Activation of AMPK/mTOR-Driven Autophagy and Suppression of the HMGB1/TLR4 Pathway with Pentoxifylline Attenuates Doxorubicin-Induced Hepatic Injury in Rats.

Despite being an effective chemotherapeutic agent, the clinical use of doxorubicin (DOX) is limited by several organ toxicities including hepatic injury. Pentoxifylline (PTX) is a methylxanthine derivative with marked anti-inflammatory and anti-apoptotic features. It is unknown, however, whether PTX can mitigate DOX-evoked hepatotoxicity. This study aims to explore the potential hepatoprotective impact of PTX in DOX-induced hepatic injury and the underlying molecular mechanisms. Histopathology, immunohistochemistry, and ELISA were used to examine liver tissues. The current findings revealed that PTX administration to DOX-intoxicated rats mitigated the pathological manifestations of hepatic injury, reduced microscopical damage scores, and improved serum ALT and AST markers, revealing restored hepatic cellular integrity. These favorable effects were attributed to PTX's ability to mitigate inflammation by reducing hepatic IL-1ß and TNF-α levels and suppressing the pro-inflammatory HMGB1/TLR4/NF-κB axis. Moreover, PTX curtailed the hepatic apoptotic abnormalities by suppressing caspase 3 activity and lowering the Bax/Bcl-2 ratio. In tandem, PTX improved the defective autophagy events by lowering hepatic SQSTM-1/p62 accumulation and enhancing the AMPK/mTOR pathway, favoring autophagy and hepatic cell preservation. Together, for the first time, our findings demonstrate the ameliorative effect of PTX against DOX-evoked hepatotoxicity by dampening the hepatic HMGB1/TLR4/NF-κB pro-inflammatory axis and augmenting hepatic AMPK/mTOR-driven autophagy. Thus, PTX could be utilized as an adjunct agent with DOX regimens to mitigate DOX-induced hepatic injury.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.