Exploring phytochemical, antioxidant, and antimicrobial properties of Plumeria pudica Jacq. leaves.

Since the emergence of the coronavirus disease, there has been a notable surge in demand for herbal remedies with minimal or no adverse effects. Notably, existing vaccines and medications employed in its treatment have exhibited significant side effects, some of which have proven fatal. Consequently, there is an increasing focus on pharmacological research aimed at identifying optimal solutions to this challenge.

Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study.

Background: Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We aimed to apply state-of-the-art, interpretable artificial intelligence (ie, predictions or prescription logic that can be easily understood) methods on real-world data to establish which groups of patients with GISTs should receive adjuvant imatinib, its optimal treatment duration, and the benefits conferred by this therapy.

Methods: In this observational cohort study, we considered for inclusion all patients who underwent resection of primary, non-metastatic GISTs at the Memorial Sloan Kettering Cancer Center (MSKCC; New York, NY, USA) between Oct 1, 1982, and Dec 31, 2017, and who were classified as intermediate or high risk according to the Armed Forces Institute of Pathology Miettinen criteria and had complete follow-up data with no missing entries.

Developing Novel Genomic Risk Stratification Models in Soft Tissue and Uterine Leiomyosarcoma.

Purpose: Leiomyosarcomas (LMS) are clinically and molecularly heterogeneous tumors. Despite recent large-scale genomic studies, current LMS risk stratification is not informed by molecular alterations. We propose a clinically applicable genomic risk stratification model.

An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study.

Background: There are several models that predict the risk of recurrence following resection of localised, primary gastrointestinal stromal tumour (GIST). However, assessment of calibration is not always feasible and when performed, calibration of current GIST models appears to be suboptimal. We aimed to develop a prognostic model to predict the recurrence of GIST after surgery with both good discrimination and calibration by uncovering and harnessing the non-linear relationships among variables that predict recurrence.

Novel Genomic Risk Stratification Model for Primary Gastrointestinal Stromal Tumors (GIST) in the Adjuvant Therapy Era.

Purpose: Traditional risk stratification schemes in gastrointestinal stromal tumors (GIST) were defined in the pre-imatinib era and rely solely on clinicopathologic metrics. We hypothesize that genomic-based risk stratification is prognostically relevant in the current era of tyrosine kinase inhibitor (TKI) therapeutics.

Experimental Design: Comprehensive mutational and copy-number profiling using MSK-IMPACT was performed.