Homeopathic treatment for peripheral nerve regeneration: an experimental study in a rat sciatic nerve transection model
Bibliographic record
Abstract
AIM: Effects of homeopathic treatment with Hypericum perforatum (Hypericum) on peripheral nerve regeneration was studied using a rat sciatic nerve transection model. METHODS: Fifty-four male healthy White Wistar rats were divided into three experimental groups (n = 18), randomly: Sham-operation (Sham), control: silicon tube (Sil) and treatment: silicon tube + Hypericum (Sil/Hypericum). In the Sham group after anesthesia left sciatic nerve was exposed through a gluteal muscle incision and after homeostasis muscle was sutured. In the Sil group the left sciatic nerve was exposed the same way and transected proximal to tibio-peroneal bifurcation leaving a 10-mm gap. Proximal and distal stumps were each inserted into a silicone tube. In the Sil/Hypericum group a silicone tube was implanted the same way and each animal received three oral drops of Hypericum 30c twice daily for 1 week. Each group was subdivided into three subgroups of six animals each studied 4, 8, 12 weeks after surgery. RESULTS: Data were analyzed statistically by factorial analysis of variance (ANOVA) and, the Bonferroni test for pair-wise comparisons. Functional study showed faster and better recovery of regenerated axons in Sil/Hypericum than in Sil group (P < 0.05). Gastrocnemius muscle mass in Sil/Hypericum was significantly greater than in Sil group. Morphometric indices of regenerated fibers showed number and diameter of the myelinated fibers in Sil/Hypericum were significantly higher than in control group. Immunohistochemistry, showed the location of reactions to S-100 in Sil/Hypericum was clearly more positive than in Sil group. CONCLUSION: Hypericum improves functional recovery of peripheral nerve regeneration in rats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".