Improved HIV-1 RNA detection using whole blood versus plasma in antiretroviral-treated individuals
Bibliographic record
Abstract
ABSTRACT Currently, nucleic acid testing (NAT) platforms detect HIV-1 in plasma. Using whole blood (WB) could improve HIV-1 detectability as cellular elements may also contain HIV-1 nucleic acids. We used well-characterized paired WB/plasma panels to evaluate HIV-1 RNA detection inhibition by WB, specificity, and enhanced HIV-1 RNA detectability by WB compared to plasma. Panels included: spiked samples; NAT−/serology−, NAT+/serology+, and NAT−/serology+ blood donor samples; samples from persons with HIV (PWH) who started antiretroviral treatment (ART) at chronic infection stages; and from PWH under ART since acute/early infection. We found one false-positive result on WB testing of 100 NAT−/serology− blood donors, and evidence of modest HIV-1 detection inhibition. Among NAT-/serology+ donors, HIV-1 RNA detectability in plasma and WB was similar ( P = 0.64). Among 50 PWH starting ART at chronic infection stages, detectability was 24% in plasma and 92% in WB ( P < 0.001). Among 345 PWH on ART since acute/early infection, detectability was 10% in plasma and 16% in WB ( P = 0.013). HIV-1 RNA detectability in both plasma and WB was progressively lower for earlier Fiebig stages at ART initiation. WB increased HIV-1 detectability relative to plasma in PWH who initiated ART at all but the earliest infection stages. We failed to find enhanced HIV-1 RNA detectability by WB in NAT−/serology+ blood donors, who may include elite controllers. Enhancing HIV-1 nucleic acid detectability could improve infection ascertainment among PWH on ART with blunted serologic reactivity; investigation of breakthrough infection in PrEP users; and potentially for virus rebound monitoring in HIV-1 cure studies. IMPORTANCE Currently, tests to detect HIV genetic materials (RNA/DNA) are done using the liquid component of a blood sample (plasma). However, HIV may be present in blood cellular components, such as white cells and platelets. Here, we investigated if using whole blood (WB; liquid + cellular components) could improve HIV RNA detectability compared to plasma. WB increased HIV RNA detectability in persons with HIV under treatment, including those with early treatment initiation, but not among blood donors with positive HIV serology and undetectable HIV RNA in the donation screening. Enhancing HIV RNA/DNA detectability would support HIV diagnosis in cases with blunted serologic response, such as persons with early antiretroviral treatment initiation or pre-exposure prophylaxis users. It would also be useful for monitoring virus rebound in HIV cure studies and in blood donation screening, where high test sensitivity is required to guarantee the safety of the blood supply.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".