Bacteria-responsive drug release platform for the local treatment of bacterial vaginosis
Bibliographic record
Abstract
Abstract Bacterial vaginosis (BV) is a common vaginal infection affecting millions of women. Vaginal anaerobic dysbiosis occurs when Lactobacillus spp., the dominant flora in healthy vagina is replaced by certain overgrown anaerobes, resulting in unpleasant symptoms such as vaginal discharge and odor. With a high recurrence rate, BV also severely impacts the overall quality of life of childbearing women by inducing preterm delivery and increasing the risks of pelvic inflammatory disease and sexually transmitted infections. Among various BV-associated bacteria, Gardnerella vaginalis ( G. vaginalis ) has been identified as a primary pathogen since it has been isolated from almost all women carrying BV and exhibits higher virulence potential over other bacteria. When dealing with BV relapse, intravaginal drug delivery systems are superior to conventional oral antibiotic therapies in improving therapeutic efficacy owing to more effective drug dose, reduced drug resistance and minimized side effects such as stomach irritation. Traditional intravaginal drug administration generally involves solids, semi-solids and delivery devices inserted into the vaginal lumen to achieve sustained drug release. However, they are mostly designed for continuous drug release and are not preventative therapies, resulting in severe side effects caused by excess dosing. Stimuli-responsive systems that can release drug only when needed (‘on-demand’) can help diminish these negative side effects. Hence, we developed a bacteria-responsive liposomal platform for the prevention and treatment of BV. This platform demonstrated sustained drug release in the presence of vaginolysin, a toxin secreted specifically by G. vaginalis . We prepared four liposome formulations and evaluated their responsiveness to G. vaginalis . The results demonstrated that the liposome formulations could achieve cumulative drug release ranging from 46.7% to 51.8% over a 3–5 d period in response to G. vaginalis and hardly any drug release in the presence of Lactobacillus crispatus ( L. crispatus ), indicating the high specificity of the system. Overall, the bacteria-responsive drug release platform has great potential, since it will be the first time to realize sustained drug release stimulated by a specific pathogen for BV prevention and treatment. This on-demand therapy can potentially provide relief to the millions of women affected by BV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".