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Enzymes are essential biocatalysts involved in all biochemical and metabolic reactions, widely used across industries, especially in food processing. Historically utilized to enhance food production, these enzymes aid in breaking down food for better digestion while improving taste, texture, and aroma. They are derived from animals, plants, or microorganisms, with microbial sources being the most preferred due to their cost-effectiveness, stability, ease of cultivation, and potential for large-scale production. Advances in biotechnology, molecular biology, and enzyme engineering have significantly deepened our understanding of microbial enzymes and enhanced their applications in the food industry. The integration of recombinant DNA technology and process engineering has further optimized enzyme-producing microbes for industrial use. However, continued research is essential to address challenges and fully harness their potential. This review focuses on microbial enzyme sources, production techniques, strain improvement methods, and their diverse applications in food processing. Graphical Abstract

The aim of the present study was to develop and optimize a gastroretentive expandable film system of itopride hydrochloride using ethylcellulose and Eudragit S100 as film-forming polymers and triethyl citrate as a plasticizer, in order to enhance gastric retention and oral bioavailability. The gastroretentive expandable film was prepared by the solvent-casting method using a solvent system comprising ethanol and dichloromethane (1:1). A 2-factor, 3-level central composite design was employed to optimize the formulation variables. The prepared films were characterized using FTIR, DSC, XRD, and SEM to evaluate drug-excipient compatibility and solid-state properties. The films were assessed for folding endurance, floating time, in-vitro drug release, and gastroretention behavior. In-vivo pharmacokinetic studies were conducted in New Zealand white rabbits, and pharmacokinetic parameters (C max , T max , AUC, MRT, and t 1/2 ) were determined from plasma concentration–time profiles. Stability studies were performed on the optimized formulation. The optimized expandable film demonstrated excellent mechanical strength with a folding endurance of 146 ± 2.1, rapid floating (9 ± 0.45 min), and nearly complete in-vitro drug release (98.9 ± 0.27%). The formulation exhibited gastroretention for more than 8 h. In-vivo pharmacokinetic evaluation revealed improved absorption, with a relative bioavailability of 106.14% compared to the marketed formulation (GANATON SR). The optimized film remained stable under storage conditions, as confirmed by stability studies. The developed gastroretentive expandable film system of itopride hydrochloride successfully achieved prolonged gastric retention, enhanced drug release, and improved oral bioavailability. This delivery system represents a promising and stable alternative to conventional sustained-release formulations for drugs requiring extended gastric residence.

The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.

A neurological condition that worsens over time, Alzheimer’s disease (AD) is typified by memory loss, cognitive decline, and functional degradation. Traditional diagnostic techniques such as neuroimaging, cerebrospinal fluid biomarkers, and neuropsychological testing are often intrusive, costly, or insensitive in the early stages. Recent years have seen the emergence of AI and ML as game-changing technologies for AD risk assessment, early detection, and customized prevention. Using sophisticated models such as deep learning, convolutional neural networks (CNNs), and graph-based algorithms, AI-driven methods achieve high performance: CNNs, for example, have reached diagnostic accuracies of 94–99% for early AD and mild cognitive impairment using multimodal MRI and PET data. However, most reported performance metrics are derived from retrospective analyses and internal validation cohorts, with limited external validation across diverse populations. These methods include multimodal data integration from neuroimaging, genetics, and clinical records. Years before symptoms appear, AI-based frameworks can predict disease progression, identify modifiable risk factors, and guide individualized treatment plans. Future developments in federated learning and explainable AI (XAI) are promising, although data privacy, algorithmic bias, and ethical ramifications are concerns. Overall, AI and ML have a great deal of promise to transform the prevention of AD, enabling precision therapy and enhancing the lives of those who are at risk. Graphical Abstract

