {{10.1371/journal.pone.0007917}},
Article-Number = {{e7917}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Gotz, Jurgen/C-2448-2014
   }},
ORCID-Numbers = {{Gotz, Jurgen/0000-0001-8501-7896
   Ittner, Lars/0000-0001-6738-3825}},
Unique-ID = {{ISI:000272004600019}},
}

@article{ ISI:000270624600025,
Author = {Li, Jialiang and Xia, Yingcun and Palta, Mari and Shankar, Anoop},
Title = {{Impact of unknown covariance structures in semiparametric models for
   longitudinal data: An application to Wisconsin diabetes data}},
Journal = {{COMPUTATIONAL STATISTICS \& DATA ANALYSIS}},
Year = {{2009}},
Volume = {{53}},
Number = {{12}},
Pages = {{4186-4197}},
Month = {{OCT 1}},
Abstract = {{Semiparametric models are becoming increasingly attractive for
   longitudinal data analysis. often there is lack of knowledge of the
   covariance structure of the response variable. Although it is still
   possible to obtain consistent estimators for both parametric and
   nonparametric components of a semipatrametric model by assuming an
   identity structure for the covariance matrix, the resulting estimators
   may not be efficient. We conducted extensive simulation studies to
   investigate the impact of an unknown covariance structure on estimators
   in semiparametric models for longitudinal data. In some situations the
   loss of efficiency could be substantial. A two-step estimator is thus
   proposed to improve the efficiency. Our study was motivated by a
   population based data analysis to examine the temporal relationship
   between systolic blood pressure and urinary albumin excretion. (C) 2009
   Elsevier B.V. All rights reserved.}},
DOI = {{10.1016/j.csda.2009.05.008}},
ISSN = {{0167-9473}},
ResearcherID-Numbers = {{Li, Jialiang/B-9132-2014}},
ORCID-Numbers = {{Li, Jialiang/0000-0002-9704-4135}},
Unique-ID = {{ISI:000270624600025}},
}

@article{ ISI:000270354100008,
Author = {Boesgaard, Trine Wellov and Gjesing, Anette Prior and Grarup, Niels and
   Rutanen, Jarno and Jansson, Per-Anders and Hribal, Marta Letizia and
   Sesti, Giorgio and Fritsche, Andreas and Stefan, Norbert and Staiger,
   Harald and Haering, Hans and Smith, Ulf and Laakso, Markku and Pedersen,
   Oluf and Hansen, Torben and EUGENE2 Consortium},
Title = {{Variant near ADAMTS9 Known to Associate with Type 2 Diabetes Is Related
   to Insulin Resistance in Offspring of Type 2 Diabetes Patients-EUGENE2
   Study}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{9}},
Month = {{SEP 30}},
Abstract = {{Backround: A meta-analysis combining results from three genome-wide
   association studies and followed by large-scale replication identified
   six novel type 2 diabetes loci. Subsequent studies of the effect of
   these variants on estimates of the beta-cell function and insulin
   sensitivity have been inconclusive. We examined these variants located
   in or near the JAZF1 (rs864745), THADA (rs7578597), TSPAN8 (rs7961581),
   ADAMTS9 (rs4607103), NOTCH2 (rs10923931) and the CDC123/CAMK1D
   (rs12779790) genes for associations with measures of pancreatic
   beta-cell function and insulin sensitivity.
   Methodology/Results: Oral and intravenous glucose stimulated insulin
   release (n=849) and insulin sensitivity (n=596) estimated from a
   hyperinsulinemic euglycemic clamp were measured in non-diabetic
   offspring of type 2 diabetic patients from five European populations.
   Assuming an additive genetic model the diabetes-associated major
   C-allele of rs4607103 near ADAMTS9 associated with reduced
   insulin-stimulated glucose uptake (p=0.002) during a hyperinsulinemic
   euglycemic clamp. However, following intravenous and oral administration
   of glucose serum insulin release was increased in individuals with the
   C-allele (p=0.003 and p=0.01, respectively). A meta-analyse combining
   clamp and IVGTT data from a total of 905 non-diabetic individuals showed
   that the C-risk allele associated with decreased insulin sensitivity
   (p=0.003) and increased insulin release (p=0.002). The major T-allele of
   the intronic JAZF1 rs864745 conferring increased diabetes risk was
   associated with increased 2(nd) phase serum insulin release during an
   IVGTT (p=0.03), and an increased fasting serum insulin level (p=0.001).
   The remaining variants did not show any associations with insulin
   response, insulin sensitivity or any other measured quantitative traits.
   Conclusion: The present studies suggest that the diabetogenic impact of
   the C-allele of rs4607103 near ADAMTS9 may in part be mediated through
   decreased insulin sensitivity of peripheral tissues.}},
DOI = {{10.1371/journal.pone.0007236}},
Article-Number = {{e7236}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Sesti, Giorgio/B-1509-2012
   Grarup, Niels/K-2807-2015
   }},
ORCID-Numbers = {{Grarup, Niels/0000-0001-5526-1070
   Sesti, Giorgio/0000-0002-1618-7688}},
Unique-ID = {{ISI:000270354100008}},
}

@article{ ISI:000269215400010,
Author = {Kusuma, K. S. and Vasudha, K. C. and Vanitha, Gowda M. N.},
Title = {{A comparative study of serum myeloperoxidase activity in type 2 diabetes
   and diabetic nephropathy}},
Journal = {{BIOMEDICAL RESEARCH-INDIA}},
Year = {{2009}},
Volume = {{20}},
Number = {{3}},
Pages = {{198-204}},
Month = {{SEP-DEC}},
Abstract = {{Inflammatory burden is high in End Stage Renal Disease (ESRD) patients.
   In Diabetic ESRD patients, the balance between pro- and anti-oxidant
   activities is shifted towards an oxidative stress. Myeloperoxidase
   (MPO), a bactericidal enzyme plays an active role in induction and
   evolution of the endothelial dysfunction associated with Type 2 Diabetes
   Mellitus (DM). However, whether MPO can serve as a marker in diabetic
   ESRD patients is doubtful. Hence, the present study was undertaken. The
   Fasting Blood Glucose (FBG), Serum Creatinine, Blood Urea Nitrogen (BUN)
   and Serum Myeloperoxidase levels were estimated in three different
   groups with 30 subjects each. The Control group includes healthy
   individuals, group I constitutes Type 2 diabetes with no nephropathy and
   group II includes Type 2 diabetes with nephropathy patients. Glomerular
   Filtration Rate (GFR) was calculated using Cockcroft Gault formula
   corrected for the Body Surface Area. Statistical software, namely, SPSS
   15.0, Stata 8.0, MedCalc 9.0.1 and Systat 11.0 were used for the
   analysis of the data and Microsoft word and Excel have been used to
   generate graphs, tables etc. Reference Ranges for MPO were established
   in our study. An increase in MPO levels was noticed in Group I, whereas
   a differential behaviour of MPO levels was noted in group II. Uremic
   diabetic nephropathy patients with a low MPO level may be at a lesser
   risk for any cardiac event compared to those uremic patients with high
   MPO levels. Hence, MPO may be taken as a biomarker to predict coronary
   events in diabetic ESRD.}},
DOI = {{10.4103/0970-938X.54857}},
ISSN = {{0970-938X}},
Unique-ID = {{ISI:000269215400010}},
}

@article{ ISI:000268955400009,
Author = {Coll, Carmen and Herrero, Alicia and Sanchez, Elena and Thome, Nestor},
Title = {{A dynamic model for a study of diabetes}},
Journal = {{MATHEMATICAL AND COMPUTER MODELLING}},
Year = {{2009}},
Volume = {{50}},
Number = {{5-6}},
Pages = {{713-716}},
Month = {{SEP}},
Abstract = {{In this paper a dynamic model for diabetes prevalence in a country is
   analyzed. The goal of this study is to estimate the prevalence of
   diabetes in a hypothetical community. The model is formulated using a
   difference-algebraic equation and then singular system theory is used to
   obtain the solution. (C) 2009 Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.mcm.2008.12.027}},
ISSN = {{0895-7177}},
ResearcherID-Numbers = {{Thome, Nestor/H-4214-2015}},
ORCID-Numbers = {{Thome, Nestor/0000-0001-5328-6637}},
Unique-ID = {{ISI:000268955400009}},
}

@article{ ISI:000267743300009,
Author = {Najarro Dearo de Morais, Juliana Aparecida and Trindade-Suedam, Ivy
   Kiemle and Pepato, Maria Teresa and Marcantonio, Jr., Elcio and Wenzel,
   Ann and Scaf, Gulnara},
Title = {{Effect of diabetes mellitus and insulin therapy on bone density around
   osseointegrated dental implants: a digital subtraction radiography study
   in rats}},
Journal = {{CLINICAL ORAL IMPLANTS RESEARCH}},
Year = {{2009}},
Volume = {{20}},
Number = {{8}},
Pages = {{796-801}},
Month = {{AUG}},
Abstract = {{Objectives
   To evaluate the effect of diabetes mellitus (DM) and insulin therapy on
   bone density around osseointegrated dental implants by digital
   subtraction radiography (DSR).
   Material and methods
   Forty implants were placed in tibiae of 40 adult rats. After a healing
   period of 2 months, the animals were divided into four groups of 10
   animals each: a 2-month control group, sacrificed at time (A), a
   diabetic group (D), an insulin-treated group (I) and a 4-month control
   group (C). During 2 months, group I received subcutaneous doses of
   insulin, whereas groups C and D received only saline. The animals in
   groups D, I and C were thereafter sacrificed. The glucose plasma levels
   (GPLs) were monitored throughout the experiment. Film radiographs were
   taken at implant surgery and on the day of sacrifice. The radiographs
   were digitized, and bone density in regions of osseointegration (OR)
   around the implants was evaluated by quantitative DSR between baseline
   and final images. Differences in shades of gray among the groups were
   assessed using ANOVA.
   Results
   GPLs were within normal range for groups A, C and I and higher for group
   D. There was a significant difference in mean gray shade values in the
   OR of subtraction images between groups D (122 +/- 7) and I (136 +/- 5)
   (P < 0.05) while there were no significant differences between control
   groups A (128 +/- 13) and C (134 +/- 10) and the insulin group I.
   Conclusions
   DM impaired bone density around osseointegrated dental implant. Further,
   insulin therapy maintained bone density in diabetic rats.
   To cite this article:de Morais JAND, Trindade-Suedam IK, Pepato MT,
   Marcantonio E Jr, Wenzel A, Scaf G. Effect of diabetes mellitus and
   insulin therapy on bone density around osseointegrated dental implants:
   a digital subtraction radiography study in rats.Clin. Oral Impl. Res.
   20, 2009; 796-801.doi: 10.1111/j.1600-0501.2009.01716.x.}},
DOI = {{10.1111/j.1600-0501.2009.01716.x}},
ISSN = {{0905-7161}},
ResearcherID-Numbers = {{Trindade-Suedam, Ivy/F-3034-2012
   Marcantonio , Elcio/A-4784-2008}},
ORCID-Numbers = {{Marcantonio , Elcio/0000-0003-1294-2305}},
Unique-ID = {{ISI:000267743300009}},
}

