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Record W2342781456 · doi:10.1139/cjpp-2015-0568

Interactions of hyperhomocysteinemia and T cell immunity in causation of hypertension

2016· review· en· W2342781456 on OpenAlexvenueno aff
Sudhakar Veeranki, Siva K. Gandhapudi, Suresh C. Tyagi

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2016
Typereview
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsHyperhomocysteinemiaHomocysteineTranssulfurationRisk factorInflammationMedicineDiseaseEpigeneticsPopulationInternal medicineImmunologyBiologyMethionineCystathionine beta synthaseGeneticsEnvironmental health

Abstract

fetched live from OpenAlex

Although hyperhomocysteinemia (HHcy) is an independent risk factor for cardiovascular diseases (CVD), there is a debate on whether HHcy is a risk factor or just a biomarker. Interestingly, homocysteine lowering strategies in humans had very little effect on reducing the cardiovascular risk, as compared with animals; this may suggest heterogeneity in human population and epigenetic alterations. Moreover, there are only few studies that suggest the idea that HHcy contributes to CVD in the presence of other risk factors such as inflammation, a known risk factor for CVD. Elevated levels of homocysteine have been shown to contribute to inflammation. Here, we highlight possible relationships between homocysteine, T cell immunity, and hypertension, and summarize the evidence that suggested these factors act together in increasing the risk for CVD. In light of this new evidence, we further propose that there is a need for evaluation of the causes of HHcy, defective remethylation or defective transsulfuration, which may differentially modulate hypertension progression, not just the homocysteine levels.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.067
GPT teacher head0.367
Teacher spread0.300 · 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 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

Citations42
Published2016
Admission routes1
Has abstractyes

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