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Record W2073420114 · doi:10.1152/ajpregu.00175.2003

Macrophages, not neutrophils, infiltrate skeletal muscle in mice deficient in P/E selectins after mechanical reloading

2003· article· en· W2073420114 on OpenAlexaff
Jéroôme Frenette, Nadia Chbinou, Charles Godbout, David Marsolais, Paul S. Frenette

Bibliographic record

VenueAmerican Journal of Physiology-Regulatory, Integrative and Comparative Physiology · 2003
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversité Laval
FundersNational Heart, Lung, and Blood Institute
KeywordsSkeletal muscleSelectinChemistryCell biologyImmunologyInflammationMedicineBiologyAnatomy

Abstract

fetched live from OpenAlex

Our objective was to test the hypothesis that endothelial selectins, P and E selectins, are necessary for leukocyte migration after muscle injury from unloading/reloading. Mice hindlimbs were suspended for 10 days followed by reloading periods of 6 or 24 h after which the soleus muscle was dissected. Light microscopic observations showed that macrophages, but not neutrophils, were able to invade soleus muscles in mice deficient in P/E selectins (P/E-/-) during reloading periods. The recruitment efficiency of neutrophils after 6 and 24 h of reloading was minimal in P/E-/- mice relative to unloaded animals. The recruitment of macrophages in the soleus muscle was preserved in P/E-/- mice. The concentration of macrophages increased by 8.1-fold compared with unloaded muscles in double-mutant mice after 24 h of reloading. The accumulation of macrophages in reloaded muscles did not lead to fiber necrosis. Together, these findings indicate that macrophages can invade skeletal muscle through cellular mechanisms that do not involve P/E selectins during skeletal muscle reloading.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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 designBench or experimental
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

Citations32
Published2003
Admission routes1
Has abstractyes

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