As a volatile organic compound (VOC), styrene can pose significant risks to human health and environment if released into the air without proper treatment. Absorption based on ionic liquids (ILs) has been widely regarded as a promising VOC treatment approach, whereas there is limited research on capturing styrene by ILs. In this work, a multilevel framework was proposed to screen IL absorbents from the largest number of cation-anion combinations in the latest COSMObase. The COSMO-RS model is applied to estimate the absorption and desorption potentials of all IL candidates for styrene capture based on solubility prediction, while important physical properties of them namely melting point, viscosity, and toxicity are estimated by a recently-reported deep learning model. A parallel screening specific to the commercially available ILs in the initial candidate database was also performed. The top-ranked three ILs, identified in both scenarios with and without limiting the commercially availability, were further evaluated in a simulated absorption-desorption process in Aspen Plus. The most promising ILs in each scenario were figured out, which vividly demonstrates the value of discovering novel ILs.
Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate-specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross-validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.
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