Bibliographic record
Abstract
The heart is a continually active pulsatile fluid pump. It generates appropriate forces by precisely timed and spaced engagement of its contractile machinery. Largely, it makes its own control signals, the most crucial of which are precisely timed and spaced fluxes of ions across the sarcolemma, achieved by the timely opening and closing of diverse voltage-gated channels (VGC). VGCs have four voltage sensors around a central ion-selective pore that opens and closes under the influence of membrane voltage. Operation of any VGC is secondarily tuned by the mechanical state (i.e., structure) of the bilayer in which it is embedded. Rates of opening and closing, in other words, vary with bilayer structure. Thus, in the intensely mechanical environment of the myocardium and its vasculature, VGCs kinetics might be routinely modulated by reversible and irreversible nano-scale changes in bilayer structure. If subtle bilayer deformations are routine in the pumping heart, VGCs could be subtly transducing bilayer mechanical signals, thereby tuning cardiac rhythmicity, collectively contributing to mechano-electric feedback. Reversible bilayer deformations would be expected with changing shear flows and tissue distension, while irreversible bilayer restructuring occurs with ischemia, inflammation, membrane remodeling, etc. I suggest that tools now available could be deployed to help probe whether/how the inherent mechanosensitivity of VGCs - an attribute substantially reflecting the dependence of voltage sensor stability on bilayer structure - contributes to cardiac rhythmicity. Chief among these tools are voltage sensor toxins (whose inhibitory efficacy varies with the mechanical state of bilayer) and arrhythmia-inducing VGC mutants with distinctive mechano-phenotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| 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".