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Record W2606563835 · doi:10.1002/jcp.25950

Cytoskeletal remodeling and regulation of cell fate in the hypertensive neonatal pulmonary artery in response to stress

2017· review· en· W2606563835 on OpenAlexaff
Dina Johar

Bibliographic record

VenueJournal of Cellular Physiology · 2017
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSenescenceHypoxia (environmental)MedicineOxidative stressApoptosisCirculatory systemPulmonary hypertensionProgrammed cell deathVasodilationCell fate determinationDiseaseCytoskeletonCellBiologyInternal medicineChemistryGeneticsTranscription factor

Abstract

fetched live from OpenAlex

Neonatal pulmonary hypertension (PHN) is a lethal progressive disease that occurs in prenatal circulatory transition. Mechanical wall strain caused by cardiac pulsation integrates with hypoxia to generate rapidly progressive myocyte cytoskeleton disassembly and failure to exert force generation. The physiological responses to such an interaction have not been investigated. The persistent phenotype does not respond to traditional vasodilator therapy; hence, there is a need for new treatment strategies to improve the morbidity and mortality outcomes. We reviewed the current research methods, models, and markers of persistent PHN relevant to oxidative and nitrosative stress as well as cell fate commitment, with an emphasis on apoptosis and proliferation. We surveyed potential investigations into the role of senescence in neonatal PHN cell fate decision programming during vasodilator treatment and suggested putative drug targets to improve clinical outcomes. We identified important signaling intermediates of senescence and cell cycle entry regulation in hypertensive pulmonary arterial tissues.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.340
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2017
Admission routes1
Has abstractyes

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