Genetic Aspects of Nutrition and Toxicology: Report of a Workshop
Bibliographic record
Abstract
The health and resilience of humans and animals is, in large part, determined by the quality and quantity of the diet. This, in turn, may influence an individual's capability to deal with stress including toxic insult. In addition, there may be specific components of the diet that modulate the toxicity of specific toxicants whether the latter are ingested as food or absorbed via other routes. Many examples attest to the importance of interactions between dietary components and toxicants after absorption in the body. Such interactions occur at every level of biological organization from the molecular to the whole organism. Some may be synergistic, others antagonistic. Some may involve direct chemical reaction between the nutrient molecule and the toxicant, others may occur by indirect action at the cellular or organ levels. All examples point to the importance of considering diet when measuring the response to toxic agents whether in animals or humans. In order to foster interaction between the sciences of nutrition and toxicology, The Heinz Institute of Nutritional Sciences as sponsoring a series of workshops. The first of these was held in June, 1999 at the University of Ulster to address evolutionary aspects of nutrition--toxicology (for report see Eur. J. Nutr, 39, 49-52, 2000). In June, 2000, a second workshop was held at the University of Toronto to address genetic aspects, and this is a brief summary of the proceedings. We are beginning to understand the molecular basis of the regulation of gene expression by dietary factors and how genetic changes can affect response to toxicants. Recent advances in technology and a detailed understanding of disease etiology has led to the ability to study molecular determinants of disease risk. The workshop provided a forum for nutritionists, toxicologists, molecular biologists, epidemiologists and others to discuss common interests and to merge their efforts towards an integrated approach to nutrition--toxicology via genetics and genomics. The first session dealt with the mechanism by which nutrients such as fatty acids (Clarke), amino acids (Jefferson) and metal ions (Cousins) can regulate gene expression. In the second session, there were presentations on the effects of nutritional factors on genes of toxicological significance such as phase I and phase II enzymes of drug metabolism (Guengerich, Goodfellow and Grant) as well as on oxidative DNA damage and its repair (Collins, Weindruch). Session three dealt with gene-nutrient interactions in the development of chronic diseases such as diabetes (Hegele, Berdanier) and cancer (Kim, Ambrosone et al.). New developments such as DNA microarrays (McGlynn) and the use of transgenic and knockout models (Sehayek) were presented in the final session.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".