Photocatalytic degradation of organic dyes using reduced graphene oxide (rGO).

Photocatalysts have developed into a successful strategy for degrading synthetic and organic toxins, such as chemicals and dyes, in wastewater. In this study, graphene oxide was reduced at different temperatures and used for degrading indigo carmine and neutral red dyes. The wide surface areas, strong adsorption sites, and oxygen functionalities of reduced graphene oxide (rGO) at 250 °C (rGO-250) produced more photocatalytic degradation efficiency and adsorption percentage. The catalyst dosage, initial dye concentration, solution pH and recyclability were all used to optimize the photocatalytic activity of rGO-250. This research presents a capable nano-adsorbent photocatalyst for the efficient degradation of organic dyes. GO and rGOs were also investigated for carbon dioxide (CO2) absorption properties. Results showed that rGO-250 has better CO2 adsorption properties than other rGOs. Overall, it was observed that rGO-250 has better photocatalytic and CO2 adsorption capabilities compared to graphene oxide reduced at different temperatures.

Perusal on the role of DMF solvent and hydrogen bonding in the formation of 1D polymeric chains in mixed ligand Ni(II) complex as an anticancer agent: a computational approach.

A novel mixed ligand Ni(II) metal complex has been investigated for the modification in structural conformation, coordination bond, and noncovalent interactions. The novel Ni(II) metal complex [Ni(TFPB)2(1,10-Ph)(DMF)] has been synthesized and structurally characterized, which featured six coordination with three bidentate ligands connected through oxygen and nitrogen atoms. The single-crystal X-ray analysis showed that the compound possessed octahedral geometry and C-H…F, C-H…O, and π…π intermolecular interactions resulting in the formation of supramolecular architecture contributed significantly towards the crystal packing and molecular stability. Hirshfeld surface analysis was carried out to validate various intermolecular interactions. Further, the 3D structural topologies were visualized using energy framework analysis. To explore the coordination stability and chemically reactive parameters of the novel Ni(II) complex, the electronic structure was optimized using density functional theory calculations. The natural bond orbital analysis revealed the various hyperconjugative interactions exhibited by the complex. In addition, the complex was screened for in silico studies to understand the antitumoricidal potential of the novel Ni(II) complex. Molecular docking studies were also performed against three targeted proteins (PDB ID: 6H0W, 6NE5, and 6E91) to investigate the binding mode and protein-ligand interactions. These results are further analyzed by molecular dynamic simulation to confirm the best possible interactions and stability in the active site of the targeted proteins with a simulation period of 100 ns.Communicated by Ramaswamy H. Sarma.

Development of a Highly Sensitive Electrochemical Sensor Using Sm2CuZrO6 Double Perovskite as an Electrocatalyst for Determination of Risperidone Antipsychotic Drug in Tablet Samples.

In this study, the preparation of Sm2CuZrO6 double perovskites was carried out through the utilization of a sol-gel technique. The Sm2CuZrO6 displayed notable conductivity, impressive electrocatalytic activity, and rapid electron transfer. The monitoring of risperidone (RIS) in tablet samples is greatly influenced by these properties. Various techniques for structural and morphological characterization were employed to confirm the formation of Sm2CuZrO6. The electrochemical properties of Sm2CuZrO6 were assessed through utilization of cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV). Interestingly, the Sm2CuZrO6 exhibited a remarkable wide linear range of 50-500 nM, along with a detection limit of 10.62 nM. Notably, it demonstrated a sensitivity of 0.4038 µA µM-1 cm-2. The constructed sensor demonstrated noteworthy selectivity, stability, and repeatability. To assess the practicality of RIS, its performance was monitored in the tablet sample, resulting in satisfactory recoveries.

Computational analysis of the phytocompounds of Mimusops elengi against spike protein of SARS CoV2 - An Insilico model.

The COVID-19 pandemic has been a global health crisis for over three years now, with the virus causing widespread illness and death. The urgent need for safe and effective therapeutic drugs has prompted the exploration of alternative medicine systems such as Ayurveda and Siddha. This study focuses on the potential therapeutic properties of the Ayurvedic plant, Mimusops elengi. In silico techniques were employed to analyze the bioactivity of the plant, including target prediction, gene ontology analysis, OMIM analysis, and molecular docking analysis. The results revealed 36 phytocompounds that interacted with 1431 receptors in the human body, and two compounds - hederagenin and quercetin - showed exceptionally high binding affinities toward their corresponding receptors, IL6 and MMP9. These results provide important insight into the potential therapeutic activity of M. elengi and its compounds in combating COVID-19. However, further research and clinical trials are necessary to validate these findings and develop safe and effective drugs. The study highlights the importance of combining traditional medicine with modern scientific methods to find effective treatments for global health challenges.

A Transcriptomic Response to Lactiplantibacillus plantarum-KCC48 against High-Fat Diet-Induced Fatty Liver Diseases in Mice.

The most prevalent chronic liver disorder in the world is fatty liver disease caused by a high-fat diet. We examined the effects of Lactiplantibacillus plantarum-KCC48 on high-fat diet-induced (HFD) fatty liver disease in mice. We used the transcriptome tool to perform a systematic evaluation of hepatic mRNA transcripts changes in high-fat diet (HFD)-fed animals and high-fat diet with L. plantarum (HFLPD)-fed animals. HFD causes fatty liver diseases in animals, as evidenced by an increase in TG content in liver tissues compared to control animals. Based on transcriptome data, 145 differentially expressed genes (DEGs) were identified in the liver of HFD-fed mice compared to control mice. Moreover, 61 genes were differentially expressed in the liver of mice fed the HFLPD compared to mice fed the HFD. Additionally, 43 common DEGs were identified between HFD and HFLPD. These genes were enriched in metabolic processes, retinol metabolism, the PPAR signaling pathway, fatty acid degradation, arachidonic metabolism, and steroid hormone synthesis. Taking these data into consideration, it can be concluded that L. plantarum-KCC48 treatment significantly regulates the expression of genes involved in hepatosteatosis caused by HFD, which may prevent fatty liver disease.

Novel Bacillus ginsengihumi CMRO6 Inhibits Adipogenesis via p38MAPK/Erk44/42 and Stimulates Glucose Uptake in 3T3-L1 Pre-Adipocytes through Akt/AS160 Signaling.

The health benefits of probiotics have been known for decades, but there has only been limited use of probiotics in the treatment of obesity. In this study, we describe, for the first time, the role of cell-free metabolites (CM) from Bacillus ginsengihumi-RO6 (CMRO6) in adipogenesis and lipogenesis in 3T3-L1 pre-adipocytes. The experimental results show that CMRO6 treatment effectively reduced lipid droplet accumulation and the expression of CCAAT/enhancer-binding protein α and ß (C/EBPα and C/EBPß), peroxisome proliferator-activated receptor γ (PPAR-γ), serum regulatory binding protein 1c (SREBP-1c), fatty acid-binding protein 4 (FABP4), fatty acid synthase (FAS), acetyl CoA carboxylase (ACC), phosphorylated p38MAPK, and Erk44/42. Additionally, CMRO6 treatment significantly increased glucose uptake and phosphorylated Akt (S473), AS160, and TBC1D1 protein expressions. Considering the results of this study, B. ginsengihumi may be a novel probiotic used for the treatment of obesity and its associated metabolic disorders.

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network.

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.