Obesity aggravates contact hypersensitivity reaction in mice
Bibliographic record
Abstract
BACKGROUND: Obesity is associated with chronic, low-grade inflammation in tissues and predisposes to various complications, including inflammatory skin diseases. However, the link between obesity and contact hypersensitivity (CHS) is not fully understood. OBJECTIVES: We sought to determine the influence of obesity on T helper 1 (Th1)-mediated CHS. METHODS: The activity/phenotype/cytokine profile of the immune cells was tested in vivo and in vitro. Using quantitative polymerase chain reaction (qPCR) and fecal microbiota transplantation (FMT), we tested the role of a high-fat diet (HFD)-induced gut microbiota (GM) dysbiosis in increasing the effects of CHS. RESULTS: Exacerbated CHS correlates with an increased inflammation-inducing GM in obese mice. We showed a proinflammatory milieu in the subcutaneous adipose tissue of obese mice, accompanied by proinflammatory CD4+ T cells and dendritic cells in skin draining lymph nodes and spleen. Obese interleukin (IL)-17A-/-B6 mice are protected from CHS aggravation, suggesting the importance of IL-17A in CHS aggravation in obesity. CONCLUSIONS: Obesity creates a milieu that induces more potent CHS-effector cells but does not have effects on already activated CHS-effector cells. IL-17A is essential for the pathogenesis of enhanced CHS during obesity. Our study provides novel knowledge about antigen-specific responses in obesity, which may help with the improvement of existing treatment and/or in designing novel treatment for obesity-associated skin disorders.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".