Dietary fat and protein interact in suppressing neuropathic pain-related disorders following a partial sciatic ligation injury in rats
Bibliographic record
Abstract
Chronic neuropathic sensory disorders (CNSD) of rats receiving a partial sciatic nerve ligation injury (the PSL model) are suppressed by dietary soy protein. Although previously shown to modify nociceptive behavior in acute pain models, dietary fat has never been tested for its putative analgesic properties in chronic pain states. Here we tested the role of dietary fat, protein and fat/protein interactions in the development of tactile allodynia and heat hyperalgesia in PSL-injured rats. Male Wistar rats were fed nine different diets, comprising of three proteins (soy, casein and albumin) and three fats (corn, soy and canola) for a week preceding PSL injury and for 2 weeks thereafter. Rats' responses to tactile and noxious heat stimuli were tested before surgery and 3, 7 and 14 days afterwards. Tactile and heat sensory abnormalities following PSL injury were significantly different among the nine dietary groups. Consumption of corn and soy fats suppressed the levels of tactile and heat allodynia and hyperalgesia, whereas consumption of soy and casein proteins was associated with lower levels of heat hyperalgesia but not tactile allodynia. A significant fat/protein interaction was found for the heat but not tactile stimuli. We conclude that dietary fat is a significant independent predictor of levels of neuropathic sensory disorders in rats and that this effect is accentuated by dietary protein. The mechanisms by which fat suppresses neuropathic disorders have yet to be determined.
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) | 0.000 | 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".