Induction of the epithelial polarizing cytokine interleukin-33 by the fungus «Cryptococcus neoformans» in genetically susceptible mice
Bibliographic record
Abstract
With the progression of the AIDS epidemic, understanding the host responseto the opportunistic fungal pathogen Cryptococcus neoformans, responsible forapproximately 650,000 deaths in Sub-Saharan Africa each year, has become animportant topic of research. Current knowledge suggests that susceptibility tocryptococcal pneumonia in both mice and humans proceeds via an allergic (TH2)pattern of lung inflammation, while resistance is attributable to a TH1 response in thelungs. The epithelial polarizing cytokines thymic stromal lymphopoietin (TSLP),interleukin-25 (IL-25) and IL-33 have been implicated in TH2 mucosal andrespiratory inflammation caused by allergens and helminths; however, their rolesduring C. neoformans infection are not known. We demonstrated that in vitrostimulation of the mouse lung epithelial cell line (MLE-12) with both the acapsularmutant C. neoformans CAP64 and the highly virulent C. neoformans H99, resulted indose- and time-dependent increases in both Il25 and Il33 mRNA expression.Correspondingly, intranasal infection of susceptible Balb/c mice with C. neoformansH99 showed time-dependent IL-33 mRNA and protein in the lungs. Furthermore,moderately virulent C. neoformans 52D induced differential Il33 mRNA expressionamong susceptible and resistant strains of mice, with susceptible C57BL/6 micedeveloping a significant increase in lung Il33 mRNA compared to resistant CBAmice. Finally, Balb/c mice lacking the IL-33 receptor T1/ST2 had significantlyreduced lung, spleen and brain fungal burdens following intratracheal instillation ofC. neoformans. These observations support a role for IL-33 in polarization of the hostinflammatory response that facilitates progressive pulmonary C. neoformansinfection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".