@article{ ISI:000268099800002,
Author = {Marling, Cindy and Shubrook, Jay and Schwartz, Frank},
Title = {{TOWARD CASE-BASED REASONING FOR DIABETES MANAGEMENT: A PRELIMINARY
   CLINICAL STUDY AND DECISION SUPPORT SYSTEM PROTOTYPE}},
Journal = {{COMPUTATIONAL INTELLIGENCE}},
Year = {{2009}},
Volume = {{25}},
Number = {{3}},
Pages = {{165-179}},
Month = {{AUG}},
Abstract = {{This paper presents a case-based decision support system prototype to
   assist patients with Type 1 diabetes on insulin pump therapy. These
   patients must vigilantly maintain blood glucose levels within prescribed
   target ranges to prevent serious disease complications, including
   blindness, neuropathy, and heart failure. Case-based reasoning (CBR) was
   selected for this domain because (a) existing guidelines for managing
   diabetes are general and must be tailored to individual patient needs;
   (b) physical and lifestyle factors combine to influence blood glucose
   levels; and (c) CBR has been successfully applied to the management of
   other long-term medical conditions. An institutional review board (IRB)
   approved preliminary clinical study, involving 20 patients, was
   conducted to assess the feasibility of providing case-based decision
   support for these patients. Fifty cases were compiled in a case library,
   situation assessment routines were encoded to detect common problems in
   blood glucose control, and retrieval metrics were developed to find the
   most relevant past cases for solving current problems. Preliminary
   results encourage continued research and work toward development of a
   practical tool for patients.}},
ISSN = {{0824-7935}},
Unique-ID = {{ISI:000268099800002}},
}

@article{ ISI:000267995500003,
Author = {Shea, Steven and Weinstock, Ruth S. and Teresi, Jeanne A. and Palmas,
   Walter and Starren, Justin and Cimino, James J. and Lai, Albert M. and
   Field, Lesley and Morin, Philip C. and Goland, Robin and Izquierdo,
   Roberto E. and Ebner, Susana and Silver, Stephanie and Petkova, Eva and
   Kong, Jian and Eimicke, Joseph P. and IDEATel Consortium},
Title = {{A Randomized Trial Comparing Telemedicine Case Management with Usual
   Care in Older, Ethnically Diverse, Medically Underserved Patients with
   Diabetes Mellitus: 5 Year Results of the IDEATel Study}},
Journal = {{JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION}},
Year = {{2009}},
Volume = {{16}},
Number = {{4}},
Pages = {{446-456}},
Month = {{JUL-AUG}},
Abstract = {{Context: Telemedicine is a promising but largely unproven technology for
   providing case management services to patients with chronic conditions
   and lower access to care.
   Objectives: To examine the effectiveness of a telemedicine intervention
   to achieve clinical management goals in older, ethnically diverse,
   medically underserved patients with diabetes.
   Design, Setting, and Patients: A randomized controlled trial was
   conducted, comparing telemedicine case management to usual care, with
   blinded outcome evaluation, in 1,665 Medicare recipients with diabetes,
   aged 55 years, residing in federally designated medically underserved
   areas of New York State.
   Interventions: Home telemedicine unit with nurse case management versus
   usual care.
   Main Outcome Measures: The primary endpoints assessed over 5 years of
   follow-up were hemoglobin A1c (HgbA1c), low density lipoprotein (LDL)
   cholesterol, and blood pressure levels.
   Results: Intention-to-treat mixed models showed that telemedicine
   achieved net overall reductions over five years of follow-up in the
   primary endpoints (HgbA1c, p = 0.001; LDL, p < 0.001; systolic and
   diastolic blood pressure, p = 0.024; p < 0.001). Estimated differences
   (95\% CI) in year 5 were 0.29 (0.12, 0.46)\% for HgbA1c, 3.84 (-0.08,
   7.77) mg/dL for LDL cholesterol, and 4.32 (1.93, 6.72) mm Hg for
   systolic and 2.64 (1.53, 3,74) mm Hg for diastolic blood pressure. There
   were 176 deaths in the intervention group and 169 in the usual care
   group (hazard ratio 1.01 {[}0.82, 1.24]).
   Conclusions: Telemedicine case management resulted in net improvements
   in HgbA1c, LDL-cholesterol and blood pressure levels over 5 years in
   medically underserved Medicare beneficiaries. Mortality was not
   different between the groups, although power was limited.}},
DOI = {{10.1197/jamia.M3157}},
ISSN = {{1067-5027}},
ResearcherID-Numbers = {{Lai, Albert/E-3453-2011
   }},
ORCID-Numbers = {{Cimino, James/0000-0003-4101-1622
   Lai, Albert/0000-0002-9241-2656}},
Unique-ID = {{ISI:000267995500003}},
}

@article{ ISI:000267572200012,
Author = {Clar, Christine and Royle, Pamela and Waugh, Norman},
Title = {{Adding Pioglitazone to Insulin Containing Regimens in Type 2 Diabetes:
   Systematic Review and Meta-Analysis}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{7}},
Month = {{JUL 1}},
Abstract = {{Background: Type 2 diabetes is treated in a stepwise manner, progressing
   from diet and physical activity to oral antidiabetic agents and insulin.
   The oral agent pioglitazone is licensed for use with insulin when
   metformin is contraindicated or not tolerated. This systematic review
   and meta-analysis investigates the extent to which adding pioglitazone
   to insulin-containing regimens produces benefits in terms of
   patient-relevant outcomes.
   Methodology/Principal Findings: Medline, Embase, and the Cochrane
   Library were searched for randomised controlled trials comparing
   pioglitazone in combination with any insulin-containing regimen in
   comparison with the same insulin regimen alone in patients with type 2
   diabetes. Outcomes investigated included HbA1c, hypoglycaemia, weight,
   and adverse events. Studies were selected, assessed and summarised
   according to standard systematic review methodology and in a
   meta-analysis. We included eight trials that examined the benefits of
   adding pioglitazone to an insulin regimen and studied a total of 3092
   patients with type 2 diabetes. All studies included patients with
   previously inadequate glucose control. Trial duration was between 12
   weeks and 34.5 months. The trials used pioglitazone doses of up to 45
   mg/day. In our meta-analysis, the mean reduction in HbA1c was 0.58\%
   (95\% CI: -0.70, -0.46, p<0.00001). Hypoglycaemic episodes were slightly
   more frequent in the pioglitazone arms ( relative risk 1.27; 95\% CI:
   0.99, 1.63, p = 0.06). Where reported, HDL-cholesterol tended to be
   increased with pioglitazone. Patients on pioglitazone tended to gain
   more weight than those who were not, with an average difference of
   almost 3 kg. Peripheral oedema was more frequent in the pioglitazone
   groups. None of the studies reported on fractures in women, and data on
   cardiovascular events were inconclusive, with most studies being too
   short or too small to assess these long-term outcomes.
   Conclusions/Significance: When added to insulin regimens, pioglitazone
   confers a small advantage in terms of HbA1c in type 2 diabetes patients
   with previous inadequate glucose control, but at the cost of increased
   hypoglycaemia and weight gain. Other considerations include the risk of
   heart failure, fractures in women, a reduced insulin dose, and the net
   financial cost.}},
DOI = {{10.1371/journal.pone.0006112}},
Article-Number = {{e6112}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000267572200012}},
}

@article{ ISI:000266432700046,
Author = {Hecht, Gil and Eventov-Friedman, Smadar and Rosen, Chava and Shezen,
   Elias and Tchorsh, Dalit and Aronovich, Anna and Freud, Enrique and
   Golan, Hana and El-Hasid, Ronit and Katchman, Helena and Hering,
   Bernhard J. and Zung, Amnon and Kra-Oz, Zipi and Shaked-Mishan, Pninit
   and Yusim, Alex and Shtabsky, Alex and Idelevitch, Pavel and Tobar, Ana
   and Harmelin, Alon and Bachar-Lustig, Esther and Reisner, Yair},
Title = {{Embryonic pig pancreatic tissue for the treatment of diabetes in a
   nonhuman primate model}},
Journal = {{PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA}},
Year = {{2009}},
Volume = {{106}},
Number = {{21}},
Pages = {{8659-8664}},
Month = {{MAY 26}},
Abstract = {{Xenotransplantation of pig tissues has great potential to overcome the
   shortage of organ donors. One approach to address the vigorous immune
   rejection associated with xenotransplants is the use of embryonic
   precursor tissue, which induces and utilizes host vasculature upon its
   growth and development. Recently, we showed in mice that embryonic pig
   pancreatic tissue from embryonic day 42 (E42) exhibits optimal
   properties as a beta cell replacement therapy. We now demonstrate the
   proof of concept in 2 diabetic Cynomolgus monkeys, followed for 393 and
   280 days, respectively. A marked reduction of exogenous insulin
   requirement was noted by the fourth month after transplantation,
   reaching complete independence from exogenous insulin during the fifth
   month after transplantation, with full physiological control of blood
   glucose levels. The porcine origin of insulin was documented by a
   radioimmunoassay specific for porcine C-peptide. Furthermore, the
   growing tissue was found to be predominantly vascularized with host
   blood vessels, thereby evading hyperacute or acute rejection, which
   could potentially be mediated by preexisting anti-pig antibodies.
   Durable graft protection was achieved, and most of the late
   complications could be attributed to the immunosuppressive protocol.
   While fine tuning of immune suppression, tissue dose, and implantation
   techniques are still required, our results demonstrate that porcine E-42
   embryonic pancreatic tissue can normalize blood glucose levels in
   primates. Its long-term proliferative capacity, its revascularization by
   host endothelium, and its reduced immunogenicity, strongly suggest that
   this approach could offer an attractive replacement therapy for
   diabetes.}},
DOI = {{10.1073/pnas.0812253106}},
ISSN = {{0027-8424}},
ORCID-Numbers = {{Shtabsky, Alexander/0000-0003-1886-4950}},
Unique-ID = {{ISI:000266432700046}},
}

@article{ ISI:000266331000014,
Author = {Uusitupa, Matti and Peltonen, Markku and Lindstrom, Jaana and Aunola,
   Sirkka and Ilanne-Parikka, Pirjo and Keinanen-Kiukaanniemi, Sirkka and
   Valle, Timo T. and Eriksson, Johan G. and Tuomilehto, Jaakko and Finnish
   Diabet Prevention Study},
Title = {{Ten-Year Mortality and Cardiovascular Morbidity in the Finnish Diabetes
   Prevention Study-Secondary Analysis of the Randomized Trial}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{5}},
Month = {{MAY 21}},
Abstract = {{Background: The Finnish Diabetes Prevention Study (DPS) was a randomized
   controlled trial, which showed that it is possible to prevent type 2
   diabetes by lifestyle changes. The aim of the present study was to
   examine whether the lifestyle intervention had an effect on the ten-year
   mortality and cardiovascular morbidity in the DPS participants
   originally randomized either into an intervention or control group.
   Furthermore, we compared these results with a population-based cohort
   comprising individuals of varying glucose tolerance states.
   Methods and Findings: Middle-aged, overweight people with IGT (n=522)
   were randomized into intensive intervention (including physical
   activity, weight reduction and dietary counseling), or control
   ``mini-intervention{''} group. Median length of the intervention period
   was 4 years and the mean follow-up was 10.6 years. The population-based
   reference study cohort included 1881 individuals (1570 with normal
   glucose tolerance, 183 with IGT, 59 with screen-detected type 2
   diabetes, 69 with previously known type 2 diabetes) with the mean
   follow-up of 13.8 years. Mortality and cardiovascular morbidity data
   were collected from the national Hospital Discharge Register and Causes
   of Death Register. Among the DPS participants who consented for register
   linkage (n=505), total mortality (2.2 vs. 3.8 per 1000 person years,
   hazard ratio HR=0.57, 95\% CI 0.21-1.58) and cardiovascular morbidity
   (22.9 vs. 22.0 per 1000 person years, HR=1.04, 95\% CI 0.72-1.51) did
   not differ significantly between the intervention and control groups.
   Compared with the population-based cohort with impaired glucose
   tolerance, adjusted HRs were 0.21 (95\% CI 0.09-0.52) and 0.39 (95\% CI
   0.20-0.79) for total mortality, and 0.89 (95\% CI 0.62-1.27) and 0.87
   (0.60-1.27) for cardiovascular morbidity in the intervention and control
   groups of the DPS, respectively. The risk of death in DPS combined
   cohort was markedly lower than in FINRISK IGT cohort (adjusted HR 0.30,
   95\% CI 0.17-0.54), but there was no significant difference in the risk
   of CVD (adjusted HR 0.88, 95\% CI 0.64-1.21).
   Conclusions: Lifestyle intervention among persons with IGT did not
   decrease cardiovascular morbidity during the first 10 years of
   follow-up. However, the statistical power may not be sufficient to
   detect small differences between the intervention and control groups.
   Low total mortality among participants of the DPS compared with
   individuals with IGT in the general population could be ascribed to a
   lower cardiovascular risk profile at baseline and regular follow-up.}},
DOI = {{10.1371/journal.pone.0005656}},
Article-Number = {{e5656}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Peltonen, Markku/F-3037-2015
   }},
ORCID-Numbers = {{Peltonen, Markku/0000-0002-3767-4223
   Aunola, Sirkka/0000-0003-2681-1541}},
Unique-ID = {{ISI:000266331000014}},
}

@article{ ISI:000264528600144,
Author = {Temurtas, Hasan and Yumusak, Nejat and Temurtas, Feyzullah},
Title = {{A comparative study on diabetes disease diagnosis using neural networks}},
Journal = {{EXPERT SYSTEMS WITH APPLICATIONS}},
Year = {{2009}},
Volume = {{36}},
Number = {{4}},
Pages = {{8610-8615}},
Month = {{MAY}},
Abstract = {{Diabetes occurs when a body is unable to produce or respond properly to
   insulin which is needed to regulate glucose. Besides contributing to
   heart disease, diabetes also increases the risks of developing kidney
   disease, blindness, nerve damage, and blood vessel damage. Diabetes
   disease diagnosis via proper interpretation of the diabetes data is an
   important classification problem. In this study, a comparative
   pima-diabetes disease diagnosis was realized. For this purpose. a
   multilayer neural network structure which was trained by
   Levenberg-Marquardt (LM) algorithm and a probabilistic neural network
   structure were used. The results of the study were compared with the
   results of the pervious studies reported focusing on diabetes disease
   diagnosis and using the same LICI machine learning database. (C) 2008
   Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.eswa.2008.10.032}},
ISSN = {{0957-4174}},
ResearcherID-Numbers = {{Temurtas, Feyzullah/I-6528-2013}},
ORCID-Numbers = {{Temurtas, Feyzullah/0000-0002-3158-4032}},
Unique-ID = {{ISI:000264528600144}},
}

@article{ ISI:000265500700019,
Author = {Kotnik, Katarina and Popova, Elena and Todiras, Mihail and Mori, Marcelo
   A. and Alenina, Natalia and Seibler, Jost and Bader, Michael},
Title = {{Inducible Transgenic Rat Model for Diabetes Mellitus Based on
   shRNA-Mediated Gene Knockdown}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{4}},
Month = {{APR 2}},
Abstract = {{The rat is an important animal model in biomedical research, but gene
   targeting technology is not established for this species. Therefore, we
   aimed to produce transgenic knockdown rats using shRNA technology and
   pronuclear microinjection. To this purpose, we employed a
   tetracycline-inducible shRNA expression system targeting the insulin
   receptor (IR). Doxycycline (DOX) treatment of the resulting transgenic
   rats led to a dose-dependent and reversible increase in blood glucose
   caused by ubiquitous inhibition of IR expression and signalling. We
   could neither detect an interferon response nor disturbances in microRNA
   processing after DOX treatment excluding toxic effects of shRNA
   expression. Low dose DOX treatment induced a chronic state of diabetes
   mellitus. In conclusion, we have developed a technology which allows the
   specific, inducible, and reversible suppression of any gene of interest
   in the rat. Our first transgenic rat line generated with this method
   represents an inducible model for diabetes mellitus.}},
DOI = {{10.1371/journal.pone.0005124}},
Article-Number = {{e5124}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{Bader, Michael/0000-0003-4780-4164}},
Unique-ID = {{ISI:000265500700019}},
}

@article{ ISI:000265490600016,
Author = {Lecube, Albert and Sampol, Gabriel and Lloberes, Patricia and Romero,
   Odile and Mesa, Jordi and Hernandez, Cristina and Simo, Rafael},
Title = {{Diabetes Is an Independent Risk Factor for Severe Nocturnal Hypoxemia in
   Obese Patients. A Case-Control Study}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{3}},
Month = {{MAR 5}},
Abstract = {{Background: Type 2 diabetes mellitus (T2DM) and obesity have become two
   of the main threats to public health in the Western world. In addition,
   obesity is the most important determinant of the sleep apnea-hypopnea
   syndrome (SAHS), a condition that adversely affects glucose metabolism.
   However, it is unknown whether patients with diabetes have more severe
   SAHS than non-diabetic subjects. The aim of this cross-sectional
   case-control study was to evaluate whether obese patients with T2DM are
   more prone to severe SAHS than obese non-diabetic subjects.
   Methodology/Principal Findings: Thirty obese T2DM and 60 non-diabetic
   women closely matched by age, body mass index, waist circumference, and
   smoking status were recruited from the outpatient Obesity Unit of a
   university hospital. The exclusion criteria included chronic respiratory
   disease, smoking habit, neuromuscular and cerebrovascular disease,
   alcohol abuse, use of sedatives, and pregnancy. Examinations included a
   non-attended respiratory polygraphy, pulmonary function testing, and an
   awake arterial gasometry. Oxygen saturation measures included the
   percentage of time spent at saturations below 90\% (CT90). A high
   prevalence of SAHS was found in both groups (T2DM: 80\%, nondiabetic:
   78.3\%). No differences in the number of sleep apnea-hypopnea events
   between diabetic and non-diabetic patients were observed. However, in
   diabetic patients, a significantly increase in the CT90 was detected
   (20.2 +/- 30.2\% vs. 6.8 +/- 13,5\%; p = 0.027). In addition, residual
   volume (RV) was significantly higher in T2DM (percentage of predicted:
   79.7 +/- 18.1 vs. 100.1 +/- 22.8; p<0.001). Multiple linear regression
   analyses showed that T2DM but not RV was independently associated with
   CT90.
   Conclusions/Significance: T2DM adversely affects breathing during sleep,
   becoming an independent risk factor for severe nocturnal hypoxemia in
   obese patients. Given that SAHS is a risk factor of cardiovascular
   disease, the screening for SAHS in T2DM patients seems mandatory.}},
DOI = {{10.1371/journal.pone.0004692}},
Article-Number = {{e4692}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Lloberes Canadell, Patricia/F-9707-2014
   Romero, Odile/N-8109-2015
   }},
ORCID-Numbers = {{Lloberes Canadell, Patricia/0000-0002-1595-8225
   Romero, Odile/0000-0002-0147-596X
   Lecube, Albert/0000-0001-9684-0183}},
Unique-ID = {{ISI:000265490600016}},
}

@article{ ISI:000265473300009,
Author = {Lu, Feng-Ping and Lin, Kun-Pei and Kuo, Hsu-Ko},
Title = {{Diabetes and the Risk of Multi-System Aging Phenotypes: A Systematic
   Review and Meta-Analysis}},
Journal = {{PLOS ONE}},
Year = {{2009}},
Volume = {{4}},
Number = {{1}},
Month = {{JAN 7}},
Abstract = {{Background: Observational studies suggested an association between
   diabetes and the risk of various geriatric conditions (i.e., cognitive
   impairment, dementia, depression, mobility impairment, disability,
   falls, and urinary incontinence). However, the magnitude and impact of
   diabetes on older adults have not been reviewed.
   Methodology/Principal Findings: MEDLINE and PSYCINFO databases were
   searched through November 2007 for published studies, supplemented by
   manual searches of bibliographies of key articles. Population-based,
   prospective cohort studies that reported risk of geriatric outcomes in
   relation to diabetes status at baseline were selected. Two authors
   independently extracted the data, including study population and
   follow-up duration, ascertainment of diabetes status at baseline,
   outcomes of interest and their ascertainment, adjusted covariates,
   measures of association, and brief results. Fifteen studies examined the
   association of DM with cognitive dysfunction. DM was associated with a
   faster decline in cognitive function among older adults. The pooled
   adjusted risk ratio (RR) for all dementia when persons with DM were
   compared to those without was 1.47 (95\% CI, 1.25 to 1.73). Summary RRs
   for Alzheimer's disease and vascular dementia comparing persons with DM
   to those without were 1.39 (CI, 1.16 to 1.66) and 2.38 (CI, 1.79 to
   3.18), respectively. Four of 5 studies found significant association of
   DM with faster mobility decline and incident disability. Two studies
   examined the association of diabetes with falls in older women. Both
   found statistically significant associations. Insulin users had higher
   RR for recurrent falls. One study for urinary incontinence in older
   women found statistically significant associations. Two studies for
   depression did not suggest that DM was an independent predictor of
   incident depression.
   Conclusions/Significance: Current evidence supports that DM is
   associated with increased risk for selected geriatric conditions.
   Clinicians should increase their awareness and provide appropriate care.
   Future research is required to elucidate the underlying pathological
   pathway.}},
DOI = {{10.1371/journal.pone.0004144}},
Article-Number = {{e4144}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{LU, FENG-PING/0000-0002-7815-8030}},
Unique-ID = {{ISI:000265473300009}},
}

