Impact of Stroke History on Cognitive Function, White Matter Hyperintensities, and Circulating BDNF Levels
Bibliographic record
Abstract
BACKGROUND: The present study aims to investigate the impact of stroke history on cognitive function, white matter hyperintensities (WMHs), and circulating brain-derived neurotrophic factor (BDNF) levels in brain lesion patients. METHODS: In this study, we enrolled 228 individuals exhibiting clinical symptoms of stroke from the Golestan Cohort Study. The participants were categorized into two groups based on their stroke history. Subsequently, 120 patients with a history of stroke and 108 patients without obvious brain lesions were subjected to comparative analysis using magnetic resonance imaging (MRI). Montreal Cognitive Assessment (MoCA) and Fazekas scores were used to evaluate cognitive function and WMH burden, respectively. In addition, circulating BDNF levels were measured using the Human BDNF Elisa kit. RESULTS: Totally, 228 patients were recruited in the study with a mean age of 63.8 years. Stroke was found in 52.6%. MoCA scores and plasma BDNF levels were significantly lower in patients with a history of stroke compared to people without such a history after adjusting for age, sex, education and type of residency (adjusted regression coefficient (RC) (95% CI)=-4.0 (-5.0 to -3.0), -3.2 (-4.2 to -2.2), respectively). In addition, the intensity burden of white matter was higher in the stroke group (adjusted RC (95% CI)=1.2 (0.8 to 1.6). CONCLUSION: The study suggests that a multi-biomarker approach, encompassing measures such as the MoCA score, Fazekas score, and circulating BDNF levels, can provide valuable insight into the neurological status of post-stroke patients and highlight potential avenues for improving patient outcomes through early detection and intervention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".