Advances in Humanized Mouse Models to Improve Understanding of HIV-1 Pathogenesis and Immune Responses
Bibliographic record
Abstract
Although antiretroviral therapy has transformed human immunodeficiency virus-type 1 (HIV-1) from a deadly infection into a chronic disease, it does not clear the viral reservoir, leaving HIV-1 as an uncurable infection. Currently, 1.2 million new HIV-1 infections occur globally each year, with little decrease over many years. Therefore, additional research is required to advance the current state of HIV management, find potential therapeutic strategies, and further understand the mechanisms of HIV pathogenesis and prevention strategies. Non-human primates (NHP) have been used extensively in HIV research and have provided critical advances within the field, but there are several issues that limit their use. Humanized mouse (Hu-mouse) models, or immunodeficient mice engrafted with human immune cells and/or tissues, provide a cost-effective and practical approach to create models for HIV research. Hu-mice closely parallel multiple aspects of human HIV infection and disease progression. Here, we highlight how innovations in Hu-mouse models have advanced HIV-1 research in the past decade. We discuss the effect of different background strains of mice, of modifications on the reconstitution of the immune cells, and the pros and cons of different human cells and/or tissue engraftment methods, on the ability to examine HIV-1 infection and immune response. Finally, we consider the newest advances in the Hu-mouse models and their potential to advance research in emerging areas of mucosal infections, understand the role of microbiota and the complex issues in HIV-TB co-infection. These innovations in Hu-mouse models hold the potential to significantly enhance mechanistic research to develop novel strategies for HIV prevention and therapeutics.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".