@inproceedings{ ISI:000268245600240,
Author = {Tung, Chia-Ming and Chiu, Yu-Hsien and Shia, Chi-Chun},
Editor = {{Lim, CT and Goh, JCH}},
Title = {{Chronic Disease Recurrence Prediction Model for Diabetes Mellitus
   Patients' Long-Term Caring}},
Booktitle = {{13TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, VOLS 1-3}},
Series = {{IFMBE Proceedings}},
Year = {{2009}},
Volume = {{23}},
Number = {{1-3}},
Pages = {{970-973}},
Note = {{13th International Conference on Biomedical Engineering (ICBME),
   Singapore, SINGAPORE, DEC 03-06, 2008}},
Organization = {{AMTI; BES Technol; ESLSYS; VICON; INSTRON; Process Engn Publishing;
   Wiley Blackwell}},
Abstract = {{Recurrence and complication prevention is crucial in caring chronic
   patients. This paper presents a chronic disease recurrence prediction
   model in long-term caring for diabetes patients. A nursing information
   system is developed and applied in an institution-based health care
   center. Values of blood sugar, vital signs, BMI and nursing records of
   diabetes patients are collected during the daily nursing procedure.
   Since data are recorded in different time with variable sampling period,
   a flexible time series analysis method is presented for exploiting
   seasonal variation and cyclical fluctuation between diabetes and
   daily-recorded signals. Expert knowledge for chronic disease is built
   based on domain specific language to extract chronic disease care
   knowledge from nursing records. Finally, a health surveillance system is
   developed by integrating multi-modal daily caring information for
   long-term caring of chronic patients. Several experiments with
   statistical testing are conducted and the results compare the
   performance of the proposed method. The expected outcome will build a
   prediction model to early warning before diabetes will be recurrence.}},
ISSN = {{1680-0737}},
ISBN = {{978-3-540-92840-9}},
Unique-ID = {{ISI:000268245600240}},
}

@inproceedings{ ISI:000268245600247,
Author = {Akter, Morium and Uddin, Mohammad Shorif and Haque, Aminul},
Editor = {{Lim, CT and Goh, JCH}},
Title = {{Diagnosis and Management of Diabetes Mellitus through a Knowledge-Based
   System}},
Booktitle = {{13TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, VOLS 1-3}},
Series = {{IFMBE Proceedings}},
Year = {{2009}},
Volume = {{23}},
Number = {{1-3}},
Pages = {{1000-1003}},
Note = {{13th International Conference on Biomedical Engineering (ICBME),
   Singapore, SINGAPORE, DEC 03-06, 2008}},
Organization = {{AMTI; BES Technol; ESLSYS; VICON; INSTRON; Process Engn Publishing;
   Wiley Blackwell}},
Abstract = {{The chronic disease diabetes mellitus is increasing worldwide. It is
   caused by absolute or relative deficiency of insulin, and is associated
   with a range of severe complications including renal and cardiovascular
   disease as well as blindness. Preventive care helps in controlling the
   severity of this disease, however, preventive measures require correct
   educational awareness and routine health checks. Medical doctors help in
   effective diagnosis as well as treatment of diabetes, although it is
   associated with high costs. With this view, the purpose of the present
   research is to develop a low-cost automated knowledge-based system that
   helps in self-diagnosis and management of this chronic disease for
   patients as well as doctors. Our knowledge-based system has an easy
   computer interface, which performs the diagnostic tasks using rules
   acquired from medical doctors on the basis of patient data. At present
   our system consists of 26 rules and it has been implemented using Prolog
   programming. Some real-life experimentations were performed, which
   confirmed the effectiveness of the developed system.}},
ISSN = {{1680-0737}},
ISBN = {{978-3-540-92840-9}},
Unique-ID = {{ISI:000268245600247}},
}

@inproceedings{ ISI:000290045000013,
Author = {Bae, Sejong and Le, Tuan D. and Singh, Karen P. and Blair, Steven N. and
   Morrow, Jame R.},
Editor = {{Anderssen, RS and Braddock, RD and Newham, LTH}},
Title = {{Association between serum ferritin, cardiorespiratory fitness and risk
   of type 2 diabetes: Aerobics Center longitudinal study}},
Booktitle = {{18TH WORLD IMACS CONGRESS AND MODSIM09 INTERNATIONAL CONGRESS ON
   MODELLING AND SIMULATION: INTERFACING MODELLING AND SIMULATION WITH
   MATHEMATICAL AND COMPUTATIONAL SCIENCES}},
Year = {{2009}},
Pages = {{103-116}},
Note = {{Combined IMACS World Congress/Modelling and Simulation
   Society-of-Australia-and-New-Zealand (MSSANZ)/18th Biennial Conference
   on Modelling and Simulation, Cairns, AUSTRALIA, JUL 13-17, 2009}},
Organization = {{IMACS; MSSANZ; CSIRO; Australian Math Sci Inst; Griffith Univ; eWater
   Cooperat Res Ctr; Dept Sustainabil \& Environm; HEMA Consulting;
   Hellenic European Res Comp Math \& Applicat; Int Council Ind Appl Math;
   Int Soc Grid Generat; Int Soc Photogrammetry \& Remote Sensing; Japan
   Soc Simulat Technol; Pacific Rim Math Assoc; Rutgers, State Univ New
   Jersey}},
Abstract = {{Recent studies suggest that an elevated serum ferritin concentration is
   considered an independent factor associated with increased risk of type
   2 diabetes, and cardiorespiratory fitness (CRF) is inversely associated
   with diabetes.
   The cross-sectional study investigates 6,230 participants' data from the
   Aerobics Center Longitudinal Study, The Cooper Institute (Dallas,
   Texas). The authors explored whether CRF level is associated inversely
   with lower serum ferritin concentration and is associated with a lower
   prevalence of type 2 diabetes.
   Participants with high ferritin levels and high triglyceride were 1.89
   and 1.57 times, respectively, more likely to have diabetes. Overweight
   or obese individuals were 1.35 to 1.40 times more likely to have
   diabetes. Participants with a family history of type 2 diabetes were
   3.96 times more likely to have diabetes.
   Lower serum ferritin concentration was associated with lower risk of
   diabetes in those participating in cardiorespiratory fitness. It
   suggests that physicians may use patients' ferritin concentrations as a
   marker for predicting their risk for new-onset diabetes, and patients
   should be encouraged to participate in cardiorespiratory fitness or
   physical activities.}},
ISBN = {{978-0-9758400-7-8}},
Unique-ID = {{ISI:000290045000013}},
}

@inproceedings{ ISI:000286342800058,
Author = {Mao, Weidong and Mao, Jinghe},
Book-Group-Author = {{IEEE}},
Title = {{The Application of Apriori-Gen Algorithm in the Association Study in
   Type 2 Diabetes}},
Booktitle = {{2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL
   ENGINEERING, VOLS 1-11}},
Year = {{2009}},
Pages = {{239-242}},
Note = {{3rd International Conference on Bioinformatics and Biomedical
   Engineering, Beijing, PEOPLES R CHINA, JUN 11-16, 2009}},
Organization = {{IEEE Engineering Med \& Biol Soc; Gordon Life Sci Inst; Fudan Univ;
   Beijing Univ Posts \& Telecommunicat; Beijing Inst Technol; Wuhan Univ;
   Journal Biomed Sci \& Engn}},
Abstract = {{Study shows type 2 diabetes is a genetic disease and evidence of a
   statistical interaction among several SNPs has been reported. Gene-gene
   interaction analysis has been used to identify common disease
   susceptibility genes. This paper explores the application of Apriori-Gen
   algorithm on the association study among SNPs. The algorithm has been
   applied to SNP data of type 2 diabetes for association study. The result
   shows the interaction among 5 SNPs with support s of 50\% and confidence
   alpha of 60\%. The risk rate RR and odds ratio OR are 2.14 and 2.92,
   respectively. The experiment results indicate that the interaction among
   those SNPs is associated with the disease.}},
ISBN = {{978-1-4244-2901-1}},
Unique-ID = {{ISI:000286342800058}},
}

@inproceedings{ ISI:000280543603321,
Author = {Valletta, John Joseph and Chipperfield, Andrew J. and Byrne, Christopher
   D.},
Book-Group-Author = {{IEEE}},
Title = {{Gaussian Process modelling of blood glucose response to free-living
   physical activity data in people with type 1 diabetes}},
Booktitle = {{2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY, VOLS 1-20}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2009}},
Pages = {{4913-4916}},
Note = {{Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Minneapolis, MN, SEP
   03-06, 2009}},
Organization = {{IEEE Engn Med \& Biol Soc}},
Abstract = {{Good blood glucose control is important to people with type 1 diabetes
   to prevent diabetes-related complications. Too much blood glucose
   (hyperglycaemia) causes long-term micro-vascular complications, while a
   severe drop in blood glucose (hypoglycaemia) can cause life-threatening
   coma. Finding the right balance between quantity and type of food
   intake, physical activity levels and insulin dosage, is a daily
   challenge. Increased physical activity levels often cause changes in
   blood glucose due to increased glucose uptake into tissues such as
   muscle. To date we have limited knowledge about the minute by minute
   effects of exercise on blood glucose levels, in part due to the
   difficulty in measuring glucose and physical activity levels
   continuously, in a free-living environment. By using a light and
   user-friendly armband we can record physical activity energy expenditure
   on a minute-by-minute basis. Simultaneously, by using a continuous
   glucose monitoring system we can record glucose concentrations. In this
   paper, Gaussian Processes are used to model the glucose excursions in
   response to physical activity data, to study its effect on glycaemic
   control.}},
DOI = {{10.1109/IEMBS.2009.5332466}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-3295-0}},
ResearcherID-Numbers = {{Chipperfield, Andrew/B-7001-2009}},
Unique-ID = {{ISI:000280543603321}},
}

