The replicative activity of human endogenous retrovirus K102 (HERV-K102) with HIV viremia
Bibliographic record
Abstract
OBJECTIVE: To address the activation and replicative activity of HERV-K102 in vivo associated with HIV viremia. DESIGN AND METHODS: Initially serology was performed on HERV-K102 specific envelope peptides to determine if HERV-K102 may become activated with HIV viremia. Before developing a quantitative PCR (qPCR) assay, we first determined whether plasma associated particles contained DNA or RNA genomes in a pilot study which surprisingly revealed predominantly DNA genomes. A relative, ddCt qPCR ratio method was then devised to detect excess levels of HERV-K102 pol DNA templates over genomic levels which served as a surrogate marker to reliably index the level of particles found in plasma. RESULTS: Both the peptide serology and ddCt qPCR excess ratio methods suggested the activation of HERV-K102 in about 70-80% of HIV viremic cases whereas only 2-3% of normal healthy adults had marginally activated HERV-K102 (P < 0.0001). Moreover, by digestion with dUTPase we were able to confirm that the vast majority of excess DNA template in plasma related to cDNA production rather than representing genomic copies. CONCLUSIONS: Our work uniquely suggests the common activation of HERV-K102 with HIV viremia and may be first to directly demonstrate HERV-K102 cDNA production in vivo. The potential implications of the induction of HERV-K102 activation and replication for the prevention and control of HIV are discussed.
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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 | 0.000 | 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) | 0.003 | 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".