Optimal Timing of Insulin Initiation Using a Mathematical Model

Author(s):  
Evrim Didem Gunes ◽  
F. Erkam Minsin ◽  
Mehtap Dursun
2000 ◽  
Vol 124 (2) ◽  
pp. 279-287 ◽  
Author(s):  
M. G. ROBERTS ◽  
M. I. TOBIAS

A mathematical model of the dynamics of measles in New Zealand was developed in 1996. The model successfully predicted an epidemic in 1997 and was instrumental in the decision to carry out an intensive MMR (measles–mumps–rubella) immunization campaign in that year. While the epidemic began some months earlier than anticipated, it was rapidly brought under control, and its impact on the population was much reduced. In order to prevent the occurrence of further epidemics in New Zealand, an extended version of the model has since been developed and applied to the critical question of the optimal timing of MMR immunization.


2009 ◽  
Vol 107 (3) ◽  
pp. 696-706 ◽  
Author(s):  
Yoseph Mebrate ◽  
Keith Willson ◽  
Charlotte H. Manisty ◽  
Resham Baruah ◽  
Jamil Mayet ◽  
...  

We examine the potential to treat unstable ventilatory control (seen in periodic breathing, Cheyne-Stokes respiration, and central sleep apnea) with carefully controlled dynamic administration of supplementary CO2, aiming to reduce ventilatory oscillations with minimum increment in mean CO2. We used a standard mathematical model to explore the consequences of phasic CO2 administration, with different timing and dosing algorithms. We found an optimal time window within the ventilation cycle (covering ∼1/6 of the cycle) during which CO2 delivery reduces ventilatory fluctuations by >95%. Outside that time, therapy is dramatically less effective: indeed, for more than two-thirds of the cycle, therapy increases ventilatory fluctuations >30%. Efficiency of stabilizing ventilation improved when the algorithm gave a graded increase in CO2 dose (by controlling its duration or concentration) for more severe periodic breathing. Combining gradations of duration and concentration further increased efficiency of therapy by 22%. The (undesirable) increment in mean end-tidal CO2 caused was 300 times smaller with dynamic therapy than with static therapy, to achieve the same degree of ventilatory stabilization (0.0005 vs. 0.1710 kPa). The increase in average ventilation was also much smaller with dynamic than static therapy (0.005 vs. 2.015 l/min). We conclude that, if administered dynamically, dramatically smaller quantities of CO2 could be used to reduce periodic breathing, with minimal adverse effects. Algorithms adjusting both duration and concentration in real time would achieve this most efficiently. If developed clinically as a therapy for periodic breathing, this would minimize excess acidosis, hyperventilation, and sympathetic overactivation, compared with static treatment.


2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi270-vi270
Author(s):  
Kosuke Aoki ◽  
Takashi Yamamoto ◽  
Hiromichi Suzuki ◽  
Sachi Maeda ◽  
Melissa Ranjit ◽  
...  

Abstract BACKGROUND In WHO grade II diffuse gliomas (low-grade gliomas, hereafter called LGGs), chemotherapy and radiotherapy contribute to prolonged survival but could induce somatic mutations. The optimal timing of treatment in LGGs remain poorly understood. To delineate this, we designed a mathematical model for tumor growth and investigate the association among the treatment, malignant transformation (MT), and the accumulation of somatic mutations revealed by whole exome sequencing (WES) in LGGs. METHODS Totally, 290 patients with LGGs between 1990 and 2018 were analyzed. We assessed the statuses of IDH mutation and 1p19q co-deletion in all tumors. Among all, 114 patients (39%) underwent MT during follow-up periods (mean: 82.6 months). Tumor volume was evaluated with FLAIR and/or T2-weighted MRI. MT was evaluated with contrast-enhanced MRI and/or pathological diagnosis. To investigate the number of somatic mutations in a cohort of LGGs and their patient matched recurrence, WES was performed on 88 serial samples collected at least two time-points from 39 patients. RESULTS Oligodendroglioma, IDH-mutant and 1p/19q-codeleted (OD) showed longer transformation-free survival compared to other subtypes. An exponential model was chosen to estimate growth rate in LGGs, since the exponential model provided a better fit to our data as compared to a linear model. The growth rate significantly decreased in the middle of chemotherapy and after radiotherapy. By contrast, these treatments increased the number of somatic mutations identified by WES and the rate of MT in each subtype. The increasing number of mutations in recurrent tumors showed strong correlation with the rise in MT rate. Based on the growth rate and the risk of MT, optimal timing of treatments could be calculated for each genetic subtype. CONCLUSIONS The mathematical model and WES analysis delineates the optimal timing of treatments in each subtype, which will help to decide the treatment for LGGs.


2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Kwang Su Kim ◽  
Giphil Cho ◽  
Il Hyo Jung

We propose a mathematical model describing tumor-immune interactions under immune suppression. These days evidences indicate that the immune suppression related to cancer contributes to its progression. The mathematical model for tumor-immune interactions would provide a new methodology for more sophisticated treatment options of cancer. To do this we have developed a system of 11 ordinary differential equations including the movement, interaction, and activation of NK cells, CD8+T-cells, CD4+T cells, regulatory T cells, and dendritic cells under the presence of tumor and cytokines and the immune interactions. In addition, we apply two control therapies, immunotherapy and chemotherapy to the model in order to control growth of tumor. Using optimal control theory and numerical simulations, we obtain appropriate treatment strategies according to the ratio of the cost for two therapies, which suggest an optimal timing of each administration for the two types of models, without and with immunosuppressive effects. These results mean that the immune suppression can have an influence on treatment strategies for cancer.


2007 ◽  
Vol 177 (4S) ◽  
pp. 128-129
Author(s):  
Christopher R. King ◽  
Stephen J. Freedland ◽  
Martha K. Terris ◽  
William J. Aronson ◽  
Christopher J. Kane ◽  
...  

2017 ◽  
Vol 23 ◽  
pp. 50
Author(s):  
Jothydev Kesavadev ◽  
Shashank Joshi ◽  
Banshi Saboo ◽  
Hemant Thacker ◽  
Arun Shankar ◽  
...  

2008 ◽  
Author(s):  
Ishii Akira ◽  
Yoshida Narihiko ◽  
Hayashi Takafumi ◽  
Umemura Sanae ◽  
Nakagawa Takeshi
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document