The Emerging Role of Circulating T Follicular Helper Cells in Dengue Virus Immunity: Balancing Protection and Pathogenesis
Bibliographic record
Abstract
Flaviviruses are a group of viruses transmitted mainly by mosquitoes and ticks, causing severe diseases in humans. Examples include dengue, Zika, West Nile virus, and yellow fever. They primarily affect individuals in tropical and subtropical regions, causing public health problems such as epidemic outbreaks and significant economic burdens due to hospitalizations and treatments. They share antigens, leading to cross-reactivity where antibodies generated against one flavivirus can react with others, complicating the accurate diagnosis of individual infections and making the development of treatments or vaccines more challenging. The role of T cells in the immune response to flaviviruses is a complex topic debated by scientists. On one hand, T cells help control infection by eliminating infected cells and protecting against disease. However, there is evidence that an excessive or dysregulated T cell response can cause tissue damage and worsen the disease, as seen in severe dengue cases. This duality underscores the complexity of the immune response to flavivirus infections, posing a significant challenge for researchers. Gaining a deeper understanding of the immune response at the cellular level, particularly the role of T follicular helper cells, can reveal new avenues of investigation that could lead to novel strategies for disease management. This review explores the dynamics of T cell responses, focusing on circulatory T follicular helper cells (cTFH), to enhance our understanding of flavivirus immunity and inform future interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".