Chemokine Receptor and Ligand Upregulation in the Diaphragm during Endotoxemia and Pseudomonas Lung Infection
Bibliographic record
Abstract
Sepsis-induced diaphragmatic inflammation has been associated with respiratory failure, but the role of chemokines in this process has not been evaluated. Here we sought to study the local expression and molecular regulation of the chemokines, regulated upon activation normal T cell expressed and secreted (RANTES) and macrophage inflammatory protein (MIP)-1alpha, in the murine diaphragm during sepsis. Constitutive expression levels of RANTES and MIP-1alpha, as well as their receptors, CCR1 and CCR5, were significantly higher in diaphragm than limb muscle. Sepsis was induced by acute lipopolysaccharide (LPS) delivery or subacutely by intratracheal administration of live Pseudomonas aeruginosa bacteria. Both sepsis models triggered a marked upregulation of RANTES and MIP-1alpha in the diaphragm. In vitro, stimulation of diaphragmatic muscle cells with LPS also led to RANTES upregulation. Inhibition of the NF-kB pathway using pharmacologic or dominant negative genetic approaches blocked the LPS-induced RANTES upregulation, while free radical scavengers had no effect. We conclude that sepsis leads to greatly increased expression of RANTES, MIP-1alpha and their cognate receptors in the diaphragm. Manipulation of the NF-kB pathway and other regulators of chemokine expression in the diaphragm could represent a novel method for mitigating the skeletal muscle inflammatory response associated with sepsis-induced diaphragmatic dysfunction.
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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".