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The SPOT-MAS assay "Screening for the Presence Of Tumor by Methylation And Size" detects the five most common cancers in Vietnam by evaluating circulating tumor DNA in the blood. Here, we validated its performance in a prospective multi-center clinical trial, K-DETEK. Our analysis of 2795 participants from 14 sites across Vietnam demonstrates its ability to detect cancers in asymptomatic individuals with a positive predictive value of 60%, with 83.3% accuracy in detecting tumor location. We present a case report to support further using SPOT-MAS as a complementary method to achieve early cancer detection and provide the opportunity for early treatment.
RESUMEN
Introduction: The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems for prenatal screening of thalassemia were created. The aim of this report was to investigate the prevalence of thalassemia in the Vietnamese population, building a clinical decision support system for prenatal screening for thalassemia. Methods: A cross-sectional study was conducted on pregnant women and their husbands visiting the Vietnam National Hospital of Obstetrics and Gynecology from October 2020 to December 2021. A total of 10112 medical records of first-time pregnant women and their husbands were collected. Results: A clinical decision support system was built, including 2 different types of systems for prenatal screening for thalassemia (an expert system and 4 AI-based CDSS). One thousand nine hundred ninety-two cases were used to train and test machine learning models, while 1555 cases were used for specialized expert system evaluation. There were ten key variables for AI-based CDSS for machine learning. The four most important features in thalassemia screening were identified. The accuracy of the expert system and AI-based CDSS was compared. The rate of patients with Alpha thalassemia is 10.73% (1085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. The expert system showed an accuracy of 98.45%. Among the AI-based CDSS developed, the multilayer perceptron (MLP) model was the most stable regardless of the training database (accuracy of 98,5% using all features and 97% using only the four most important features). Conclusions: When comparing the expert system with the AI-based CDSS, the accuracy of the expert system and AI-based models was comparable. The developed expert system for prenatal thalassemia screening showed high accuracy. AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice.
RESUMEN
AIMS: In this study, we evaluated the effect of mevinolin on the expressions of osteogenic genes and surface molecules expression during osteogenesis. MAIN METHODS: D1 cells were cultured in osteogenic differentiation medium (ODM) for 6 days, treated with mevinolin for 2 days, and then subjected to alizarin red S staining, MTT assays, alkaline phosphatase (ALP) activity determinations, energy dispersive X-ray spectrophotometry (EDX), real-time PCR, Western blot, fluorescence microscopy and FACS analysis. KEY FINDINGS: Mevinolin is commonly prescribed and widely used to lower cholesterol levels, and offers an important, effective approach to the treatment of hypercholesterolemia and arteriosclerosis. However, the direct effect of mevinolin on osteogenesis in vitro has not been clarified. ODM has been previously shown to increase the osteoblast differentiation of D1 cells. In the present study, we investigated the expressions of osteogenic genes and surface molecules during osteoblast differentiation induced by mevinolin. We found that the induction of ALP, type I collagen, osteocalcin, CD44, CD47 and CD51 by mevinolin is responsible for the osteoblastic differentiation of D1 cells. SIGNIFICANCE: Our data show that mevinolin enhances the expressions of proteins and surface molecules related to osteogenesis.