The Confluence of HIV‐1 and HIV‐2: Implications for Disease Progression and Insights for Therapy
Bibliographic record
Abstract
Two distinct types of human immunodeficiency virus (HIV), namely, HIV-1 and HIV-2 exist. HIV-1 is responsible for the global pandemic and has an aggressive pathogenesis. On the contrary, HIV-2 is not only less aggressive but also confined to West and Central African regions. Even after four decades of HIV research, a scalable cure or effective vaccine against HIV remains elusive. Consequently, the concept of a functional cure or vaccine, targeting to limit disease progression, allowing sufficient time for the immune response to clear the virus, has gained traction. Efforts to identify new therapeutic targets for development of a functional cure have focused on elite controllers, that is, individuals who naturally control HIV-1 infection in the absence of antiretroviral therapy. However, little progress has been associated with these efforts perhaps due to the scarcity of elite controllers, who make up only 0.15% of HIV-1 infected population globally. A distinct but largely unexplored subset of HIV patients comprise HIV-1 and HIV-2 dually infected individuals. This group of patients naturally presents with an attenuated disease progression phenotype akin to natural controllers. In this review, we discuss the attenuated disease progression phenomenon in dually infected individuals and offer potential explanations for this unanticipated observation. Additionally, we propose potential therapeutic and/or vaccine strategies that could leverage interactions of HIV-1 and HIV-2. Such strategies are likely to inform alternative therapeutics. A thorough understanding of the mechanism underlying the attenuated disease progression phenotype in HIV dually infected individuals is crucial for the design of a functional cure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".