TEG and our collaborators have published extensive literature in the fields of disease management and in silico modeling, among other topics. 

Characterizing glucose changes antecedent to hypoglycemic events in the intensive care unit.

Cook C, Potter D, Kongable G. EndocrPract 2012; 18:317-324. Read Full Publication

Most Commonly Used Approaches for T2 Insulin Treatment

Click Here for T2 Insulin Treatment Summary

DMMS.R is a computer application designed for conducting clinical studies in virtual subjects with T1, T2 or Pre-Diabetes. It provides a unique in silico environment for testing diabetes treatment and monitoring interventions and is ideal for modeling new devices or examining treatment protocols and dosing algorithms.

Ubl M, Koutny T, Della Cioppa A, De Falco I, Tarantino E, Scafuri U. Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment>Conf Proc IEEE Eng Med Biol Soc. Sensors (Basel). 2022 Dec 2;22(23):9445  Link

Sun Q, Jankovic MV, Mougiakakou SG. Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy. Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:3609-3612  Link

Sieber J, Weinheimer M, Kongable G, Riddle S, Chang YY, Flacke F. In Silico Examination of Initiation of Long-Acting Insulin Analogs Toujeo Compared to Lantus Under 3 Dosing Titration Rules in Virtual Type 2 Diabetes Subjects. J Diabetes Sci Technol. 2019 Jul 9:1932296819861586. doi: 10.1177/1932296819861586 Link

T1DMS is revolutionizing the process by which researchers are making new discoveries for treating and managing Type 1 Diabetes.  See below for peer-reviewed scientific manuscripts that are related to or made possible by T1DMS technologies.

Olçomendy L, Cassany L, Pirog A, Franco R, Puginier E, Jaffredo M, Gucik-Derigny D, Ríos H, Ferreira de Loza A, Gaitan J, Raoux M, Bornat Y, Catargi B, Lang J, Henry D, Renaud S, Cieslak J. Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes Front Endocrinol (Lausanne). 2022 Apr 22;13:795225. Link

Ozaslan B, Deshpande S, Doyle FJ 3rd, Dassau E Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes Front Endocrinol (Lausanne). 2022 Mar 22;12:768639. Link

Daniels J, Herrero P, Georgiou P A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems Sensors (Basel). 2022 Jan 8;22(2):466. Link

De Bois M, Yacoubi MAE, Ammi M. GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes Med Biol Eng Comput. 2021 Nov 9. doi: 10.1007/s11517-021-02437-4. Link

Noaro G, Cappon G, Sparacino G, Boscari F, Bruttomesso D, Facchinetti A. Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical TrialJ Diabetes Sci Technol. 2021 Sep 6:19322968211043162. Link

Olcomendy L, Pirog A, Lebreton F, Jaffredo M, Cassany L, Gucik Derigny D, Cieslak J, Henry D, Lang J, Catargi B, Raoux M, Bornat Y, Renaud S. Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept IEEE Trans Biomed Eng. 2021 Sep 1;PP. doi: 10.1109/TBME.2021.3109096. Online ahead of print. Link

Rios YY, García-Rodríguez JA, Sanchez EN, Alanis AY, Ruiz-Velázquez E, Pardo Garcia A. Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction ISA Trans. 2021 Aug 11:S0019-0578(21)00412-2. doi: 10.1016/j.isatra.2021.07.045. Online ahead of print. Link

Schmitzer J, Strobel C, Blechschmidt R, Tappe A, Peuscher H. Efficient Closed Loop Simulation of Do-It-Yourself Artificial Pancreas Systems  J Diabetes Sci Technol. 2021 Jul 30:19322968211032249. doi: 10.1177/19322968211032249 Link

Chakrabarty A, Healey E, Shi D, Zavitsanou S, Doyle FJ 3rd, Dassau E. Embedded Model Predictive Control for a Wearable Artificial Pancreas IEEE Trans Control Syst Technol. 2020 Nov;28(6):2600-2607. Link

Astillo PV, Choudhary G, Duguma DG, Kim J, You I. TrMAps: Trust Management in Specification-based Misbehavior Detection System for IMD-Enabled Artificial Pancreas System  IEEE J Biomed Health Inform. 2021 Mar 2;PP. doi: 10.1109/JBHI.2021.3063173. Link

