Acetyl-L-carnitine prevents and reduces oxaliplation-evoked painful peripheral neuropathy
Bibliographic record
Abstract
AIM: To examine the potential efficacy of acetyl-L-carnitine(ALCAR) to prevent and treat oxaliplation-evoked pain.METHODS: 20 adult male Sprague-Dawley rats(150-200 g) were randomly divided into two groups,ALCAR group and control group.Each group had 10 rats.A stock solution of oxaliplatin is diluted to 2 mg/mL with 5% dextrose in distilled water and injected IP at 2 mg/kg on five consecutive days(d 0-d 4) in a volume of 1.0 mL/kg.ALCAR(100 mg/kg;p.o.) or vehicle was given daily starting on day 0(the day of the first oxaliplatin injection) and continuing until day 21(i.e.,15 days after the last oxaliplatin injection.Mechano-allodynia and mechano-hyperalgesia were assessed using von Frey hairs with bending forces of 4 g and 15 g,respectively,on d 8,d 22,d 27,d 35,and d 41 postoperatively.Withdrawal responses were counted and expressed as an overall percentage response.RESULTS:Mechano-allodynia(4 g) and mechano-hyperalgesia(15 g) of the rats in ALCAR group were significantly and persistently reduced(P0.01) on d 8,d 22,d 27,d 35,and d 41 postoperatively in comparison with control group.CONCLUSION: It is concluded that ALCAR may be useful in the prevention and treatment of oxaliplatin-induced painful peripheral neuropathy.
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".