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Redox signaling, Nox5 and vascular remodeling in hypertension

2015· review· en· W955847907 on OpenAlexfundno aff
Augusto C. Montezano, Sofia Tsiropoulou, Maria Dulak‐Lis, Adam Harvey, Lívia L. Camargo, Rhian M. Touyz

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2015
Typereview
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchBritish Heart Foundation
KeywordsRedoxMedicineChemistry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Extensive data indicate a role for reactive oxygen species (ROS) and redox signaling in vascular damage in hypertension. However, molecular mechanisms underlying these processes remain unclear, but oxidative post-translational modification of vascular proteins is critical. This review discusses how proteins are oxidatively modified and how redox signaling influences vascular smooth muscle cell growth and vascular remodeling in hypertension. We also highlight Nox5 as a novel vascular ROS-generating oxidase. RECENT FINDINGS: Oxidative stress in hypertension leads to oxidative imbalance that affects vascular cell function through redox signaling. Many Nox isoforms produce ROS in the vascular wall, and recent findings show that Nox5 may be important in humans. ROS regulate signaling by numerous processes including cysteine oxidative post-translational modification such as S-nitrosylation, S-glutathionylation and sulfydration. In vascular smooth muscle cells, this influences cellular responses to oxidative stimuli promoting changes from a contractile to a proliferative phenotype. SUMMARY: In hypertension, Nox-induced ROS production is increased, leading to perturbed redox signaling through oxidative modifications of vascular proteins. This influences mitogenic signaling and cell cycle regulation, leading to altered cell growth and vascular remodeling in hypertension.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0020.002
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 head0.123
GPT teacher head0.336
Teacher spread0.214 · 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

Citations93
Published2015
Admission routes1
Has abstractyes

Explore more

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