Therapeutic Targeting of Inflammatory Pathways with Emphasis onNLRP3 Inflammasomes by Natural Products: A Novel Approach forthe Treatment of Inflammatory Eye Diseases
Bibliographic record
Abstract
There is an increase in the incidence of inflammatory eye diseases worldwide. Several dysregulated inflammatory pathways, including the NOD-like receptor protein 3 (NLRP3) inflammasome, have been reported to contribute significantly to the pathogenesis and progression of ophthalmic diseases. Although the available allopathic/ conventional medicine has demonstrated effectiveness in managing eye diseases, there is an ongoing global demand for alternative therapeutics with minimal adverse drug reactions, easy availability, increase in patient compliance, and better disease outcomes. Therefore, several studies are investigating the utilization of natural products and herbal formulations in impeding inflammatory pathways, including the NLRP3 inflammasome, in order to prevent or manage eye diseases. In the present review, we highlight the recently reported inflammatory pathways with special emphasis on NLRP3 Inflammasomes involved in the development of eye diseases. Furthermore, we present a variety of natural products and phytochemicals that were reported to interfere with these pathways and their underlying mechanisms of action. These natural products represent potential therapeutic applications for the treatment of several inflammatory eye diseases.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".