Search Results (213)

The identification of brain clearance failure as a precursor to a large variety of neurodegenerative diseases has shifted fluid dynamics from a secondary to a tertiary target of brain health. The identification of the glymphatic system, detailing cerebrospinal fluid entry along perivascular spaces and exit via perivenous and meningeal lymphatic pathways, provided a challenge to previous diffusion models and established aquaporin-4–dependent astroglial polarity as a governing principle of solute transport. Multiple lines of evidence now support a coupled glymphatic–venous axis, wherein vasomotion, venous outflow, and lymphatic drainage are functionally interrelated. Failure of any axis will cascade and affect the entire axis, linking venous congestion, aquaporin-4 disassembly, and meningeal lymphatic failure to protein aggregation, neuroinflammation, edema, and intracranial hypertension. Specific lines of evidence from diffusion tensor imaging along vascular spaces, clearance MRI, and multi-omic biomarkers can provide a measure of transport. Therapeutic strategies are rapidly advancing from experimental strategies to translational approval, including behavioral optimization, closed-loop sleep stimulation, vascular and lymphatic therapies, focused ultrasound, pharmacological polarity recoupling, and regenerative bioengineering. Novel computational approaches, such as digital twin dynamic modeling and adaptive trial designs, suggest that clearance measures may serve as endpoints to be approved by the FDA. This review is intended to bridge relevant mechanistic and translational reviews, focusing on impaired clearance as an exploitable systems defect rather than an incapacitating secondary effect. Improving our understanding of the glymphatic-venous axis Injury may lead to future target strategies that advance cognitive resilience, alleviate disease burden, and improve quality of life. By clarifying the glymphatic–venous axis, we provide a mechanistic link between impaired interstitial clearance and the pathological accumulation of amyloid-β, tau, and α-synuclein in neurodegenerative diseases. The repair of aquaporin-4 polarity, venous compliance, and lymphatic drainage might therefore open new avenues for the diagnosis and treatment of Alzheimer’s and Parkinson’s disease, supplying both biomarkers of disease progression and new targets for early intervention. These translational implications not only locate clearance failure as an epiphenomenon of neurodegeneration but, more importantly, as a modifiable driver of the course of neurodegeneration. Full article

Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0–C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article

Insecticide resistance has become a critical issue threatening global agricultural production and food security. Previous studies have primarily focused on resistance mechanisms such as target-site mutations, enhanced metabolic detoxification, and reduced cuticular penetration. However, growing evidence in recent years indicates that odorant-binding proteins (OBPs) and chemosensory proteins (CSPs)—beyond their roles in chemoreception—also play key roles in the development of insecticide resistance. Research has revealed that these proteins significantly modulate insect susceptibility to insecticides through various mechanisms, including direct binding to insecticides, regulation of detoxification metabolic pathways, and influence on behavioral adaptations in pests. This review also systematically summarizes modern research strategies employed to investigate OBPs/CSPs functions, including high-throughput omics technologies, RNA interference, CRISPR-Cas9 gene editing, and molecular docking, while discussing the potential of targeting these proteins for developing novel insecticides and resistance management strategies. Although significant progress has been made in laboratory studies, the practical application of OBPs/CSPs-mediated resistance mechanisms still faces multiple challenges. Future research should prioritize multi-gene targeting strategies, cross-species functional validation, and field trial implementation to facilitate the development of green and precise pest control approaches based on OBPs and CSPs, thereby offering new pathways for sustainable agriculture. Full article

Stuttering, a complex and multifactorial speech disorder, has long presented an enigma regarding its etiology. While earlier approaches often emphasized psychosocial influences, historical clinical and speech-language strategies have considered multiple contributing factors. By integrating genomic, transcriptomic and phenomic evidence, the ongoing research illustrates how functional genomics can unravel the biological architecture of complex speech disorders. In particular, advances in omic technologies have unequivocally positioned genetics and underlying biological pathways at the forefront of stuttering research. I have experienced stuttering and lived with it since my early childhood. This perspective article presents findings from omic studies, highlighting relevant aspects such as gene discoveries, implicated cellular mechanisms, and the intricate genetic architecture of developmental stuttering. As a person who stutters, I offer an intimate perspective on how these scientific insights are not merely academic but profoundly impactful for the affected community. A multi-omic integration strategy, combining large-scale genetic discovery with deep phenotyping and functional validation, is advocated to accelerate understanding in this field. Additionally, a bibliometric analysis using an international database was conducted to map trends and identify directions in stuttering research within the omic context. Ultimately, these scientific endeavors hold the potential to inform not only personalized interventions but also critical policy and regulatory changes, enhancing accessibility, support, and the recognized rights of people who stutter. Full article

Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, DeepInsight, Fotomics, and a novel saliency-guided convolutional neural network (CNN), for transcriptomic classification. DeepInsight utilises dimensionality reduction to spatially arrange gene features, while Fotomics applies Fourier transforms to encode expression patterns into structured images. The proposed method transforms each single-cell gene expression profile into an RGB image using PCA, UMAP, or t-SNE, enabling CNNs such as ResNet to learn spatially organised molecular features. Gradient-based saliency maps are employed to highlight gene regions most influential in model predictions. Evaluation is conducted on two biologically and technologically different datasets: single-cell RNA-seq from glioblastoma GSM3828672 and bulk microarray data from medulloblastoma GSE85217. Outcomes demonstrate that image-based deep learning methods, particularly those incorporating saliency guidance, provide a robust and interpretable framework for uncovering biologically meaningful patterns in complex high-dimensional omics data. For instance, ResNet-18 achieved the highest accuracy of 97.25% on the GSE85217 dataset and 91.02% on GSM3828672, respectively, outperforming other baseline models across multiple metrics. Full article

Autoimmune diseases are chronic inflammatory conditions characterized by the breakdown of immune tolerance to self-antigens. While genetic and environmental factors play key roles, growing evidence highlights chronic stress as a significant contributor to immune dysregulation through its impact on the hypothalamic–pituitary–adrenal (HPA) axis. The HPA axis, primarily via cortisol secretion, serves as the major neuroendocrine mediator of stress responses, influencing both immune regulation and systemic homeostasis. This review synthesizes current literature on HPA axis physiology, the mechanisms of cortisol signaling, and the maladaptive effects of chronic stress. Emphasis is placed on clinical and experimental findings linking HPA dysfunction to immune imbalance and autoimmunity, as well as organ-specific consequences across neuroimmune, endocrine, cardiovascular, gastrointestinal, integumentary, and musculoskeletal systems. Chronic stress leads to impaired HPA axis feedback, glucocorticoid receptor resistance, and paradoxical cortisol dysregulation, fostering a pro-inflammatory state. This dysregulation promotes cytokine imbalance, weakens protective immune mechanisms, and shifts the immune response toward autoimmunity. Evidence from both human and animal studies associates persistent HPA dysfunction with diseases such as systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis. HPA axis dysregulation under chronic stress constitutes a critical mechanistic link between psychological stress and autoimmune disease. Understanding these pathways provides opportunities for therapeutic interventions, including stress management, lifestyle modification, and neuroendocrine-targeted treatments. Future research should focus on multi-omics and longitudinal approaches to clarify the reversibility of HPA alterations and identify resilience factors. Full article

Photodynamic therapy (PDT) has evolved considerably over the past decades, progressing from first-generation porphyrins to second- and third-generation photosensitizers, including nanocarrier-based systems with improved selectivity and bioavailability. In parallel, matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) has become a gold standard for the characterisation of complex biomolecules, enabling precise determination of molecular mass, purity and stability. This narrative review explores the intersection of these two fields, focusing on how MALDI-TOF MS supports the development, characterisation and clinical application of photosensitizers used in PDT. Literature searches were performed across PubMed, Web of Science, Scopus and Base-search, followed by targeted retrieval of studies on MALDI and PDT applications. Findings indicate that MALDI-TOF MS plays a crucial role at multiple stages: confirming the synthesis and chemical integrity of novel photosensitizers, monitoring their metabolic stability in biological systems and characterising photodegradation products after PDT. Moreover, MALDI imaging mass spectrometry (MALDI-IMS) enables spatial mapping of photosensitizer distribution in tissues, while rapid pathogen identification by MALDI-TOF supports antimicrobial PDT applications. Collectively, the evidence highlights that MALDI-MS is not only a tool for molecular characterisation but also a versatile analytical platform with a direct translational impact on PDT. Its integration with other omics and multimodal imaging approaches is expected to enhance the personalization and clinical effectiveness of photodynamic therapy. Full article