@inproceedings{ ISI:000280543604135,
Author = {Georga, Eleni and Protopappas, Vasilios and Guillen, Alejandra and Fico,
   Giuseppe and Ardigo, Diego and Teresa Arredondo, Maria and Exarchos,
   Themis P. and Polyzos, Demosthenes and Fotiadis, Dimitrios I.},
Book-Group-Author = {{IEEE}},
Title = {{Data Mining for Blood Glucose Prediction and Knowledge Discovery in
   Diabetic Patients: The METABO Diabetes Modeling and Management System}},
Booktitle = {{2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY, VOLS 1-20}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2009}},
Pages = {{5633-5636}},
Note = {{Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Minneapolis, MN, SEP
   03-06, 2009}},
Organization = {{IEEE Engn Med \& Biol Soc}},
Abstract = {{METABO is a diabetes monitoring and management system which aims at
   recording and interpreting patient's context, as well as, at providing
   decision support to both the patient and the doctor. The METABO system
   consists of (a) a Patient's Mobile Device (PMD), (b) different types of
   unobtrusive biosensors, (c) a Central Subsystem (CS) located remotely at
   the hospital and (d) the Control Panel (CP) from which physicians can
   follow-up their patients and gain also access to the CS. METABO provides
   a multi-parametric monitoring system which facilitates the efficient and
   systematic recording of dietary, physical activity, medication and
   medical information (continuous and discontinuous glucose measurements).
   Based on all recorded contextual information, data mining schemes that
   run in the PMD are responsible to model patients' metabolism, predict
   hypo/hyper-glycaemic events, and provide the patient with short and
   long-term alerts. In addition, all past and recently-recorded data are
   analyzed to extract patterns of behavior, discover new knowledge and
   provide explanations to the physician through the CP. Advanced tools in
   the CP allow the physician to prescribe personalized treatment plans and
   frequently quantify patient's adherence to treatment.}},
DOI = {{10.1109/IEMBS.2009.5333635}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-3295-0}},
ORCID-Numbers = {{Fico, Giuseppe/0000-0003-1551-4613}},
Unique-ID = {{ISI:000280543604135}},
}

@inproceedings{ ISI:000280543605102,
Author = {Seto, Emily and Istepanian, Robert S. H. and Cafazzo, Joseph A. and
   Logan, Alexander and Sungoor, Ala},
Book-Group-Author = {{IEEE}},
Title = {{UK and Canadian Perspectives of the Effectiveness of Mobile Diabetes
   Management Systems}},
Booktitle = {{2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY, VOLS 1-20}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2009}},
Pages = {{6584-6587}},
Note = {{Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Minneapolis, MN, SEP
   03-06, 2009}},
Organization = {{IEEE Engn Med \& Biol Soc}},
Abstract = {{The use of mobile technologies for self-monitoring of blood glucose and
   blood pressure for diabetes patients is becoming increasingly popular
   worldwide. This is propelled by the proliferation of the wider usage of
   mobile phones and other wireless technologies and computing platforms in
   the healthcare sector. Such technologies can play a pivotal role in
   chronic disease management and patient self-care. There have been
   several clinical trials in recent years on mobile diabetes management in
   UK and Canada. However, no studies to date have addressed and correlated
   the technological and clinical outcomes concerning the use of mobile
   chronic disease management systems for diabetes from the UK and Canadian
   perspectives. In this paper we address some of these correlative issues
   based on similar clinical trials on mobile type-2 diabetes management
   systems deployed in these two countries. In particular, the outcomes of
   these trials supported the use of telemonitoring for effective blood
   pressure control, but telemonitoring was less effective at managing
   blood glucose control. Some of the clinical results and challenges are
   presented together with future work and suggestions that aim to validate
   a generic platform for mobile diabetes management.}},
DOI = {{10.1109/IEMBS.2009.5333998}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-3295-0}},
ResearcherID-Numbers = {{Meyer, Michelle/B-4019-2012
   }},
ORCID-Numbers = {{Cafazzo, Joseph A./0000-0002-3114-4440}},
Unique-ID = {{ISI:000280543605102}},
}

@inproceedings{ ISI:000288686500161,
Author = {Kala, Rahul and Shukla, Anupam and Tiwari, Ritu},
Editor = {{Abraham, A and Herrera, F and Carvalho, A and Pai, V}},
Title = {{Comparative Analysis of Intelligent Hybrid Systems for detection of PIMA
   Indian Diabetes}},
Booktitle = {{2009 WORLD CONGRESS ON NATURE \& BIOLOGICALLY INSPIRED COMPUTING (NABIC
   2009)}},
Series = {{World Congress on Nature and Biologically Inspired Computing}},
Year = {{2009}},
Pages = {{946-951}},
Note = {{World Congress on Nature and Biologically Inspired Computing,
   Coimbatore, INDIA, DEC 09-12, 2009}},
Abstract = {{The past few years have seen a lot of applications of Hybrid Soft
   Computing approaches that seem to have completely replaced the
   traditional uni-system approaches. The added abilities that come from
   the hybrid approaches motivate their use in every system. Bio-Medical
   Engineering is yet another field which has seen a major change in he
   past few years. We find various new approaches being applied to this
   field as well as many new models being proposed. At this juncture, we
   study the effectiveness of various new hybrid approaches in the field of
   Bio-medicals in this paper. PIMA Indian database has been used for this
   purpose from the UCI Machine Learning Repository. The basic aim is to
   compare the various hybrid approaches from the recent literature and
   compare their performances. We have chosen 3 major Hybrid Systems and
   standard Back Propagation Algorithm for this purpose. These are Adaptive
   Neuro Fuzzy Inference Systems, Ensembles and Evolutionary Artificial
   Neural Networks. We also try to explain the results from our theoretical
   understanding of the individual Hybrid Systems.}},
ISSN = {{2164-7364}},
ISBN = {{978-1-4244-5053-4}},
Unique-ID = {{ISI:000288686500161}},
}

@incollection{ ISI:000267508400013,
Author = {Fernandez, Margarita and Villasana, Minaya and Streja, Dan},
Editor = {{Sidhu, AS and Dillon, TS}},
Title = {{The Minimal Model of Glucose Disappearance in Type I Diabetes}},
Booktitle = {{BIOMEDICAL DATA AND APPLICATIONS}},
Series = {{Studies in Computational Intelligence}},
Year = {{2009}},
Volume = {{224}},
Pages = {{295-315}},
Abstract = {{In this chapter we evaluate the ability of the minimal model of glucose
   disappearance to describe experimental data collected from 9 diabetic
   patients controlled subcutaneously by an insulin pump. Two versions of
   the minimal model are used: the nonlinear classic minimal model
   developed by Bergman et al. (MM) and the linear approximation proposed
   by Fernandez et al. (LMM). All data windows (n = 13) show residuals that
   are correlated for both the LMM and MM (p-value < 0.01). The results
   also show that both the LMM and MM provide an equivalent goodness of fit
   with R-2 C values that are statistically equivalent (p-value > 0.05).
   This study confirms that the minimal model of glucose disappearance,
   either the classic or linear version, is unable to describe the observed
   experimental data possibly as a result of the physiological constraints
   imposed by the minimal model approach on the system dynamics, together
   with possible errors derived from the unmeasured insulin dynamics.
   Further testing on more complex models should be performed.}},
ISSN = {{1860-949X}},
ISBN = {{978-3-642-02192-3}},
Unique-ID = {{ISI:000267508400013}},
}

@inproceedings{ ISI:000267058000077,
Author = {Martinez-Sarriegui, Inaki and Zhu, Hong and Shan, Lijun and Garcia-Saez,
   Gema and Gomez, Enrique J. and Elena Hernando, M.},
Editor = {{Fred, A}},
Book-Author = {{Filipe, J}},
Title = {{MODELLING AND FORMAL SPECIFICATION OF A MULTIAGENT TELEMEDICINE SYSTEM
   FOR DIABETES CARE}},
Booktitle = {{ICAART 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON AGENTS AND
   ARTIFICIAL INTELLIGENCE}},
Year = {{2009}},
Pages = {{507-512}},
Note = {{1st International Conference on Agents and Artificial Intelligence, Inst
   Syst \& Technol Informat, Control \& Commun, Porto, PORTUGAL, JAN 19-21,
   2009}},
Organization = {{Workflow Manage Coalit; Assoc Adv Artificial Intell; Special Interest
   Grp Artificial Intell}},
Abstract = {{This paper presents the modelling and formal specification of a
   telemedicine system for diabetes care. In such scenario, the multiagent
   technology supports the distributed autonomy of several Personal
   Assistants; the communications between them and the hospital's agents;
   the control of the system's access and multitask functionality;
   scalability; adaptability; robustness; and the provision to the
   physicians with the necessary automatic processing tools for the
   analysis of the large amounts of data generated by patients. We
   evaluated the AOIS meta-model and the CAMLE's modelling environment
   concluding that this methodology is adequate to represent a complex
   medical system like the one presented. The model and the formal
   specification provide a more complete view of the system and contain
   very useful information to cope with the future system evolution.}},
ISBN = {{978-989-8111-66-1}},
ResearcherID-Numbers = {{Hernando, M. Elena/A-9575-2008
   Zhu, Hong/B-3231-2011}},
ORCID-Numbers = {{Hernando, M. Elena/0000-0001-6182-313X
   Zhu, Hong/0000-0001-9393-5005}},
Unique-ID = {{ISI:000267058000077}},
}

