The microbiota-brain interaction is a complex network connecting gut microbiota to the enteric nervous system (ENS) and the central nervous system (CNS) that are crucial for neurotransmission and neurological health. Metabolites such as short-chain fatty acids (SCFA), neurotransmitters, and neuromodulators from the gut microbiota influence behavior and brain function. This review focuses on the study related with gut bacteria, including Bifidobacterium infantis and Lactobacillus species, producing various metabolites in gut including bile acids, SCFA, histamine, and others to communicate with the brain or CNS. Dysbiosis can lead to neurological conditions such as anxiety, depression, PD, and AD. SCFAs from gut bacteria bind to the free fatty acid receptors of intestinal epithelial cells (IECs), affecting neurones and influencing neuroactivity. Gut bacteria also produce neurotransmitters that regulate growth hormone release through interactions with the CNS and endocrine systems. Brain signals interact directly with the pituitary and adrenal glands through the hypothalamic-pituitary-adrenal (HPA) axis, which in turn communicates with enteroendocrine cells (EECs). Investigating probiotics, prebiotics, and dietary changes could open new avenues for treatment for mental and neurological problems.

Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. While timely reperfusion therapies such as percutaneous coronary intervention (PCI) and thrombolysis are essential for salvaging ischemic myocardium, they can paradoxically exacerbate tissue injury through a process known as myocardial infarction reperfusion injury (MIRI). MIRI can contribute to up to 50% of the final infarct size, significantly diminishing the benefits of revascularization and leading to worsened cardiac outcomes. The pathophysiology of MIRI involves complex, interrelated mechanisms including oxidative stress, calcium overload, mitochondrial dysfunction, inflammatory responses, apoptosis, and dysregulated autophagy. Post-reperfusion recovery is further complicated by structural and functional abnormalities such as microvascular obstruction, endothelial dysfunction, and myocardial stunning. Clinically, distinguishing reperfusion injury from ischemic damage is challenging and often requires the use of sensitive biomarkers, such as cardiac troponins, alongside advanced imaging modalities. Although a range of pharmacological (e.g., antioxidants, calcium channel blockers, mitochondrial stabilizers, anti-inflammatory agents) and non-pharmacological (e.g., hypothermia, gene therapy, stem cell-based therapies) interventions have shown promise in preclinical studies, their clinical translation remains limited. This is largely due to the multifactorial and dynamic nature of MIRI. In this context, network pharmacology offers a systems-level approach to understanding the complex biological interactions involved in MIRI, facilitating the identification of multi-target therapeutic strategies. Integrating network pharmacology with omics technologies and precision medicine holds potential for advancing cardioprotective therapies. This review provides a comprehensive analysis of the molecular mechanisms underlying MIRI, examines the current clinical challenges, and explores emerging therapeutic strategies. Emphasis is placed on bridging the translational gap through validated, multi-target approaches and large-scale, multicenter clinical trials. Ultimately, this work aims to support the development of innovative and effective interventions for improving outcomes in patients with myocardial infarction.

Background/Objectives: Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder, which causes memory loss and loss of cognitive functioning, along with behavioral changes. Early detection is important to delay disease progression, timely intervention and to increase patients’ and caregivers’ quality of life (QoL). One of the major and primary challenges for preventing any disease is to identify the disease at the initial stage through a quick and reliable detection process. Different researchers across the world are still working relentlessly, coming up with significant solutions. Artificial intelligence-based solutions are putting great importance on identifying the disease efficiently, where deep learning with medical imaging is highly being utilized to develop disease detection frameworks. In this work, a novel and optimized detection framework has been proposed that comes with remarkable performance that can classify the level of Alzheimer’s accurately and efficiently. Methods: A powerful vision transformer model (ViT-B16) with three efficient Convolutional Neural Network (CNN) models (VGG19, ResNet152V2, and EfficientNetV2B3) has been trained with a benchmark dataset, ‘OASIS’, that comes with a high volume of brain Magnetic Resonance Images (MRI). Results: A weighted average ensemble technique with a Grasshopper optimization algorithm has been designed and utilized to ensure maximum performance with high accuracy of 97.31%, precision of 97.32, recall of 97.35, and F1 score of 0.97. Conclusions: The work has been compared with other existing state-of-the-art techniques, where it comes with high efficiency, sensitivity, and reliability. The framework can be utilized in IoMT infrastructure where one can access smart and remote diagnosis services.

Cancer stem cells (CSCs) represent a small but highly resilient tumor subpopulation responsible for sustained growth, metastasis, therapeutic resistance, and recurrence. Their survival is supported by aberrant activation of developmental and inflammatory pathways, including Wnt/β-catenin, Notch, Hedgehog, PI3K/Akt/mTOR, STAT3, and NF-κB, as well as epithelial–mesenchymal transition (EMT) programs and niche-driven cues. Increasing evidence shows that phytochemicals, naturally occurring bioactive compounds from medicinal plants, can disrupt these networks through multi-targeted mechanisms. This review synthesizes current findings on prominent phytochemicals such as curcumin, sulforaphane, resveratrol, EGCG, genistein, quercetin, parthenolide, berberine, and withaferin A. Collectively, these compounds suppress CSC self-renewal, reduce sphere-forming capacity, diminish ALDH⁺ and CD44⁺/CD24⁻ fractions, reverse EMT features, and interfere with key transcriptional regulators that maintain stemness. Many phytochemicals also sensitize CSCs to chemotherapeutic agents by downregulating drug-efflux transporters (e.g., ABCB1, ABCG2) and lowering survival thresholds, resulting in enhanced apoptosis and reduced tumor-initiating potential. This review further highlights the translational challenges associated with poor solubility, rapid metabolism, and limited bioavailability of free phytochemicals. Emerging nanotechnology-based delivery systems, including polymeric nanoparticles, lipid carriers, hybrid nanocapsules, and ligand-targeted formulations, show promise in improving stability, tumor accumulation, and CSC-specific targeting. These nanoformulations consistently enhance intracellular uptake and amplify anti-CSC effects in preclinical models. Overall, the consolidated evidence supports phytochemicals as potent modulators of CSC biology and underscores the need for optimized delivery strategies and evidence-based combination regimens to achieve meaningful clinical benefit.