Hughes J, Gautier T, Colmegna P, Fabris C, Breton MD. Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes  J Diabetes Sci Technol. 2020 Nov 20:1932296820973193. doi: 10.1177/1932296820973193. Online ahead of print. Link

Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning  Sensors (Basel). 2020 Sep 6;20(18):E5058. doi: 10.3390/s20185058. Link

Çankaya, N, Aydoğdu, Ö. Three Parameter Control Algorithm for Obtaining Ideal Postprandial Blood Glucose in Type 1 Diabetes Mellitus   IEEE Access 2020 Aug 20 epub ahead of print_152305-152315_ DOI: 10.1109/ACCESS.2020.3015454 Link

Garcia-Tirado J, Corbett JP, Boiroux D, Jørgensen JB, Breton MD. Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control   J Process Control. 2019 Aug;80:202-210. doi: 10.1016/j.jprocont.2019.05.017. Epub 2019 Jun 23.. Link

Zhu T, Li K, Herrero P, Georgiou P. Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation  IEEE J Biomed Health Inform. 2020 Aug 5;PP. doi: 10.1109/JBHI.2020.3014556. Online ahead of print.. Link

Lee S, Kim J, Park SW, Jin SM, Park SM. Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation IEEE J Biomed Health Inform. 2020 Jun 12;PP. doi: 10.1109/JBHI.2020.3002022. Online ahead of print. Link

Meneghetti L, Facchinetti A, Del Favero S. Model-Based Detection and Classification of Insulin Pump Faults and Missed Meal Announcements in Artificial Pancreas Systems for Type 1 Diabetes Therapy  IEEE Trans Biomed Eng. 2020 Jun 22;PP. doi: 10.1109/TBME.2020.3004270. Online ahead of print. Link

Noaro G, Cappon G, Vettoretti M, Sparacino G, Del Favero S, Facchinetti A. Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy IEEE Trans Biomed Eng. 2020 Jun 22;PP. doi: 10.1109/TBME.2020.3004031. Online ahead of print. Link

Fushimi E, Serafini MC, De Battista H, Garelli F. Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study Med Biol Eng Comput. 2020 Jul 24. doi: 10.1007/s11517-020-02213-w. Online ahead of print. Link

Colmegna P, Cengiz E, Garcia-Tirado J, Kraemer K, Breton MD. Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination J Diabetes Sci Technol. 2020 Jun 17:1932296820928067. doi: 10.1177/1932296820928067. Online ahead of print. Link

Roversi C, Vettoretti M, Del Favero S, Facchinetti A, Sparacino G. Modeling carbohydrate counting error in type 1 diabetes management. Diabetes Technol Ther. 2020 Mar 30. doi: 10.1089/dia.2019.0502. [Epub ahead of print] Link

Amar Y, Shilo S, Oron T, Amar E, Phillip M, Segal E.  Clinically Accurate Prediction of Glucose Levels in Patients With Type 1 Diabetes  Diabetes Technol Ther. 2020 Mar 6. doi: 10.1089/dia.2019.0435. Online ahead of print. Link

Schiavon M, Visentin R, Giegerich C, Sieber J, Dalla Man C, Cobelli C, Klabunde T. IN SILICO HEAD-TO-HEAD COMPARISON OF INSULIN GLARGINE 300 U/ML AND INSULIN DEGLUDEC 100 U/ML IN TYPE 1 DIABETES.  Diabetes Technol Ther. 2020 Mar 3. doi: 10.1089/dia.2020.0027. [Epub ahead of print  Link

Moscoso-Vasquez M, Colmegna P, Rosales N, Garelli F, Sanchez-Pena R. Control-Oriented Model with Intra-Patient Variations for an Artificial Pancreas. IEEE J Biomed Health Inform. 2020 Jan 27. doi: 10.1109/JBHI.2020.2969389. [Epub ahead of print] Link

Visser SAG, Kandala B, Fancourt C, Krug AW, Cho CR.  A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making. Clin Pharmacol Ther. 2019 Dec 30. doi: 10.1002/cpt.1729. [Epub ahead of print]  Link

Camerlingo N, Vettoretti M, Del Favero S, Cappon G, Sparacino G, Facchinetti A. In-silico Assessment of Preventive Hypotreatment Efficacy and Development of a Continuous Glucose Monitoring Based Algorithm to Prevent/Mitigate Hypoglycemia in Type 1 Diabetes. Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:4133-4136. Link