@inproceedings{ ISI:000281139900007,
Author = {Gribovschi, M. and Fodor, A. and Grosu, G. and Hancu, N.},
Editor = {{Vlad, S and Ciupa, RV and Nicu, AI}},
Title = {{Assessing Glycemic Control in Diabetes: From Self Monitoring of Blood
   Glucose to Continuous Glucose Monitoring System}},
Booktitle = {{INTERNATIONAL CONFERENCE ON ADVANCEMENTS OF MEDICINE AND HEALTH CARE
   THROUGH TECHNOLOGY}},
Series = {{IFMBE Proceedings}},
Year = {{2009}},
Volume = {{26}},
Pages = {{29-32}},
Note = {{International Conference on Advancements of Medicine and Health Care
   Through Technology, Cluj Napoca, ROMANIA, SEP 23-26, 2009}},
Organization = {{Tech Univ Cluj Napoca; Romanian Natl Soc Med Engn \& Biol Technol}},
Abstract = {{The incidence of diabetes is increasing at an alarming rate. Maintaining
   tight glycemic control is essential to prevent microvascular and
   macrovascular complications as demonstrated by many studies. Current
   recommendation for HbAlc is less then 7\% and even 6.5\% in special
   individuals. HbAlc measurement is considered the gold standard of
   diabetes care and serves as a surrogate of diabetes-related
   complications, but it does not provide information about glycemic
   excursions. For these reasons, self-monitoring of blood glucose (SMBG)
   levels is considered an important adjunct to HbAlc measurements for
   achieving and maintaining glycemic control and consequently for reducing
   diabetes-related complications. A new method that offers us a complete
   investigation of glucose excursions is continuous glucose monitoring
   system (CGMS) that measure glucose concentration in subcutaneous
   interstitial fluid which can reflect changes in blood glucose
   concentrations reasonably quickly every 5 min throughout 72 hours (3
   days).
   The aim of this paper was to evaluate the different methods of assessing
   glucose control in individuals with diabetes mellitus and to compare
   themselves in order to establish the clinical relevance. We assessed the
   glycemic control by HbA1c, SMBG and CGMS Gold simultaneously in a number
   of 97 individuals with diabetes. 20 individuals have a HbAlc between 6,5
   - 7,4\%, 33 patients between 7,5 - 8,4\%, 15 between 8,5-9,4\%, 10
   between 9,5 - 10,5\% and 7 persons more than 10,5\%. It was observed
   that between HbAlc level and the period spent in glycemic values more
   than 180 mg/di there is not a direct relationship as we expected. The
   same trend was observed in near normal glycemic status but in different
   way.
   Assessing of glycemia in diabetes is crucially important to the
   prevention of both acute and long - term complications. The 3
   fundamental approaches to assessment, HbAlc, SMBG and CGM provide
   fundamentally different but complementary information.}},
DOI = {{10.1007/978-3-642-04292-8\_7}},
ISSN = {{1680-0737}},
ISBN = {{978-3-642-04291-1}},
Unique-ID = {{ISI:000281139900007}},
}

@incollection{ ISI:000270954800014,
Author = {Deja, Rafal},
Editor = {{Tkacz, E and Kapczynski, A}},
Title = {{Applying Rough Set Theory to the System of Type 1 Diabetes Prediction}},
Booktitle = {{INTERNET - TECHNICAL DEVELOPMENT AND APPLICATIONS}},
Series = {{Advances in Intelligent and Soft Computing}},
Year = {{2009}},
Volume = {{64}},
Pages = {{119-127}},
Abstract = {{In this paper we present the results of analysis of the genetic data of
   children with diabetes mellitus type 1 (DMT1) and their up to now
   healthy siblings. Base on the data the decision support system has been
   developed to classify and later on to predict the illness among the
   children with genetic susceptibility to DMT1. The system can recommend
   to include a person to pre-diabetes therapy. The data mining and
   classification algorithms are based on the rough set theory.}},
ISSN = {{1867-5662}},
ISBN = {{978-3-642-05018-3}},
Unique-ID = {{ISI:000270954800014}},
}

@inproceedings{ ISI:000291667000110,
Author = {Nagakura, Toshiaki and Inada, Kazuki and Susuki, Yuuto and Yoshida,
   Naohiro and Yamada, Akira and Ikeuchi, Masashi and Ikuta, Koji},
Editor = {{Dissel, O and Schlegel, WC}},
Title = {{The study of micro liter Insulin injection system by Osmotic pressure
   for Diabetes therapy}},
Booktitle = {{MICRO-AND NANOSYSTEMS IN MEDICINE, ACTIVE IMPLANTS, BIOSENSORS}},
Series = {{IFMBE Proceedings}},
Year = {{2009}},
Volume = {{25}},
Number = {{8}},
Pages = {{382-383}},
Note = {{World Congress on Medical Physics and Biomedical Engineering, Munich,
   GERMANY, SEP 07-12, 2009}},
Organization = {{IUPESM; IOMP}},
Abstract = {{There is diabetes mellitus more than 15 million people, and this disease
   cause various serious complication. Diabetes mellitus therapy is insulin
   injection therapy, but the quantity of insulin is decided by experience.
   So it has danger of hypoglycemia attack. Then it is expected for insulin
   therapy to be done by measuring blood sugar level, but it is not still
   practical method. We developed that insulin valve system driven by
   osmotic change (blood sugar level change). This system actuation was
   confirmed by preliminary experiment. We tried to fabricate the small
   size valve system which could control micro liter insulin injection. The
   smaller osmotic valve, the better for a treatment of diabetes mellitus
   therapy. We tried to confirm that this small valve could control blood
   sugar level of rat.}},
ISSN = {{1680-0737}},
ISBN = {{978-3-642-03886-0}},
Unique-ID = {{ISI:000291667000110}},
}

@inproceedings{ ISI:000279253400040,
Author = {Verma, Anju and Fiasche, Maurizio and Cuzzola, Maria and Iacopino,
   Pasquale and Morabito, Francesco C. and Kasabov, Nikola},
Editor = {{Leung, CS and Lee, M and Chan, JH}},
Title = {{Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis:
   An Integrated Approach}},
Booktitle = {{NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2009}},
Volume = {{5864}},
Pages = {{360-366}},
Note = {{16th International Conference on Neural Information Processing (ICONIP
   2009), King Mongluts Univ Technol Thonburi, Sch Informat Technol,
   Bangkok, THAILAND, DEC 01-05, 2009}},
Organization = {{Asia Pacific Neural Network Assembly; Int Neural Network Soc; Japanese
   Neural Network Soc; European Neural Network Soc; IEEE Computat
   Intelligence Soc}},
Abstract = {{A novel ontology based type 2 diabetes risk analysis system framework is
   described, which allows the creation of global knowledge representation
   (ontology) and personalized modeling for a decision support system. A
   computerized model focusing on organizing knowledge related to three
   chronic diseases and genes has been developed in an ontological
   representation that is able to identify interrelationships for the
   ontology-based personalized risk evaluation for chronic diseases. The
   personalized modeling is a process of model creation for a single
   person, based on their personal data and the information available in
   the ontology. A transductive neuro-fuzzy inference system with weighted
   data normalization is used to evaluate personalized risk for chronic
   disease. This approach aims to provide support for further discovery
   through the integration of the ontological representation to build an
   expert system in order to pinpoint genes of interest and relevant diet
   components.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-10682-8}},
ResearcherID-Numbers = {{Morabito, Fortunato/I-7819-2012
   }},
ORCID-Numbers = {{Maria, Cuzzola/0000-0001-8749-5402
   MORABITO, Francesco Carlo/0000-0003-0734-9136
   FIASCHE', MAURIZIO/0000-0002-9253-7272}},
Unique-ID = {{ISI:000279253400040}},
}

@inproceedings{ ISI:000276905000048,
Author = {Karimipour, Hadis and Shandiz, Heydar Toosian},
Editor = {{Jusoff, K and Mahmoud, SS and Sivakumar, R}},
Title = {{Simulation Studies based on the Pharmacokinetic model Of Diabetes
   Mellitus}},
Booktitle = {{SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING,
   VOL 1, PROCEEDINGS}},
Series = {{International Conference on Computer and Electrical Engineering ICCEE}},
Year = {{2009}},
Pages = {{224-228}},
Note = {{2nd International Conference on Computer and Electrical Engineering,
   Dubai, U ARAB EMIRATES, DEC 28-30, 2009}},
Organization = {{Int Assoc Comp Sci \& Informat Technol}},
Abstract = {{this study illustrates that the overall model based on pharmacokinetic
   diagrams of insulin and glucose is able to follow the dynamics of
   glucose in Type I diabetes. Both models are capable of predicting the
   blood glucose and insulin levels, total glucose uptake and the renal
   glucose excretion. A four-daily dose and continuous injection of insulin
   therapy has been used for suppressing and normalizing the change of
   blood glucose. The results show that continuous injection has more
   precise control than the traditional regime of daily injections.}},
DOI = {{10.1109/ICCEE.2009.89}},
ISBN = {{978-0-7695-3925-6}},
Unique-ID = {{ISI:000276905000048}},
}

@article{ ISI:000265452200005,
Author = {Fischer-Rosinsky, Antje and Fisher, Eva and Kovacs, Peter and Blueher,
   Matthias and Moehlig, Matthias and Pfeiffer, Andreas F. H. and Boeing,
   Heiner and Spranger, Joachim},
Title = {{Lack of Association between the Tagging SNP A+930 -> G of SOCS3 and Type
   2 Diabetes Mellitus: Meta-Analysis of Four Independent Study Populations}},
Journal = {{PLOS ONE}},
Year = {{2008}},
Volume = {{3}},
Number = {{12}},
Month = {{DEC 4}},
Abstract = {{Background: The suppressor of cytokine signalling 3 (SOCS3) provides a
   link between cytokine action and their negative consequences on insulin
   signalling. Thus SOCS3 is a potential candidate gene for type 2 diabetes
   (T2DM).
   Methodology/Principal Findings: Based on HapMap we identified the
   polymorphism A+930 -> G (rs4969168) as a haplotype tagging SNP (htSNP)
   sufficiently covering the genetic variation of the whole gene. We
   therefore examined the association between rs4969168 within SOCS3 and
   T2DM in three independent study populations; one prospective case-cohort
   study and two cross-sectional study populations. Due to the low
   frequency of individuals being homozygous for the polymorphism a
   dominant model of inheritance was assumed. The case-cohort study with
   2,957 individuals (764 of them with incident T2DM) showed no effect of
   the polymorphism on diabetes risk (hazard ratio (95\%Cl): 0.86
   (0.66-1.13); p = 0.3). Within the MeSyBePo-study population 325 subjects
   had T2DM from a total of 1,897 individuals, while the second
   cross-sectional cohort included 851 cases of T2DM within a total of 1653
   subjects. According to the results in the prospective study, no
   association with T2DM was found (odds ratio (95\%Cl): 0.78 (0.54-1.12)
   for MesyBepo and 1.13 (0.90-1.42) for the Leipzig study population).
   There was also no association with metabolic subtraits such as insulin
   sensitivity (p = 0.7), insulin secretion (p = 0.8) or the hyperbolic
   relation of both, the disposition index (p = 0.7). In addition, no
   evidence for interaction with BMI or sex was found. We subsequently
   performed a meta-analysis, additionally including the publicly available
   data from the T2DM-subcohort of the WTCCC (n = 4,855). The overall odds
   ratio within that meta-analysis was 0.96 (0.88-1.06).
   Conclusions/Significance: There is no strong effect of the common
   genetic variation within the SOCS3 gene on the development of T2DM.}},
DOI = {{10.1371/journal.pone.0003852}},
Article-Number = {{e3852}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000265452200005}},
}

