An Alternative Treatment for Vaginal Cuff Wart: a Case Report

A Scoping Review of Complementary and Alternative Medicine for Human Papillomavirus Infections and Cervical Dysplasia

OBJECTIVE

The goal of this scoping review is to synthesize clinically relevant scientific literature on current complementary and alternative medications that address human papillomavirus (HPV) infections and cervical dysplasia.

MATERIALS AND METHODS

A systematic search of published studies was performed December 2021 for the following concepts: human papilloma virus, cervical dysplasia, and complementary and alternative medicine (CAM). Relevant publications were identified by searching Ovid MEDLINE ALL, Embase, Cochrane Library, AMED, and MEDLINE databases, in addition to clinical trial databases. Data were extracted based on specific study selection criteria and analyzed by 3 authors independently using Covidence software.

RESULTS

A total of 2324 studies were identified of which 56 met inclusion criteria. Treatment outcomes measured regression of HPV, improvement of cervical cytology, and/or regression of histopathology with varied definitions of success across all studies. The CAM therapies found to have the most clinical benefit and best supporting data via randomized control trials were topical mushroom ( Coriolus versicolor) gel, oral and topical selenium therapies, and oral indol-3-carbinol. Adverse events were reported in only 28/56 (50%) of included studies.

CONCLUSIONS

The evidence for treating HPV and cervical dysplasia with CAM is of low quality because of lack of standardized, clinically relevant treatment outcomes, lack of standardization of products, and minimal reporting on adverse and long-term effects. Future large, randomized control trials are needed to further assess efficacy and safety of CAM therapies to address HPV and cervical dysplasia.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy

Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to the health of female. Based on drug repositioning strategy, we trained and benchmarked multiple machine learning models so as to predict potential effective antiviral drugs for HPV infection in this work. Through optimizing models, measuring models' predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by the United States Food and Drug Administration, and benchmarking different models' predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naïve Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 pairs of antiviral-HPV protein associations. Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.