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Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L‐Net and W‐Net deep learning models. The proposed cascaded model leverages the strengths of U‐Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L‐Net excels in capturing the mask, while the W‐Net focuses on fine‐grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi‐modal MRI images. The experimental results demonstrate that the cascaded L‐Net and W‐Net model consistently outperforms individual models and other state‐of‐the‐art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the cardioprotective effect of bioassay-guided isolated fractions of Cleome viscosa methanolic extract in streptozotocin-induced diabetic rats. Methods: Total phenolics, flavonoids, and in vitro antioxidant activities of the methanolic extract of Cleome viscosa were evaluated. The FD-40 fraction from this extract was further evaluated for antihyperglycemic efficacy (insulin and HbA1c), antioxidant activity, and cardioprotective effects (creatine kinase-MB, lactate dehydrogenase, and histopathology) in streptozotocin-induced diabetic rats. In silico studies were also conducted to assess the bioactivity of FD-40. Results: Cleome viscosa methanolic extract exhibited the highest antioxidant activity in DPPH, ABTS, H2O2, and FRAP assays compared to other extracts. Treatment with FD-40 (40 mg/kg b.w.) isolated from this extract normalized blood glucose, insulin, HbA1c, creatine kinase-MB, and lactate dehydrogenase levels in diabetic rats. It also significantly reduced oxidative stress by increasing antioxidant enzymes, decreasing lipid peroxidation as well as restoring the levels of ascorbic acid and glutathione. Histological study demonstrated that FD-40 treatment improved cardiac structure in diabetic rats. Molecular docking analysis revealed that phytocompounds from FD-40 had strong binding affinities with cardiac markers and oxidative enzymes. Hexose (5TMS) demonstrated greater binding affinity with xanthine oxidase and lactate dehydrogenase. Conclusions: FD-40 of Cleome viscosa methanolic extract exhibits significant cardioprotective effects in diabetic rats by regulating cardiac markers and reducing oxidative stress. The underlying mechanisms need to be elucidated in the future.

Background: The AYUSH 64 formulation helps to treat mild to moderate cases of COVID-19. Although several drugs have been proposed to combat COVID-19, no medication is available for SARS-CoV-2 infection. The RNA-dependent RNA polymerase (RdRp) is the pivotal enzyme of SARS-CoV-2 replication, so it could be considered a better drug target for experimental studies. Objective: The AYUSH-64 formulation plants exhibited multiple therapeutic properties; thus, the present study aims to screen the phytocompounds of these plants against SARS CoV2 RdRp to identify specific compounds that could potentially affect COVID-19 infection. Materials and methods: PatchDock and AutoDock tools were used for docking experiments. MD simulations and Density Functional Theory (DFT) calculations of protein-ligand Picroside-I and Remdesivir complexes were carried out in GROMACS v2019.4 and Gaussian 09 software, respectively. Results: Among the tested, five phytocompounds (Picroside I, Oleanolic acid, Arvenin I, II, and III) from AYUSH-64 medicinal plants showed possible binding with RdRp catalytic residues (Ser759, Asp760, and Asp761). Of these, Picroside I exhibited hydrogen bond interactions with NTP entry channel residues (Arg553 and Arg555). The MM-PBSA free energy, RMSD, Rg, PCA, and RMSF analysis suggested that the Picroside I complex showed stable binding interactions with RdRp in the 50 ns simulation. In addition to this, Picroside I revealed its robust and attractive nature toward the target protein, as confirmed by DFT. Conclusion: The results of this study have proposed that Picroside I from AYUSH 64 medicinal plant compounds was the selective binder of catalytic and NTP entry channel residues of SARS-CoV2 RdRp thereby; it may considered as a potential inhibitor of SARS-CoV2 RdRp.

Abstract The Internet of Things (IoT) permeates various sectors, including healthcare, smart cities, and agriculture, alongside critical infrastructure management. However, its susceptibility to malware due to limited processing power and security protocols poses significant challenges. Traditional antimalware solutions fall short in combating evolving threats. To address this, the research work developed a feature selection-based classification model. At first stage, a preprocessing stage enhances dataset quality through data smoothing and consistency improvement. Feature selection via the Zebra Optimization Algorithm (ZOA) reduces dimensionality, while a classification phase integrates the Graph Attention Network (GAN), specifically the Dual-channel GAN (DGAN). DGAN incorporates Node Attention Networks and Semantic Attention Networks to capture intricate IoT device interactions and detect anomalous behaviors like botnet activity. The model's accuracy is further boosted by leveraging both structural and semantic data with the Sooty Tern Optimization Algorithm (STOA) for hyperparameter tuning. The proposed STOA-DGAN model achieves an impressive 99.87% accuracy in botnet activity classification, showcasing robustness and reliability compared to existing approaches.

A total of ten species of bats including two Megachiropterans (Pteropus medius and Cynopterus sphinx) and eight Microchiropterans (Rhinopoma hardwickii, Lyroderma lyra, Taphozous melanopogon, Hipposideros speoris, Scotophilus heathii, Scotophilus kuhlii, Pipistrellus ceylonicus and Pipistrellus tenuis) were documented to be present in different habitats of Adilabad district of Telangana State, India. Echolocation calls of eight species of microchiropteran bats from this region is being reported for the first time. The echolocating bats belonged to constant frequency (CF), frequency modulated (FM) and frequency-modulated/quasi constant frequency (FM/QCF) call emitters. The details on the distribution and roosting ecology of all the bats are provided. [ABSTRACT FROM AUTHOR]

Information on scorpion diversity of Telangana State is meager and an attempt was made to compile an updated checklist of these poorly studied groups of arthropods based on published literature and field studies between 2014 and 2021. A total of 13 species of scorpions belonging to nine genera and three families are known to be present in Telangana State, India. Recent taxonomic changes with respect to the giant forest scorpions have been included in the present checklist. [ABSTRACT FROM AUTHOR]

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

Influenza is an acute respiratory infectious disease caused by the influenza virus, affecting people globally and causing significant social and economic losses. Due to the inevitable limitations of vaccines and approved drugs, there is an urgent need to discover new anti-influenza drugs with different mechanisms. The viral ribonucleoprotein complex (vRNP) plays an essential role in the life cycle of influenza viruses, representing an attractive target for drug design. In recent years, the functional area of constituent proteins in vRNP are widely used as targets for drug discovery, especially the PA endonuclease active site, the RNA-binding site of PB1, the cap-binding site of PB2 and the nuclear export signal of NP protein. Encouragingly, the PA inhibitor baloxavir has been marketed in Japan and the United States, and several drug candidates have also entered clinical trials, such as favipiravir. This article reviews the compositions and functions of the influenza virus vRNP and the research progress on vRNP inhibitors, and discusses the representative drug discovery and optimization strategies pursued.

Novel therapies are urgently needed to improve global treatment of SARS-CoV-2 infection. Herein, we briefly provide a concise report on the medicinal chemistry strategies towards the development of effective SARS-CoV-2 inhibitors with representative examples in different strategies from the medicinal chemistry perspective.

Abstract Background Rilpivirine (RPV) was approved by the U.S. FDA (Food and Drug Administration) in 2011 to treat individuals infected with human immunodeficiency virus 1 (HIV-1). Significantly, rilpivirine is three fold more potent than etravirine. Once-daily, it is used with a low oral dose (25 mg/tablet), decreasing the drug administration and bringing a better choice to the patients. However, there are many shortcomings in the existing synthesis route of RPV, such as the high cost, prolonged reaction time and low yield (18.5%). Results This article describes our efforts to develop an efficient and practical microwave-promoted method to synthesize rilpivirine using less toxic organic reagents and low boiling solvents. The last step's reaction time decreased from 69 h to 90 min through this optimized synthetic procedure, and the overall yield improved from 18.5 to 21%. In addition, the yield of intermediate 3 increased from 52 to 62% compared to the original patent. Conclusion Overall, through a series of process optimization, we have developed a practical synthesis method of rilpivirine, which is easy to scale with higher yield and shorter reaction time.