The role of γδ T cells in flavivirus infections: Insights into immune defense and therapeutic opportunities
Bibliographic record
Abstract
γδ T cells are a unique subset of unconventional T cells and an important component of the innate immune system. Unlike conventional αβ T cells, γδ T cells can respond rapidly during the early stages of infection, and their antigen recognition is not restricted by MHC molecules. These distinctive features underscore the important role of γδ T cells in viral clearance and infection control. Therefore, γδ T cell-based immunotherapies have been extensively explored for the treatment of a variety of diseases, including viral infections and cancers. Several therapeutic strategies based on γδ T cells have advanced to clinical trials, demonstrating promising safety and efficacy. Currently, there are no effective treatments for flavivirus infections, which are typically characterized by acute onset. Research has shown that γδ T cells can rapidly expand during the early phases of flavivirus infections and effectively suppress viral replication, making them an attractive target for the development of novel therapies for flavivirus infections. This review aims to highlight the immunological roles of γδ T cells in flavivirus infections and to explore the potential of γδ T cell-based therapeutic strategies for the prevention and treatment of these infections.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".