Characterization of HIV‐1 Particles Co‐Purified With Three Extracellular Vesicle Subtypes From the Raji CD4 DCIR Cell Line, a Hybrid Model of CD4 T Cells and Dendritic Cells
Bibliographic record
Abstract
HIV-1 proteins and RNA are incorporated into extracellular vesicles (EVs) via the EV biogenesis machinery. Due to their similar size and content, EVs and HIV-1 particles are hard to separate, and current purification methods often overlook EVs' effects on infectivity. This study co-characterized HIV-1 particles and three EV subtypes to assess their impact on infection. The HIV-infected Raji CD4 DCIR cells' supernatants were harvested 2 and 8 days after infection. The 2-day supernatant was treated with proteinase K to discard viral components outside the EVs. The supernatants were fractionated into three pellets by differential centrifugation: 3K, 17K and 100K. EVs and viral particles were co-characterized for their host and viral contents and the pellets obtained after 8 days post-infection were tested for infectivity. Proteinase K reduced HIV-1 RNA in EVs without affecting p24 concentration. The p24 protein was mostly found in the 17K pellet and HIV-1 RNA was the most abundant in the 100K pellet for both 2- and 8-day productions. Nevertheless, the 3K pellet had the highest infectivity when cells were infected with an equal quantity of virus. Each EV subtype were co-purified with functional virus and uniquely influenced HIV-1 infectivity, underscoring the importance of considering EVs in viral preparations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".