Dendritic Cells: Multifunctional Roles in Host Defenses to Cryptococcus Infections
Bibliographic record
Abstract
Fungal infections are an increasingly growing public health concern, and Cryptococcus is one of the most problematic fungal organisms causing substantial mortality and morbidity worldwide. Clinically, this high incidence of cryptococcosis is most commonly seen in immunocompromised patients, especially those who lack an adaptive T cell response, such as HIV/AIDS patients. However, patients with other underlying immunodeficiencies are also at an increased risk for cryptococcosis. The adaptive immune response, in particular the Th1/Th17 T-cell-mediated responses, to pulmonary Cryptococcus infections are required for host protection. Dendritic cells (DCs), encompassing multiple subsets identified to date, are recognized as the major professional antigen-presenting cell (APC) subset essential for the initiation and execution of T-cell immunity. Apart from their prominent role in orchestration of the adaptive arm of the immune defenses, DCs are fully armed cells from the innate immune system capable of the recognition, uptake, and killing of the fungal cells. Thus, DCs serve as a critical point for the endpoint outcomes of either fungal control or unrestrained fungal infection. Multiple studies have shown that DCs are required for anti-cryptococcal defense in the lungs. In addition, the role of DCs in Cryptococcus gattii infections is just starting to be elucidated. C. gattii has recently risen to prominence with multiple outbreaks in the US and Canada, demonstrating increased virulence in non-immunocompromised individuals. C. gattii infection fails to generate an inflammatory immune response or a protective Th1/Th17 T cell response, at least in part, through a lack of proper DC function. Here we summarize the multiple roles of DCs, including subsets of DCs in both mouse and human models, the roles of DCs during cryptococcal infection, and mechanisms by cryptococcal cells to attempt to undermine these host defenses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".