Novel intravaginal nanomedicine for the targeted delivery of saquinavir to CD4+ immune cells
Bibliographic record
Abstract
Sidi Yang,1,2 Yufei Chen,1,2 Kaien Gu,1,2 Alicia Dash,1,2 Casey L Sayre,1 Neal M Davies,1 Emmanuel A Ho1,2 1Faculty of Pharmacy, 2Laboratory for Drug Delivery and Biomaterials, University of Manitoba, Winnipeg, MB, Canada Abstract: The goal of this study was to develop and characterize an intravaginal nanomedicine for the active targeted delivery of saquinavir (SQV) to CD4+ immune cells as a potential strategy to prevent or reduce HIV infection. The nanomedicine was formulated into a vaginal gel to provide ease in self-administration and to enhance retention within the vaginal tract. SQV-encapsulated nanoparticles (SQV-NPs) were prepared from poly(lactic-co-glycolic acid)(PLGA) and conjugated to antihuman anti-CD4 antibody. Antibody-conjugated SQV-NPs (Ab-SQV-NPs) had an encapsulation efficiency (EE%) of 74.4% ± 3.7% and an antibody conjugation efficiency (ACE%) of 80.95% ± 1.10%. Over 50% of total loaded SQV was released from NPs over 3 days. NPs were rapidly taken up by Sup-T1 cells, with more than a twofold increase in the intracellular levels of SQV when delivered by Ab-SQV-NPs in comparison to controls 1 hour post-treatment. No cytotoxicity was observed when vaginal epithelial cells were treated for 24 hours with drug-free Ab-NPs (1,000 µg/mL), 1% HEC placebo gel (200 mg/mL), or 1% HEC gel loaded with drug-free Ab-NPs (5 mg NPs/g gel, 200 mg/mL of gel mixture). Overall, we described an intravaginal nanomedicine that is nontoxic and can specifically deliver SQV into CD4+ immune cells. This platform may demonstrate potential utility in its application as postexposure prophylaxis for the treatment or reduction of HIV infection, but further studies are required. Keywords: nanoparticles, saquinavir, antibody conjugation, intravaginal gel, HIV/AIDS, microbicide
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".