Induction of Cell Death in Human Immunodeficiency Virus-Infected Macrophages and Resting Memory CD4 T Cells by TRAIL/Apo2L
Bibliographic record
Abstract
Because the persistence of human immunodeficiency virus (HIV) in cellular reservoirs presents an obstacle to viral eradication, we evaluated whether tumor necrosis factor-related apoptosis-inducing ligand (TRAIL/Apo2L) induces apoptosis in such reservoirs. Lymphocytes and monocyte-derived macrophages (MDM) from uninfected donors do not die following treatment with either leucine zipper human TRAIL (LZhuTRAIL) or agonistic anti-TRAIL receptor antibodies. By contrast, such treatment induces apoptosis of in vitro HIV-infected MDM as well as peripheral blood lymphocytes from HIV-infected patients, including CD4(+) CD45RO(+) HLA-DR(-) lymphocytes. In addition, LZhuTRAIL-treated cells produce less viral RNA and p24 antigen than untreated controls. Whereas untreated cultures produce large amounts of HIV RNA and p24 antigen, of seven treated CD4(+) CD45RO(+) HLA-DR(-) cell cultures, viral RNA production was undetectable in all, p24 antigen was undetectable in six, and proviral DNA was undetectable in four. These data demonstrate that TRAIL induces death of cells from HIV-infected patients, including cell types which harbor latent HIV reservoirs.
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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) | 0.001 | 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".