Brain inflammation is a common occurrence following responses to varied insults such as bacterial infections, stroke, traumatic brain injury and neurodegenerative disorders. A common mediator for these varied inflammatory responses is prostaglandin E2 (PGE2), produced by the enzymatic activity of cyclooxygenases (COX) 1 and 2. Previous attempts to reduce neuronal inflammation through COX inhibition, by use of nonsteroidal anti-inflammatory drugs (NSAIDs), have met with limited success. We are proposing the two-hit model for neuronal injury—an initial localized inflammation mediated by PGE2 (first hit) and the simultaneous release of adenosine triphosphate (ATP) by injured cells (second hit), which significantly enhances the inflammatory response through increased synthesis of PGE2. Several evidences on the role of exogenous ATP in inflammation have been reported, including contrary instances where extracellular ATP reduces inflammatory events. In this review, we will examine the current literature on the role of P2 receptors, to which ATP binds, in modulating inflammatory reactions during neurodegeneration. Targeting the P2 receptors, therefore, provides a therapeutic alternative to reduce inflammation in the brain. P2 receptor-based anti-inflammatory drugs (PBAIDs) will retain the activities of essential COX enzymes, yet will significantly reduce neuroinflammation by decreasing the enhanced production of PGE2 by extracellular ATP.

Background Lumpy skin disease (LSD) is a contagious viral disease of cattle caused by lumpy skin disease virus (LSDV). LSD has recently spread in Asia following outbreaks in the Middle East and Europe. The disease emerged in Bangladesh in July 2019 in the Chattogram district, then rapidly spread throughout the entire country. We investigated six LSD outbreaks in Bangladesh to record the clinical signs and collect samples for diagnostic confirmation. Furthermore, we performed the molecular characterization of Bangladesh isolates, analyzing the full RPO30 and GPCR genes and the partial EEV glycoprotein gene. Results Clinical observations revealed common LSD clinical signs in the affected cattle. PCR and real-time PCR, showed the presence of the LSDV genome in samples from all six districts. Phylogenetic analysis and detailed inspection of multiple sequence alignments revealed that Bangladesh isolates differ from common LSDV field isolates encountered in Africa, the Middle East, and Europe, as well as newly emerged LSDV variants in Russia and China. Instead, they were closely related to LSDV KSGP-0240, LSDV NI2490, and LSDV Kenya. Conclusions These results show the importance of continuous monitoring and characterization of circulating strains and the need to continually refine the strategies for differentiating vaccine strains from field viruses.

The PI3K-Akt-mechanistic (formerly mammalian) target of the rapamycin (mTOR) signaling pathway is important in a variety of biological activities, including cellular proliferation, survival, metabolism, autophagy, and immunity. Abnormal PI3K-Akt-mTOR signalling activation can promote transformation by creating a cellular environment conducive to it. Deregulation of such a system in terms of genetic mutations and amplification has been related to several human cancers. Consequently, mTOR has been recognized as a key target for the treatment of cancer, especially for treating cancers with elevated mTOR signaling due to genetic or metabolic disorders. In vitro and in vivo, rapamycin which is an immunosuppressant agent actively suppresses the activity of mTOR and reduces cancer cell growth. As a result, various sirolimus-derived compounds have now been established as therapies for cancer, and now these medications are being investigated in clinical studies. In this updated review, we discuss the usage of sirolimus-derived compounds and other drugs in several preclinical or clinical studies as well as explain some of the challenges involved in targeting mTOR for treating various human cancers.

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.