Inhibition of Virulence Associated Traits by β-Sitosterol Isolated from Hibiscus rosa-sinensis Flowers Against Candida albicans: Mechanistic Insight and Molecular Docking Studies.

The emerging drug resistance and lack of safer and more potent antifungal agents make Candida infections another hot topic in the healthcare system. At the same time, the potential of plant products in developing novel antifungal drugs is also in the limelight. Considering these facts, we have investigated the different extracts of the flowers of Hibiscus rosa-sinensis of the Malvaceae family for their antifungal efficacy against five different pathogenic Candida strains.

Self-microemulsifying drug delivery system-based gastroretentive in situ raft of pazopanib with enhanced solubility and bioavailability.

Pazopanib hydrochloride (PZH) is a Biopharmaceutics Classification System class II drug that faces challenges at the formulation forefront including low aqueous solubility (0.043 mg/mL) and poor oral bioavailability (14-39%). The present investigation aimed to develop a self-microemulsifying drug delivery system (SMEDDS) of PZH using a blend of Capryol® 90, Labrasol®, and propylene glycol to improve its solubility.

Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence.

This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer.

Objectives: To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging.

Methods: Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings.