MétaCan
Menu
Back to cohort
Record W2935979948 · doi:10.1165/rcmb.2018-0410tr

Cellular Senescence: The Trojan Horse in Chronic Lung Diseases

2019· review· en· W2935979948 on OpenAlex
Shruthi Hamsanathan, Jonathan K. Alder, Jacobo Sellarés, Mauricio Rojas, Aditi U. Gurkar, Ana L. Mora

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Respiratory Cell and Molecular Biology · 2019
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsInstitute of Aging
FundersNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsSenescenceCellular senescenceTrojan horseBiologyCell biologyMedicineGeneticsPhenotypeComputer science

Abstract

fetched live from OpenAlex

Senescence is a cell fate decision characterized by irreversible arrest of proliferation accompanied by a senescence-associated secretory phenotype. Traditionally, cellular senescence has been recognized as a beneficial physiological mechanism during development and wound healing and in tumor suppression. However, in recent years, evidence of negative consequences of cellular senescence has emerged, illuminating its role in several chronic pathologies. In this context, senescent cells persist or accumulate and have detrimental consequences. In this review, we discuss the possibility that in chronic obstructive pulmonary disease, persistent senescence impairs wound healing in the lung caused by secretion of proinflammatory senescence-associated secretory phenotype factors and exhaustion of progenitor cells. In contrast, in idiopathic pulmonary fibrosis, chronic senescence in alveolar epithelial cells exacerbates the accumulation of senescent fibroblasts together with production of extracellular matrix. We review how cellular senescence may contribute to lung disease pathology.

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.

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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.325
Teacher spread0.307 · 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