Helicobacter pylori infection attenuates 2,4-dinitrochlorobenzene-induced atopic dermatitis-like skin lesions in C57/BL6 mice
Bibliographic record
Abstract
BACKGROUND: Although numerous studies have suggested a negative correlation between Helicobacter pylori (H. pylori) infection and allergies, there has been limited research on the relationship between H. pylori infections and atopic dermatitis (AD). The present study aimed to investigate the effects of H. pylori infection in an AD mouse model and identify potential mechanisms related to type 2 immunity, skin barrier defects, and pruritus. METHODS: A model of AD-like symptoms was established with 2,4-dinitrochlorobenzene (DNCB) after infection of the gastric cavity with H. pylori. Analysis of the expression of key inflammatory cytokines and serum levels of immunoglobulin E (IgE) was based on enzyme-linked immunosorbent assay (ELISA). The expression of filaggrin (FLG) and loricrin (LOR) were analyzed by immunohistochemistry staining. The evaluation of STAT1, STAT3, phosphorylated STAT1 (phospho-STAT1), and phosphorylated STAT3 (phospho-STAT1) expression levels in skin lesions was performed using western blot. RESULTS: The present study showed that the H. pylori-positive AD group (HP+AD+) exhibited milder skin lesions, including erythema, erosion, swelling, and scaling, than the H. pylori-negative AD group (HP-AD+). Additionally, HP+AD+ displayed lower levels of IgE in serum, and downregulated expression of interleukins 4 and 31 (IL-4 and IL-31) in serum. Furthermore, HP+AD+ demonstrated higher expression of filaggrin and loricrin than HP-AD+. Notably, H. pylori significantly reduced the amount of phosphorylated STAT1 and STAT3. CONCLUSION: Helicobacter pylori infection negatively regulates the inflammatory response by affecting inflammatory factors in the immune response, and repairs the defective epidermal barrier function. In addition, H. pylori infection may reduce IL-31, thereby alleviating pruritus. These effects may be associated with the inhibition of JAK-STAT signaling activation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".