Thrombolome and Its Emerging Role in Chronic Kidney Diseases
Bibliographic record
Abstract
Patients with chronic kidney disease (CKD) are at an increased risk of thromboembolic complications, including myocardial infarction, stroke, deep vein thrombosis, and pulmonary embolism. These complications lead to increased mortality. Evidence points to the key role of CKD-associated dysbiosis and its effect via the generation of gut microbial metabolites in inducing the prothrombotic phenotype. This phenomenon is known as thrombolome, a panel of intestinal bacteria-derived uremic toxins that enhance thrombosis via increased tissue factor expression, platelet hyperactivity, microparticles release, and endothelial dysfunction. This review discusses the role of uremic toxins derived from gut-microbiota metabolism of dietary tryptophan (indoxyl sulfate (IS), indole-3-acetic acid (IAA), kynurenine (KYN)), phenylalanine/tyrosine (p-cresol sulfate (PCS), p-cresol glucuronide (PCG), phenylacetylglutamine (PAGln)) and choline/phosphatidylcholine (trimethylamine N-oxide (TMAO)) in spontaneously induced thrombosis. The increase in the generation of gut microbial uremic toxins, the activation of aryl hydrocarbon (AhRs) and platelet adrenergic (ARs) receptors, and the nuclear factor kappa B (NF-κB) signaling pathway can serve as potential targets during the prevention of thromboembolic events. They can also help create a new therapeutic approach in the CKD population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".