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Record W4401928488 · doi:10.1002/jcp.31424

Phenotypic switch of vascular smooth muscle cells in COVID‐19: Role of cholesterol, calcium, and phosphate

2024· review· en· W4401928488 on OpenAlexaff
Laura Ghanem, Dina Essayli, Jana Kotaich, Mohammad Al Zein, Amirhossein Sahebkar, Ali H. Eid

Bibliographic record

VenueJournal of Cellular Physiology · 2024
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsPharma Medica Research (Canada)
Fundersnot available
KeywordsVascular smooth musclePhenotypeContext (archaeology)CoronavirusCoronavirus disease 2019 (COVID-19)BiologyCalcificationMechanism (biology)CalciumRespiratory distressDiseaseInternal medicineEndocrinologyMedicineSmooth muscleGeneticsGeneInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Although the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), primarily manifests as severe respiratory distress, its impact on the cardiovascular system is also notable. Studies reveal that COVID-19 patients often suffer from certain vascular diseases, partly attributed to increased proliferation or altered phenotype of vascular smooth muscle cells (VSMCs). Although the association between COVID-19 and VSMCs is recognized, the precise mechanism underlying SARS-CoV-2's influence on VSMC phenotype remains largely under-reviewed. In this context, while there is a consistent body of literature dissecting the effect of COVID-19 on the cardiovascular system, few reports delve into the potential role of VSMC switching in the pathophysiology associated with COVID-19 and the molecular mechanisms involved therein. This review dissects and critiques the link between COVID-19 and VSMCs, with particular attention to pathways involving cholesterol, calcium, and phosphate. These pathways underpin the interaction between the virus and VSMCs. Such interaction promotes VSMC proliferation, and eventually potentiates vascular calcification as well as worsens prognosis in patients with COVID-19.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.406
Teacher spread0.352 · 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 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

Citations10
Published2024
Admission routes1
Has abstractyes

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