Bibliographic record
Abstract
Transient receptor potential vanilloid 1 channels (TRPV1) which are playing an important role in conduction of pain signals to dorsal root ganglion (DRG), can be interacted by many external and internal factors. Food ingredients and herbal products have a great impact on these receptors. Topical application or oral consumption of these products are effective in reducing pain signals with different mechanisms of action. TRPV1 is involved in a various processes including nociception, thermosensation and energy homeostasis. Role of capsaicin, unsaturated omega fatty acids, minerals, and herbal products in pain relief and molecular mechanisms are being discussed. However, some dietary supplementation with TRPV1 activity, such as capsaicin, show conflicting results. TRPV1 channels and their agonist elements may play a great impact in decreasing the risk of obesity and diabetes through different mechanisms including reducing inflammation. Therefore, TRPV1 could be dysregulated in obesity leading to the development of obesity, diabetes. Further, TRPV1 channels look like to be responsible in pancreatic insulin secretion. Hopefully, we could make it possible to produce natural food supplements to reduce pain by focusing on the role of TRPV1 channels. This will further help clinicians and surgeons to reduce pain post-surgical procedures just by modifying the patient’s diet.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".