Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease

BackgroundIn patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models.MethodsEight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)]. Twelve clinical and blood biomarker features assessed on admission were used to inform the models. We compared the best-performing ML model and established the pre-test probability of CAD (updated Diamond-Forrester and CAD consortium) models.ResultsThe CatBoost algorithm model showed the best performance (area under the receiver operating characteristics, AUROC, 0.796, and 95% confidence interval, CI, 0.740–0.853; Matthews correlation coefficient, MCC, 0.448) compared to the seven other algorithms. The CatBoost algorithm model improved risk prediction compared with the CAD consortium clinical model (AUROC 0.727; 95% CI 0.664–0.789; MCC 0.313). The accuracy of the ML model was 74.6%. Age, sex, hypertension, high-sensitivity cardiac troponin T, hemoglobin A1c, triglyceride, and high-density lipoprotein cholesterol levels contributed most to obstructive CAD prediction.ConclusionThe ML models using clinically relevant biomarkers provided high accuracy for stable obstructive CAD prediction. In real-world practice, employing such an approach could improve discrimination of patients with suspected obstructive CAD and help select appropriate non-invasive testing for ischemia

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.N

Preparation, characterization, and application of TiO2-patterned polyimide film as a photocatalyst for oxidation of organic contaminants

Photocatalytically active TiO2-patterned polyimide (PI) films (PI-TiO2) were fabricated using thermal transfer patterning (TTP). When subjected to hot pressing, the TiO2 nanoparticles electrosprayed on steel mesh templates were successfully transferred and formed checker plate patterns on PI film. FE-SEM studies confirmed that pressing at 350 degrees C and 100 MPa was optimum for obtaining patterns with uniform TiO2 coverage. When the quantity of TiO2 on the template increased, the amount of it immobilized on PI film also increased from 0.3245 to 1.2378 mg per 25 cm(2). XPS studies confirmed the presence TiO2 on the patterns, and indicated the formation of carboxylic acid and amide groups on the PI surface during TTP. When tested under UVA irradiation, PI-TiO2 with 1.2378 mg/25 cm(2) TiO2 loading exhibited the highest photocatalytic performance for methylene blue (10 mu M) degradation, with a rate constant of 0.0225 min(-1) and stable photocatalytic efficacy for 25 cycles of reuse. The PI-TiO2 was also successfully used to degrade amoxicillin, atrazine, and 4-chlorophenol. During photocatalysis, the toxicity of 4-chlorophenol against Vibrio fischeri and the antibiotic activity of amoxicillin against Escherichia coli were decreased. Overall, TTP was found to be a potentially scalable method for fabricating robust immobilized TiO2 photocatalyst