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Record W2124734965 · doi:10.1152/ajpheart.00701.2005

Cellular and molecular mechanisms underlying LPS-associated myocyte impairment

2005· article· en· W2124734965 on OpenAlexaff
Samantha A. Tavener, Paul Kubes

Bibliographic record

VenueAmerican Journal of Physiology-Heart and Circulatory Physiology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMyocyteInternal medicineTumor necrosis factor alphaTLR4EndocrinologyCardiac myocyteMacrophageInflammationReceptorLipopolysaccharideBiologyNitric oxide synthaseImmunologyNitric oxideMedicineBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Recently we reported that Toll-like receptor 4 (TLR4)-positive immune cells of unknown identity were responsible for the LPS-induced depression of cardiac myocyte shortening. The aim of this study is to identify the TLR4-positive cell type that is responsible for the LPS-induced cardiac dysfunction. Neither neutrophil depletion alone nor mast cell deficiency had any impact on the impairment of myocyte shortening during LPS treatment. In contrast, LPS-treated, macrophage-deficient mice demonstrated a partial reduction in shortening compared with saline-treated, macrophage-deficient mice. Because the removal of macrophages could only partially restore myocyte shortening, we also investigated the effects of removing both neutrophils and macrophages on myocyte shortening. Interestingly, endotoxemic, neutrophil-depleted, and macrophage-deficient mice had completely restored myocyte shortening. Because both macrophages and neutrophils can produce nitric oxide (NO) and TNF-alpha, we examined LPS-treated inducible NO synthase knockout (iNOSKO) mice and TNF receptor (TNFR)-deficient mice. Eliminating both TNFR1 and TNFR2 was required to restore myocyte shortening during LPS treatment, whereas iNOS deficiency had no effect. These data suggest that macrophages and to a lesser degree neutrophils cause cardiac impairment, presumably via TNF-alpha.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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 teacher head, not a consensus.

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

Citations52
Published2005
Admission routes1
Has abstractyes

Explore more

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