@article{ ISI:000265114100006,
Author = {Lund, Soren Sogaard and Tarnow, Lise and Astrup, Anne Sofie and Hovind,
   Peter and Jacobsen, Peter Karl and Alibegovic, Amra Ciric and Parving,
   Ida and Pietraszek, Lotte and Frandsen, Merete and Rossing, Peter and
   Parving, Hans-Henrik and Vaag, Allan Arthur},
Title = {{Effect of Adjunct Metformin Treatment in Patients with Type-1 Diabetes
   and Persistent Inadequate Glycaemic Control. A Randomized Study}},
Journal = {{PLOS ONE}},
Year = {{2008}},
Volume = {{3}},
Number = {{10}},
Month = {{OCT 9}},
Abstract = {{Background: Despite intensive insulin treatment, many patients with
   type-1 diabetes (T1DM) have longstanding inadequate glycaemic control.
   Metformin is an oral hypoglycaemic agent that improves insulin action in
   patients with type-2 diabetes. We investigated the effect of a one-year
   treatment with metformin versus placebo in patients with T1DM and
   persistent poor glycaemic control.
   Methodology/Principal Findings: One hundred patients with T1DM,
   preserved hypoglycaemic awareness and HaemoglobinA(1c) (HbA(1c)) >=
   8.5\% during the year before enrolment entered a one-month run-in on
   placebo treatment. Thereafter, patients were randomized (baseline) to
   treatment with either metformin (1 g twice daily) or placebo for 12
   months (double-masked). Patients continued ongoing insulin therapy and
   their usual outpatient clinical care. The primary outcome measure was
   change in HbA(1c) after one year of treatment. At enrolment, mean
   (standard deviation) HbA(1c) was 9.48\% (0.99) for the metformin group
   (n=49) and 9.60\% (0.86) for the placebo group (n=51). Mean (95\%
   confidence interval) baseline-adjusted differences after 12 months with
   metformin (n=48) versus placebo (n=50) were: HbA(1c), 0.13\% (-0.19;
   0.44), p=0.422; Total daily insulin dose, -5.7 U/day (-8.6; -2.9), p <
   0.001; body weight, -1.74 kg (-3.32; -0.17), p=0.030. Minor and overall
   major hypoglycaemia was not significantly different between treatments.
   Treatments were well tolerated.
   Conclusions/Significance: In patients with poorly controlled T1DM,
   adjunct metformin therapy did not provide any improvement of glycaemic
   control after one year. Nevertheless, adjunct metformin treatment was
   associated with sustained reductions of insulin dose and body weight.
   Further investigations into the potential cardiovascular-protective
   effects of metformin therapy in patients with T1DM are warranted.}},
DOI = {{10.1371/journal.pone.0003363}},
Article-Number = {{e3363}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000265114100006}},
}

@article{ ISI:000257617100008,
Author = {Kahramanli, Humar and Allahverdi, Novruz},
Title = {{Design of a hybrid system for the diabetes and heart diseases}},
Journal = {{EXPERT SYSTEMS WITH APPLICATIONS}},
Year = {{2008}},
Volume = {{35}},
Number = {{1-2}},
Pages = {{82-89}},
Month = {{JUL-AUG}},
Abstract = {{Data can be classified according to their properties. Classification is
   implemented by developing a model with existing records by using sample
   data. One of the aims of classification is to increase the reliability
   of the results obtained from the data. Fuzzy and crisp values are used
   together in medical data. Regarding to this, a new method is presented
   for classification of data of a medical database in this study. Also a
   hybrid neural network that includes artificial neural network (ANN) and
   fuzzy neural network (FNN) was developed. Two real-time problem data
   were investigated for determining the applicability of the proposed
   method. The data were obtained from the University of California at
   Irvine (UCI) machine learning repository. The datasets are Pima Indians
   diabetes and Cleveland heart disease. In order to evaluate the
   performance of the proposed method accuracy, sensitivity and specificity
   performance measures that are used commonly in medical classification
   studies were used. The classification accuracies of these datasets were
   obtained by k-fold cross-validation. The proposed method achieved
   accuracy values 84.24\% and 86.8\% for Pima Indians diabetes dataset and
   Cleveland heart disease dataset, respectively. It has been observed that
   these results are one of the best results compared with results obtained
   from related previous studies and reported in the UCI web sites. (C)
   2007 Published by Elsevier Ltd.}},
DOI = {{10.1016/j.eswa.2007.06.004}},
ISSN = {{0957-4174}},
Unique-ID = {{ISI:000257617100008}},
}

@article{ ISI:000257794700016,
Author = {Zai, Adrian H. and Grant, Richard W. and Estey, Greg and Lester, William
   T. and Andrews, Carl T. and Yee, Ronnie and Mort, Elizabeth and Chueh,
   Henry C.},
Title = {{Lessons from implementing a combined workflow-informaties system for
   diabetes management}},
Journal = {{JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION}},
Year = {{2008}},
Volume = {{15}},
Number = {{4}},
Pages = {{524-533}},
Month = {{JUL-AUG}},
Note = {{Annual Symposium of the American-Medical-Informatics-Association,
   Chicago, IL, NOV 10-14, 2007}},
Organization = {{Amer Med Informat Assoc}},
Abstract = {{Shortcomings surrounding the care of patients with diabetes have been
   attributed largely to a fragmented, disorganized, and duplicative health
   care system that focuses more on acute conditions and complications than
   on managing chronic disease. To address these shortcomings, we developed
   a diabetes registry population management application to change the way
   our staff manages patients with diabetes. Use of this new application
   has helped us coordinate the responsibilities for intervening and
   monitoring patients in the registry among different users. Our
   experiences using this combined workflow-informatics intervention system
   suggest that integrating a chronic disease registry into clinical
   workflow for the treatment of chronic conditions creates a useful and
   efficient tool for managing disease.}},
DOI = {{10.1197/jamia.M2598}},
ISSN = {{1067-5027}},
EISSN = {{1527-974X}},
ORCID-Numbers = {{Zai, Adrian/0000-0002-2972-6839}},
Unique-ID = {{ISI:000257794700016}},
}

@article{ ISI:000260586600029,
Author = {Chen, Nelson K. F. and Wong, Jen San and Kee, Irene H. C. and Lai, Siang
   Hui and Thng, Choon Hua and Ng, Wai Har and Ng, Robert T. H. and Tan,
   Soo Yong and Lee, Shu Yen and Tan, Mark E. H. and Sivalingam,
   Jaichandran and Chow, Pierce K. H. and Kon, Oi Lian},
Title = {{Nonvirally Modified Autologous Primary Hepatocytes Correct Diabetes and
   Prevent Target Organ Injury in a Large Preclinical Model}},
Journal = {{PLOS ONE}},
Year = {{2008}},
Volume = {{3}},
Number = {{3}},
Month = {{MAR 5}},
Abstract = {{Background: Current gene- and cell-based therapies have significant
   limitations which impede widespread clinical application. Taking
   diabetes mellitus as a paradigm, we have sought to overcome these
   limitations by ex vivo electrotransfer of a nonviral insulin expression
   vector into primary hepatocytes followed by immediate autologous
   reimplantation in a preclinical model of diabetes.
   Methods and Results: In a single 3-hour procedure, hepatocytes were
   isolated from a surgically resected liver wedge, electroporated with an
   insulin expression plasmid ex vivo and reimplanted intraparenchymally
   under ultrasonic guidance into the liver in each of 10
   streptozotocin-induced diabetic Yorkshire pigs. The vector was comprised
   of a bifunctional, glucose-responsive promoter linked to human insulin
   cDNA. Ambient glucose concentrations appropriately altered human insulin
   mRNA expression and C-peptide secretion within minutes in vitro and in
   vivo. Treated swine showed correction of hyperglycemia, glucose
   intolerance, dyslipidemia and other metabolic abnormalities for >= 47
   weeks. Metabolic correction correlated significantly with the number of
   hepatocytes implanted. Importantly, we observed no hypoglycemia even
   under fasting conditions. Direct intrahepatic implantation of
   hepatocytes did not alter biochemical indices of liver function or
   induce abnormal hepatic lobular architecture. About 70\% of implanted
   hepatocytes functionally engrafted, appeared histologically normal,
   retained vector DNA and expressed human insulin for >= 47 weeks. Based
   on structural tissue analyses and transcriptome data, we showed that
   early correction of diabetes attenuated and even prevented pathological
   changes in the eye, kidney, liver and aorta.
   Conclusions: We demonstrate that autologous hepatocytes can be
   efficiently, simply and safely modified by electroporation of a nonviral
   vector to express, process and secrete insulin durably. This strategy,
   which achieved significant and sustained therapeutic efficacy in a large
   preclinical model without adverse effects, warrants consideration for
   clinical development especially as it could have broader future
   applications for the treatment of other acquired and inherited diseases
   for which systemic reconstitution of a specific protein deficiency is
   critical.}},
DOI = {{10.1371/journal.pone.0001734}},
Article-Number = {{e1734}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000260586600029}},
}

@article{ ISI:000253634600008,
Author = {Nelson, Matthew R. and Daniel, Kurt R. and Carr, J. Jeffrey and
   Freedman, Barry I. and Prineas, Ronald J. and Bowden, Donald W. and
   Herrington, David M.},
Title = {{Associations between electrocardiographic interval durations and
   coronary artery calcium scores: The Diabetes Heart Study}},
Journal = {{PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY}},
Year = {{2008}},
Volume = {{31}},
Number = {{3}},
Pages = {{314-321}},
Month = {{MAR}},
Abstract = {{Background: The mechanisms underlying the associations between QT
   interval duration and risk of cardiovascular disease (CVD) remain
   unclear. It has been assumed that these associations are driven by
   abnormal myocardial repolarization. We examined the relationship between
   coronary artery disease, measured by coronary artery calcified plaque
   (CAC), and the duration of QRS, JT, and QT intervals, among
   predominantly type two diabetic participants.
   Methods: The study sample included 1,123 subjects from the Diabetes
   Heart Study, of whom 85\% had type 2 diabetes. Correlations between
   electrocardiogram interval durations and log-transformed coronary artery
   calcified (CAC) were assessed in univariate and sequential multivariable
   generalized estimating equation models adjusted for familial
   correlations, heart rate, age, race, gender, diabetes status,
   hypertension status, Body Mass Index (BMI), smoking status, systolic
   blood pressure, Low Density Lipoprotein (LDL) cholesterol, QT-prolonging
   medications, and use of exogenous estrogen.
   Results: QT interval duration significantly correlated with the extent
   of CAC in univariate (r = 0.09, P = 0.01) and multivariable models (r =
   0.08, P = 0.01). We observed strong correlations between the QRS
   duration and CAC in univariate (r = 0.23, P < 0.0001) and adjusted
   models (r = 0.10, P = 0.01). In contrast, the JT interval was not
   associated with CAC. A strong correlation existed between the QRS
   interval and CAC in men (QRS: r = 0.24, P <= 0.0001) and diabetics (QRS:
   r = 0.25, P <= 0.0001) but was absent in women and nondiabetics. These
   relationships were not modified by CVD, race, or presence of bundle
   branch block.
   Conclusion: QT duration correlates with the amount of CAC in a
   predominantly diabetic population. The association between QT duration
   and CAC is driven by QRS and not JT interval duration.}},
DOI = {{10.1111/j.1540-8159.2008.00991.x}},
ISSN = {{0147-8389}},
ResearcherID-Numbers = {{Carr, John/A-1938-2012}},
ORCID-Numbers = {{Carr, John/0000-0002-4398-8237}},
Unique-ID = {{ISI:000253634600008}},
}

