Systemic inflammation activates the nuclear factor-κB regulatory pathway in adipose tissue
Bibliographic record
Abstract
Adipose tissue (AT) is a store of energy but also an endocrine organ with the capacity to produce and release proinflammatory mediators into the circulation. The mechanism that may trigger human AT inflammation on a cellular level still remains largely unknown. The aim of this study was to investigate whether an acute systemic inflammation increases AT inflammatory activity, focused on innate immunity. Open heart surgery results in an extensive acute systemic inflammation. Therefore, we investigated the in vivo gene expression and production of inflammatory mediators in omental and subcutaneous AT stimulated by surgery. Biopsies from omental and subcutaneous AT were collected before and after cardiopulmonary bypass. Blood samples were collected at the same time as the AT biopsies and plasma IL-6 levels were measured with ELISA. RT-PCR was used for quantification of relative AT gene expression. To verify the gene expression results on a protein level, we used immunohistochemistry and microdialysis. After surgery, in both omental and subcutaneous AT, there was a strong upregulation of nuclear factor-kappaB-regulated genes, e.g., chemokine ligand-2, E-selectin, IL-1beta, IL-6, IL-8, and Toll-like receptor-2. Immunohistochemistry showed staining for E-selectin associated with a high number of macrophages in close contact with and in the vascular wall. Increased levels of IL-6 were detected in microdialysate from subcutaneous AT. In conclusion, we present the novel finding that this model of inflammation induced a strong inflammatory response in both omental and subcutaneous AT including adhesion of macrophages to an activated endothelium and release of IL-6 from AT interstitium. It can be hypothesized that AT exerts a modulatory effect on innate immunity in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".