Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus ( HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multiscale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of 94.54%, and accuracy of 94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.

With the COVID-19 outbreak, many challenges are posed before the scientific world to curb this pandemic. The diagnostic testing, treatment, and vaccine development for this infection caught the scientific community's immediate attention. Currently, despite the global proliferation of COVID-19 vaccination, the specific treatment for this disease is yet unknown. Meanwhile, COVID-19 detection or diagnosis using polymerase chain reaction (PCR)-based me hods is expensive and less reliable. Moreover, this technique needs much time to furnish the results. Thus, the elaboration of a highly sensitive and fast method of COVID-19 diagnostics is of great importance. The spectroscopic approach is herein suggested as an efficient detection methodology for COVID-19 diagnosis, particularly Raman spectroscopy, infrared spectroscopy, and mass spectrometry.

The characteristics of an LED lighting system consisting of a commercial violet LED and a green phosphor based on CsPbBr nanocrystals are studied in the context of development of LED illumination sources with antibacterial effects but without harmful effects on human health. The internal efficiency of the nanocrystalline phosphor in a silicone compound was found to exceed 40%, dropping noticeably because of heating for an electric current of ~0.1 A (phosphor excitation intensity ~0.1 W/mm). This undesirable feature can be diminished by using a remote phosphor design for the illuminators and by using chemical techniques to improve the thermal stability of the nanocrystals.

Methods for determining the oxidant/antioxidant activity of free radicals, antioxidant compounds, and free-radical oxidation products in biological fluids are discussed. General approaches to the analysis of the antioxidant potential of complex natural objects using differential spectroscopy are presented.

The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.