Magdelaine N, Rivadeneira PS, Chaillous L, Fournier-Guilloux AL, Krempf M, MohammadRidha T, Ait-Ahmed M, Moog CH. Hypoglycaemia-free artificial pancreas project. IET Syst Biol. 2020 Feb;14(1):16-23. Link

Amar Y, Shilo S, Oron T, Amar E, Phillip M, Segal E. Clinically accurate prediction of glucose levels in patients with type 1 diabetes. Diabetes Technol Ther. 2020 Jan 13. doi: 10.1089/dia.2019.0435. Link

Toffanin C, Kozak M, Sumnik Z, Cobelli C, Petruzelkova L. In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems. Diabetes Technol Ther. 2020 Feb;22(2):112-120. Link

Garcia-Tirado J, Colmegna P, Corbett JP, Ozaslan B, Breton MD.  In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System. J Diabetes Sci Technol. 2019 Nov;13(6):1054-1064. Link

Moscardó V, Díez JL, Bondia J. Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions. J Diabetes Sci Technol. 2019 Nov;13(6):1026-1034. Link

Fabris C, Ozaslan B, Breton MD.  Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution. Sensors (Basel). 2019 Dec 6;19(24). pii: E5386. doi: 10.3390/s19245386. Link

Meneghetti L, Susto GA, Del Favero S. Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms. J Diabetes Sci Technol. 2019 Oct 14:1932296819881452. doi: 10.1177/1932296819881452 Link

Liu C, Vehí J, Avari P, Reddy M, Oliver N, Georgiou P, Herrero P. Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal. Sensors (Basel). 2019 Oct 8;19(19). pii: E4338. doi: 10.3390/s19194338. Link

Camerlingo N, Vettoretti M, Del Favero S, Cappon G, Sparacino G, Facchinetti A. A Real-Time Continuous Glucose Monitoring Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events.  Diabetes Technol Ther. 2019 Jul 25. doi: 10.1089/dia.2019.0139. [Epub ahead of print] Link

Mandal S, Sutradhar A. Robust multi-objective blood glucose control in Type-1 diabetic patient. IET Syst Biol. 2019 Jun;13(3):136-146. doi: 10.1049/iet-syb.2018.5093. Link

Garcia-Tirado J, Zuluaga-Bedoya C, Breton MD Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems.  J Diabetes Sci Technol. 2018 Sep;12(5):937-952 Link

Li K, Daniels J, Liu C, Herrero-Vinas P, Georgiou P.  Convolutional Recurrent Neural Networks for Glucose Prediction. IEEE J Biomed Health Inform. 2019 Apr 1. doi: 10.1109/JBHI.2019.2908488 Link

AlMatouq AA, Laleg-Kirati TM, Novara C, Rabbone I, Vincent T. Sparse Reconstruction of Glucose Fluxes using Continuous Glucose Monitors. IEEE/ACM Trans Comput Biol Bioinform. 2019 Mar 15. doi: 10.1109/TCBB.2019.2905198. [Epub ahead of print]Link

Visentin R, Schiavon M, Giegerich C, Klabunde T, Dalla Man C, Cobelli C. Incorporating Long-Acting Insulin Glargine into the UVAPadova Type 1 Diabetes Simulator for In Silico Testing of MDI TherapiesIEEE Trans Biomed Eng. 2019 Feb 6. doi: 10.1109/TBME.2019.2897851. [Epub ahead of print]Link

Shi D, Dassau E, Doyle FJ 3rd. Multivariate learning framework for long-term adaptation in the artificial pancreas. Bioeng Transl Med. 2018 Nov 12;4(1):61-74. Link

Xie J, Wang Q. A Data-Driven Personalized Model of Glucose Dynamics Taking Account of the Effects of Physical Activity for Type 1 Diabetes: An In Silico Study.  J Biomech Eng. 2019 Jan 1;141(1). doi: 10.1115/1.4041522. Link

Visentin R, Schiavon M, Giegerich C, Klabunde T, Man CD, Cobelli C. Long-acting Insulin in Diabetes Therapy: In Silico Clinical Trials with the UVA/Padova Type 1 Diabetes Simulator.  Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:4905-4908. doi: 10.1109/EMBC.2018.85132 Link

