The peptide hormone leptin regulates food intake, body mass, and reproductive function and plays a role in fetal growth, proinflammatory immune responses, angiogenesis and lipolysis. Leptin is a product of the obese (ob) gene and, following synthesis and secretion from fat cells in white adipose tissue, binds to and activates its cognate receptor, the leptin receptor (LEP-R). LEP-R distribution facilitates leptin’s pleiotropic effects, playing a crucial role in regulating body mass via a negative feedback mechanism between adipose tissue and the hypothalamus. Leptin resistance is characterized by reduced satiety, over-consumption of nutrients, and increased total body mass. Often this leads to obesity, which reduces the effectiveness of using exogenous leptin as a therapeutic agent. Thus, combining leptin therapies with leptin sensitizers may help overcome such resistance and, consequently, obesity. This review examines recent data obtained from human and animal studies related to leptin, its role in obesity, and its usefulness in obesity treatment.

Even though type 2 diabetes mellitus (T2DM) represents a worldwide chronic health issue that affects about 462 million people, specific underlying determinants of insulin resistance (IR) and impaired insulin secretion are still unknown. There is growing evidence that chronic subclinical inflammation is a triggering factor in the origin of T2DM. Increased C-reactive protein (CRP) levels have been linked to excess body weight since adipocytes produce tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which are pivotal factors for CRP stimulation. Furthermore, it is known that hepatocytes produce relatively low rates of CRP in physiological conditions compared to T2DM patients, in which elevated levels of inflammatory markers are reported, including CRP. CRP also participates in endothelial dysfunction, the production of vasodilators, and vascular remodeling, and increased CRP level is closely associated with vascular system pathology and metabolic syndrome. In addition, insulin-based therapies may alter CRP levels in T2DM. Therefore, determining and clarifying the underlying CRP mechanism of T2DM is imperative for novel preventive and diagnostic procedures. Overall, CRP is one of the possible targets for T2DM progression and understanding the connection between insulin and inflammation may be helpful in clinical treatment and prevention approaches.

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

Mangroves are unique, and endangered, coastal ecosystems that play a vital role in the tropical and subtropical environments. A comprehensive description of the microbial communities in these ecosystems is currently lacking, and additional studies are required to have a complete understanding of the functioning and resilience of mangroves worldwide. In this work, we carried out a metagenomic study by comparing the microbial community of mangrove sediment with the rhizosphere microbiome of Avicennia marina, in northern Red Sea mangroves, along the coast of Saudi Arabia. Our results revealed that rhizosphere samples presented similar profiles at the taxonomic and functional levels and differentiated from the microbiome of bulk soil controls. Overall, samples showed predominance by Proteobacteria, Bacteroidetes and Firmicutes, with high abundance of sulfate reducers and methanogens, although specific groups were selectively enriched in the rhizosphere. Functional analysis showed significant enrichment in 'metabolism of aromatic compounds', 'mobile genetic elements', 'potassium metabolism' and 'pathways that utilize osmolytes' in the rhizosphere microbiomes. To our knowledge, this is the first metagenomic study on the microbiome of mangroves in the Red Sea, and the first application of unbiased 454-pyrosequencing to study the rhizosphere microbiome associated with A. marina. Our results provide the first insights into the range of functions and microbial diversity in the rhizosphere and soil sediments of gray mangrove (A. marina) in the Red Sea.

More people die from cardiovascular diseases (CVD) than from any other cause. Cardiovascular complications are thought to arise from enhanced levels of free radicals causing impaired “redox homeostasis,” which represents the interplay between oxidative stress (OS) and reductive stress (RS). In this review, we compile several experimental research findings that show sustained shifts towards OS will alter the homeostatic redox mechanism to cause cardiovascular complications, as well as findings that show a prolonged antioxidant state or RS can similarly lead to such cardiovascular complications. This experimental evidence is specifically focused on the role of glutathione, the most abundant antioxidant in the heart, in a redox homeostatic mechanism that has been shifted towards OS or RS. This may lead to impairment of cellular signaling mechanisms and elevated pools of proteotoxicity associated with cardiac dysfunction.

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.