Cross Talk Between Natural Killer T and Dendritic Cells and Its Impact on T Cell Responses in Infections
Bibliographic record
Abstract
Both innate and adaptive immunity is vital for host defense against infections. Dendritic cells (DCs) are critical for initiating and modulating adaptive immunity, especially for T-cell responses. Natural killer T (NKT) cells are a small population of innate-like T cells distributed in multiple organs. Many studies have suggested that the cross-talk between these two immune cells is critical for immunobiology and host defense mechanisms. Not only can DCs influence the activation/function of NKT cells, but NKT cells can feedback on DCs also, thus modulating the phenotype and function of DCs and DC subsets. This functional feedback of NKT cells on DCs, especially the preferential promoting effect on CD8α+ and CD103+ DC subsets in lymphoid and non-lymphoid tissues, significantly impacts the systemic and local adaptive CD4 and CD8 T cell responses in infections. This review focuses on the two-way interaction between NKT cells and DCs, emphasizing the importance of NKT cell feedback on DCs in bridging innate and adaptive immune responses for host defense purposes.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".