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Record W2809951543 · doi:10.3389/fphys.2018.00951

Mechanobiological Feedback in Pulmonary Vascular Disease

2018· review· en· W2809951543 on OpenAlexaff
Paul B. Dieffenbach, Marcy Maracle, Daniel J. Tschumperlin, Laura E. Fredenburgh

Bibliographic record

VenueFrontiers in Physiology · 2018
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsWestern University
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthAmerican Heart Association
KeywordsPulmonary hypertensionMedicineExtracellular matrixPulmonary arteryPathogenesisArterial stiffnessCardiologyVascular diseasePathologyInternal medicineBlood pressureBiologyCell biology

Abstract

fetched live from OpenAlex

Vascular stiffening in the pulmonary arterial bed is increasingly recognized as an early disease marker and contributor to right ventricular workload in pulmonary hypertension. Changes in pulmonary artery stiffness throughout the pulmonary vascular tree lead to physiologic alterations in pressure and flow characteristics that may contribute to disease progression. These findings have led to a greater focus on the potential contributions of extracellular matrix remodeling and mechanical signaling to pulmonary hypertension pathogenesis. Several recent studies have demonstrated that the cellular response to vascular stiffness includes upregulation of signaling pathways that precipitate further vascular remodeling, a process known as mechanobiological feedback. The extracellular matrix modifiers, mechanosensors, and mechanotransducers responsible for this process have become increasingly well-recognized. In this review, we discuss the impact of vascular stiffening on pulmonary hypertension morbidity and mortality, evidence in favor of mechanobiological feedback in pulmonary hypertension pathogenesis, and the major contributors to mechanical signaling in the pulmonary vasculature.

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 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 score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.324
Teacher spread0.281 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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