Exosomal <scp>miR</scp>‐let‐7c‐5p is involved in the cognitive function of type 2 diabetes mellitus patients by interleukin 10: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Interleukin (IL)-10 plays a notable role in the inflammatory-associated mild cognitive impairment (MCI). We aimed to investigate whether IL-10 and its upstream factors exert an impact on MCI in type 2 diabetes mellitus (T2DM) patients. METHODS: A total of 117 T2DM patients were recruited and divided into Control group and MCI group based on the presence or absence of MCI. Clinical parameters were collected. The Montreal Cognitive Assessment (MoCA) was conducted for global cognitive function. Digit Span Test (DST), Verbal Fluency Test (VFT), and Trail Making Test-B (TMTB) were used to evaluate the executive functions of the diabetic patients. Trail Making Test-A (TMTA) was performed to examine the information processing speed function. Patients' scene memory was examined by Logical Memory Test (LMT). After the baseline data were compared, correlation and regression analyses were performed to explore the relationship among IL-10, miR-let-7c-5p and cognitive function. RESULTS: Compared to 80 patients in the control group, 37 patients in the MCI group exhibited lower IL-10 in plasma and higher miR-let-7c-5p levels in exosomes from plasma. The IL-10 level was negatively associated with MoCA. Likewise, miR-let-7c-5p levels were negatively correlated with IL-10 levels and MoCA. Elevated miR-let-7c-5p levels and decreased IL-10 levels are risk factors for MCI in T2DM patients. Increased miR-let-7c-5p and downregulated IL-10 may influence VFT and TMTB, respectively, associated with executive function. CONCLUSIONS: We demonstrated that IL-10 is correlated to the executive function of T2DM patients. Decreased IL-10 may result from the regulation of miR-let-7c-5p in exosomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".