[no title]
Bibliographic record
Abstract
Extensive research work has been carried out to define the exact significance and contribu- \ntion of regulated necrosis-like cell death program, such as necroptosis to cardiac ischemic injury. This \ncell damaging process plays a critical role in the pathomechanisms of myocardial infarction (MI) and \npost-infarction heart failure (HF). Accordingly, it has been documented that the modulation of key \nmolecules of the canonical signaling pathway of necroptosis, involving receptor-interacting protein \nkinases (RIP1 and RIP3) as well as mixed lineage kinase domain-like pseudokinase (MLKL), elicit \ncardioprotective effects. This is evidenced by the reduction of the MI-induced infarct size, alleviation \nof myocardial dysfunction, and adverse cardiac remodeling. In addition to this molecular signaling \nof necroptosis, the non-canonical pathway, involving Ca2+/calmodulin-dependent protein kinase II \n(CaMKII)-mediated regulation of mitochondrial permeability transition pore (mPTP) opening, and \nphosphoglycerate mutase 5 (PGAM5)–dynamin-related protein 1 (Drp-1)-induced mitochondrial \nfission, has recently been linked to ischemic heart injury. Since MI and HF are characterized by an \nimbalance between reactive oxygen species production and degradation as well as the occurrence of \nnecroptosis in the heart, it is likely that oxidative stress (OS) may be involved in the mechanisms of \nthis cell death program for inducing cardiac damage. In this review, therefore, several observations \nfrom different studies are presented to support this paradigm linking cardiac OS, the canonical and \nnon-canonical pathways of necroptosis, and ischemia-induced injury. It is concluded that a multiple \ntherapeutic approach targeting some specific changes in OS and necroptosis may be beneficial in \nimproving the treatment of ischemic heart disease
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.086 |
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".