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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".