Circulating PIWI-interacting RNAs in Acute Ischemic Stroke patients
Bibliographic record
Abstract
Background: Stroke refers to an abrupt neurological deficit, caused by an acute focal injury of the central nervous system via infarction or hemorrhage due to impaired vascularity, and remains among the leading causes of disability and death worldwide. Stroke is often preceded by an episode of neuronal deficit termed transient ischemic attack (TIA), which presents an effective opportunity for mitigating the risk of an eminent acute ischemic stroke (AIS). Circulating non-coding RNAs (ncRNAs) have emerged as important biomarkers for stroke, but PIWI-interacting RNAs (piRNAs), a class of small regulatory ncRNAs, have not been previously explored as diagnostic or prognostic biomarkers for stroke. Methods: We conducted comprehensive circulating piRNA profiling of AIS and TIA patients using RNA-seq on serum samples collected within 24 h of clinical diagnosis. The study cohort was divided into discovery and cross-validation datasets to identify replicated piRNAs using stringent analysis cut-offs. The expression levels of the panel of differentially regulated piRNAs between AIS and TIA patients were also compared with healthy controls. Results: We identified a panel of 10 differentially regulated piRNAs between AIS and TIA patients; hsa-piR-28272, -piR-32972, -piR-28247, -piR-24553, -piR-24552, -piR-28275, -piR-28707 and -piR-32882 were upregulated, while hsa-piR-23058 and -piR-23136 were downregulated in AIS patients. Moreover, these 10 piRNAs were also differentially expressed in AIS patients compared to healthy controls. In addition, we investigated the potential gene targets of the dysregulated piRNAs and their plausible involvement in pathophysiological processes affected in stroke. Conclusions: The imbalances in the circulating piRnome of AIS and TIA patients presented herein provide important insights into the roles of piRNAs following ischemic brain injury and potentially provide opportunities to mitigate stroke-induced mortality and morbidity.
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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) | 0.001 | 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) | 0.002 | 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".