Search Results (1,951)

HER2-positive breast cancer represents a biologically aggressive subtype associate with poor prognosis, despite advances in targeted therapies. Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i), initially approved for hormone-receptor-positive, HER2-negative disease, are now being explored in HER2-positive settings due to their mechanistic synergy with the HER2 signaling pathway. This review synthesizes evolving clinical evidence from trials and highlights further research into biomarker discovery. CDK4/6i may redefine treatment paradigms in HER2-positive breast cancer, offering a potential, non-chemotherapy option with durable benefit in select patient populations. Full article

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. Full article

This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy—though validation in BTC-specific cohorts remains essential—accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7–10 years, contingent on successful completion of validation studies addressing current evidence gaps. Full article

Cellular senescence is a fundamental hallmark of aging, contributing to tissue dysfunction and chronic disease through the senescence-associated secretory phenotype (SASP). The SASP encompasses a diverse and dynamic collection of secreted cytokines, chemokines, growth factors, and proteases that vary depending on cell type, senescence trigger, and microenvironmental context. Accurate quantification of SASP components is critical to understanding the mechanisms linking senescence to pathology and for advancing senotherapeutic strategies. However, measuring the SASP presents significant technical and biological challenges due to its complexity, heterogeneity, and context dependence. This review provides a comprehensive overview of the principal methodologies used to measure SASP components across different biological levels—transcriptional, translational, and functional—and sample types, including cell cultures, tissues, and systemic fluids. We discuss the advantages and limitations of widely used RNA-level techniques (e.g., qRT-PCR, RNA sequencing, in situ hybridization), protein-level assays (e.g., ELISA, Western blotting, mass spectrometry, Luminex, MSD), and spatial detection methods (e.g., immunohistochemistry, immunofluorescence). By organizing current SASP detection strategies by molecular level and sample source, this review highlights the importance of multiparametric approaches to capture the full spectrum of senescent cell activity. We also identify key methodological gaps and propose directions for refining SASP biomarker discovery in aging and disease research. Full article

In recent years, the bidirectional regulatory mechanism of the bone-brain axis has become a hotspot for interdisciplinary research. In this paper, we systematically review the anatomical and functional links between bone and the central nervous system, focusing on the regulation of brain function by bone-derived signals and their clinical translational potential. At the anatomical level, the blood–brain barrier permeability mechanism and the unique structure of the periventricular organs establish the anatomical basis for bone-brain information transmission. Innovative discoveries indicate that the bone cell network (bone marrow mesenchymal stem cells, osteoblasts, osteoclasts, and bone marrow monocytes) directly regulates neuroplasticity and the inflammatory microenvironment through the secretion of factors such as osteocalcin, lipid transporter protein 2, nuclear factor κB receptor-activating factor ligand, and fibroblast growth factor 23, as well as exosome-mediated remote signaling. Clinical studies have revealed a bidirectional vicious cycle between osteoporosis and Alzheimer’s disease: reduced bone density exacerbates Alzheimer’s disease pathology through pathways such as PDGF-BB, while AD-related neurodegeneration further accelerates bone loss. The breakthrough lies in the discovery that anti-osteoporotic drugs, such as bisphosphonates, improve cognitive function. In contrast, neuroactive drugs modulate bone metabolism, providing new strategies for the treatment of comorbid conditions. Additionally, whole-body vibration therapy shows potential for non-pharmacological interventions by modulating bone-brain interactions through the mechano-osteoclast signaling axis. In the future, it will be essential to integrate multiple groups of biomarkers to develop early diagnostic tools that promote precise prevention and treatment of bone-brain comorbidities. This article provides a new perspective on the mechanisms and therapeutic strategies of neuroskeletal comorbidities. Full article

Coronary artery disease (CAD) is a leading cause of death worldwide, encompassing a broad spectrum of pathological conditions ranging from chronic to acute coronary syndromes. It underlies complex biological mechanisms, among which an emerging role is played by extracellular vesicles (EVs). EVs are non-replicable cell-derived particles enclosed by lipid bilayers acting as mediators of cellular interactions. In the past two decades, there has been a growing interest in EVs as potential diagnostic, prognostic and therapeutic tools in cardiovascular disease. We reviewed the most recent studies on circulating EVs in CAD with a particular focus on their role in biomarker discovery. Our aim was to evaluate the feasibility of translating these findings into routine clinical practice. To this end, we underlie the development and application of integrated indicators, referred to as “Bioscores”, which combine clinical, laboratory, and molecular data to enhance diagnostic and prognostic accuracy. We briefly discuss the opportunity and pitfalls related to the emerging use of Machine Learning (ML) algorithms. Moreover, we highlight that further investigation of mechanistic pathways is required beyond the initially predicted associations generated by in silico studies. Finally, we analyzed the key limitations, challenges, and unmet needs in the field, including small and unrepresentative sample sizes, a lack of external validation, overlapping and often contradictory effects on targeted pathways, difficulties in standardizing EV isolation and characterization methods, as well as concerns regarding affordability and clinical reliability. Full article

