Sai Nath University is a private university located in Ranchi, Jharkhand, India. Sai Nath University was set up in 2012 vide Sai Nath University, Jharkhand Act 2012 and is duly recognised by the University Grants Commission as a private university. Sai Nath University is approved by PCI , BCI , NCTE, AICTE and State Nursing Council. The University is established with the aims to uplift the level of human resource in order to synchronise with ever changing corporate world. And Secondly to impart the higher education in India especially in the state of Jharkhand at cost-effective quality education where the students can also benefited with various scholarship schemes of the state and central government.
For over five decades, sodium alginate (SA) has been widely employed in pharmaceutical formulations due to its environmentally friendly, biodegradable, and biocompatible nature. Primarily sourced from brown seaweed, this natural polymer plays a crucial role in enhancing drug delivery control. It serves as a versatile excipient in numerous dosage forms such as capsules, tablets, liposomes, and microspheres. SA ionotropic gelation with calcium ions allows drug and enzyme encapsulation for controlled release. Structurally, SA consists of α-L-guluronic acid and β-D-mannuronic acid monomers linked via glycosidic bonds, enabling the formation of strong, porous hydrogels capable of carrying bioactive substances. These hydrogels have been extensively utilized in ocular and oral drug delivery systems for prolonged therapeutic effect. This review highlights the structural and physicochemical characteristics of SA, its pharmaceutical and biomedical applications, food industry relevance, and recent innovations, while also discussing its potential as a multifunctional material for future drug delivery systems.
A bioactive glass–ceramic with the composition 53SiO₂-20CaO-23Na₂O-4P₂O₅ (wt
Chronic and complex wounds represent a major clinical and economic burden, requiring frequent assessment, early detection of complications, and accurate prediction of healing outcomes. Conventional wound evaluation is often subjective and time-intensive, motivating the development of automated approaches. Machine learning (ML) has emerged as a powerful tool for wound management by enabling wound detection, segmentation, tissue characterisation, infection assessment, and healing prediction using wound images, clinical records, and sensor-derived data. This structured narrative review summarises key ML and deep learning (DL) methods applied in wound care, including commonly used architectures, data modalities, and evaluation strategies. We critically discuss persistent barriers limiting clinical adoption, such as limited dataset size and diversity, annotation inconsistency, poor generalizability, bias, lack of external and prospective validation, and challenges related to privacy, regulation, and clinical workflow integration. Finally, we highlight emerging opportunities including multimodal learning, self-supervised learning, federated learning, explainable AI (XAI), and mobile or wearable technologies for continuous wound monitoring. Overall, ML has strong potential to improve the objectivity, efficiency, and personalization of wound care; however, clinically deployable solutions will require standardised datasets, transparent reporting, rigorous validation, and interdisciplinary collaboration.
Tissue engineering, rooted in cell biology and materials science, focuses on repairing or replacing injured tissues and organs, offering groundbreaking solutions in regenerative medicine. Although progress has been made in scaffold design, growth factor utilization, and stem cell applications, challenges such as prolonged production timelines, high costs, and unpredictable tissue outcomes persist. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), which integrates computer science with large-scale data, have emerged as innovative tools with the potential to address these limitations. By leveraging ML and DL, researchers can optimize biomaterial selection, enhance scaffold fabrication, predict tissue growth, and improve organ transplantation outcomes. This paper explores the latest applications of ML and AI-driven DL approaches in tissue engineering, focusing on property prediction and optimization of biomaterials, scaffold design, tissue regeneration, and 3D bioprinting. Additionally, it highlights the challenges of data quality, model interpretability, and standardization, which currently hinder the full integration of these technologies. Despite these obstacles, the convergence of AI, ML, and DL with tissue engineering holds immense promises for accelerating scientific discoveries and improving therapeutic outcomes. This review aims to provide a comprehensive overview of current advancements, address existing challenges, and outline future directions, offering valuable insights for researchers to drive innovation in this interdisciplinary field.
Electroculture is defined by the use of electromagnetic stimulation (EM) to stimulate seed germination, plant growth, and/or increase yields. The study of EM has existed sporadically over nearly three hundred years. Recently, electroculture has re-emerged into mainstream discussion due to trending social media content pertaining to inexpensive “passive” EM garden devices made with copper-wrapped garden stakes. In this paper, we will provide an overview of past research on electroculture, describe the physiology-based explanations that have been proposed to account for observed effects of EM on plant growth; evaluate the evidence supporting classical studies and modern research efforts; and differentiate scientific “active” electroculture techniques from what appear to be unsupported “passive” approaches based upon limited data.
