Cellular and molecular mechanisms underlying LPS-associated myocyte impairment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".