Study on metabolic pathway of mild cognitive impairment in type 2 diabetes patients
Bibliographic record
Abstract
Abstract Background The Montreal Cognitive Assessment Scale (MoCA) survey was used to examine the risk factors contributing to the development of mild cognitiveMethods impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) in combination with clinical practice, the Montreal Cognitive, The Assessment Scale (MoCA) was used to assess cognitive function. Based on the MoCA scale scores, subjects were included in a total of 147 cases in the type 2 diabetes mellitus with mild cognitive impairment group (T2DM-MCI group) (MoCA score < 26) and a total of 53 cases in the group with type 2 diabetes mellitus group with normal cognitive function (T2DM-NCI group) (MoCA score ≥ 26 points). While venous serum samples were collected from the patients, the metabolic data were analyzed using ultra-performance liquid chromatography-mass spectrometry (UPLC-Q/TOF-MS) for the T2DM-MCI and T2DM-NCI groups to identify the metabolites with differential expression to analyze their related metabolic pathways between the two groups and to investigate the metabolic characteristics of MCI in T2DM patients.Results The results of comparing general clinical data between the T2DM-MCI group and the T2DM-NCI group showed that there were significant differences in the training and age of the patients.Conclusions Patients with type 2 diabetes mellitus with advanced age, short educational period, high D-dimer level and high glycosylated hemoglobin are more likely to have mild cognitive impairment. Caffeine metabolism and sphingolipid metabolism were the main metabolic pathways, and the abnormalities of their metabolic pathways may lead to the occurrence and development of cognitive dysfunction in T2DM patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".