Mitogen-induced upregulation of hepatitis C virus expression in human lymphoid cells
Bibliographic record
Abstract
Considering growing evidence indicating that hepatitis C virus (HCV) replicates in lymphoid cells, establishment of a reliable and sensitive method for detection of HCV in these cells may provide means for monitoring the infection and the efficacy of sterilizing antiviral therapy. In this study, conditions for ex vivo augmentation and detection of the HCV genome in peripheral blood mononuclear cells (PBMCs) from patients with chronic hepatitis C (CHC) or after a sustained virological response (SVR) to antiviral treatment were assessed. Following stimulation with combinations of mitogens and/or cytokines, PBMCs and, in certain cases, affinity-purified T and B cells were examined for HCV positive- and negative-strand RNA by using RT-PCR followed by nucleic acid hybridization, while the presence of viral NS3 protein was determined by flow cytometry. HCV RNA augmentation was assessed by quantification of Southern and dot-blot hybridization signals. The results showed that treatment of peripheral lymphoid cells with mitogens stimulating T- and B-cell proliferation and with cytokines supporting their growth significantly increased HCV RNA detection in patients with both CHC and SVR. This enhancement was up to 100-fold for the HCV genome and fivefold for the NS3 protein compared with untreated cells. In conclusion, HCV RNA can be readily detected in circulating lymphoid cells in progressing hepatitis C and following SVR after ex vivo cell stimulation. As such, this method offers a new investigative tool to study HCV lymphotropism and to monitor virus presence during the course of HCV infection.
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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) | 0.002 | 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".