Malassezia Globosa Aggravates Atopic Dermatitis by Influencing the Th1/Th2 Related Cytokines in Mouse Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (9.000.89),(10.170.87),(9.170.75),while those of the AD + M group were (11.000.82),(10.830.75),(10.830.75)(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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it