Optimized ex vivo stimulation identifies multi-functional HBV-specific T cells in a majority of chronic hepatitis B patients
Bibliographic record
Abstract
Abstract High antigen burden during chronic hepatitis B (CHB) results in a low frequency HBV-specific T cell response with restricted functionality. However, this observation is based on limited data because low T cell frequencies have hindered effective ex vivo analysis. We adapted the ELISpot assay to overcome this obstacle to measure ex vivo T cell responses in CHB patients. We modified the key variables of cell number and the peptide pulsing method to improve ex vivo detection of HBV-specific T cells. We detected IFN-γ responses in 10/15 vaccinated controls and 20/30 CHB patients, averaging 195 and 84 SFUs/2 × 106 PBMCs respectively. Multi-analyte FluoroSpots improved functional characterization of T cells. We detected IFN-γ responses in all tested vaccinated controls (n = 10) and CHB patients (n = 13). IL-2 responses were detectable in 9/10 controls and 10/13 patients. TNF-α displayed less sensitivity, detectable in only 7/10 controls and 7/13 patients. Antigen-specific analysis demonstrated that IFN-γ responses were dominated by polymerase and core, with weak responses to envelope and X. IL-2 responses were found in 3/5 patients and equally directed towards polymerase and core. While their ex vivo frequency is extremely low, a fraction of HBV-specific T cells are detectable and display multi-functionality ex vivo.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".