Metabolic reprogramming plays a crucial role in cancer progression, therapeutic resistance, and tumor-microenvironment remodelling. Monoamine oxidase-B (MAO-B), a mitochondrial enzyme involved in oxidative deamination, has recently been identified as a metabolic regulator that influences reactive oxygen species (ROS) production, mitochondrial homeostasis, and redox-dependent signaling in tumors. Selegiline, an MAO-B inhibitor traditionally used in neurological disorders, is now gaining attention for its potential role in modulating tumor metabolism. Elevated MAO-B activity contributes to oxidative stress, genomic instability, immune suppression, and metabolic adaptations that support tumor survival. By inhibiting MAO-B, selegiline reduces ROS generation, alters mitochondrial respiration, regulates glycolytic flux, and disrupts hypoxia-associated pathways, making it a promising modulator of metabolic checkpoints in oncology. Relevant literature was collected from PubMed, Google Scholar, and ScienceDirect using keywords such as Selegiline, MAO-B inhibitor, tumor metabolism, oxidative stress, and drug repurposing in cancer. Relevant studies from the past 5 years, with inclusion criteria focusing on mechanistic, preclinical, and translational evidence related to MAO-B and selegiline-mediated metabolic regulation. Recent findings indicate that selegiline not only modulates cancer cell metabolism but also influences the tumor microenvironment by reducing inflammatory cytokine production, altering macrophage polarization, and enhancing susceptibility to therapeutic stress. Additionally, combination approaches with chemotherapeutics, metabolic inhibitors, and immunotherapies show synergistic potential. This review summarizes current insights into selegiline’s role in metabolic reprogramming, highlights existing challenges, and discusses future opportunities for repositioning selegiline as a targeted metabolic modulator in cancer therapy.

Cellular stress responses and intercellular communication play a crucial role in the pathogenesis of Inflammatory bowel disease (IBD). Among these, endoplasmic reticulum (ER) stress and exosome-mediated signaling have emerged as interconnected drivers of chronic intestinal inflammation. Persistent ER stress, primarily through unfolded protein response pathways involving PERK, IRE1, and ATF6, disrupts epithelial barrier integrity, alters immune cell function, and promotes pro-inflammatory gene expression. ER stress not only affects intracellular homeostasis but also modulates intercellular communication through the secretion of exosomes, which carry proteins, lipids, and nucleic acids. This bidirectional relationship ensures that stress-altered exosomes can amplify ER stress and inflammatory signals in neighboring cells, sustaining intestinal inflammation. For this review, relevant research and review articles were retrieved from established search engines and databases, including PubMed, Google Scholar, and ScienceDirect, using key terms such as “endoplasmic reticulum stress,” “exosome secretion,” “exosome cargo,” “inflammatory bowel disease,” “intestinal inflammation,” and “intercellular communication.” The literature search primarily focused on studies published in the last 5 years, prioritizing clinical and preclinical studies ( in vivo and in vitro models). Published literature addressing ER stress, exosome biology, and their interconnection in IBD were included, whereas studies lacking relevance or study quality were excluded. Recent findings highlight a dynamic interconnection between ER stress and exosomes, where ER stress modulates exosome biogenesis, secretion, and cargo composition. In contrast, stress-altered exosomes amplify ER stress signals and inflammatory mediators in neighboring cells. This review aims to summarize the current evidences on the interconnection of ER stress and exosomes in modulating the intestinal microenvironment, driving inflammation, and contributing to epithelial and immune dysregulation in IBD. This review also highlights experimental insights, existing challenges, and therapeutic prospects for targeting the ER stress–exosome axis to restore mucosal homeostasis in IBD management.

Stem cells are the basis of organogenesis and regeneration, providing cellular support during the development, maintenance, and repair of tissues. This review provides a brief overview of the major stem cell types and their sources, as well as the key stages of organogenesis that depend on stem cell activity. This review highlights critical signalling pathways, including Wnt, Notch, Hedgehog, and BMP. These pathways regulate the fate and lineage specification of stem cells. The review identifies the roles of embryonic stem cells and induced pluripotent stem cells in organ formation as well as the newly arising methods for directed differentiation. Mesenchymal stem cells play a crucial role in tissue regeneration and therapeutic repair. Organoids are potent experimental models for studying development and disease. The impact of stem cell niches and microenvironmental regulation is discussed, along with the cellular and molecular processes that underlie recovery after damage. The review encompasses the translational progress of stem-cell-based therapies, current clinical trials, and the challenges in safety and efficacy. Moreover, the review also explores the introduction of advanced technologies, such as CRISPR, 3D bioprinting, and synthetic biology, as well as theoretical considerations, including future directions and ethical issues. Together, these insights provide a comprehensive overview of stem cell biology and highlight their potential for clinical translation. Graphical Abstract

