Efficacy of oxiracetam combined with nimodipine in the treatment of cognitive impairment patients with type 2 diabetes
Bibliographic record
Abstract
Objective To evaluate the clinical efficacy and safety of oxiracetam combined with nimodipine on cognitive impairment patients with type 2 diabetes and to investigate its mechanisms. Methods Ninety patients with cognitive impairment in type 2 diabetes were randomly divided into oxiracetam group( A)( n = 30) with oxiracetam 800 mg,tid,po; nimodipine group( B)( n = 30) with nimodipine 20 mg,tid,po; and combined group( C)( n = 30) with oxiracetam plus nimodipine of the same dose. After tweleve-week treatment,the Montreal cognitive assessment( Mo CA) and activities of daily living( ADL) scale were evaluated. Neuron-specific enolase( NSE) and S-100β protein expressions in blood serum and drug adverse reactions were also evaluated. Results After tweleve-week treatment,Mo CA score was significantly increased( P 0. 05) and ADL scale was significantly reduced in all of the three groups( P 0. 05),and the changes in the C group was much more obvious than other two groups( P 0. 01). After treatment,NSE and S-100β protein expressions in blood serum of group A and B were obviously reduced compared with before treatment( P 0. 05),while group C were more dramatically reduced compared with A and Bgroup( P 0. 01). No obvious side effect was observed in the period of treatment in all the three groups.Conclusion Oxiracetam combined with nimodipine treatment was superior to either of the single regiment in the treatment of cognitive impairment in type 2 diabetes without increasing the risk of developing side effect by inhibiting NSE and S-100β expressions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".