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Record W3082819950 · doi:10.1002/cti2.1167

Resident macrophages as potential therapeutic targets for cardiac ageing and injury

2020· review· en· W3082819950 on OpenAlexaff
Shiqing Zhang, Rong Chen, Subrata Chakrabarti, Zhaoliang Su

Bibliographic record

VenueClinical & Translational Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsWestern University
FundersGovernment of Jiangsu ProvinceSix Talent Peaks Project in Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsInflammationCardiac function curveNeuroscienceRegeneration (biology)MedicineFunction (biology)SenescenceHeart RhythmBiologyCell biologyHeart failureCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Cardiac-resident macrophages (CRMs) play critical roles in maintaining cardiac homoeostasis and removing senescent and dying cells. Recent preclinical data have re-energised the area of cardioimmunology and provided improved understanding of the modulation of compositional and functional phenotypes of CRMs. These data can aid in achieving improved cardiac regeneration, repair and functional remodelling following cardiac injury. In this review, we discuss the composition and renewal of various subsets of CRMs. Specific attention has been given to delineate the roles of various CRM subsets with respect to (1) facilitation of cardiac development and maintenance of physiological function such as electrical conduction and rhythm; (2) promotion of cardiac regeneration, inflammation resolution and functional remodelling following a cardiac injury; and (3) therapeutic potential. We have also highlighted the relationship between CRM replenishment and cardiomyocyte senescence as well as cardiovascular diseases development. Finally, we have addressed future perspectives and directions in basic research and potentially clinical applications of CRMs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.430
Teacher spread0.365 · 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 teacher head, not a consensus.

Study designOther design
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

Citations16
Published2020
Admission routes1
Has abstractyes

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