Diabetic neuropathy (DN) is a common and debilitating complication of diabetes mellitus, often presenting with chronic pain and sensory or motor deficits. Current treatment options provide only partial symptomatic relief and are frequently associated with adverse effects, underscoring the need for more effective and targeted approaches. Microneedle (MN) technology has emerged as a minimally invasive, highly efficient, transdermal-delivery strategy offering increased drug absorption, sustained drug release, and patient compliance. Different designs of MN, such as solid, coated, dissolving, and hydrogel-based, are available and provide specific strategies in DN management. The patient-specific and solid microneedles, like the lipid-cast microneedles functionalized with the lidocaine and dexamethasone, have demonstrated the possibility to provide long-term pain relief. Emulsion formulations of microneedles made of hydrogel loaded with nerve growth factor show potential in enhancing nerve repair. Biodegradable polymers such as polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP) can be dosed well. Still, more advanced materials, such as polylactide (PLA) and polycaprolactone (PCL), need to be engineered for scalable and cost-efficient production. It has its main obstacles that include drug stability and production, mass culturing, and complicated regulatory processes. The combination of MN platforms, nanomedicine and advanced delivery systems could provide synergistic therapeutic effects that could be the next step towards personalised, effective, and endurable DN treatment. This review identifies new developments, limitations, and areas to explore in terms of using microneedle technology as a method of targeted drug delivery for diabetic neuropathy. Graphical abstract

The retinoblastoma gene (RB1), which is located on chromosome 13q14.2, is mutated in retinoblastoma (RB), the most common malignant intraocular tumor in children. About 8000 new cases of retinoblastoma are diagnosed globally each year, accounting for approximately 1 in 17,000 live births. RB is prototypically considered hereditary by nature as thirty to forty percent of cases have autosomal dominant inheritance, and the remaining sixty to seventy percent have non-inherited sporadic inheritance. RB is the most treatable juvenile malignancy, with a high percentage of survival; nevertheless, advanced tumors restrict the amount of globe salvage and are frequently linked to high-risk histological characteristics that indicate spread. Investigating the disease’s molecular causes has also helped to understand its subsequent processes, which has resulted in the identification of biomarkers and relevant targeted treatments. Additionally, advancements in molecular biology techniques facilitated the creation of effective strategies for early disease detection, genetic counseling, and prevention. In the present review, we discuss the risk factors, epidemiology, pathology, and therapeutic approaches for retinoblastoma. We specifically focus on the genetic and molecular characteristics of retinoblastoma, including mutations that cause key signaling pathways involved in the DNA repair, cellular plasticity, and cell proliferation to become dysregulated.

The application of machine learning (ML) methods and predictive analytics in disaster management has made a drastic change in this field over the past few years. With their unparalleled ability to forecast, prepare, and respond, these advanced technologies are transforming the complete paradigm of disaster and emergency management. Much of this work is reinforced by machine learning models, an artificial intelligence domain that analyses huge amounts of data to establish patterns and forecast future disasters. This research proposes novel techniques in disaster management-based healthcare system utilizing machine learning model for sustainable environment. The study utilizes a dataset Centre for Research on Epidemiology of Disasters (CRED) launched Emergency Events Database (EM-DAT) in 1988. Data on frequency as well as effects of about 15,700 incidents since 1900 can be found in International Disaster Database, or EM-DA, which is preprocessed for noise removal and normalization. The processed data features have been extracted utilizing deep adversarial gaussian multilayer perceptron and the features has been optimized using firefly swarm binary grasshopper optimization. Experimental analysis is carried out in terms of random accuracy, precision, recall, AUC, F-1 score. Proposed technique random accuracy 98%, precision 95%, F-1 score 94%, AUC 96%, Recall 97%.