Schiavon M, Acciaroli G, Vettoretti M, Giaretta A, Visentin R. A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements. Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:159-162. doi: 10.1109/EMBC.2018.8512257.. Link

Cappon G, Vettoretti M, Marturano F, Facchinetti A, Sparacino G. Optimal Insulin Bolus Dosing in Type 1 Diabetes Management: Neural Network Approach Exploiting CGM Sensor Information. Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8512250. Link

Feng J, Hajizadeh I, Yu X, Rashid M, Turksoy K, Samadi S, Sevil M, Hobbs N, Brandt R, Lazaro C, Maloney Z, Littlejohn E, Philipson LH, Cinar A. Multi-level Supervision and Modification of Artificial Pancreas Control System. Comput Chem Eng. 2018 Apr 6;112:57-69. doi: 10.1016/j.compchemeng.2018.02.002. Epub 2018 Feb 10. Link

Nandi S, Singh T. Glycemic Control of People with Type 1 Diabetes based on Probabilistic Constraints. IEEE J Biomed Health Inform. 2018 Sep 10. doi: 10.1109/JBHI.2018.2869365. [Epub ahead of print] Link

Garcia-Tirado J, Zuluaga-Bedoya C, Breton MD. Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems. J Diabetes Sci Technol. 2018 Sep;12(5):937-952. doi: 10.1177/1932296818788873. Epub 2018 Aug 10. Link

Shi D, Dassau E, Doyle Iii FJ. Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties. IEEE Trans Biomed Eng. 2018 Aug 21. doi: 10.1109/TBME.2018.2866392. [Epub ahead of print] Link

Cappon G, Marturano F, Vettoretti M, Facchinetti A, Sparacino G. In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change.  J Diabetes Sci Technol. 2018 May 1:1932296818777524. doi: 10.1177/1932296818777524. [Epub ahead of print] Link

Bertachi A, Beneyto A, Ramkissoon CM, Vehí J. Assessment of Mitigation Methods to Reduce the Risk of Hypoglycemia for Announced Exercise in a Uni-hormonal Artificial Pancreas. Diabetes Technol Ther. 2018 Apr;20(4):285-295. doi: 10.1089/dia.2017.0392. Epub 2018 Apr 2.] Link

Torrent-Fontbona F, Lopez Ibanez B. Personalised Adaptive CBR Bolus Recommender System for Type 1 Diabetes.  IEEE J Biomed Health Inform. 2018 Mar 9. doi: 10.1109/JBHI.2018.2813424. [Epub ahead of print] Link

Cappon G, Vettoretti M, Marturano F, Facchinetti A, Sparacino G. A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring.  J Diabetes Sci Technol. 2018 Mar;12(2):265-272 Link

Visentin R, Campos-Náñez E, Schiavon M, Lv D, Vettoretti M, Breton M, Kovatchev BP, Dalla Man C, Cobelli C. The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day. J Diabetes Sci Technol. 2018 Feb 1:1932296818757747. doi: 10.1177/1932296818757747 Link

Steil GM. Best Use of Models to Advance the Artificial Pancreas. Diabetes Technol Ther. 2018 Feb 15. doi: 10.1089/dia.2017.0420. [Epub ahead of print]] Link

Samadi S, Rashid M, Turksoy K, Feng J, Hajizadeh I, Hobbs N, Lazaro C, Sevil M, Littlejohn E, Cinar A.  Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.  Diabetes Technol Ther. 2018 Feb 6. doi: 10.1089/dia.2017.0364. [Epub ahead of print] Link

Schiavon M, Dalla Man C, Cobelli C. Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy. Diabetes Technol Ther. 2018 Feb;20(2):98-105. Link

MohammadRidha T, Ait-Ahmed M, Chaillous L, Krempf M, Guilhem I, Poirier JY, Moog CH. Model Free iPID Control for Glycemia Regulation of Type-1 DiabetesIEEE Trans Biomed Eng. 2018 Jan;65(1):199-206. Link

Walsh J. Issues and Ideas in Bolus Advisor Research With Commentary on “A Methodology to Compare Insulin Dosing Algorithms in Real-Life Settings”.  J Diabetes Sci Technol. 2017 Nov;11(6):1183-1186. Link

