Efficacy of autologous bone marrow mononuclear cell transplantation therapy in patients with refractory diabetic peripheral neuropathy
Bibliographic record
Abstract
BACKGROUND: Owing to the multifactorial nature of the pathogenesis of diabetic peripheral neuropathy (DPN), conventional drug therapies have not been effective. The application of stem cells transplantation may be useful for the treatment of DPN. This study was designed to assess the safety and therapeutic effects of autologous bone marrow mononuclear cells (BMMNCs) transplantation on the treatment of refractory DPN. METHODS: One hundred and sixty-eight patients with refractory DPN were recruited and enrolled in the study. They received intramuscular injection of BMMNCs and followed at 1, 3, 6, 12, 18, 24, and 36 months after the transplantation. Clinical data, Toronto Clinical Scoring System (TCSS), and nerve conduction studies (NCSs) were compared before and after the transplantation. RESULTS: The signs and symptoms of neuropathy were significantly improved after BMMNCs transplantation. The values of the TCSS scores at 1 month (9.68 ± 2.49 vs. 12.55 ± 2.19, P < 0.001) and 3 months (8.47 ± 2.39 vs. 12.55 ± 2.19, P < 0.001) after the treatment reduced significantly compared with the baseline value. This decrement remained persistent until the end of the study. The conduction velocity and action potential and sensory nerves were significantly improved after transplantation (3 and 12 months after the treatment vs. the baseline: motor nerve conduction velocity, 40.24 ± 2.80 and 41.00 ± 2.22 m/s vs. 38.21 ± 2.28 m/s, P < 0.001; sensory nerve conduction velocity, 36.96 ± 2.26 and 39.15 ± 2.61 m/s vs. 40.41 ± 2.22 m/s, P < 0.001; compound muscle action potential, 4.67 ± 1.05 and 5.50 ± 1.20 μV vs. 5.68 ± 1.08 μV, P < 0.001; sensory nerve action potential, 4.29 ± 0.99 and 5.14 ± 1.26 μV vs. 5.41 ± 1.14 μV, P < 0.001). No adverse event associated with the treatment was observed during the follow-up period. CONCLUSIONS: Autologous transplantation of BMMNCs may be an effective and promising therapeutic strategy for the treatment of refractory DPN.
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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) | 0.001 | 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".