Immunotherapy-induced cytotoxic T follicular helper cells reduce numbers of retrovirus-infected reservoir cells in B cell follicles
Bibliographic record
Abstract
Antiretroviral therapy (ART) transformed HIV from a life-threatening disease to a chronic condition. However, eliminating the virus remains an elusive therapy goal. For several decades, Friend virus (FV) infection serves as a murine model to study retrovirus immunity. Similar to HIV, FV persists at low levels in lymph nodes B cell follicles avoiding elimination by immune cells. Such immune-privileged reservoirs exclude cytotoxic T cells from entry. However, CXCR5+ T cells are permitted to traffic through germinal centers. This marker is predominantly expressed by CD4+ follicular helper T cells (Tfh). Therefore, we explored immunotherapy to induce cytotoxic Tfh, which are rarely found under physiological conditions. The TNF receptor family member CD137 was first identified as a promising target for cancer immunotherapy. We demonstrated that FV-infected mice treatment with αCD137 antibody resulted in an induction of the cytotoxic program in Tfh. The therapy significantly increased numbers of cytotoxic Tfh within B cell follicles and contributed to viral load reduction. Moreover, αCD137 antibody combined with ART delayed virus rebound upon treatment termination without disturbing the lymph node architecture or antibody responses. Thus, αCD137 antibody therapy might be a novel strategy to target the retroviral reservoir and an interesting approach for HIV cure research.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".