Visual exposure to green light therapy reduces knee joint pain and alters the lipidome in osteoarthritic rats
Bibliographic record
Abstract
ABSTRACT: Visual exposure to dim, green, light has been found to reduce pain levels in patients living with migraine, low back pain, and fibromyalgia. Preclinical studies discovered that the analgesic effect of green light was due to the central release of endogenous opioids and a reduction in inflammatory cytokines in the cerebrospinal fluid. The present study assessed the effect of green light therapy (GLT) on joint pain in a rat model of osteoarthritis (OA) and investigated the role of endolipids. Male and female Wistar rats (207-318 g) received an intra-articular injection of sodium monoiodoacetate (3 mg in 50 μL saline) into the knee to induce OA. On day 9, animals were placed in a room illuminated by either white (neutral-white 4000K; 20 lux) or green (wavelength: 525 nm; luminance: 20 lux) light for 5 days (8 hours per day). Joint nociception was assessed by von Frey hair algesiometry, dynamic weight bearing, and in vivo single unit extracellular recordings from knee joint mechanonociceptors. Compared to white light, GLT significantly reduced secondary mechanical hypersensitivity in both sexes and improved hindlimb weight bearing in females only. There was no effect of GLT on joint nociceptor activity in either sex. Serum lipidomics indicated an increase in circulating analgesic endolipids in response to GLT, particularly the N -acyl-glycines. Partial blockade of the endocannabinoid system with the G protein receptor-18/cannabinoid-1 receptor antagonist AM281 (500 μg/kg i.p.) attenuated GLT-induced analgesia. These data show for the first time that GLT acts to reduce OA pain by upregulating circulating analgesic endolipids, which then engage the endocannabinoid system.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".