Search Results (68)

Oxidative stress is a defining and pervasive driver of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). As a molecular accelerant, reactive oxygen species (ROS) and reactive nitrogen species (RNS) compromise mitochondrial function, amplify lipid peroxidation, induce protein misfolding, and promote chronic neuroinflammation, creating a positive feedback loop of neuronal damage and cognitive decline. Despite its centrality in promoting disease progression, attempts to neutralize oxidative stress with monotherapeutic antioxidants have largely failed owing to the multifactorial redox imbalance affecting each patient and their corresponding variation. We are now at the threshold of precision redox medicine, driven by advances in syndromic multi-omics integration, Artificial Intelligence biomarker identification, and the precision of patient-specific therapeutic interventions. This paper will aim to reveal a mechanistically deep assessment of oxidative stress and its contribution to diseases of neurodegeneration, with an emphasis on oxidatively modified proteins (e.g., carbonylated tau, nitrated α-synuclein), lipid peroxidation biomarkers (F2-isoprostanes, 4-HNE), and DNA damage (8-OHdG) as significant biomarkers of disease progression. We will critically examine the majority of clinical trial studies investigating mitochondria-targeted antioxidants (e.g., MitoQ, SS-31), Nrf2 activators (e.g., dimethyl fumarate, sulforaphane), and epigenetic reprogramming schemes aiming to re-establish antioxidant defenses and repair redox damage at the molecular level of biology. Emerging solutions that involve nanoparticles (e.g., antioxidant delivery systems) and CRISPR (e.g., correction of mutations in SOD1 and GPx1) have the potential to transform therapeutic approaches to treatment for these diseases by cutting the time required to realize meaningful impacts and meaningful treatment. This paper will argue that with the connection between molecular biology and progress in clinical hyperbole, dynamic multi-targeted interventions will define the treatment of neurodegenerative diseases in the transition from disease amelioration to disease modification or perhaps reversal. With these innovations at our doorstep, the future offers remarkable possibilities in translating network-based biomarker discovery, AI-powered patient stratification, and adaptive combination therapies into individualized/long-lasting neuroprotection. The question is no longer if we will neutralize oxidative stress; it is how likely we will achieve success in the new frontier of neurodegenerative disease therapies. Full article

Metformin, a long-established antidiabetic agent, is undergoing a renaissance as a prototype gerotherapeutic and immunometabolic oncology adjuvant. Mechanistic advances reveal that metformin modulates an integrated network of metabolic, immunological, microbiome-mediated, and epigenetic pathways that impact the hallmarks of aging and cancer biology. Clinical data now demonstrate its ability to reduce cancer incidence, enhance immunotherapy outcomes, delay multimorbidity, and reverse biological age markers. Landmark trials such as UKPDS, CAMERA, and the ongoing TAME study illustrate its broad clinical impact on metabolic health, cardiovascular risk, and age-related disease trajectories. In oncology, trials such as MA.32 and METTEN evaluate its influence on progression-free survival and tumor response, highlighting its evolving role in cancer therapy. This review critically synthesizes the molecular underpinnings of metformin’s polypharmacology, examines results from pivotal clinical trials, and compares its effectiveness with emerging gerotherapeutics and senolytics. We explore future directions, including optimized dosing, biomarker-driven personalization, rational combination therapies, and regulatory pathways, to expand indications for aging and oncology. Metformin stands poised to play a pivotal role in precision strategies that target the shared roots of aging and cancer, offering scalable global benefits across health systems. Full article

Background: Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are genetically complex and often linked to structural genomic variations such as copy number variants (CNVs). Current diagnostic strategies face challenges in interpreting the clinical significance of such variants. Methods: We developed a customized, gene-oriented chromosomal microarray (CMA) targeting 6026 genes relevant to neurodevelopment, aiming to improve diagnostic yield and candidate gene prioritization. A total of 39 patients with unexplained developmental delay, intellectual disability, and/or ASD were analyzed using this custom platform. Systems biology approaches were employed for downstream interpretation, including protein–protein interaction networks, centrality measures, and tissue-specific functional module analysis. Results: Pathogenic or likely pathogenic CNVs were identified in 31% of cases (9/29). Network analyses revealed candidate genes with key topological properties, including central “hubs” (e.g., NPEPPS, PSMG1, DOCK8) and regulatory “bottlenecks” (e.g., SLC15A4, GLT1D1, TMEM132C). Tissue- and cell-type-specific network modeling demonstrated widespread gene involvement in both prenatal and postnatal developmental modules, with glial and astrocytic networks showing notable enrichment. Several novel CNV regions with high pathogenic potential were identified and linked to neurodevelopmental phenotypes in individual patient cases. Conclusions: Customized CMA offers enhanced detection of clinically relevant CNVs and provides a framework for prioritizing novel candidate genes based on biological network integration. This approach improves diagnostic accuracy in NDDs and identifies new targets for future functional and translational studies, highlighting the importance of glial involvement and immune-related pathways in neurodevelopmental pathology. Full article

Memory impairment, ranging from mild memory impairment to neurodegenerative diseases such as Alzheimer’s disease, poses an escalating global health challenge that necessitates multi-targeted interventions to prevent progression. Health functional foods (HFFs), which include bioactive dietary compounds that not only provide basic nutrition but also function beyond that to modulate physiological pathways, offer a promising non-pharmacological strategy to preserve memory function. This review presents an integrative framework for the discovery, evaluation, and clinical translation of biomarkers responsive to HFFs in the context of preventing memory impairment. We examine both established clinical biomarkers, such as amyloid-β and tau in the cerebrospinal fluid, neuroimaging indicators, and memory assessments, as well as emerging nutritionally sensitive markers including cytokines, microRNAs, gut microbiota signatures, epigenetic modifications, and neuroactive metabolites. By leveraging systems biology approaches, we explore how network pharmacology, gut–brain axis modulation, and multi-omics integration can help to elucidate the complex interactions between HFF components and memory-related pathways such as neuroinflammation, oxidative stress, synaptic plasticity, and metabolic regulation. The review also addresses the translational pipeline for HFFs, from formulation and standardization to regulatory frameworks and clinical development, with an emphasis on precision nutrition strategies and cross-disciplinary integration. Ultimately, we propose a paradigm shift in memory health interventions, positioning HFFs as scientifically validated compounds for personalized nutrition within a preventative memory function framework. Full article

Multiple myeloma is a hematologic malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. The tumor microenvironment plays a crucial role in multiple myeloma pathogenesis, progression, and drug resistance. Traditional two-dimensional cell culture models have been instrumental in multiple myeloma research. However, they fail to recapitulate the complex in vivo bone marrow microenvironment, leading to limited predictive value for clinical outcomes. Three-dimensional cell culture models emerged as more physiologically relevant systems, offering enhanced insights into multiple myeloma biology. Scaffold-based systems (e.g., hydrogels, collagen, and Matrigel), scaffold-free spheroids, and bioprinted models have been developed to simulate the bone marrow microenvironment, incorporating key components like mesenchymal stromal cells, osteoblasts, endothelial cells, and immune cells. These models enable the functional assessment of cell adhesion-mediated drug resistance, cytokine signaling networks, and hypoxia-induced adaptations, which are often lost in 2D cultures. Moreover, 3D platforms demonstrated improved predictive value in preclinical drug screening, facilitating the evaluation of novel agents and combination therapies in a setting that better mimics the in vivo tumor context. Hence, 3D cultures represent a pivotal step toward bridging the gap between basic myeloma research and translational applications, supporting the development of more effective and patient-specific therapies. Full article

Milk fever, or periparturient hypocalcemia, in dairy cows has traditionally been addressed as an acute calcium deficiency, leading to interventions like supplementation and adjustments in dietary cation–anion balance. Although these measures have improved clinical outcomes, milk fever remains a widespread and economically significant issue for the dairy industry. Emerging findings demonstrate that a narrow emphasis on blood calcium concentration overlooks the complex interactions of immune, endocrine, and metabolic pathways. Inflammatory mediators and bacterial endotoxins can compromise hormone-driven calcium regulation and induce compensatory calcium sequestration, thereby worsening both clinical and subclinical hypocalcemia. Recent insights from systems biology illustrate that milk fever arises from nonlinear interactions among various physiological networks, rather than a single deficiency. Consequently, this review contends that a holistic strategy including integrating nutrition, immunology, microbiology, genetics, and endocrinology is vital for comprehensive management and prevention of milk fever. By embracing a multidisciplinary perspective, producers and veterinarians can develop more robust, customized solutions that not only safeguard animal well-being but also bolster profitability. Such an approach promises to meet the evolving demands of modern dairy operations by reducing disease prevalence and enhancing overall productivity. Tackling milk fever through integrated methods may unlock possibilities for improved herd health and sustainable dairy farming. Full article

Improved image quality, better scanners, innovative software technologies, enhanced computational power, superior network connectivity, and the ease of virtual image reproduction and distribution are driving the potential use of digital pathology for diagnosis and education. Although relatively common in clinical oncology, its application in liver pathology is under development. Digital pathology and improving subjective histologic scoring systems could be essential in managing obesity-associated steatotic liver disease. The increasing use of digital pathology in analyzing liver specimens is particularly intriguing as it may offer a more detailed view of liver biology and eliminate the incomplete measurement of treatment responses in clinical trials. The objective and automated quantification of histological results may help establish standardized diagnosis, treatment, and assessment protocols, providing a foundation for personalized patient care. Our experience with artificial intelligence (AI)-based software enhances reproducibility and accuracy, enabling continuous scoring and detecting subtle changes that indicate disease progression or regression. Ongoing validation highlights the need for collaboration between pathologists and AI developers. Concurrently, automated image analysis can address issues related to the historical failure of clinical trials stemming from challenges in histologic assessment. We discuss how these novel tools can be incorporated into liver research and complement post-diagnosis scenarios where quantification is necessary, thus clarifying the evolving role of digital pathology in the field. Full article

Medical treatment of glioblastoma presents a significant challenge. A conventional medication has limited effectiveness, and a single-target therapy is usually effective only in the early stage of the treatment. Recently, there has been increasing focus on multi-target therapies, but the vast range of possible combinations makes clinical experimentation and implementation difficult. From the perspective of systems biology, this study conducted simulations for multi-target glioblastoma therapy based on dynamic analysis of previously established endogenous networks, validated with glioblastoma single-cell RNA sequencing data. Several potentially effective target combinations were identified. The findings also highlight the necessity of multi-target rather than single-target intervention strategies in cancer treatment, as well as the promise in clinical applications and personalized therapies. Full article

Background: In the United States, approximately 1 in 5 children experience comorbidities with mental illness, including depression and anxiety, which lead to poor general health outcomes. Adolescents with substance use disorders exhibit high rates of co-occurring mental illness, with over 60% meeting diagnostic criteria for another psychiatric condition in community-based treatment programs. Comorbidities are influenced by both genetic (DNA antecedents) and environmental (epigenetic) factors. Given the significant impact of psychiatric comorbidities on individuals’ lives, this study aims to uncover common mechanisms through a Genome-Wide Association Study (GWAS) meta-meta-analysis. Methods: GWAS datasets were obtained for each comorbid phenotype, followed by a GWAS meta-meta-analysis using a significance threshold of p < 5E−8 to validate the rationale behind combining all GWAS phenotypes. The combined and refined dataset was subjected to bioinformatic analyses, including Protein–Protein Interactions and Systems Biology. Pharmacogenomics (PGx) annotations for all potential genes with at least one PGx were tested, and the genes identified were combined with the Genetic Addiction Risk Severity (GARS) test, which included 10 genes and eleven Single Nucleotide Polymorphisms (SNPs). The STRING-MODEL was employed to discover novel networks and Protein–Drug interactions. Results: Autism Spectrum Disorder (ASD) was identified as the top manifestation derived from the known comorbid interaction of anxiety, depression, and attention deficit hyperactivity disorder (ADHD). The STRING-MODEL and Protein–Drug interaction analysis revealed a novel network associated with these psychiatric comorbidities. The findings suggest that these interactions are linked to the need to induce “dopamine homeostasis” as a therapeutic outcome. Conclusions: This study provides a reliable genetic and epigenetic map that could assist healthcare professionals in the therapeutic care of patients presenting with multiple psychiatric manifestations, including anxiety, depression, and ADHD. The results highlight the importance of targeting dopamine homeostasis in managing ASD linked to these comorbidities. These insights may guide future pharmacogenomic interventions to improve clinical outcomes in affected individuals. Full article

Cell surface receptors are pivotal to cancer cell transformation, disease progression, metastasis, early detection, targeted therapy, drug responses, and clinical outcomes. Since they coordinate complex signaling communication networks in the tumor microenvironment, mapping the physical interaction partners of cell surface receptors in vivo is vital for understanding their roles, functional states, and suitability as therapeutic targets. Yet traditional methods like immunoprecipitation and affinity purification–mass spectrometry often fail to detect key but weak or transient receptor–protein interactions. Proximity labeling, a cutting-edge proteomics technology, addresses these technical challenges by enabling precise mapping of protein neighborhoods around a receptor target on the cell surface of cancer cells. This technique has been successfully applied in vitro and in vivo for proteomic mapping across various model systems. This review explores the fundamental principles, technologies, advantages, limitations, and applications of proximity labeling in cancer biology, focusing on mapping receptor microenvironments. By advancing mechanistic insights into cancer cell receptor signaling mechanisms, proximity labeling is poised to transform cancer research, improve targeted therapies, and illuminate avenues to overcome drug resistance. Full article

Late-onset Alzheimer’s disease (LOAD) is a subtype of dementia that manifests after the age of 65. It is characterized by progressive impairments in cognitive functions, behavioral changes, and learning difficulties. Given the progressive nature of the disease, early diagnosis is crucial. Early-onset Alzheimer’s disease (EOAD) is solely attributable to genetic factors, whereas LOAD has multiple contributing factors. A complex pathway mechanism involving multiple factors contributes to LOAD progression. Employing a systems biology approach, our analysis encompassed the genetic, epigenetic, metabolic, and environmental factors that modulate the molecular networks and pathways. These factors affect the brain’s structural integrity, functional capacity, and connectivity, ultimately leading to the manifestation of the disease. This study has aggregated diverse biomarkers associated with factors capable of altering the molecular networks and pathways that influence brain structure, functionality, and connectivity. These biomarkers serve as potential early indicators for AD diagnosis and are designated as early biomarkers. The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. This study has compiled research papers on Alzheimer’s disease (AD) diagnosis utilizing machine learning (ML) methodologies from both categories of biomarker data, including the applications of ML techniques for AD diagnosis. The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer’s disease (AD) diagnosis utilizing various types of biomarker data. Furthermore, this study has addressed the potential scope of using generative AI and the Synthetic Minority Oversampling Technique (SMOTE) for data augmentation. Full article

Metastatic colorectal cancer is a leading cause of cancer-related death across the world. The treatment paradigm has shifted away from systemic chemotherapy alone to include targeted therapy and immunotherapy. The past two decades have been characterized by increased investigation into molecular profiling of colorectal cancer. These molecular profiles help physicians to better understand colorectal cancer biology among patients with metastatic disease. Additionally, improved data on genetic pathways allow for specific therapies to be targeted at the underlying molecular profile. Investigation of the EGFR, VEGF, HER2, and other pathways, as well as deficient mismatch repair, has led to the development of multiple targeted therapies that are now utilized in the National Comprehensive Cancer Network guidelines for colon and rectal cancer. While these new therapies have contributed to improved survival for metastatic colorectal cancer, long-term survival remains poor. Additional investigation to understand resistance to targeted therapy and development of new targeted therapy is necessary. New therapies are under development and are being tested in the preclinical and clinical settings. The aim of this review is to provide a comprehensive evaluation of molecular profiling, currently available therapies, and ongoing obstacles in the field of colorectal cancer. Full article

Helminths have developed intricate mechanisms to survive and evade the host’s immune responses. Hence, understanding the excretory-secretory products (ESPs) by helminths is crucial for developing control tools, including drug targets, vaccines, and potential therapies for inflammatory and metabolic disorders caused by them. Proteomics, the large-scale analysis of proteins, offers a powerful approach to unravel the complex proteomes of helminths and gain insights into their biology. Proteomics, as a science that delves into the functions of proteins, has the potential to revolutionize clinical therapies against parasitic infections that have developed anthelminthic resistance. Proteomic technologies lay a framework for accompanying genomic, reverse genetics, and pharmacokinetic approaches to provide more profound or broader coverage of the cellular mechanisms that underlie the response to anthelmintics. With the development of vaccines against helminth infections, proteomics has brought a major change to parasitology. The proteome of helminths can be analyzed comprehensively, revealing the complex network of proteins that enable parasite survival and pathogenicity. Furthermore, it reveals how parasites interact with hosts’ immune systems. The current article reviews the latest advancements in helminth proteomics and highlights their valuable contributions to the search for anthelminthic vaccines. Full article

Alzheimer’s disease has become a major public health issue. While extensive research has been conducted in the last few decades, few drugs have been approved by the FDA to treat Alzheimer’s disease. There is still an urgent need for understanding the disease pathogenesis, as well as identifying new drug targets for further drug discovery. Alzheimer’s disease is known to arise from a build-up of amyloid beta (Aβ) plaques as well as tangles of tau proteins. Along similar lines to Alzheimer’s disease, inflammation in the brain is known to stem from the degeneration of tissue and build-up of insoluble materials. A minireview was conducted in this work assessing the genes, proteins, reactions, and pathways that link brain inflammation and Alzheimer’s disease. Existing tools in Systems Biology were implemented to build protein interaction networks, mainly for the classical complement pathway and G protein-coupled receptors (GPCRs), to rank the protein targets according to their interactions. The top 10 protein targets were mainly from the classical complement pathway. With the consideration of existing clinical trials and crystal structures, proteins C5AR1 and GARBG1 were identified as the best targets for further drug discovery, through computational approaches like ligand–protein docking techniques. Full article

Traditional clinical methodologies often fall short of revealing the complex interplay of multiple components and targets within the human body. This study was designed to explore the complex and synergistic effects of phytochemicals in a plant-based multivitamin/mineral supplement (PBS) on oxidative stress and inflammation in healthy individuals. Utilizing a systems biology framework, we integrated clinical with multi-omics analyses, including UPLC-Q-TOF-MS for 33 phytochemicals, qPCR for 42 differential transcripts, and GC-TOF-MS for 17 differential metabolites. A Gene Ontology analysis facilitated the identification of 367 biological processes linked to oxidative stress and inflammation. As a result, a comprehensive network was constructed consisting of 255 nodes and 1579 edges, featuring 10 phytochemicals, 26 targets, and 218 biological processes. Quercetin was identified as having the broadest target spectrum, succeeded by ellagic acid, hesperidin, chlorogenic acid, and quercitrin. Moreover, several phytochemicals were associated with key genes such as HMOX1, TNF, NFE2L2, CXCL8, and IL6, which play roles in the Toll-like receptor, NF-kappa B, adipocytokine, and C-type lectin receptor signaling pathways. This clinical data-driven network system approach has significantly advanced our comprehension of a PBS’s effects by pinpointing pivotal phytochemicals and delineating their synergistic actions, thus illuminating potential molecular mechanisms. Full article