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Record W4379054087 · doi:10.1038/s41514-023-00107-3

Cadmium, von Willebrand factor and vascular aging

2023· review· en· W4379054087 on OpenAlexaff
Xia Wang, М. Н. Стародубцева, Carolyn Kapron, Ju Liu

Bibliographic record

Venuenpj Aging · 2023
Typereview
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsTrent University
FundersShandong First Medical UniversityTaishan Scholar Project of Shandong ProvinceNational Natural Science Foundation of China
KeywordsVon Willebrand factorEndothelial dysfunctionInflammationTissue factorVascular smooth muscleInternal medicineCoagulationOxidative stressVascular diseaseEndocrinologyMedicineEndothelial stem cellImmunologyEndotheliumBiologyPlateletIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Vascular aging is a major contributing factor to cardiovascular disease. The aged blood vessels, characterized by vascular wall thickening and stiffening, are instigated by endothelial cell dysfunction induced by oxidative stress and inflammation. von Willebrand Factor (vWF) is a glycoprotein known for its role in coagulation, and plasma levels of vWF are increased with age. Elevated vWF promotes thrombosis, atherosclerotic plaque formation, inflammation and proliferation of vascular smooth muscle cells. Cadmium (Cd) is an environmental pollutant associated with increased morbidity and mortality of cardiovascular disease. At low concentrations, Cd activates pro-survival signaling in endothelial cells, however enhances intima-media thickness and atherogenesis. A non-cytotoxic dose of Cd also increases endothelial vWF expression and secretion in vivo and in vitro. In this review, we summarize the molecular mechanisms underlying vWF-promoted vascular aging-associated pathologies and Cd-induced vWF expression. In addition, we propose that exposure to low-dose Cd is a risk factor for vascular aging, through elevation of plasma vWF.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.089
GPT teacher head0.342
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2023
Admission routes1
Has abstractyes

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