Novel Nanotechnology-Based Approaches for Targeting HIV Reservoirs
Bibliographic record
Abstract
Highly active anti-retroviral therapy (HAART) is prescribed for HIV infection and, to a certain extent, limits the infection's spread. However, it cannot completely eradicate the latent virus in remote and cellular reservoir areas, and due to the complex nature of the infection, the total eradication of HIV is difficult to achieve. Furthermore, monotherapy and multiple therapies are not of much help. Hence, there is a dire need for novel drug delivery strategies that may improve efficacy, decrease side effects, reduce dosing frequency, and improve patient adherence to therapy. Such a novel strategy could help to target the reservoir sites and eradicate HIV from different biological sanctuaries. In the current review, we have described HIV pathogenesis, the mechanism of HIV replication, and different biological reservoir sites to better understand the underlying mechanisms of HIV spread. Further, the review deliberates on the challenges faced by the current conventional drug delivery systems and introduces some novel drug delivery strategies that have been explored to overcome conventional drug delivery limitations. In addition, the review also summarizes several nanotechnology-based approaches that are being explored to resolve the challenges of HIV treatment by the virtue of delivering a variety of anti-HIV agents, either as combination therapies or by actively targeting HIV reservoir sites.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".