Hydrogen-Rich Saline Attenuated Neuropathic Pain by Reducing Oxidative Stress
Bibliographic record
Abstract
BACKGROUND: Reactive oxygen species (ROS) are often associated with persistent pains such as neuropathic and inflammatory pain. Hydrogen gas can reduce ROS and alleviate cerebral, myocardial, and hepatic ischemia/reperfusion injuries. In the present study, we aim to investigate whether hydrogen-rich saline can reduce neuropathic pain in a rat model of chronic constriction injury (CCI). METHODS: Thirty SD rats were randomly divided into three groups: sham group was administered sodium chloride by intrathecal injection (n=10); control groups underwent CCI surgery and were administered sodium chloride by intrathecal injection (n=10); vehicle group underwent CCI surgery and was administered hydrogen-rich saline by intrathecal injection (n=10). Drugs were administered in the dose of 100 ul/kg once a day at 0.5 hours before and 1-7 day after CCI surgery. The mechanical thresholds were tested at one day before and 3-14 day after CCI surgery. RESULTS: We found that hydrogen-rich saline significantly elevated the mechanical thresholds of neuropathic pain compared to vehicle (physiologic saline) control in CCI rats (p<0.05); it also decreased the levels of myeloperoxidase, maleic dialdehyde, and protein carbonyl in spinal cord by 7 days post-chronic constriction injury(p<0.05). In addition, hydrogen-rich saline also suppressed the expression of p38-mitogen-activated protein kinase (p38MAPK) and brain-derived neurotrophic factor (BDNF) in the spinal cord by 7 days post-chronic constriction injury (p<0.01, p<0.01, respectively), but had no effect on P2X4R (p>0.05), an ATP receptor. CONCLUSION: Intrathecal injection of hydrogen-rich saline can decrease oxidative stress and the expression of p38MAPK and BDNF that may contribute to the elevated threshold of neuropathic pain in rat CCI model. Le salin riche en hydrogène atténue la douleur névropathique en réduisant le stress oxydatif.
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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 | 0.001 |
| 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".