First-in-human immunoPET imaging of HIV-1 infection using 89Zr-labeled VRC01 broadly neutralizing antibody
Bibliographic record
Abstract
Abstract A major obstacle to achieving long-term antiretroviral (ART) free remission or functional cure of HIV infection is the presence of persistently infected cells that establish a long-lived viral reservoir. HIV largely resides in anatomical regions that are inaccessible to routine sampling, however, and non-invasive methods to understand the longitudinal tissue-wide burden of HIV persistence are urgently needed. Positron emission tomography (PET) imaging is a promising strategy to identify and characterize the tissue-wide burden of HIV. Here, we assess the efficacy of using immunoPET imaging to characterize HIV reservoirs and identify anatomical foci of persistent viral transcriptional activity using a radiolabeled HIV Env-specific broadly neutralizing antibody, 89 Zr-VRC01, in HIV-infected individuals with detectable viremia and on suppressive ART compared to uninfected controls (NCT03729752). We also assess the relationship between PET tracer uptake in tissues and timing of ART initiation and direct HIV protein expression in CD4 T cells obtained from lymph node biopsies. We observe significant increases in 89 Zr-VRC01 uptake in various tissues (including lymph nodes and gut) in HIV-infected individuals with detectable viremia ( N = 5) and on suppressive ART ( N = 5) compared to uninfected controls ( N = 5). Importantly, PET tracer uptake in inguinal lymph nodes in viremic and ART-suppressed participants significantly and positively correlates with HIV protein expression measured directly in tissue. Our strategy may allow non-invasive longitudinal characterization of residual HIV infection and lays the framework for the development of immunoPET imaging in a variety of other infectious diseases.
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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 | 0.000 |
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".