Increased serum concentrations of Mesencephalic astrocyte-derived neurotrophic factor in patients and rats with ischemic stroke
Bibliographic record
Abstract
OBJECTIVES: Although Mesencephalic astrocyte-derived neurotrophic factor (MANF) shows protection in multiple cells, the role of circulating MANF in patients with acute ischemic stroke (AIS) and transient ischemic attack (TIA) remains unclear. Here, we aimed to explore the value of circulating MANF levels in cerebral ischemic events. MATERIALS AND METHODS: Using a rat cerebral ischemic model, MANF expression in ischemic brains and serum was detected. 50 AIS patients, 56 TIA patients and 48 controls were enrolled, and MANF mRNA, inflammatory cytokines and MANF concentrations in serum and different blood cell types were detected. The National Institutes of Health Stroke Scale (NIHSS) score and Alberta Stroke Program Early CT Score (ASPECTS) were used to evaluate stroke severity. Cerebrovascular recurrence within 90 d was documented during TIA follow-up. RESULTS: MANF expression increased at 2h, peaking at 24h and decreased to baseline at 7d in rat ischemic brains and serum. Serum MANF concentrations increased at 24h and 7d in AIS patients compared to controls and were correlated with NIHSS score, ASPECTS and inflammatory cytokines. MANF protein was present in blood cells, while MANF mRNA levels did not differ between AIS patients and controls. MANF levels revealed a good value to diagnose TIA with area under the curve (AUC) of 0.949 (95% CI: 0.9093-0.9892). MANF levels were lower in TIA patients with recurrence compared to non-recurrence patients. The AUC for MANF to predict a re-event was 0.80 (95% CI: 0.6746-0.9282). CONCLUSIONS: Serum MANF levels correlate with neuroprotection, stroke severity, inflammation, and TIA recurrence.
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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 | 0.000 |
| 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".