Contreras I, Oviedo S, Vettoretti M, Visentin R, Vehí J. Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models. PLoS One. 2017 Nov 7;12(11):e0187754. Link

Campos-Náñez E, Layne JE, Zisser HC.  In Silico Modeling of Minimal Effective Insulin Doses Using the UVA/PADOVA Type 1 Diabetes Simulator. J Diabetes Sci Technol. 2017 Sep 1:1932296817735341. Link

Fortwaengler K, Campos-Náñez E, Parkin CG, Breton MD. The Financial Impact of Inaccurate Blood Glucose Monitoring Systems. J Diabetes Sci Technol. 2017 Sep 1:193229681773142. Link

Vettoretti M, Facchinetti A, Sparacino G, Cobelli C. Type 1 diabetes patient decision simulator for in silico testing safety and effectiveness of insulin treatments  IEEE Trans Biomed Eng. 2017 Aug 29. doi: 10.1109/TBME.2017.2746340. Link

Herrero P, Bondia J, Adewuyi O, Pesl P, El-Sharkawy M, Reddy M, Toumazou C, Oliver N, Georgiou P.  Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability. Comput Methods Programs Biomed. 2017 Jul;146:125-131.  Link

Breton MD, Hinzmann R, Campos-Nañez E, Riddle S, Schoemaker M, Schmelzeisen-Redeker G. Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator. J Diabetes Sci Technol. 2017 May;11(3):545-552./1932296817710474. Link

Campos-Náñez E, Fortwaengler K, Breton MD. Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study. J Diabetes Sci Technol. 2017 May 1:1932296817710474. doi: 10.1177/1932296817710474. Link

Chakrabarty A, Zavitsanou S, Doyle Iii F, Dassau E. Event-Triggered Model Predictive Control For Embedded Artificial Pancreas Systems. IEEE Trans Biomed Eng. 2017 May 23. doi: 10.1109/TBME.2017.2707344. Link

Xie J, Wang Q. A Variable State Dimension Approach to Meal Detection and Meal Size Estimation: In Silico Evaluation Through Basal-Bolus Insulin Therapy for Type 1 Diabetes. IEEE Trans Biomed Eng. 2017 Jun;64(6):1249-1260. Link

Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng H. 2017 May;231(5):455-466. Link

Samadi S, Turksoy K, Hajizadeh I, Feng J, Sevil M, Cinar A. Meal Detection and Carbohydrate Estimation Using Continuous Glucose Sensor Data. IEEE J Biomed Health Inform. 2017 Mar 3. doi: 10.1109/JBHI.2017.2677953. [Epub ahead of print] Link

Toffanin C, Visentin R, Messori M, Di Palma F, Magni L, Cobelli C. Towards a Run-to-Run Adaptive Artificial Pancreas: In Silico Results. IEEE Trans Biomed Eng. 2017 Jan 11. doi: 10.1109/TBME.2017.2652062 Link

Vettoretti M, Facchinetti A, Sparacino G, Cobelli C. Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity. Diabetes Technol Ther. 2016 Nov;18(11):694-704. Link

Daskalaki E, Diem P, Mougiakakou SG. Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes. PLoS One. 2016 Jul 21;11(7):e0158722. doi: 10.1371/journal.pone.0158722. eCollection 2016. Link

Messori M, Toffanin C, Del Favero S, De Nicolao G, Cobelli C, Magni L. Model individualization for artificial pancreas. Comput Methods Programs Biomed. 2016 Jul 5. pii: S0169-2607(15)30443-0. doi: 10.1016/j.cmpb.2016.06.006. [Epub ahead of print] Link

Visentin R, Giegerich C, Jäger R, Dahmen R, Boss A, Grant M, Dalla Man C, Cobelli C, Klabunde T. Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator. Diabetes Technol Ther. 2016 Jun 22. [Epub ahead of print] Link

Visentin R, Man C, Cobelli C. One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Towards a Single-Day UVA/Padova Type 1 Diabetes Simulator. IEEE Trans Biomed Eng. 2016 Feb 26. [Epub ahead of print]. Link

