Regulation of therapeutic apoptosis: a potential target in controlling hypertensive organ damage
Bibliographic record
Abstract
Cell growth and survival are potential therapeutic targets for the control of complications associated with hypertension. In most cardiovascular disorders, cardiac fibroblasts and large-vessel smooth muscle cells can replicate and thus contribute to the disease. We propose that cardiovascular hyperplasia may be reversed via therapeutic apoptosis induction with drugs that are safe and already used in the clinic. We first reported that, irrespective of the drug class, those drugs that are able to induce regression of cardiovascular hypertrophy are also able to reverse cardiovascular hyperplasia via apoptosis. Drugs active in this regard include inhibitors of the renin-angiotensin system, calcium channel blockers, and beta-blockers. Moreover, the effects of these drugs on cell survival is not merely secondary to blood pressure reduction. Therapeutic apoptosis in the cardiovascular system of the spontaneously hypertensive rat is characterized by a rapid and transient onset following initiation of antihypertensive treatment. Herein, the induction and termination of therapeutic apoptosis during drug treatment of hypertension will be briefly reviewed and supported by novel data suggesting that reversal of cardiovascular hyperplasia is associated with reduced cell growth and a resistance to further induction of therapeutic apoptosis, as shown in spontaneously hypertensive rats receiving an intermittent regime of nifedipine therapy. We propose that the presence of a cell subpopulation with defective cell cycle regulation may determine organ susceptibility to undergo therapeutic apoptosis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".