Poststroke Neuropsychiatric Symptoms: Relationships with IL-17 and Oxidative Stress
Bibliographic record
Abstract
Stroke variably activates interleukin- (IL-) 17 expression, reduces regulatory T cells, and induces oxidative stress, which may support neurodegeneration. Ischemic stroke patients were screened for depressive symptoms (Center for Epidemiological Studies Depression (CES-D)) and cognitive status (Mini Mental State Examination). Proinflammatory cytokines (IL-17, IL-23, and interferon- [IFN-] γ), anti-inflammatory cytokine IL-10, and lipid hydroperoxide (LPH), a measure of oxidative stress, were assayed from fasting serum. Of 47 subjects (age 71.8 ± 14.4 years, 36% female), 19 had depressive symptoms (CES-D ≥ 16), which was associated with poorer cognitive status (F 1,46 = 8.44, P = 0.006). IL-17 concentrations did not differ between subjects with and without depressive symptoms (F 1,46 = 8.44, P = 0.572); however, IL-17 was associated with poorer cognitive status in subjects with depressive symptoms (F 1,46 = 9.29, P = 0.004). In those subjects with depressive symptoms, IL-17 was associated with higher LPH (ρ = 0.518, P = 0.023) and lower IL-10 (ρ = -0.484, P = 0.036), but not in those without. In conclusion, poststroke depressive symptoms may be associated with cognitive vulnerability to IL-17 related pathways, involving an imbalance between proinflammatory and anti-inflammatory activity and increased oxidative stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".