Silymarin: a promising modulator of apoptosis and survival signaling in cancer.

Cancer, one of the deadliest diseases, has remained the epicenter of biological research for more than seven decades. Yet all the efforts for a perfect therapeutic cure come with certain limitations. The use of medicinal plants and their phytochemicals as therapeutics has received much attention in recent years.

Optimizing glycemic control in type 2 diabetes: the impact of the GLIDE program's personalized digital health intervention.

Background: The integration of digital health applications into type 2 diabetes mellitus (T2DM) management presents promising opportunities for optimizing glycemic control, enhancing adherence, and improving health outcomes. MyTatva's Glycemic Lifestyle Intervention in Diabetes Empowerment (GLIDE) program, which integrates dietary and exercise regimens, cognitive behavioral therapy (CBT), and Internet of Things (IoT) devices, potentiates this approach. This study aimed to evaluate the effectiveness of the GLIDE program's personalized, comprehensive approach in improving glycemic control over 90 days among T2DM patients.

Characterization of Uncharacterized Proteins From Multiple Strains of .

() is a multi-strain, spore-forming, Gram-positive, opportunistic enteropathogen bacteria, majorly associated with nosocomial infections, resulting in severe diarrhoea and colon inflammation. Several antibiotics including penicillin, tetracycline, and clindamycin have been employed to control infection, but studies have suggested that injudicious use of antibiotics has led to the development of resistance in strains. However, many proteins from its genome are still considered uncharacterized proteins that might serve crucial functions and assist in the biological understanding of the organism.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches.

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate.

Proteome analysis using machine learning approaches and its applications to diseases.

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL).