Molecular Pathways and Crosstalk Characterizing the Cardiorenal Syndrome
Bibliographic record
Abstract
The risk of developing cardiovascular diseases (CVD) is dramatically increased in patients with chronic kidney diseases (CKD). Mechanisms leading to this cardiorenal syndrome (CRS) are multifactorial, and combined analyses of both failing organs may provide routes toward developing strategies for early risk assessment, prognosis, and consequently effective therapy. In order to identify molecular mechanisms involved in the crosstalk between the diseased cardiovascular system and kidney, we analyzed tissue specific transcriptomics profiles on atherosclerosis and diabetic nephropathy together with gene sets associated with cardiovascular and chronic kidney diseases that derived from a literature mining approach. We focused on enriched molecular pathways and highlight molecular interactions found within as well as between affected pathways identified for the two organs. Analysis on the level of molecular pathways pointed out the role of PPAR signaling, coagulation, inflammation, and focal adhesion pathways in formation and progression of the CRS. The proteins apolipoprotein A1 (APOA1) and albumin (ALB) turned out to be of particular importance in the context of dyslipidemia, one of the major risk factors for the development of CVD. In summary, our analyses highlight mechanisms associated with dyslipidemia, hemodynamic regulation, and inflammation on the interface between the cardiovascular and the renal system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".