HIV disrupts CD34 <sup>+</sup> progenitors involved in T-cell differentiation
Bibliographic record
Abstract
Abstract HIV-1 causes the loss of CD4 + T cells via depletion or impairment of their production. The latter involves infection of thymocytes, but the involvement of other cells including haematopoietic CD34 + cells remains unclear even though HIV-positive patients frequently manifest myelosuppression. This study utilised the OP9-DL1 coculture system, which supports in vitro T-lineage differentiation of human haematopoietic stem/progenitor cells. Cord-derived CD34 + cells were infected with CXCR4-tropic HIV-1 NL4-3 and cocultured. HIV-infected cocultures exhibited sustained viral replication for 5 weeks, as well as reduced CD4 + T-cell growth at weeks 3–5. It was further revealed that CD34 + CD7 + CXCR4 + cells can be quickly depleted as early as in 1 week after infection of the subset, and this was accompanied by the emergence of CD34 + CD7 + CD4 + cells. These results indicate that CXCR4-tropic HIV-1 strains may disrupt CD34 + CD7 + lymphoid progenitor cell pools, presumably leading to impaired T-cell production potential.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".