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Record W2044468754 · doi:10.1165/ajrcmb.23.4.3944

A Role for Hyaluronan in Macrophage Accumulation and Collagen Deposition after Bleomycin-Induced Lung Injury

2000· article· en· W2044468754 on OpenAlexaff
Rashmin C. Savani, Guangpei Hou, Ping Liu, Chao Wang, Elinor Simons, Paul C. Grimm, Robert Stern, Arnold H. Greenberg, Horace M. DeLisser, Nasreen Khalil

Bibliographic record

VenueAmerican Journal of Respiratory Cell and Molecular Biology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsUniversity of Manitoba
FundersNational Heart, Lung, and Blood Institute
KeywordsBleomycinMacrophageHyaluronic acidDeposition (geology)LungChemistryGlycosaminoglycanAlveolar macrophagePathologyCell biologyMedicineCancer researchInternal medicineBiologyBiochemistryAnatomyChemotherapy

Abstract

fetched live from OpenAlex

Elevated concentrations of hyaluronan (HA) are associated with the accumulation of macrophages in the lung after injury. We have investigated the role of HA in the inflammatory and fibrotic responses to lung injury using the intratracheal instillation of bleomycin in rats as a model. After bleomycin-induced lung injury, both HA content in bronchoalveolar lavage (BAL) and staining for HA in macrophages accumulating in injured areas of the lung were maximal at 4 d. Increased HA in BAL correlated with increased locomotion of isolated alveolar macrophages. HA-binding peptide was able to specifically block macrophage motility in vitro. Importantly, systemic administration of HA-binding peptide to rats before injury not only decreased alveolar macrophage motility and accumulation in the lung, but also reduced lung collagen alpha (I) messenger RNA and hydroxyproline contents. We propose a model in which HA plays a critical role in the inflammatory response and fibrotic consequences of acute lung injury.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.294
Teacher spread0.287 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations81
Published2000
Admission routes1
Has abstractyes

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