The Physiopathology of Cardiorenal Syndrome: A Review of the Potential Contributions of Inflammation
Bibliographic record
Abstract
Inter-organ crosstalk plays an essential role in the physiological homeostasis of the heart and other organs, and requires a complex interaction between a host of cellular, molecular, and neural factors. Derangements in these interactions can initiate multi-organ dysfunction. This is the case, for instance, in the heart or kidneys where a pathological alteration in one organ can unfavorably affect function in another distant organ; attention is currently being paid to understanding the physiopathological consequences of kidney dysfunction on cardiac performance that lead to cardiorenal syndrome. Different cardiorenal connectors (renin-angiotensin or sympathetic nervous system activation, inflammation, uremia, etc.) and non-traditional risk factors potentially contribute to multi-organ failure. Of these, inflammation may be crucial as inflammatory cells contribute to over-production of eicosanoids and lipid second messengers that activate intracellular signaling pathways involved in pathogenesis. Indeed, inflammation biomarkers are often elevated in patients with cardiac or renal dysfunction. Epigenetics, a dynamic process that regulates gene expression and function, is also recognized as an important player in single-organ disease. Principal epigenetic modifications occur at the level of DNA (i.e., methylation) and histone proteins; aberrant DNA methylation is associated with pathogenesis of organ dysfunction through a number of mechanisms (inflammation, nitric oxide bioavailability, endothelin, etc.). Herein, we focus on the potential contribution of inflammation in pathogenesis of cardiorenal syndrome.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".