Dietary factors and cancer chemoprevention
Bibliographic record
Abstract
Obesity is a growing health problem in developed nations and in countries that are in the process of westernization like India. Obesity is linked with several health disorders such as hypertension and cardiovascular diseases, Type 2 diabetes, dyslipidemia and certain cancers. Currently, obesity-related malignancies, e.g., cancers of the breast, prostate and colon are the leading cancers in the industrialized societies. An increased amount of fat or adipose tissue in an overweight or obese person probably influences the development of cancer by releasing several hormone-like factors or adipokines. The majority of adipokines are pro-inflammatory, which promote pathological conditions like insulin resistance and cancer. On the other hand, many recent studies have shown that adiponectin, an anti-inflammatory adipokine, has anti-cancer and insulin-sensitizing effects. Adiponectin exerts its physiological functions chiefly by activation of AMP kinase via adiponectin receptors. Interestingly, several fruits and vegetables may contain adiponectin-like molecules or may increase the biosynthesis of adiponectin in our body. Studies on adiponectin analogues or adiponectin receptor agonists are a promising area of cancer chemoprevention research. In general, fruits and vegetables contain various dietary substances such as vitamins, minerals (like calcium and selenium), fiber and phytochemicals or phenolic compounds (like flavonoids and vanilloids), which may act as anti-cancer agents. Similarly, several dietary constituents including phytochemicals may have anti-obesity effects. Consumption of such dietary compounds along with caloric restriction and physical activity may be helpful in preventing obesity-related cancers. For this review article, we searched PubMed primarily to get the relevant literature.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".