Correlations of vascular cognitive impairment with brain-derived neurotrophic factor and trace elements
Bibliographic record
Abstract
Abstract Background Cognitive impairment has become one of the major public health problems due to population aging and the increased morbidity associated with stroke. In this study, we aimed to analyze the correlations of vascular cognitive impairment (VCI) with brain-derived neurotrophic factor (BDNF) and trace elements. Methods Between January 2022 and January 2024, a total of 206 subjects were included in the study, of which 103 were VCI patients treated in our hospital (a cognitive impairment group), and 103 were volunteers undergoing physical examination (a control group). Comparisons were conducted on the levels of BDNF and trace elements (Cu, Fe, Zn, Ca, Mg, Se, As, and Al) between the two groups. Results In comparison with the control group, the cognitive impairment group had significantly reduced levels of BDNF, Cu, Fe and Zn (p<0.05), a significantly raised Al level (p<0.05), and decreases in the total score of Mini-Mental State Examination (MMSE) and corrected total score of Montreal Cognitive Assessment (MoCA) (p<0.05). The total score of MMSE and corrected total score of MoCA were positively correlated with the levels of BDNF, Cu, Fe, and Zn (p<0.05) and negatively correlated with the Al level in both groups (p<0.05). BDNF <5.39 μg/L, Cu <10.87 μmol/L, Fe <5.97 μmol/L, Zn <77.32 μmol/L, and Al >0.72 μmol/L were risk factors for VCI. Conclusions VCI patients have significantly lower levels of BDNF and trace elements (Cu, Fe, and Zn) and a significantly higher Al level than those of healthy populations. Excessively low levels of BDNF and trace elements (Cu, Fe, and Zn) and an overly high level of harmful element Al are risk factors for VCI.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".