Cerebral-Cardiac Syndrome and Diabetes: Cardiac Damage After Ischemic Stroke in Diabetic State
Bibliographic record
Abstract
Cerebral-cardiac syndrome (CCS) refers to cardiac dysfunction following varying brain injuries. Ischemic stroke is strongly evidenced to induce CCS characterizing as arrhythmia, myocardial damage, and heart failure. CCS is attributed to be the second leading cause of death in the post-stroke stage; however, the responsible mechanisms are obscure. Studies indicated the possible mechanisms including insular cortex injury, autonomic imbalance, catecholamine surge, immune response, and systemic inflammation. Of note, the characteristics of the stroke population reveal a common comorbidity with diabetes. The close and causative correlation of diabetes and stroke directs the involvement of diabetes in CCS. Nevertheless, the role of diabetes and its corresponding molecular mechanisms in CCS have not been clarified. Here we conclude the features of CCS and the potential role of diabetes in CCS. Diabetes drives establish a "primed" inflammatory microenvironment and further induces severe systemic inflammation after stroke. The boosted inflammation is suspected to provoke cardiac pathological changes and hence exacerbate CCS. Importantly, as the key element of inflammation, NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome is indicated to play an important role in diabetes, stroke, and the sequential CCS. Overall, we characterize the corresponding role of diabetes in CCS and speculate a link of NLRP3 inflammasome between them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".