Pandora's Box: mitochondrial defects in ischaemic heart disease and stroke
Bibliographic record
Abstract
Ischaemic heart disease and stroke are vascular events with serious health consequences worldwide. Recent genetic and epigenetic techniques have revealed many genetic determinants of these vascular events and simplified the approaches to research focused on ischaemic heart disease and stroke. The pathogenetic mechanisms of ischaemic heart disease and stroke are complex, with mitochondrial involvement (partially or entirely) recently gaining substantial support. Not only can mitochondrial reactive oxygen species give rise to ischaemic heart disease and stroke by production of oxidised low-density lipoprotein and induction of apoptosis, but the impact on pericytes contributes directly to the pathogenesis. Over the past two decades, publications implicate the causative role of nuclear genes in the development of ischaemic heart disease and stroke, in contrast to the potential role of mitochondrial DNA (mtDNA) in the pathophysiology of the disorders, which is much less understood, although recent studies do demonstrate that the involvement of mitochondria and mtDNA in the development of ischaemic heart disease and stroke is likely to be larger than originally thought, with the novel discovery of links among mitochondria, mtDNA and vascular events. Here we explore the molecular events and mtDNA alterations in relation to the role of mitochondria in ischaemic heart disease and stroke.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".