Review on Variants in Genes Associated with Cancer Risk and Red Meat Metabolism
Bibliographic record
Abstract
With the advent of human genome sequencing project, came the wave of personalized genomics. Scientists have now gone beyond scanning of individual genes and epigenetic variations that might alter an individual’s predisposition to developing complex diseases. Nutritional genomics is a science which is fast catching up. Efforts to explain the diet-gene interactions often recapitulate the effects of genetic makeup in determining the exact fate of the meal we ate last. Diet-gene interactions play a major role in the metabolism and detoxification of food-derived mutagens and carcinogens. Heterocyclic amines (HCAs), polycyclic aromatic hydrocarbons (PAHs), and N-nitroso compounds (NOCs) are a class of mutagens or carcinogens found in red and processed meat that can lead to various types of cancers. Harboring unfavourable mutations or single nucleotide polymorphisms (SNPs) involved in metabolism of HCAs, PAHs, and NOCs can promote cancers. Increasing risks of several types of cancers, such as cancer of the colorectum, breast, prostate, esophagus, and lung, have been associated with high intake of red and processed meat. We attempt to compile some of the variants based on reports published during the past five years on variations involved in red meat metabolism which aims to provide useful insight in aiding us to regulate our red meat intake to avoid spurring of cancer.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".