Nutrigenomics and Complementary Alternative Medicine
Bibliographic record
Abstract
ABSTRACT Nutrigenomics applied high-throughput ‘omics’ techniques in nutrition research to enable investigation into interactions between nutrients with the genome at a molecular level. One of the emerging areas of research in nutri-genomics includes Complementary Alternative Medicine (CAM), which can be used to treat various diseases including type2 diabetes, cancer and obesity. Research in CAM includes identification of the active compounds present in various herbal and dietary products, and evaluating these compounds for their effects on human health. Only few studies have explored the effects of these compounds when used in synergistic, additive or antagonistic combinations. One of the striking features of CAM is the low toxicity of natural compounds used as supplements. However most of the active ingredients are not “hydrophyllic” but are “lypophyllic”, resulting in limited absorption from GI tract, when ingested orally. This has led to limited bioavailability and it needs application of innovative techniques to overcome this problem. We have evaluated combination therapy and its effects as a solution to this problem. This report will discuss general concepts in nutrigenomics related to CAM, effects of combination therapies and possible mechanisms of action that come in to play with combination therapies, with particular focus on phytocompounds used as anti-cancer agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".