GLA University

Nutrition, encompassing vital macro- and micronutrients (including carbs, proteins, fats, vitamins such as D and C, and minerals like iron and calcium), is fundamental to supporting life, fostering healthy ageing, and averting disease. The information on the combined impact of dietary patterns, nutritional content, and lifestyle factors on metabolic health, disease risk, and healthy ageing is summarised in this publication. According to the discussion, both macronutrient and micronutrient surpluses and deficiencies upset metabolic homeostasis, which leads to obesity, insulin resistance, cardiovascular disease, and hepatic steatosis. Dietary habits are one of the three cornerstones of health and longevity, according to biogerontological theories of ageing. The free radical theory emphasises oxidative stress in cellular damage, and the disposable soma hypothesis emphasises the trade-off between reproduction and somatic upkeep. Clinical and epidemiological data indicate that cardiometabolic outcomes, including elevated insulin sensitivity, lipid profiles, and inflammatory markers, are strongly influenced by diet quality, dietary timing (such as time-restricted eating), and overall energy balance. The review also demonstrates how nutrients can control gene expression, signalling pathways, and epigenetic alterations in addition to serving as energy sources. Nutrient availability and disease susceptibility are largely determined by interactions between the gut microbiota, food, and human metabolism. Furthermore, individual reactions to diet are very variable due to genetic, metabolic, and environmental factors, which explains why customised nutrition plans are becoming more and more popular. The findings also show that dietary habits during childhood and adolescence have an impact on long-term disease prevention, and that combining lifestyle treatments (diet and exercise) can consistently reduce the risk of chronic illness. In order to optimise health and wellness over the course of a lifetime, the data generally supports switching from reductionist, single-nutrient dietary methods to integrated, systems-oriented strategies that take into account molecular, clinical, and sociocultural factors.

The incorporation of artificial intelligence (AI) and machine learning (ML) into microalgal research is transforming biomass generation, biofuel synthesis, and wastewater remediation strategies. Sophisticated ML techniques, such as artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA), facilitate precise simulation and forecasting of highly intricate microalgal systems. Although constraints related to data accessibility and model scalability persist, ML-based methodologies are increasingly demonstrating their value in enhancing the sustainability and operational efficiency of microalgal processes. Simultaneously, technoeconomic analysis (TEA) has become an indispensable framework for assessing biorefinery viability through systematic evaluation of life-cycle environmental burdens. Recent progress in TEA methodologies has strengthened iterative design optimization, uncertainty quantification, and user accessibility via open-source computational platforms. Broader systems boundaries now account for policy mechanisms, performance during end-use phase, and international market dynamics, thereby reinforcing TEA’s contribution to sustainable bioeconomic advancement. Collectively, these computational and analytical innovations are expediting the deployment of scalable and economically feasible microalgal technologies. Highlights

The most often occurring primary liver cancer, hepatocellular carcinoma (HCC), continues to be a major worldwide health burden due to few treatment choices and high rates of recurrence and death. Though frequently hampered by acquired resistance and dose-limiting toxicity, sorafenib-a first-in-class oral multikinase inhibitor-is still a standard systemic treatment for late-stage HCC. The pharmacological effects of sorafenib in HCC are critically analysed in this review, along with their molecular mechanisms, resistance pathways, and genetic and epigenetic elements affecting response. Using databases including PubMed, Scopus, Embase, and Web of Science, an extensive literature review was carried out to include FDA-approved medications, clinical trials, and peer-reviewed research concentrating on mechanisms of resistance, therapeutic combinations, and artificial intelligence (AI)-based precision medicine approaches. While VEGFR-mediated angiogenesis is the main target of sorafenib, resistance comes via compensatory activation of alternate pathways, including PI3K/AKT/mTOR and JAK/STAT, as well as efflux transporter upregulation. Preclinical research shows that combining sorafenib with natural compounds like berberine, wogonin, and quercetin improves pro-apoptotic effects and lowers toxicity. Second-generation kinase inhibitors, including Regorafenib and lenvatinib, provide better survival; immune checkpoint inhibitors, including Nivolumab and atezolizumab, show promise in combination treatments. Moreover, artificial intelligence (AI) applications in radiomics, genomics, and predictive modelling are showing high accuracy in helping to optimize HCC diagnosis and therapy selection. Overcoming the constraints of sorafenib demands the development of combination therapies, biomarker-based personalization, and validated artificial intelligence integration into clinical practice, even if it is still a mainstay of advanced HCC treatment.

Traumatic brain injury (TBI) and Alzheimer’s disease (AD), are two prominent neurological disorders. Both present a large number of clinical and socio-economic issues, but they are increasingly seen as having common pathways through which they cause disease. For both TBI and AD, oxidative stress and neuro-inflammatory responses lead to neuronal damages, impairment in synaptic function, and continued neuro-degeneration. After TBI, excessive production of oxygen and nitrogen free radicals damages mitochondrial function, while also activating microglial and astroglial cells chronically to cause inflammation. In patients with AD, continued oxidative damage leads to an accumulation of amyloid‑β protein and also leads to an increase in tau phosphorylation. There is an increasing body of literature demonstrating that the pathways by which TBI and AD occur may functionally connect short-term brain injuries to long-term neuro-degenerative processes. This article reviews the available data related to what currently drives the neuronal damage associated with TBI and AD through oxidative stress and inflammation and the available and generating treatment options—particularly antioxidants, anti-inflammatory agents, compounds protecting mitochondria, nanoparticles designed to deliver drugs, and phytochemicals with multi-targeted activity. The focus of this paper is on the scientific data supporting both the mechanistic pathways underlying the damage and the pharmacological/biological therapies that might treat the damage from a single point of view. The innovative aspect of this article lies in its demonstration of the relationship between oxidative stress and inflammation and its impact on the continuum of neuronal disease causing TBI leading to AD, and the potential of multi-targeted therapies to modify the ongoing disease processes.

Abstract We investigate static equilibrium configurations of compact stars composed of an admixture of bosonic dark matter and dark energy within the framework of regularized four-dimensional Einstein–Gauss–Bonnet (4DEGB) gravity. The stellar interior is modeled as a two-fluid system in which self-interacting bosonic dark matter and Chaplygin-gas dark energy coexist and interact solely through gravity. Employing a scalar–tensor formulation of 4DEGB gravity, we derive the modified Tolman–Oppenheimer–Volkoff equations governing hydrostatic equilibrium and solve them numerically for a range of central densities. We systematically explore the impact of higher-curvature corrections by varying the Gauss–Bonnet coupling parameter, as well as the role of dark-matter microphysics by changing the bosonic particle mass. Our analysis shows that positive Gauss–Bonnet coupling significantly enhances the maximum supported mass and compactness of the configurations relative to the general relativistic limit, while remaining compatible with current observational constraints from GW170817, PSR J0740+6620, and HESS J1731–347. In contrast, variations in the bosonic dark-matter particle mass within the considered range induce only mild modifications to global stellar properties. The mass-central-density criterion, the relativistic adiabatic index, and the causality condition on the sound speed are all satisfied throughout the stellar interior, and stability is evaluated accordingly. These findings indicate that compact stars with dark-matter-dark-energy admixtures in regularized 4DEGB gravity are stable and feasible astrophysical configurations. They also emphasize the significant influence of higher-curvature effects on the macroscopic structure of these structures.