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Record W2935979948 · doi:10.1165/rcmb.2018-0410tr

Cellular Senescence: The Trojan Horse in Chronic Lung Diseases

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

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.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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

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

Citations89
Published2019
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Respiratory Cell and Molecular BiologySame topicTelomeres, Telomerase, and SenescenceFrench-language works237,207