Serum Galectin‐3 as a Potential Predictive Biomarker Is Associated with Poststroke Cognitive Impairment
Bibliographic record
Abstract
Objective. Galectin‐3, an inflammatory mediator derived from microglia, participates in the pathophysiological process of various neurological diseases. However, the relationship between galectin‐3 and poststroke cognitive impairment (PSCI) remains ambiguous. This research purposed to prove whether serum galectin‐3 can predict PSCI. Methods. In the end, an aggregate of 416 patients with the first acute ischemic stroke (AIS) were continuously and prospectively enrolled in the study. Upon admission, the baseline data of AIS patients were collected, and their serum galectin‐3 levels were measured. Three months after the stroke, the Montreal Cognitive Scale (MoCA) was utilized to measure the cognitive function of AIS patients, and PSCI was defined as a MoCA score less than 26 points. Results. Premised on the MoCA scores, patients were categorized into PSCI cohort and non‐PSCI cohort. The two AIS patient cohorts did not exhibit any statistical difference in their baseline characteristics (p > 0.05). However, the serum galectin‐3 level of AIS patients in the PSCI cohort was considerably elevated (p < 0.001). Pearson correlation analysis illustrated that serum galectin‐3 level was negatively linked to MoCA score (r = −0.396, p < 0.05). The findings from the receiver‐operating curve (ROC) illustrated that the sensitivity of serum galectin‐3 as a possible biomarker for diagnosing PSCI was 66%, and the specificity was 94%. The cut‐off value of serum galectin‐3 to diagnose PSCI is 6.3 ng/mL (OR = 5.49, p < 0.001). Upon controlling for different variables, serum galectin‐3 level remained to be an independent predictor of PSCI (p < 0.001). Conclusions. Elevated serum galectin‐3 levels are linked to a higher risk of PSCI. Serum galectin‐3 could be a prospective biomarker for predicting PSCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".