Quantitative Detection of Hepadnavirus-Infected Lymphoid Cells by In Situ PCR Combined with Flow Cytometry: Implications for the Study of Occult Virus Persistence
Bibliographic record
Abstract
The detection of small amounts of viral pathogens in infected cells by classical PCR is hampered by a partial loss of virus nucleic acid due to extraction and by difficulties in discrimination between truly intracellular virus genome material and that possibly adhered to the cell surface. These impediments limit reliable identification of virus traces within infected cells, which are typically encountered in latent and persistent occult infections. In this study, hepadnavirus-specific in situ PCR combined with the enzymatic elimination of extracellular virus and flow cytometry permitted detection of viral genomes in lymphoid cells without nucleic acid isolation and allowed quantification of infected cells during the course of persistent infection with woodchuck hepatitis virus (WHV). The validity of the procedure was confirmed by hybridization analysis of the in situ-amplified viral sequences. The results showed that hepadnavirus can be directly detected within lymphoid cells not only in serologically accountable infection, but also years after recovery from viral hepatitis and in the course of primary occult virus carriage. Percentages of infected peripheral lymphoid cells in symptomatic WHV hepatitis fluctuate between 3.4 and 20.4% (mean +/- standard error of the mean, 9.6% +/- 1.7%), whereas those in persistent, serologically mute WHV infection range from 1.1 to 14.6% (mean +/- standard error of the mean, 4.8% +/- 0.8%) (P = 0.005). The data obtained provide further evidence that WHV infection continues indefinitely in the lymphatic system independently of whether it is symptomatic or concealed. They document that hepadnavirus can be detected in a significant proportion of circulating lymphoid cells in both immunovirologically apparent as well as occult persistent 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".