Nutritional Genomic: A Multi-Directional Approach to Address Complex Diseases with Multi-Functional Nutrition
Bibliographic record
Abstract
Nutritional genomics describes the biological interactions between genes and diet, their effects on the metabolism, and susceptibility to develop diseases. This approach covers both nutrigenomics that explores the effects of nutrients on the genome; and nutrigenetics that explores the effects of genetic polymorphisms on diet/disease interactions. These interactions vary because individuals have unique combinations of common genetic polymorphisms that are differentially affected by diet. Diseases causality is associated to certain genetic polymorphisms providing predictive biomarkers for diagnostic accuracy. Specific nutrient can modify the expression of genes through the interaction with receptors that activate the transcription of target genes and affect signal pathways. Nutritional genomics is aimed to prevent onset of diseases and maintain human health, identify individuals who are responders and can benefit from specific dietary interventions, and identify how genetic variation affects human nutritional requirements. Nutritional genomics has many potential therapeutic and preventive applications: in individuals with a genetic predisposition to complex diseases including cancer, diabetes and cardiovascular disorders; in those already suffering from these diseases; and in those with memory impairment during aging. This review describes nutritional facts linked to genomic aspects to manage multigenic diseases. It presents some notable example of nutrients with proven modulating gene activity, and the role of nutrition associated with nutritional genomics. Hereafter we briefly review the health-promoting properties of two well-known edible plants, i.e. dandelion and artichoke whose presence in the diet could simultaneously exert positive influence on molecular genomic mechanisms related to risk factors for chronic diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".