Hypertension as One of the Main Non-Myocardial Infarction-Related Causes of Increased Cardiospecific Troponins: From Mechanisms to Significance in Current Medical Practice
Bibliographic record
Abstract
It is well known that many pathological conditions of both cardiovascular diseases (CVDs) (coronary heart disease, myocardial infarction, arrhythmias, myocarditis, cardiomyopathy, etc.) and non-cardiac (sepsis, anemia, kidney diseases, diabetes mellitus, etc.) origin in the course of their development cause injury to contractile cardiac muscle cells - myocardial cells (MCs). One of the most sensitive and specific criteria for detecting MC injury are cardiospecific troponins (CTs), which are regulatory protein molecules that are released into the blood serum from MC upon their death or injury. Current methods for determining CTs are called high-sensitive ones, and their main advantage is a very low minimum detectable concentration (limit of detection) (average 1 - 10 ng/L or less), which allows early detection of minor MC injury at the earliest stages of CVDs, and therefore they can change the understanding of disease development mechanisms and open up new diagnostic possibilities. One of the most common and dangerous early diseases of the cardiovascular system is hypertension (HT). The novelty of this article lies in the discussion of a new diagnostic direction - predicting the risk of developing CVDs and their dangerous complications in patients with HT by determining the concentration of CTs. In addition, pathophysiological mechanisms underlying MC injury and the release of CTs into the bloodstream and the elimination of CTs into the urine are proposed. This information will contribute to additional fundamental and clinical research to verify the new diagnostic possibility of using CTs in clinical practice (for the management of patients with HT).
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".