Potential Use of Antimicrobial Peptides as Vaginal Spermicides/Microbicides
Bibliographic record
Abstract
The concurrent increases in global population and sexually transmitted infection (STI) demand a search for agents with dual spermicidal and microbicidal properties for topical vaginal application. Previous attempts to develop the surfactant spermicide, nonoxynol-9 (N-9), into a vaginal microbicide were unsuccessful largely due to its inefficiency to kill microbes. Furthermore, N-9 causes damage to the vaginal epithelium, thus accelerating microbes to enter the women's body. For this reason, antimicrobial peptides (AMPs), naturally secreted by all forms of life as part of innate immunity, deserve evaluation for their potential spermicidal effects. To date, twelve spermicidal AMPs have been described including LL-37, magainin 2 and nisin A. Human cathelicidin LL-37 is the most promising spermicidal AMP to be further developed for vaginal use for the following reasons. First, it is a human AMP naturally produced in the vagina after intercourse. Second, LL-37 exerts microbicidal effects to numerous microbes including those that cause STI. Third, its cytotoxicity is selective to sperm and not to the female reproductive tract. Furthermore, the spermicidal effects of LL-37 have been demonstrated in vivo in mice. Therefore, the availability of LL-37 as a vaginal spermicide/microbicide will empower women for self-protection against unwanted pregnancies and STI.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".