Visentin R, Klabunde T, Grant M, Dalla Man C, Cobelli C. Incorporation of inhaled insulin into the FDA accepted University of Virginia/Padova Type 1 Diabetes Simulator. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:3250-3. doi: 10.1109/EMBC.2015.7319085. Link

Li P, Yu L, Fang Q, Lee SY. A simplification of Cobelli’s glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation. Med Biol Eng Comput. 2015 Dec 30. [Epub ahead of print] Link

Colmegna P, Sanchez-Pena R, Gondhalekar R, Dassau E, Doyle F. Switched LPV Glucose Control in Type 1 Diabetes. IEEE Trans Biomed Eng. 2015 Oct 5. [Epub ahead of print] Link

Zavitsanou S, Mantalaris A, Georgiadis MC, Pistikopoulos EN. In Silico Closed-Loop Control Validation Studies for Optimal Insulin Delivery in Type 1 Diabetes. IEEE Trans Biomed Eng. 2015 Oct;62(10):2369-78. doi: 10.1109/TBME.2015.2427991. Epub 2015 Apr 29. Link

Herrero P, Pesl P, Reddy M, Oliver N, Georgiou P, Toumazou C. Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning. IEEE J Biomed Health Inform. 2015 May;19(3):1087-96. Link

Zhao C, Yu C. Rapid model identification for online subcutaneous glucose concentration prediction for new subjects with type I diabetes. Comput Methods Programs Biomed. 2015 Apr;119(1):1-8. doi: 10.1016/j.cmpb.2015.02.003. Epub 2015 Feb 16. Link

Herrero P, Pesl P, Bondia J, Reddy M, Oliver N, Georgiou P, Toumazou C. Method for automatic adjustment of an insulin bolus calculator: in silico robustness evaluation under intra-day variability. IEEE Trans Biomed Eng. 2015 May;62(5):1333-44. doi: 10.1109/TBME.2014.2387293. Epub 2015 Jan 1. Link

Hu R, Li C. An Improved PID Algorithm Based on Insulin-on-Board Estimate for Blood Glucose Control with Type 1 Diabetes. Comput Math Methods Med. 2015;2015:281589. doi: 10.1155/2015/281589. Epub 2015 Oct 5. Link

Visentin R, Dalla Man C, Kudva YC, Basu A, Cobelli C. Circadian variability of insulin sensitivity: physiological input for in silico artificial pancreas. Diabetes Technol Ther. 2015 Jan;17(1):1-7. doi: 10.1089/dia.2014.0192. Link

Colmegna P, Sanchez Pena RS, Gondhalekar R, Dassau E, Doyle Iii FJ. Reducing risks in type 1 diabetes using H∞ control. IEEE Trans Biomed Eng. 2014 Dec;61(12):2939-47. doi: 10.1109/TBME.2014.2336772. Epub 2014 Jul 9. Link

Visentin R, Dalla Man C, Kovatchev B, Cobelli C. The university of Virginia/Padova type 1 diabetes simulator matches the glucose traces of a clinical trial. Diabetes Technol Ther. 2014 Jul;16(7):428-34. doi: 10.1089/dia.2013.0377. Epub 2014 Feb 26. Link

Wang Q, Molenaar P, Harsh S, Freeman K, Xie J, Gold C, Rovine M, Ulbrecht J. Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach. J Diabetes Sci Technol. 2014 Mar 24;8(2):331-345. [Epub ahead of print] Link

Turksoy K, Quinn L, Littlejohn E, Cinar A. Multivariable adaptive identification and control for artificial pancreas systems. IEEE Trans Biomed Eng. 2014 Mar;61(3):883-91. doi: 10.1109/TBME.2013.2291777. [Epub ahead of print] Link

Man CD, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C. The UVA/PADOVA Type 1 Diabetes Simulator: New Features. J Diabetes Sci Technol. 2014 Jan 1;8(1):26-34. [Epub ahead of print] Link

Abbes IB, Richard PY, Lefebvre MA, Guilhem I, Poirier JY. A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism. J Diabetes Sci Technol. 2013 May 1;7(3):699-707. Link

Facchinetti A, Del Favero S, Sparacino G, Cobelli C. An online failure detection method of the glucose sensor-insulin pump system: improved overnight safety of type-1 diabetic subjects. IEEE Trans Biomed Eng. 2013 Feb;60(2):406-16. doi: 10.1109/TBME.2012.2227256. Epub 2012 Nov 15. Link

Zhao C, Dassau E, Jovanovič L, Zisser HC, Doyle FJ 3rd, Seborg DE. Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus. J Diabetes Sci Technol 2012; May 1; 6(3):617-33. Link

Zarkogianni K, Vazeou A, Mougiakakou SG, Prountzou A, Nikita KS. An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Trans Biomed Eng. 2011 Sep;58(9):2467-77. doi: 10.1109/TBME.2011.2157823. Epub 2011 May 27. Link

Cameron F, Bequette BW, Wilson DM, Buckingham BA, Lee H, Niemeyer G. A closed-loop artificial pancreas based on risk management. J Diabetes Sci Technol 2011; Mar 1; 5(2):368-79. Link

Kovatchev B, Cobelli C, Renard E, Anderson S, Breton M, Patek S, Clarke W, Bruttomesso D, Maran A, Costa S, Avogaro A, Dalla Man C, Facchinetti A, Magni L, De Nicolao G, Place J, Farret A. Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: summary of the results. J Diabetes Sci Technol 2010; Nov 1; 4(6):1374-81. Link

Grosman B, Dassau E, Zisser HC, Jovanovic L, Doyle FJ 3rd. Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events. J Diabetes Sci Technol 2010; Jul 1; 4(4):961-75. Link

Wang Y, Dassau E, Zisser H, Jovanovič L, Doyle FJ 3rd. Automatic bolus and adaptive basal algorithm for the artificial pancreatic β-cell. Diabetes Technol Ther 2010; Nov; 12(11):879-87. Epub 2010 Sep 30. Link

Mauseth R, Wang Y, Dassau E, Kircher R Jr, Matheson D, Zisser H, Jovanovic L, Doyle FJ 3rd. Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. J Diabetes Sci Technol 2010; Jul 1; 4(4):913-22. Link

Kovatchev BP, Breton MD, Dalla Man C, Cobelli C. In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes. J Diabetes Sci Technol 2009; 3(1): 44-45. Link

Patek SD, Bequette W, Breton M, Buckingham BA, Dassau E, Doyle FJ 3rd, Lum J, Magni L, and Zisser H. In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type I Diabetes Mellitus. J Diabetes Sci Technol 2009; 3(2): 269-282. Link

Clarke W, Kovatchev B. Statistical tools to analyze continuous glucose monitor data. Diabetes Technol Ther 2009; Jun; 11 Suppl 1:S45-54. Link

Man CD, Breton MD, Cobelli C Physical activity into the meal glucose-insulin model of type 1 diabetes: in silico studies. J Diabetes Sci Technol. 2009 Jan;3(1):56-67. Link

Magni L, Raimondo DM, Man CD, Breton M, Patek S, Nicolao GD, Cobelli C, Kovatchev BP. Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diabetes Sci Technol 2008; Jul; 2(4):630-5. Link

Breton M, Kovatchev B. Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. J Diabetes Sci Technol 2008; 2(5): 853-862. Link

Dalla Man C, Rizza RA, Cobelli C. Meal Simulation Model of the Glucose-Insulin System. IEEE Trans Biomed Eng 2007; 54(10): 1740-1749. Link

Dalla Man C, Camilleri M, Cobelli C. A System Model of Oral Glucose Absorption: Validation on Gold Standard Data. IEEE Trans Biomed Eng 2006; 53(12): 2472-2478. Link

Sandridge LC, Baglioni AJ Jr, Kongable GL, Harthun NL. Evaluation of the effect of endovascular options on infrarenal abdominal aortic aneurysm repair. Am Surg 2006; 72(8):700-4; disc 704-6. Link

Zito D, Kongable G, Anderson M. The Impact of Intensive Insulin Protocols on the Clinical Laboratory. J Ligand Assay 2005; 28(4):202-206. Link

Kovatchev BP, Gonder-Frederick LA, Cox DJ, and Clarke WL. Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors: Continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. DiabetesCare 2004;27:1922-1928. Link

The following publications by TEG and collaborators are shaping the future of diabetes treatment and management strategies.


