Metabolic inflammation: role of cytokines in the crosstalk between adipose tissue and liver
Bibliographic record
Abstract
The innate immune system and its major mediators, i.e., cytokines, are increasingly recognized as being of crucial importance in metabolic inflammation as observed in morbid obesity and type 2 diabetes (T2D). Morbid obesity is commonly associated with adipose tissue inflammation. Adipose tissue inflammation is characterized by an increased expression of various pro-inflammatory cytokines such as tumor necrosis factor-alpha, interleukin-1 and -6, and by a rather heterogenous cellular infiltrate including monocytes/macrophages, neutrophils, B lymphocytes, T lymphocytes, and others. It has been demonstrated that in patients with severe obesity and fatty liver disease, expression of these pro-inflammatory cytokines in adipose tissue is 100-1000 times higher compared with that in the liver. Therefore, the adipose tissue can be considered in the state of severe obesity as the "cytokine factory" of the body. Rapid weight loss almost entirely eliminates pro-inflammatory cytokines in the adipose tissue, and therefore provides a very potent anti-inflammatory strategy. In conclusion, there is increasing evidence that peripheral tissues such as the adipose tissue may affect disease processes in target organs such as the liver, pancreas, heart, or blood vessels, and may therefore significantly contribute to chronic inflammation as observed in obesity and T2D.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".