Quantifying Anti-HIV Envelope-Specific Antibodies in Plasma from HIV Infected Individuals
Bibliographic record
Abstract
Quantifying HIV Envelope (Env)-specific antibodies in HIV+ plasma is useful for interpreting antibody dependent cellular cytotoxicity assay results. HIV Env, the only viral protein expressed on the surface of infected cells, has a native trimeric closed conformation on cells infected with wild-type HIV. However, CD4+ uninfected bystander cells in HIV+ cell cultures bind gp120 shed from HIV+ cells exposing CD4-induced epitopes normally hidden in native Env. We used flow-cytometry based assays to quantify antibodies in HIV+ plasma specific for native trimeric Env or gp120/CD4 conjugates using CEM.NKr.CCR5 (CEM) cells infected with HIV (iCEM) or coated with recombinant gp120 (cCEM), as a surrogate for gp120+ HIV- bystander cells. Results from both assays were compared to those of a plate-based ELISA to monomeric gp120. The levels of Env-specific antibodies to cCEM and iCEM, measured by flow cytometry, and to gp120 by ELISA were positively correlated. More antibodies in HIV+ plasma recognized the gp120 conformation exposed on cCEM than on iCEM. Comparisons of plasma from untreated progressors, treated progressors, and elite controllers revealed that antibodies to Env epitopes were the lowest in treated progressors. Plasma from elite controllers and untreated progressors had similarly high levels of Env-specific antibodies, despite elite controllers having undetectable HIV viral loads, while untreated progressors maintained high viral loads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".