Beneficial effect of TNF-α inhibition on diabetic peripheral neuropathy
Bibliographic record
Abstract
BACKGROUND: Tumor necrosis factor-α (TNF-α) is an important inflammatory factor produced by activated macrophages and monocytes and plays an important role in the pathogenesis of diabetic peripheral neuropathy (DPN). To evaluate the effect of TNF-α signaling suppression and the potential of TNF-α in the treatment of DPN, a recombinant human TNF-α receptor-antibody fusion protein (rhTNFR:Fc) was used. We focused on the pathophysiology of the sciatic nerve and examined the expression of myelin basic protein (MBP) under DPN status with or without TNF-α inhibition. METHODS: The DPN rat model was generated by intraperitoneal injection of streptozotocin and by feeding with a high-fat, high-sugar diet. The nerve conduction velocity (NCV) in sciatic nerve of rat was monitored over a period of four weeks. The histopathological changes in nerve tissue were examined through traditional tissue histology and ultrastructure transmission electron microscopy (TEM). The expression of MBP was examined through western blot analysis. RESULTS: The DPN induced rats showed significant signs of nerve damage including lower NCV, demyelination of nerve fibers, disorganization of lamellar and axonal structures, and decreased expression of MBP in the nerve tissue. The inhibition of TNF-α in the DPN rats resulted in a significant recovery from those symptoms compared to the DPN rats. CONCLUSIONS: Our study demonstrates that TNF-α plays a key role in the pathogenesis of DPN and its inhibition by rhTNFR:Fc can prove to be a useful therapeutic strategy for the treatment of and/or prevention from DPN symptoms.
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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".