Therapeutic enzymes as non-conventional targets in cardiovascular impairments: A comprehensive review
Bibliographic record
Abstract
Over the last few decades, substantial progress has been made towards the understanding of cardiovascular diseases. In-depth mechanistic insights have also provided opportunities to explore novel therapeutic targets and to discover new treatment regimens. Therapeutic enzymes are examples of such opportunities. The enzymes protect against a variety of cardiovascular diseases, however, even minor malfunctioning of these enzymes may lead to deleterious outcomes. Owing to their great versatility, the inhibition and activation of these enzymes are key regulatory approaches to counter the onset and progression of several cardiovascular impairments. While cardiovascular remedies are already available in excess and are efficacious, a comprehensive description of novel therapeutic enzymes to combat cardiovascular diseases would still be of great benefit. In the light of this, the regulation of functional activities of these enzymes also opens a new avenue for the treatment approaches to be employed. This review describes the importance of non-conventional enzymes such as nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX), phosphodiesterase (PDE), arginase, superoxide dismutase (SOD), thioredoxin reductase (TXNRD) and selenoprotein T (SELENOT), cytochrome b5 reductase 3 (CYB5R3), epoxide hydrolase (EHs), xanthine oxidoreductase (XOR), matrix metalloprotease (MMPs), and dopamine beta hydroxylase (DBH), as potential candidates in several cardiovascular disorders while highlighting some of the recently targeted therapeutic enzymes in cardiovascular diseases. We also discuss the role of intrinsic antioxidant defense system involved in cardioprotection followed by addressing some of the clinical investigations considering the use of antioxidant as a preferred therapy of cardiovascular complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".