EVALUATION OF TNF-Α LEVELS IN MALE PATIENTS WITH STROKE: PROGNOSTIC IMPLICATIONS.
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Following a stroke, activation of the immune system leads to a cascade of events collectively known as neuroinflammation, which involves the release of pro-inflammatory cytokines [such as tumor necrosis factor-alpha (TNF-α)], recruitment of immune cells, and disruption of the blood-brain barrier. Herein, we sought to investigate levels TNF-α in patients with strokes (both ischemic and hemorrhagic). METHODS: Blood or serum specimens were collected from male patients with stroke from the Republic of Georgia (Adjarian) population. A total of 48 patients (ischemic stroke = 27; and hemorrhagic stroke=21) formed the basis of this study analysis. An enzyme-linked immunosorbent assay (ELISA) was employed to assess the TNF-α levels in all specimens. The reference level was <8 pg/mL. Statistical analyses were performed using GraphPad Prism 9. A p-value of <0.05 was considered statistically significant. RESULTS: Both ischemic and hemorrhagic stroke patients had relatively higher levels of TNF-α (15.26±8.68 vs. 9.18±5.66 pg/mL; p=0.0065). Approximately 1.66-fold increase in TNF-α level was observed in patients with ischemic stroke as compared to the hemorrhagic stroke patients. CONCLUSION: Our analysis suggests that significantly increased levels of TNF-α are associated with both ischemic and hemorrhagic strokes male patients; however, the levels in ischemic stroke patients are more pronounced. Thus, monitoring/targeting TNF-α may help identify high-risk patients and guide new therapeutic strategies to improve recovery in male stroke patients.
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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".