Whither digitalis? What we can still learn from cardiotonic steroids about heart failure and hypertension
Bibliographic record
Abstract
-ATPase or NKA) and identification of a circulating ligand, endogenous ouabain (EO), a cardiotonic steroid (CTS), triggered seminal discoveries regarding EO and its NKA receptor in cardiovascular function and the pathophysiology of heart failure (HF) and hypertension. Cardiotonic digitalis preparations were a preferred treatment for HF for two centuries, but digoxin was only marginally effective in a large clinical trial (1997). This led to diminished digoxin use. Missing from the trial, however, was any consideration that endogenous CTS might influence digitalis' efficacy. Digoxin, at therapeutic concentrations, acutely inhibits NKA but, remarkably, antagonizes ouabain's action. Prolonged treatment with ouabain, but not digoxin, causes hypertension in rodents; in this model, digoxin lowers blood pressure (BP). Furthermore, NKA-bound ouabain and digoxin modulate different protein kinase signaling pathways and have disparate long-term cardiovascular effects. Reports of "brain ouabain" led to the elucidation of a new, slow neuromodulatory pathway in the brain; locally generated EO and the α2 NKA isoform help regulate sympathetic drive to the heart and vasculature. The roles of EO and α2 NKA have been studied by EO assay, ouabain-resistant mutation of α2 NKA, and immunoneutralization of EO with ouabain-binding Fab fragments. The NKA α2 CTS binding site and its endogenous ligand are required for BP elevation in many common hypertension models and full expression of cardiac remodeling and dysfunction following pressure overload or myocardial infarction. Understanding how endogenous CTS impact hypertension and HF pathophysiology and therapy should foster reconsideration of digoxin's therapeutic utility.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".