The Efficiency of Wastewater Treatment Plants for the Removal of Antibiotics

Undoubtedly domestic Wastewater Treatment Plants (WWTPs) are not designed for the removal of some pollutants such as antibiotics. This chapter summarizes the occurrence and fate of six groups of the most widely used antibiotics (β-lactams, sulfonamides, quinolones, tetracyclines, macrolides, and others) in domestic WWTPs. The literature showed that the six groups of antibiotics have been frequently detected during wastewater treatment train (influent, primary treatment, secondary treatment, tertiary treatment, effluent, and sludge treatment) of domestic WWTPs. Also, it was clear that the main removal routes of antibiotics during sewage treatment of domestic WWTPs were adsorption, biodegradation, membrane filtration, and disinfection. Domestic WWTPs cannot remove most of the antibiotics which finally enter the environment through treated effluent and sludge

Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early

Therapeutic Value of miRNAs in Coronary Artery Disease

Atherosclerotic ischemic coronary artery disease (CAD) is a significant community health challenge and the principal cause of morbidity and mortality in both developed and developing countries for all ethnic groups. The progressive chronic coronary atherosclerosis is the main underlying cause of CAD. Although enormous progress occurred in the last three decades in the management of cardiovascular diseases, the prevalence of CAD continues to increase worldwide, indicating the need for discovery of deeper molecular insights of CAD mechanisms, biomarkers, and innovative therapeutic targets. Recently, several research groups established that microRNAs essentially regulate various cardiovascular development and functions, and a deregulated cardiac enriched microRNA profile plays a vital role in the pathogenesis of CAD and its biological aging. Numerous studies established that over- or downregulation of a single miRNA gene by ago-miRNA or anti-miRNA is enough to modify the CAD disease process, significantly prevent age-dependent cardiac cell death, and markedly improve cardiac function. In the light of more recent experimental and clinical evidences, we briefly reviewed and discussed the involvement of miRNAs in CAD and their possible diagnostic/therapeutic values. Moreover, we also focused on the role of miRNAs in the initiation and progression of the atherosclerosis plaque as the strongest risk factor for CAD