Hypoxia signaling in hepatocellular carcinoma: Challenges and therapeutic opportunities.

Hepatocellular carcinoma (HCC) is one of the most common cancers with a relatively high cancer-related mortality. The uncontrolled proliferation of HCC consumes a significant amount of oxygen, causing the development of a hypoxic tumor microenvironment (TME). Hypoxia-inducible factors (HIFs), crucial regulators in the TME, activate several cancer hallmarks leading to the hepatocarcinogenesis of HCC and resistance to current therapeutics. As such, HIFs and their signaling pathways have been explored as potential therapeutic targets for the future management of HCC. This review discusses the current understanding of the structure and function of HIFs and their complex relationship with the various cancer hallmarks. To address tumor hypoxia, this review provides an insight into the various potential novel therapeutic agents for managing HCC, such as hypoxia-activated prodrugs, HIF inhibitors, nanomaterials, antisense oligonucleotides, and natural compounds, that target HIFs/hypoxic signaling pathways in HCC. Because of HCC's relatively high incidence and mortality rates in the past decades, greater efforts should be put in place to explore novel therapeutic approaches to improve the outcome for HCC patients.

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet.

Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

Phytochemicals as Invaluable Sources of Potent Antimicrobial Agents to Combat Antibiotic Resistance.

Plants have been used for therapeutic purposes against various human ailments for several centuries. Plant-derived natural compounds have been implemented in clinics against microbial diseases. Unfortunately, the emergence of antimicrobial resistance has significantly reduced the efficacy of existing standard antimicrobials. The World Health Organization (WHO) has declared antimicrobial resistance as one of the top 10 global public health threats facing humanity. Therefore, it is the need of the hour to discover new antimicrobial agents against drug-resistant pathogens. In the present article, we have discussed the importance of plant metabolites in the context of their medicinal applications and elaborated on their mechanism of antimicrobial action against human pathogens. The WHO has categorized some drug-resistant bacteria and fungi as critical and high priority based on the need to develope new drugs, and we have considered the plant metabolites that target these bacteria and fungi. We have also emphasized the role of phytochemicals that target deadly viruses such as COVID-19, Ebola, and dengue. Additionally, we have also elaborated on the synergetic effect of plant-derived compounds with standard antimicrobials against clinically important microbes. Overall, this article provides an overview of the importance of considering phytogenous compounds in the development of antimicrobial compounds as therapeutic agents against drug-resistant microbes.