Bersoux S, Cook CB, Kongable GL, Shu J, Zito DR. Benchmarking glycemic control in u.s. Hospitals. Endocr Pract. 2014 Sep;20(9):876-83. doi: 10.4158/EP13516.OR. Link

Bersoux S, Cook CB, Kongable GL, Shu J. Trends in glycemic control over a 2-year period in 126 US hospitals. J Hosp Med. 2013 Mar;8(3):121-5. doi: 10.1002/jhm.1997. Epub 2012 Dec 19. Link

Cook C, Potter D, Kongable G. Characterizing glucose changes antecedent to hypoglycemic events in the intensive care unit. EndocrPract 2012; 18:317-324. Link

Cook CB, Wellik KE, Kongable GL, Shu J. Assessing inpatient glycemic control: what are the next steps? J Diabetes Sci Technol 2012; Mar 1; 6(2):421-7. Link

Swanson C, Potter D, Kongable G. Update on inpatient glycemic control in hospitals in the United States. EndocrPract 2011; 17(6):853-861. Link

Cook C, Elias B, Kongable G et al. Diabetes and Hyperglycemia Quality Improvement Efforts in Hospitals in the United States: Current Status, Practice Variation and Barriers to Implementation. EndocrPract 2010; 16(2):219-230. Link

Cook C, Kongable G, Potter D et al. Inpatient glucose control: a glycemic survey of 126 U.S. Hospitals. J Hosp Med 2009; 4(9):E7-E14. Link

Biesma B, van de Wef PR, Melissant CF, Brok RG. Anaemia management with epoetin alfa in lung cancer patients in The Netherlands. Lung Cancer 2007; 58(1):104-11. Link

Roberts D, Meakem T, Dalton C, Haverstick D, Lynch C. Prevalence of Hyperglycemia in a Pre-Surgical Population. The Internet Journal of Anesthesiology 2007; 12(1). Link

Cook C, Moghissi E, Joshi R, Kongable G, Abad V. Inpatient Point-of-Care Bedside Glucose Testing: Preliminary Data on Use of Connectivity Informatics to Measure Hospital Glycemic Control. Diabetes Technol Ther 2007; 9(6):493-500. Link

Juneja R, Roudebush C, Kumar N, Macy A, Golas A, Wall D, Wolverton C, Nelson D, Carroll J, Flanders SJ. Utilization of a computerized intravenous insulin infusion program to control blood glucose in the intensive care unit. Diabetes Technol Ther 2007; 9(3):232-40. Link

Moghissi E, Kongable G, Abad V, Leija D. Current State of Inpatient Diabetes Burden and Care, and Goal of Conference. EndocrPract 2006; 12(Supplement 3):1-10. Link

Lameire N, Stevens, Raptis S, Thomas S, Schernthaner G. Individualized risk management in diabetics: how to implement best practice guidelines–design and concept of the IRIDIEM studies. Kidney Blood Press Res 2004; 27(3): 127-133. Link

Ludwig H, et al. The European Cancer Anaemia Survey (ECAS): A large, multinational, prospective survey defining the prevalence, incidence, and treatment of anaemia in cancer patients. European Journal of Cancer 2004; 40(15): 2293-2306. Link

Vincent JL, et al. Anemia and blood transfusion in critically ill patients. JAMA 2002; Sep 25; 288(12):1499-1507. Link

The following are textbook references to studies by TEG and our collaborators.

Klipp E, Liebermeister W., Wierling C., Kowald A., Systems Biology, A Textbook, 2nd Edition, 2016; Wiley-Blackwell, Hoboken, NJ

DiStefano JJ 3rd. Dynamic Systems Biology Modeling and Simulation. First Edition, 2014, Rev Edition 2015.

Bonate PL. Pharmacokinetic-Pharmacodynamic Modeling and Simulation. 2010; Springer Science + Business Media, Inc., New York, NY.

Cobelli C, Carson E. Introduction to Modeling in Physiology and Medicine. 2008; Elsevier, Burlington, MA.

Cobelli C, Foster D, Toffolo G. Tracer Kinetics in Biomedical Research From Data to Model. 2000; Kluwer Academic/Plenum Publishers, New York, NY.

Jacquez JA. Modeling with Compartments. 1999; BioMedware, Ann Arbor, MI.

Jacquez JA. Compartmental Analysis in Biology and Medicine. 3rd Ed. 1996; BioMedware, Ann Arbor, MI.

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