Patients with Fontan circulation are at lifelong risk for a range of complications involving multiple organ systems. As survival into adulthood increases, there is an urgent need to refine strategies for long-term follow-up and the early detection of Fontan-related sequelae. This narrative review aims to provide a comprehensive summary of the current evidence regarding the use of circulating blood biomarkers as non-invasive tools for assessing and monitoring Fontan physiology. We critically analyzed available studies investigating serum biomarkers related to key pathological mechanisms associated with Fontan failure, encompassing not only cardiac dysfunction but also systemic inflammation, endothelial dysfunction, hepatic and renal impairment, and altered bone metabolism. Several biomarkers have shown promise in reflecting global systemic impairments as well as end-organ involvement in Fontan patients. However, current data are insufficient to support evidence-based clinical recommendations for standardized specific biomarkers, mainly due to the small sample sizes, heterogeneous patient populations, and limited longitudinal data in the available studies. Only a large-scale, prospective, multi-center, and multidisciplinary research will permit us to identify a panel of specific biomarkers of clinical utility in this population. Artificial intelligence (AI) and machine learning (ML) approaches could be applied to integrate all these heterogeneous datasets. Furthermore, “omics”-based studies, including proteomics, metabolomics, lipidomics, and microRNA profiling, hold great potential for uncovering novel biomarkers and pathophysiological pathways, ultimately paving the way for precision medicine in the management of Fontan patients. Full article

In the last years, the zebrafish model has become a primary model system for vertebrate tissue regeneration, particularly for neurodegeneration and metabolic disease. Zebrafish (Danio rerio) are small freshwater teleosts valued for disease modelling, which are widely used in genetic laboratories, as a key model for studying neurodegenerative, metabolic, cardiac and dystrophic diseases, supporting the goal of identifying new therapeutic targets and approaches. Zebrafish can proliferate and produce/regenerate neurons. In response to retinal injury, zebrafish can regenerate multiple classes of retinal neurons and particularly, Müller glia-derived progenitor cells (MGPCs) can regenerate all types of neurons and restore visual function upon injury. The Jak/Stat-pathway of zebrafish retina represents one of the cell-signalling pathways involved in reprogramming Müller glia into MGPCs. In this era characterized by a revolution in experimental models and the future of omics, zebrafish might represent a suitable animal model for studying retinal degeneration and regeneration. In this context, the review is not meant to be entirely comprehensive of the zebrafish field, but it will highlight the usefulness of this model in discovering some mechanisms underlying retinal repair and regeneration. Full article

Breast cancer is one of the most frequently diagnosed malignancies and remains the leading cause of cancer-related death among women worldwide. Emerging evidence implicates the microbiota to be a potential contributor to its pathogenesis and progression. This review summarizes emerging evidence of microbial alterations across various body niches in breast cancer patients, including gut, breast tissue, nipple aspirate fluid (NAF), oral cavity, skin, urinary and reproductive tracts, and blood. Reductions in commensal taxa such as Faecalibacterium, Bifidobacterium, Lachnospira, Akkermansia, and Sphingomonas, along with an increase in pro-inflammatory genera like Prevotella, Fusobacterium, and Desulfovibrio, may promote breast tumor development and progression through multiple pathways including modulation of estrogen metabolism, production of microbial metabolites, and immunoregulation. The presence of cross-niche overlaps and possible translocation of microbiota between niches through the bloodstream suggests the existence of a complex interconnected oral–gut–breast microbiota axis. Progress in the field will depend on integrative multi-omics, translational approaches, and longitudinal studies to give a clearer mechanistic understanding of microbiota–host interactions to develop feasible microbiota-based biomarkers and therapeutic strategies in breast cancer. Full article

