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Record W4221014636 · doi:10.3390/biomedicines10030661

Role of CCR2-Positive Macrophages in Pathological Ventricular Remodelling

2022· review· en· W4221014636 on OpenAlexaff
Veera Ganesh Yerra, Andrew Advani

Bibliographic record

VenueBiomedicines · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Structural Anomalies and Repair
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsHeart failureChemokineCCR2PathologicalImmune systemChemokine receptorBiologyVentricular remodelingInflammationMacrophageCell typeReceptorImmunologyMedicineCell biologyCellPathologyInternal medicine

Abstract

fetched live from OpenAlex

Even with recent advances in care, heart failure remains a major cause of morbidity and mortality, which urgently needs new treatments. One of the major antecedents of heart failure is pathological ventricular remodelling, the abnormal change in the size, shape, function or composition of the cardiac ventricles in response to load or injury. Accumulating immune cell subpopulations contribute to the change in cardiac cellular composition that occurs during ventricular remodelling, and these immune cells can facilitate heart failure development. Among cardiac immune cell subpopulations, macrophages that are recognized by their transcriptional or cell-surface expression of the chemokine receptor C-C chemokine receptor type 2 (CCR2), have emerged as playing an especially important role in adverse remodelling. Here, we assimilate the literature that has been generated over the past two decades describing the pathological roles that CCR2+ macrophages play in ventricular remodelling. The goal is to facilitate research and innovation efforts in heart failure therapeutics by drawing attention to the importance of studying the manner by which CCR2+ macrophages mediate their deleterious effects.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

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