Dendritic Cells and Their Crucial Role in Modulating Innate Lymphoid Cells for Treating and Preventing Infectious Diseases
Bibliographic record
Abstract
Two key players in the immune system, dendritic cells (DCs) and innate lymphoid cells (ILCs), interact in a crucial way to fight infectious diseases. DCs play a key role in recognizing pathogens, and ILCs respond to cytokines released by DCs. This response triggers the production of specific effector cytokines that help control pathogens and maintain the body's barrier integrity. DCs have various receptors, including Toll-like receptors (TLRs), that detect microbial components and trigger immune responses. Likewise, ILCs act as essential initial responders in the immune system in viral, bacterial, and parasitic infections. Successfully managing diseases caused by pathogens mainly depends on the combined actions of DCs and ILCs, which work to suppress and eliminate pathogens. DCs also play a crucial role in activating innate and adaptive immune cell subsets, including ILCs. Furthermore, the use of DCs in developing vaccines and immunotherapy for cancers, along with the dedication of many researchers to improve immune responses through DCs, has increased interest in the potential of DC therapies for treating and preventing infectious diseases. This review examines approaches that may enhance DC vaccines and boost anti-infection immune responses by fostering better interactions of DCs with ILCs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".