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Record W4411029946 · doi:10.1128/jcm.01904-24

Improved HIV-1 RNA detection using whole blood versus plasma in antiretroviral-treated individuals

2025· article· en· W4411029946 on OpenAlexaff
Vivian Iida Avelino‐Silva, Mars Stone, Leilani Montalvo, Clara Di Germanio, Sonia Bakkour, Marion C. Lanteri, Eduard Grebe, Brian Custer, Xutao Deng, Renata Buccheri, Karen Harrington, Steven Kleinman, Sandhya Vasan, Nittaya Phanuphak, Carlo Sacdalan, Siriwat Akapirat, Mark de Souza, Esper G. Kallás, Sheila de Oliveira Garcia Mateos, Éster Cerdeira Sabino, Philip J. Norris, M. Busch

Bibliographic record

VenueJournal of Clinical Microbiology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental HealthNational Heart, Lung, and Blood Institute
KeywordsSerologyNatNucleic acidHuman immunodeficiency virus (HIV)VirologyViral loadWhole bloodRNAImmunologyNucleic acid testMedicineBiologyAntibodyInternal medicineDiseaseGeneCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.040
GPT teacher head0.361
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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