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Record W4402934020 · doi:10.3389/fimmu.2024.1437821

Immunological perspectives on atherosclerotic plaque formation and progression

2024· review· en· W4402934020 on OpenAlexaff
Hui Pi, Guangliang Wang, Yu Wang, Ming Zhang, Qin He, Xi‐Long Zheng, Kai Yin, Guojun Zhao, Ting Jiang

Bibliographic record

VenueFrontiers in Immunology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsImmune systemInnate immune systemImmunologyAcquired immune systemInflammationBiologyMacrophageImmunityPattern recognition receptor

Abstract

fetched live from OpenAlex

Atherosclerosis serves as the primary catalyst for numerous cardiovascular diseases. Growing evidence suggests that the immune response is involved in every stage of atherosclerotic plaque evolution. Rapid, but not specific, innate immune arms, including neutrophils, monocytes/macrophages, dendritic cells (DCs) and other innate immune cells, as well as pattern-recognition receptors and various inflammatory mediators, contribute to atherogenesis. The specific adaptive immune response, governed by T cells and B cells, antibodies, and immunomodulatory cytokines potently regulates disease activity and progression. In the inflammatory microenvironment, the heterogeneity of leukocyte subpopulations plays a very important regulatory role in plaque evolution. With advances in experimental techniques, the fine mechanisms of immune system involvement in atherosclerotic plaque evolution are becoming known. In this review, we examine the critical immune responses involved in atherosclerotic plaque evolution, in particular, looking at atherosclerosis from the perspective of evolutionary immunobiology. A comprehensive understanding of the interplay between plaque evolution and plaque immunity provides clues for strategically combating atherosclerosis.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.284
Teacher spread0.261 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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