Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure.

Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.

Smartphone-based pH responsive 3-channel colorimetric biosensor for non-enzymatic multi-antibiotic residues.

Antibiotic residues in animal-derived food pose a risk to food safety and human health. Here, a smartphone-based pH-responsive 3-channel colorimetric biosensor is constructed for rapid detection of non-enzymatic multi-antibiotic residues in milk. In this system, a magnetic separation and enrichment approach is designed to specifically capture different antibiotic residues in complex environment. Indicators loaded on polydopamine-silver nanoparticles with excellently pH responsive visualization properties are utilized to ensure the high sensitivity of detection system. Moreover, smartphones are introduced to fulfill the demand for portable and on-site inspection of practical applications. It achieves simultaneous detection of oxytetracycline, kanamycin and streptomycin in the linear range of 1-105 pg/mL with detection limits of 0.085, 0.168, and 0.307 pg/mL, respectively. The practicality of the reported multi-antibiotic residues detection system is successfully demonstrated and evaluated challenging milk samples. Therefore, this system demonstrates the wide applications in multi-antibiotic residue analysis and food safety guarantee.