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Record W4404583237 · doi:10.1093/cvr/cvae248

Anaplerotic filling in heart failure: a review of mechanism and potential therapeutics

2024· review· en· W4404583237 on OpenAlexafffund
Karm A Alhasan, Melissa A. King, Badal S B Pattar, Ian A. Lewis, Gary D. Lopaschuk, Steven C. Greenway

Bibliographic record

VenueCardiovascular Research · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsLibin Cardiovascular Institute of AlbertaAlberta Children's HospitalUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsHeart failureMechanism (biology)Citric acid cycleEnergy metabolismCellular metabolismMedicineTricarboxylic acidBioinformaticsBiologyPharmacologyMetabolismInternal medicine

Abstract

fetched live from OpenAlex

Heart failure (HF) is a complex syndrome and a leading cause of mortality worldwide. While current medical treatment is based on known pathophysiology and is effective for many patients, the underlying cellular mechanisms are poorly understood. Energy deficiency is a characteristic of HF, marked by complex alterations in metabolism. Within the tricarboxylic acid cycle, anaplerosis emerges as an essential metabolic process responsible for replenishing lost intermediates, thereby playing a crucial role in sustaining energy metabolism and consequently cardiac function. Alterations in cardiac anaplerosis are commonly observed in HF, demonstrating potential for therapeutic intervention. This review discusses recent advances in understanding the anaplerotic adaptations that occur in HF. We also explore therapeutics that can directly modulate anaplerosis or are likely to confer cardioprotective effects through anaplerosis, which could potentially be implemented to rescue the failing heart.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.117
GPT teacher head0.400
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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