Melatonin, immune function and aging
Bibliographic record
Abstract
Aging is associated with a decline in immune function (immunosenescence), a situation known to correlate with increased incidence of cancer, infectious and degenerative diseases. Innate, cellular and humoral immunity all exhibit increased deterioration with age. A decrease in functional competence of individual natural killer (NK) cells is found with advancing age. Macrophages and granulocytes show functional decline in aging as evidenced by their diminished phagocytic activity and impairment of superoxide generation. There is also marked shift in cytokine profile as age advances, e.g., CD3+ and CD4+ cells decline in number whereas CD8+ cells increase in elderly individuals. A decline in organ specific antibodies occurs causing reduced humoral responsiveness. Circulating melatonin decreases with age and in recent years much interest has been focused on its immunomodulatory effect. Melatonin stimulates the production of progenitor cells for granulocytes-macrophages. It also stimulates the production of NK cells and CD4+ cells and inhibits CD8+ cells. The production and release of various cytokines from NK cells and T-helper lymphocytes also are enhanced by melatonin. Melatonin presumably regulates immune function by acting on the immune-opioid network, by affecting G protein-cAMP signal pathway and by regulating intracellular glutathione levels. Melatonin has the potential therapeutic value to enhance immune function in aged individuals and in patients in an immunocompromised state.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".