In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. When the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into four classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on a severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG-16 with a test accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with a test accuracy of 98.9 %, whereas the ResNet-18 worked best for severity classification achieving a test accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

Adaptable polymer-based solid-state electrolytes can be a game-changer to safe, lightweight flexible batteries. We present a robust Bakelite-type organic polymer covalently decked with viologen, triazine, and phenolic moieties. Its flexible...

Peptide-based biomimetic catalysts are promising materials for efficient catalytic activity in various biochemical transformations. However, their lack of operational stability and fragile nature in non-aqueous media limit their practical applications. In this study, we have developed a cladding technique to stabilize biomimetic catalysts within porous covalent organic framework (COF) scaffolds. This methodology allows for the homogeneous distribution of peptide nanotubes inside the COF (TpAzo and TpDPP) backbone, creating strong noncovalent interactions that prevent leaching. We synthesized two different peptide-amphiphiles, C10FFVK and C10FFVR, with lysine (K) and arginine (R) at the C-termini, respectively, which formed nanotubular morphologies. The C10FFVK peptide-amphiphile nanotubes exhibit enzyme-like behavior and efficiently catalyze C–C bond cleavage in a buffer medium (pH 7.5). We produced nanotubular structures of TpAzo–C10FFVK and TpDPP–C10FFVK through COF cladding by using interfacial crystallization (IC). The peptide nanotubes encased in the COF catalyze C–C bond cleavage in a buffer medium as well as in different organic solvents (such as acetonitrile, acetone, and dichloromethane). The TpAzo–C10FFVK catalyst, being heterogeneous, is easily recoverable, enabling the reaction to be performed for multiple cycles. Additionally, the synthesis of TpAzo–C10FFVK thin films facilitates catalysis in flow. As control, we synthesized another peptide-amphiphile, C10FFVR, which also forms tubular assemblies. By depositing TpAzo COF crystallites on C10FFVR nanotubes through IC, we produced TpAzo–C10FFVR nanotubular structures that expectedly did not show catalysis, suggesting the critical role of the lysines in the TpAzo–C10FFVK.

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