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Myocardial Cell Signaling During the Transition to Heart Failure

2019· article· en· W4409120595 on OpenAlexaff
Matthew R. Zeglinski, Adel Rezaei Moghadam, Sudharsana Rao Ande, Kimia Sheikholeslami, Pooneh Mokarram, Zahra Sepehri, Haleh Rokni, Nima Khadem Mohtaram, Mansour Poorebrahim, Anahita Masoom, Mehnosh Toback, Niketa Sareen, Saravanan Sekaran, Davinder S. Jassal, Mohammad Hashemi, Hassan Marzban, Dedmer Schaafsma, Pawan K. Singal, Jeffrey T. Wigle, Michael P. Czubryt, Mohsen Akbari, Ian Dixon, Saeid Ghavami, Joseph W. Gordon, Sanjiv Dhingra

Bibliographic record

VenueComprehensive physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsChildren's Hospital Research Institute of ManitobaLibin Cardiovascular Institute of AlbertaUniversity of VictoriaUniversity of WaterlooUniversity of TorontoUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsHeart failureTransition (genetics)Cell biologyCellInternal medicineChemistryCardiologyMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Cardiovascular disease leading to heart failure (HF) remains a leading cause of morbidity and mortality worldwide. Improved pharmacological and interventional coronary procedures have led to improved outcomes following acute myocardial infarction. This success has translated into an unforeseen increased incidence in HF. This review summarizes the signaling pathways implicated in the transition to HF following cardiac injury. In addition, we provide an update on cell death signaling and discuss recent advances in cardiac fibrosis as an independent event leading to HF. Finally, we discuss cell‐based therapies and their possible use to avert the deteriorating nature of HF. © 2019 American Physiological Society. Compr Physiol 9:75‐125, 2019.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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