Research on Mechanism and Effect of Vitamin C on Mild Cognitive Impairment in Type 2 Diabetes Mellitus Patients
Bibliographic record
Abstract
Objective To investigate the effects and possible mechanism of Vitamin C on mild cognitive impairment(MCI) in type 2 diabetes mellitus(T2DM) patients.Methods Senile T2DM patients with MCI were randomly divided into control group(30 cases) and experimental group (30 cases).Patients from control group were given routine treatment,while patients from experimental group took additional Vitamin C 0.2g,3/d despite the routine treatment,with the course of treatment lasting one year.MMSE Scale and MoCA Scale were used and 8-iso-PGF2α was tested before and after the treatment respectively.Results Before treatment,there were no significant differences between control group and treatment group in MMSE scale,MoCA scale test and serum 8-iso-PGF2α 〔with MMSE (26.7±1.0) score,MoCA (24.9±1.6) score and 8-iso-PGF2α(49.5±19.9 )ng/L in control group,and MMSE (26.0±0.6) score,MoCA (25.2±1.1)score and 8-iso-PGF2α(50.8±21.9) ng/L in treatment group,t=-0.511,-0.625 and 0.455,all P0.05〕;after treatment,significant differences was found between the two groups 〔with MMSE (26.3±1.2) score,MoCA (26.2±1.4)score and 8-iso-PGF2α(46.7±21.2) ng/L in control group,and MMSE (28.0±1.1)score,MoCA(28.3±1.2) score and 8-iso-PGF2α(31.5±12.4) ng/L in treatment group,t= 2.095,2.014 and 3.184,(all P0.01)〕.Conclusion Vitamin C could ameliorate the MCI of Senile T2DM patients,probably by alleviating oxidative-stress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".