Zinc Nutrition and Inflammation in the Aging Retina
Bibliographic record
Abstract
Abstract Zinc is an essential nutrient for human health. It plays key roles in maintaining protein structure and stability, serves as catalytic factor for many enzymes, and regulates diverse fundamental cellular processes. Zinc is important in affecting signal transduction and, in particular, in the development and integrity of the immune system, where it affects both innate and adaptive immune responses. The eye, especially the retina‐choroid complex, has an unusually high concentration of zinc compared to other tissues. The highest amount of zinc is concentrated in the retinal pigment epithelium (RPE) (RPE‐choroid, 292 ± 98.5 µg g−1 dry tissue), followed by the retina (12362.2 µg g−1 dry tissue). The interplay between zinc and inflammation has been explored in other parts of the body but, so far, has not been extensively researched in the eye. Several lines of evidence suggest that ocular zinc concentration decreases with age, especially in the context of age‐related disease. Thus, a hypothesis that retinal function could be modulated by zinc nutrition is proposed, and subsequently trialled clinically. In this review, the distribution and the potential role of zinc in the retina‐choroid complex is outlined, especially in relation to inflammation and immunity, and the clinical studies to date are summarized.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".