Control of HIV Infection In Vivo Using Gene Therapy with a Secreted Entry Inhibitor
Bibliographic record
Abstract
HIV entry inhibitors are highly effective in controlling virus replication. We have developed a lentiviral vector that expresses a secreted entry inhibitor, soluble CD4 (sCD4), which binds to the HIV envelope glycoproteins and inactivates the virus. We have shown that sCD4 was secreted from gene-modified CD4 + T cells, as well as from human umbilical cord blood-derived CD34 + hematopoietic stem/progenitor cells (HSPCs), and protected unmodified HIV target cells from infection in vitro. To investigate the in vivo application of our approach, we injected gene-modified HSPCs into NOD/SCID/γc null (NSG) mice. NSG hosts supported multi-lineage differentiation of human gene-modified HSPCs. Upon challenge with HIV, humanized mice capable of secreting sCD4 demonstrated a reduction of viral load over time compared to control humanized mice. In contrast to gene therapy approaches that render only gene-modified HIV target cells resistant to infection, our approach also showed protection of unmodified CD4 + T cells in the peripheral blood and tissues. Our findings provide support for the continuous delivery of secreted entry inhibitors via gene therapy as an alternative to oral administration of antiretroviral drugs or injection of antiretroviral proteins, including antibodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".