Malassezia Globosa Aggravates Atopic Dermatitis by Influencing the Th1/Th2 Related Cytokines in Mouse Models
Bibliographic record
Abstract
Purpose: To establish atopic dermatitis (AD) mouse models infected with Malassezia globosa and study its effects and potential mechanisms. Methods: Twenty - four male BALB/c mice were randomly allocated into four groups: control, AD, M (normal mice treated with olive oil fungus suspension), and AD + M (AD mice treated with the same suspension). DNFB was used to induce the AD model. The M and AD + M groups were treated with Malassezia suspension. Body weight, scratching behavior, and skin lesion scores of mice were recorded. Skin tissues underwent HE and PAS staining, viable fungal flora counting, and Th1/Th2 cytokine detection via flow cytometry. Results: The AD mouse models infected with Malassezia globosa were successfully set up. The AD + M group scratched more often. On days 8, 12, and 16, the AD group’s skin lesion scores were (9.00± 0.89), (10.17± 0.87), (9.17± 0.75), while those of the AD + M group were (11.00± 0.82), (10.83± 0.75), (10.83± 0.75) (P< 0.05). The AD + M group had more Malassezia colonization (P< 0.001). The M group displayed a Th1 response. The AD + M group enhanced Th1 response and increased Th2 cytokines like IL - 4 and IL - 10 (P< 0.05). The control group had normal skin with minimal scratching and low fungal counts. Conclusion: Malassezia causes inflammation in normal and AD - like skin, with worse inflammation when the skin barrier is damaged. Targeting Malassezia might alleviate AD inflammation, offering new AD treatment directions. Keywords: atopic dermatitis, mouse model, immune response, inflammatory cytokines, fungal colonization
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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) | 0.001 | 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".