Effect of miR-146a polymorphism on lipoic acid therapy in patients with T2DM peripheral polyneuropthy
Bibliographic record
Abstract
For investigating the impact of miR-146a rs2910164 polymorphism on the therapeutic efficacy of lipoic acid therapy in patients with type 2 diabetes mellitus (T2DM) peripheral neuropathy (DPN). 106 T2DM-DPN patients in our hospital from Jan. 2020- 2022 were selected. The probe detection method was utilized to determine the polymorphism of the miR-146a rs2910164 gene in peripheral blood. All patients were treated with zinc sulfate for 3 weeks period. According to the treatment effect, 37 patients who were ineffective in treatment will be divided into an ineffective group, and 79 patients who were effective in treatment will be divided into an effective group. The condition of miR-146a gene peptides was analyzed after treatment in both groups. The motor nerve conduction velocity (MNCV), sensory nerve conduction velocity (SNCV), and Toronto Clinical Scoring System (TCSS) scores of the median nerve and common peroneal nerve with different genotypes were compared between the 2 sets. The genotype frequencies of alleles G, GG, and GC in the valid group were lower than those in the invalid group; After treatment, MNCV and SNCV of CC genotype median nerve and common peroneal nerve in DPN patients were higher than those before treatment; The TCSS scores of the three genotypes less than post-treatment. The above results showed statistically significant differences (P<0.05). Lipoic acid is influenced by the miR-146a polymorphism gene in the treatment of T2DM-DPN patients, with the CC genotype having a lower susceptibility and the best clinical treatment effect.
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 | 0.001 |
| 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".