Cytokines and endotoxin induce cytokine receptors in skeletal muscle
Bibliographic record
Abstract
Proinflammatory cytokines are important factors in the regulation of diverse aspects of skeletal muscle function; however, the muscle cytokine receptors mediating these functions are uncharacterized. Binding kinetics (dissociation constant = 39+/-4.7 x 10(-9) M, maximal binding = 3.5+/-0.23 x 10(-12) mol/mg membrane protein) of muscle tumor necrosis factor (TNF) receptors were obtained. Skeletal muscle was found to express mRNAs encoding interleukin-1 type I and II receptors, interleukin-6 receptor (IL-6R), and interferon-gamma receptor by RT-PCR, but these receptors were below limits of detection of ligand-binding assay (> or =1 fmol binding sites/mg protein). Twenty-four hours after intraperitoneal administration of endotoxin to rats, TNF receptor type II (TNFRII) and IL-6R mRNA were increased in skeletal muscle (P<0.05). In cultured L6 cells, the expression of mRNA encoding TNFRII and IL-6R receptors was induced by TNF-alpha, and all six cytokine receptor mRNA were induced by a mixture of TNF-alpha, IFN-gamma, and endotoxin (P<0.05). This suggests that the low level of cytokine receptor expression is complemented by a capacity for receptor induction, providing a clear mechanism for amplification of cytokine responses at the muscle level.
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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) | 0.002 | 0.001 |
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".