Frequency of Human CD45+ Target Cells is a Key Determinant of Intravaginal HIV-1 Infection in Humanized Mice
Bibliographic record
Abstract
Abstract Approximately 40% of HIV-1 infections occur in the female genital tract (FGT), primarily through heterosexual transmission. FGT factors determining outcome of HIV-1 exposure are incompletely understood, limiting prevention strategies. Here, humanized NOD-Rag1−/−γc−/−mice differentially reconstituted with human CD34+ -enriched hematopoietic stem cells (Hu-mice), were used to assess target cell frequency and viral inoculation dose as determinants of HIV-1 infection following intravaginal (IVAG) challenge. Results revealed a significant correlation between HIV-1 susceptibility and hCD45+ target cells in the blood, which correlated with presence of target cells in the FGT, in the absence of local inflammation. HIV-1 plasma load was associated with viral dose at inoculation and frequency of target cells. Events following IVAG HIV-1 infection; viral dissemination and CD4 depletion, were not affected by these parameters. Following IVAG inoculation, HIV-1 titres peaked, then declined in vaginal lavage while plasma showed a reciprocal pattern. The greatest frequency of HIV-1-infected (p24+) cells were found one week post-infection in the FGT versus blood and spleen, suggesting local viral amplification. Five weeks post-infection, HIV-1 disseminated into systemic tissues, in a dose-dependent manner, followed by depletion of hCD45+ CD3+ CD4+ cells. Results indicate target cell frequency in the Hu-mouse FGT is a key determinant of HIV-1 infection, which might provide a useful target for prophylaxis in women.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".