Multiscale Metabolic Modeling Approach for Predicting Blood Alcohol Concentration
Bibliographic record
Abstract
Alcohol is one of the most widely consumed and abused substances, and is a major factor in many alcohol-related diseases, incidents of impaired driving, and crimes. In this letter, we develop a mechanistic model for alcohol metabolism in the human body based on the dynamic parsimonious flux balance analysis technique. The developed whole body alcohol metabolic model contains two main mechanisms for ethanol metabolism in the body, namely, oxidative and non-oxidative mechanisms. The model is able to demonstrate the effect of variations in biochemical kinetics associated with the alcohol dehydrogenase enzyme, gender differences, physiological properties of the human body such as age, weight, and height, and the meal effect on the alcohol clearance from the body. Simulation results show that the model predictions are consistent with in vivo studies. The results from this letter indicate that the proposed metabolic modeling approach may open the door to new opportunities in the area of metabolic nutrition research and personalized medicine since it accounts for physiological properties and biochemical information related to the human body.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".