Exosomes are extracellular vesicles secreted by all cell types, transporting nucleic acids, proteins, lipids, and metabolites. They are known to influence tumor biology by modulating cellular proliferation, invasion, and apoptosis. In acute myeloid leukemia (AML), the precise functions of exosomes remain incompletely characterized. Here, we present an integrated multi-omics study combining single-cell RNA sequencing (scRNA-seq) of bone marrow aspirates from AML patients and healthy donors with transcriptomic profiling of purified exosomes. This approach uniquely allowed us to link cellular transcriptional states with exosome content and function. We discovered a significant upregulation of exosome-related transcriptional activity in AML cells. Purified AML exosomes showed enhanced translational, transcriptional, and metabolic activity compared to those from healthy donors. Notably, these exosomes were highly enriched in transforming growth factor-β (TGF-β), a key regulator of tumor progression. Functional assays confirmed that AML-derived exosomes promote leukemic cell proliferation and migration. Mechanistically, these effects are mediated via activation of the Smad2/3–MMP2 and ERK1/2 signaling pathways. Furthermore, cell–cell interaction analysis revealed that AML exosomes reshape the bone marrow immune microenvironment by upregulating multiple immunoregulatory genes and pathways, revealing a novel immunomodulatory role. This study provides the first integrative demonstration that TGF-β–enriched exosomes actively drive AML progression through combined enhancement of leukemic aggressiveness and immune microenvironment remodeling. Our findings highlight exosomes and their signaling cascades as promising therapeutic targets, offering new avenues for innovative AML treatments. Full article

Natriuretic peptides (NPs) have significantly improved the assessment and management of patients with heart failure (HF), but they present several limitations. It is now clear that no single biomarker can adequately guide the diagnosis, prognosis, and outcomes of HF. Therefore, the use of multiple biomarkers, correlated with HF pathophysiology, may improve HF management. An exponential number of emerging biomarkers have been reported in the literature, and when combined, they demonstrate greater clinical relevance than when used alone. They are also increasingly used as targets in the development of innovative treatments, such as targeted and personalized therapies. Their identification and clinical relevance could also be facilitated by the application of artificial intelligence models and the use of multi-omics approaches. This article presents a detailed overview of emerging biomarkers, potential targets, and innovative therapies, illustrating their advantages and limitations, as well as possible solutions to overcome them, and highlighting their strong, promising potential. This could enable the prediction of the spread of this disease in the general population, enabling early diagnosis and limiting complications and mortality. The path to achieving this goal is arduous, but it is achievable. This will require further efforts by researchers and clinicians with diverse multidisciplinary expertise. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

Asthma has traditionally been viewed as a single disease, but recent research reveals its clinical and molecular complexity. This perspective highlights the need to shift from a traditional, uniform treatment paradigm to one that embraces the heterogeneity of asthma across individuals. Each patient presents a unique clinical story shaped by a complex interplay of genetic predispositions, developmental programming during critical early-life windows, the influence of sex and hormones, and lifelong environmental exposures. Asthma comprises multiple subtypes with distinct clinical and biological features. Furthermore, lifestyle factors such as obesity and smoking, along with highly prevalent comorbidities like allergic rhinitis and gastroesophageal reflux disease, significantly modify the disease’s course and response to treatment. This article explores how classifying the disease into clinical phenotypes (observable characteristics) and molecular endotypes (underlying mechanisms)—particularly the distinction between T2-high and T2-low inflammation—provides a crucial framework for managing this complexity. The application of this framework, guided by biomarkers, has enabled the development of targeted biologic therapies that can transform care for specific patient subgroups. Despite these advances, significant challenges remain. The pathophysiology of certain subgroups, particularly non-T2 asthma, remains poorly defined, and there is an urgent need for reliable predictive biomarkers to guide therapy and monitor outcomes. It is our opinion that future studies must adopt a systems-biology strategy, with a multi-omics approach that constructs a comprehensive molecular profile of each patient. This integrative methodology will require the use of advanced computational methods, including machine learning and artificial intelligence, to decipher the complex pathways linking genetic and environmental inputs to clinical disease. In conclusion, this article argues for a more personalized understanding of asthma, urging clinicians and researchers to consider each patient’s unique clinical presentation. Full article