Aberrant highly prokineticin 2 and its association with inflammatory indexes and functional recovery in acute ischemic stroke patients
Bibliographic record
Abstract
Background Prokineticin 2 is associated with the macrophages-mediated biological process, neuronal death, oxidative stress, and inflammatory processes, while its clinical value in patients with acute ischemic stroke (AIS) has not been explored. This study aimed to evaluate the level of prokineticin 2 and its association with inflammatory indexes and functional recovery in AIS patients. Methods Serum samples in 210 AIS patients at admission and in 30 healthy subjects at enrollment were collected. Then, prokineticin 2 levels were determined by enzyme-linked immunosorbent assay. Results Prokineticin 2 level was higher in AIS patients than healthy subjects (p < 0.001). Prokineticin 2 showed an acceptable ability to distinguish the AIS patients from healthy subjects (area under the curve: 0.812) with the best cut-off value at 4 ng/mL. No matter dividing the prokineticin 2 by continuous variable or quartiles, its value was positively correlated with the high sensitivity C reactive protein (HsCRP), tumor necrosis factor-alpha (TNF-α), and interleukin 17A (IL-17A) (all p < 0.001). Prokineticin 2 showed a higher trend in AIS patients with Modified Rankin Scale (mRS) score>2 compared with those with mRS score ≤2, but without statistical significance (p = 0.095). Besides, there was no association between prokineticin 2 by quartiles and the percentage of mRS > 2 (p > 0.05). Conclusion Prokineticin 2 aberrantly highly expresses, and it indicates the inflammatory status, but with limited ability to predict the neural functional recovery in AIS 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 | 0.000 |
| 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".