@article{ ISI:000252473600013,
Author = {Klinke, II, David J.},
Title = {{Integrating epidemiological data into a mechanistic model of type 2
   diabetes: Validating the prevalence of virtual patients}},
Journal = {{ANNALS OF BIOMEDICAL ENGINEERING}},
Year = {{2008}},
Volume = {{36}},
Number = {{2}},
Pages = {{321-334}},
Month = {{FEB}},
Abstract = {{Mathematical models are playing an increasing role in understanding the
   complexity of multifactorial diseases like type 2 diabetes. The
   objective of this study was to validate a population of virtual patients
   against a real population of patients with type 2 diabetes. A population
   of virtual patients was created that incorporates different underlying
   pathogenic lesions consistent with a type 2 diabetic phenotype. These
   virtual patients were created within the Metabolism PhysioLab platform,
   a non-linear coupled differential algebraic model that incorporates the
   salient causal mechanisms underlying glucose homeostasis and substrate
   metabolism. The weights of each individual virtual patient were
   determined to reproduce the diversity in a real type 2 diabetic
   population obtained from the NHANES III study. As a validation test,
   this virtual population reproduced a series of clinical studies that
   identify less invasive biomarkers for insulin sensitivity. This approach
   demonstrates how computational bridges can be constructed between
   statistical approaches common in epidemiology and deterministic
   approaches common in biomedical engineering.}},
DOI = {{10.1007/s10439-007-9410-y}},
ISSN = {{0090-6964}},
ResearcherID-Numbers = {{Klinke, David/A-7133-2008
   }},
ORCID-Numbers = {{Klinke, David/0000-0003-3299-4938}},
Unique-ID = {{ISI:000252473600013}},
}

@article{ ISI:000253025500006,
Author = {Lin, Jessica and Lee, Dominic and Chase, J. Geoffrey and Shaw, Geoffrey
   M. and Le Compte, Aaron and Lotz, Thomas and Wong, Jason and Lonergan,
   Timothy and Hann, Christopher E.},
Title = {{Stochastic modelling of insulin sensitivity and adaptive glycemic
   control for critical care}},
Journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
Year = {{2008}},
Volume = {{89}},
Number = {{2}},
Pages = {{141-152}},
Month = {{FEB}},
Note = {{6th IFAC Symposium on Modelling and Control in Biomedical Systems,
   Reims, FRANCE, AUG 20-22, 2006}},
Organization = {{IFAC; Univ Reims Champagne Ardenne; Soc l'Electricite; IFAC Tech
   Committee Biolog \& Med Syst; Inst Union Phys \& Eng Sci Med; Inst Elect
   \& Elect Eng; Eng Med \& Biolog Soc; Int Measurement Confederation; GDR
   MACS Res Network CNRS; Soc Francaise Genie Biolog Med; Ctr Recherche
   STIC, Univ Reims; French Minist Res; Region Champagne Ardenne; City
   Reims; Dept Marne}},
Abstract = {{Targeted, tight model-based glycemic control in critical care patients
   that can reduce mortality 18-45\% is enabled by prediction of insulin
   sensitivity, S-I. However, this parameter can vary significantly over a
   given hour in the critically ill as their condition evolves. A
   stochastic model of S-I variability is constructed using data from 165
   critical care patients. Given S-I for an hour, the stochastic model
   returns the probability density function of S-I for the next hour.
   Consequently, the glycemic distribution following a known intervention
   can be derived, enabling pre-determined likelihoods of the result and
   more accurate control.
   Cross validation of the S-I variability model shows that 86.6\% of the
   blood glucose measurements are within the 0.90 probability interval, and
   54.0\% are within the interquartile interval. ``Virtual Patients{''}
   with S-I behaving to the overall S-I variability model achieved similar
   predictive performance in simulated trials (86.8\% and 45.7\%). Finally,
   adaptive control method incorporating S-I variability is shown to
   produce improved glycemic control in simulated trials compared to
   current clinical results. The validated stochastic model and methods
   provide a platform for developing advanced glycemic control methods
   addressing critical care variability. (c) 2007 Elsevier Ireland Ltd. All
   rights reserved.}},
DOI = {{10.1016/j.cmpb.2007.04.006}},
ISSN = {{0169-2607}},
ResearcherID-Numbers = {{Chase, James/B-1584-2012}},
ORCID-Numbers = {{Chase, James/0000-0001-9989-4849}},
Unique-ID = {{ISI:000253025500006}},
}

@inproceedings{ ISI:000262404500074,
Author = {Stahl, F. and Johansson, R.},
Book-Group-Author = {{IEEE}},
Title = {{Short-Term Diabetes Blood Glucose Prediction Based On Blood Glucose
   Measurements}},
Booktitle = {{2008 30th Annual International Conference of the IEEE Engineering in
   Medicine and Biology Society, Vols 1-8}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2008}},
Pages = {{291-294}},
Note = {{30th Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Vancouver, CANADA, AUG
   20-24, 2008}},
Organization = {{DEVICIX; Green Coll; Natl Inst Hlth; NIBIB; NSF; PLEXON Inc; UBC Engn
   Biomed Engn; Univ Washington, Coll Engn; Bentham Sci Publ Ltd; Recent
   Patents Biomed Engn; Recent Patents Engn}},
Abstract = {{Insulin Dependent Diabetes Mellitus; (IDDM) is a chronic disease
   characterized by the inability of the pancreas to produce sufficient
   amounts of Insulin. Daily compensation of the deficiency requires 4-6
   insulin injections to be taken daily, the aim of this insulin therapy
   being to maintain normoglycemia-i.e., a blood glucose level between 4-7
   mmol/L. To determine the quantity and timing of these Injections,
   various different approaches are used. Currently, mostly qualitative and
   semi-quantitative models and reasoning are used to design such a
   therapy. Here, an attempt Is made to show how system identification and
   control may be used to estimate predictive quantitative models to be
   used in design of optimal insulin regimens.
   The system was divided into three subsystems, the insulin subsystem, the
   glucose subsystem and the insulin-glucose interaction. The Insulin
   subsystem aims to describe the absorbtion of injected insulin from the
   subcutaneous depots and the glucose subsystem the absorbtion of glucose
   from the gut following a meal. These subsystems were modeled using
   compartment models and proposed models found in the literature. Several
   black-box modets and grey-box models describing the insulin/glucose
   Interaction were developed and analysed. These models were fitted to
   real data monitored by a IDDM patient. Many difficulties were
   encountered, typical of biomedical systems: Non-uniform and scarce
   sampling, time-varying dynamics and severe nonlinearities were some of
   the difficulties encountered during the modeling. None of the proposed
   models were able to describe the system accurately In all aspects during
   all conditions. However, all the linear models shared some dynamics.
   Based on the estimated models, short-term blood glucose predictors for
   up to two-hour-ahead blood glucose prediction were investigated.}},
DOI = {{10.1109/IEMBS.2008.4649147}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-1814-5}},
ResearcherID-Numbers = {{Johansson, Rolf/C-1845-2013}},
Unique-ID = {{ISI:000262404500074}},
}

@inproceedings{ ISI:000262404501001,
Author = {Kovacs, Levente and Kulcsar, Balazs and Bokor, Jozsef and Benyo, Zoltan},
Book-Group-Author = {{IEEE}},
Title = {{Model-based Nonlinear Optimal Blood Glucose Control of Type I Diabetes
   Patients}},
Booktitle = {{2008 30th Annual International Conference of the IEEE Engineering in
   Medicine and Biology Society, Vols 1-8}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2008}},
Pages = {{1607-1610}},
Note = {{30th Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Vancouver, CANADA, AUG
   20-24, 2008}},
Organization = {{DEVICIX; Green Coll; Natl Inst Hlth; NIBIB; NSF; PLEXON Inc; UBC Engn
   Biomed Engn; Univ Washington, Coll Engn; Bentham Sci Publ Ltd; Recent
   Patents Biomed Engn; Recent Patents Engn}},
Abstract = {{Using induced L(2)-norm minimization, a robust controller was developed
   for insulin delivery in Type I diabetic patients. The high-complexity
   nonlinear diabetic patient Sorensen-model {[}1] was considered. LPV
   (Linear Parameter Varying) methodology was used to develop open loop
   model and robust controller. Considering the normoglycemic set point
   (81.1 mg/dL), a polytopic set was created over the physiologic
   boundaries of the glucose-insulin interaction of the Sorensen-model. In
   this way, LPV model formalism was defined. The robust control was
   developed considering input and output multiplicative uncertainties with
   other weighting functions.}},
DOI = {{10.1109/IEMBS.2008.4649480}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-1814-5}},
ResearcherID-Numbers = {{Benyo, Zoltan/H-2900-2012
   Kovacs, Levente/A-7539-2015
   Bokor, Jozsef/K-3346-2015}},
ORCID-Numbers = {{Kovacs, Levente/0000-0002-3188-0800
   Bokor, Jozsef/0000-0002-3461-4807}},
Unique-ID = {{ISI:000262404501001}},
}

@inproceedings{ ISI:000262404501228,
Author = {Fishman, Ezra and Melanson, Dave and Lamport, Ron and Levine, Andy},
Book-Group-Author = {{IEEE}},
Title = {{A Novel Endoscopic Delivery System for Placement of a Duodenal-Jejunal
   Implant for the Treatment of Obesity and Type 2 Diabetes}},
Booktitle = {{2008 30th Annual International Conference of the IEEE Engineering in
   Medicine and Biology Society, Vols 1-8}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2008}},
Pages = {{2501-2503}},
Note = {{30th Annual International Conference of the
   IEEE-Engineering-in-Medicine-and-Biology-Society, Vancouver, CANADA, AUG
   20-24, 2008}},
Organization = {{DEVICIX; Green Coll; Natl Inst Hlth; NIBIB; NSF; PLEXON Inc; UBC Engn
   Biomed Engn; Univ Washington, Coll Engn; Bentham Sci Publ Ltd; Recent
   Patents Biomed Engn; Recent Patents Engn}},
DOI = {{10.1109/IEMBS.2008.4649708}},
ISSN = {{1557-170X

repository: newbiettn/paper-review

repo_name: newbiettn/paper-review