MétaCan
Menu
Back to cohort
Record W4413108190 · doi:10.1152/ajplung.00222.2025

Bioenergetics and metabolism of the pulmonary endothelium. Scientific session I: ReSPIRE 2025

2025· review· en· W4413108190 on OpenAlexaff
R.P. Stevens, Justin T. Roberts, Wolfgang M. Kuebler, Ji Lee, Karthik Suresh, Rebecca F. Hough

Bibliographic record

VenueAmerican Journal of Physiology-Lung Cellular and Molecular Physiology · 2025
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsColumbia College
FundersNational Heart, Lung, and Blood Institute
KeywordsBioenergeticsARDSHypoxia (environmental)EndotheliumBiologyPulmonary hypertensionBioinformaticsCell biologyInternal medicineMedicineLungChemistryMitochondrionEndocrinologyOxygen

Abstract

fetched live from OpenAlex

Session I of the inaugural biennial Research Symposium on Pulmonary Injury and Repair of the Endothelium (ReSPIRE) highlighted recent advances in endothelial bioenergetics and metabolism and their role in pulmonary vascular diseases. Emerging evidence suggests that the maladaptation of metabolic pathways in the lung endothelium contributes to the progression of the acute respiratory distress syndrome (ARDS) and pulmonary arterial hypertension (PAH). The conference highlighted several new aspects of endothelial metabolism, including the use of alternative fuel sources such as fructose and fatty acids, inflammatory signaling mediated by mitochondrial depolarization, bioenergetic reprogramming through isoform switching of genes during hypoxia, and feedback regulation of metabolism by hypercapnia. Ultimately, these findings point to future research directions aimed at identifying mechanisms of dysregulated endothelial metabolism, which could serve as therapeutic targets for pulmonary vascular diseases.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.007

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 head0.011
GPT teacher head0.291
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 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physiology-Lung Cellular and Molecular PhysiologySame topicAdipose Tissue and MetabolismFrench-language works237,207