Kinetic Analysis by Real-Time PCR of Hepatitis C Virus (HCV)-Specific T Cells in Peripheral Blood and Liver after Challenge with HCV
Bibliographic record
Abstract
Intrahepatic virus-specific CD8(+) T cells are thought to be important for the control of hepatitis C virus (HCV) infection, yet the precise kinetics for the expansion of epitope-specific T cells over the course of infection are difficult to determine with currently available methods. We used a real-time PCR assay to measure the frequency of clonotypic HCV-specific CD8(+) T cells in peripheral blood and snap-frozen liver biopsy specimens of two chimpanzees (Pan troglodytes) with previously resolved HCV infection who were rechallenged with HCV. In response to rechallenge, the magnitude of each clonotypic response was 10-fold higher in the liver than in the blood, and the peak clonotype frequency was concurrent with the peak viral load. The higher frequency of HCV-specific clonotypes in the liver than in peripheral blood was maintained for at least 3 months after the clearance of viremia. After antibody-mediated CD8(+) T-cell depletion and another viral challenge, the rebound of these clonotypes was seen prior to an appreciable reconstitution of CD8(+) T-cell values and, again, at higher frequencies in the liver than in peripheral blood. These data demonstrate the importance of intrahepatic virus-specific CD8(+) T cells for the clearance of infection and the rapid kinetics of expansion after virus challenge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".