Inhibition of monocyte chemoattractant protein-1 prevents diaphragmatic inflammation and maintains contractile function during endotoxemia
Bibliographic record
Abstract
INTRODUCTION: Respiratory muscle weakness is common in sepsis patients. Proinflammatory mediators produced during sepsis have been implicated in diaphragmatic contractile dysfunction, but the role of chemokines has not been explored. This study addressed the role of monocyte chemoattractant protein-1 (MCP-1, also known as CCL2), in the pathogenesis of diaphragmatic inflammation and weakness during endotoxemia. METHODS: Mice were treated as follows (n = 6 per group): (a) saline, (b) endotoxin (25 μg/g IP), (c) endotoxin + anti-MCP-1 antibody, and (d) endotoxin + isotype control antibody. Muscles were also exposed to recombinant MCP-1 in vivo and in vitro. Measurements were made of diaphragmatic force generation, leukocyte infiltration, and proinflammatory mediator (MCP-1, IL-1α, IL-1β, IL-6, NF-κB) expression/activity. RESULTS: In vivo, endotoxin-treated mice showed a large decrease in diaphragmatic force, together with upregulation of MCP-1 and other cytokines, but without an increase in intramuscular leukocytes. Antibody neutralization of MCP-1 prevented the endotoxin-induced force loss and reduced expression of MCP-1, IL-1α, IL-1β, and IL-6 in the diaphragm. MCP-1 treatment of nonseptic muscles also led to contractile weakness, and MCP-1 stimulated its own transcription independent of NF-κB activation in vitro. CONCLUSIONS: These results suggest that MCP-1 plays an important role in the pathogenesis of diaphragmatic weakness during sepsis by both direct and indirect mechanisms. We speculate that its immunomodulatory properties and ability to modify skeletal muscle function make MCP-1 a potential therapeutic target in critically ill patients with sepsis and associated respiratory muscle weakness.
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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 | 0.001 |
| 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".