The effects of vitamin D on glucose-insulin dynamics: mathematical model and simulation
Bibliographic record
Abstract
Background: Maintenance of glucose level for diabetic people is very important and challenging. Many factors seem to affect the level of glucose in our body, out of which vitamin D is found to be one of the most important factors. It inspires us to develop a model that is capable of predicting the effect of vitamin D on glucose-insulin dynamics of human body. Objective: The main objective of the study is to develop a model to capture the effect of vitamin D on the glucose-insulin dynamics of non-diabetic, T2DM and T1DM people. Method: A minimal model previously developed by Bergman is extended to include the effects of vitamin D via parameters. Stability analysis and numerical simulation has been performed on the vitamin D model to analyze the behavior of the model. Comparisons are made to observe the blood glucose and insulin level in non-diabetic, T2DM and T1DM people. Result: The model assess the changes in glucose-insulin dynamics after the induction of different values of vitamin D parameters in their respective range. Conclusion and future work: The vitamin D model captured the glucose-insulin dynamics effectively and should be recommended in our daily routine, especially for the diabetic people. Further, the dosage of vitamin D may be calculated clinically because of the variation in the population and severity of the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".