Background: Type 2 diabetes (T2D), the most common form of diabetes, is associated with a significantly elevated risk of cardiovascular and cerebrovascular complications. However, circulating metabolic signatures that reliably predict the transition to insulin resistance, and are potentially linked to increased vascular risk, remain incompletely characterized. Rodent models, particularly those induced by a high-fat diet (HFD) combined with low-dose streptozotocin (STZ), are widely used to study the progression of T2D. However, the systemic metabolic shifts associated with this model, especially at the plasma level, are poorly defined. Methods: In this study, we performed untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomic profiling on plasma samples from control, HFD-only (obese, insulin-sensitive), and HFD + STZ (obese, insulin-resistant) C57BL/6 mice. Results: In the HFD + STZ cohort, plasma profiles showed a global shift toward lipid classes; depletion of aromatic and branched-chain amino acids (BCAAs); accumulation of phenylalanine-derived co-metabolites, consistent with gut–liver axis dysregulation; elevations in glucose, fructose-6-phosphate, and nucleoside catabolites, indicating impaired glucose handling and heightened nucleotide turnover; increased free fatty acids, reflecting membrane remodeling and lipotoxic stress; and higher cAMP, thyroxine, hydrocortisone, and uric acid, consistent with endocrine and redox imbalance. By contrast, HFD-only mice exhibited elevations in aromatic amino acids and BCAAs relative to controls, a pattern compatible with early obesity-associated adaptation while insulin signaling remained partially preserved. KEGG analysis revealed disturbances in carbohydrate metabolism, amino acid degradation, nucleotide turnover, and hormone-related pathways, and HMDB mapping linked these changes to T2D, obesity, heart failure, and renal dysfunction. Conclusion: Collectively, these findings delineate insulin resistance-specific plasma signatures of metabolic inflexibility and inflammatory stress in the HFD + STZ model, distinguishing it from HFD alone and supporting its utility for mechanistic studies and biomarker discovery. Importantly, this plasma metabolomics study shows that insulin-sensitive and insulin-resistant states exhibit distinct variation in circulating metabolites and cardiovascular risk factors, underscoring the translational value of plasma profiling. Full article

Background/Objectives: Cocaine is a widely used illicit stimulant with significant toxicity. Despite its clinical relevance, the broader metabolic alterations associated with cocaine use remain incompletely characterized. This study aims to identify novel biomarkers for cocaine exposure by applying untargeted metabolomics to retrospective urine drug screening data. Methods: We conducted a retrospective analysis of a raw mass spectrometry (MS) dataset from urine comprehensive drug screening (UCDS) from 363 patients at the University of Pittsburgh Medical Center Clinical Toxicology Laboratory. The liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-qToF-MS) data were preprocessed with MS-DIAL and subjected to multiple statistical analyses to identify features significantly associated with cocaine-enzyme immunoassay (EIA) results. Significant features were further evaluated using MS-FINDER for feature annotation. Results: Among 14,883 features, 262 were significantly associated with cocaine-EIA results. A subset of 37 more significant features, including known cocaine metabolites and impurities, nicotine metabolites, norfentanyl, and a tryptophan-related metabolite (3-hydroxy-tryptophan), was annotated. Cluster analysis revealed co-varying features, including parent compounds, metabolites, and related ion species. Conclusions: Features associated with cocaine exposure, including previously underrecognized cocaine metabolites and impurities, co-exposure markers, and alterations in an endogenous metabolic pathway, were identified. Notably, norfentanyl was found to be significantly associated with cocaine -EIA, reflecting current trends in illicit drug use. This study highlights the potential of repurposing real-world clinical toxicology data for biomarker discovery, providing a valuable approach to identifying exposure biomarkers and expanding our understanding of drug-induced metabolic disturbances in clinical toxicology. Further validation and exploration using complementary analytical platforms are warranted. Full article

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation. Full article

Glycosylation, the enzymatic addition of glycans to proteins and lipids, is a critical post-translational modification that influences protein folding, stability, trafficking, immune modulation, and cell signaling. The vast structural diversity of glycans arising from differences in monosaccharide composition, branching, and terminal modifications such as sialylation, fucosylation, and sulfation underpins their functional specificity and regulatory capacity. This review provides a comprehensive overview of glycan biosynthesis, with a focus on N-glycans, O-glycans, glycosaminoglycans (GAGs), and glycolipids. It explores their essential roles in maintaining cellular homeostasis, development, and immune surveillance. In health, glycans mediate cell–cell communication, protein interactions, and immune responses. In disease, however, aberrant glycosylation is increasingly recognized as a hallmark of numerous pathological conditions, including cancer, neurodegenerative disorders, autoimmune diseases, and a wide range of infectious diseases. Glycomic alterations contribute to tumor progression, immune evasion, therapy resistance, neuroinflammation, and synaptic dysfunction. Tumor-associated carbohydrate antigens (TACAs) and disease-specific glycoforms present novel opportunities for biomarker discovery and therapeutic targeting. Moreover, glycan-mediated host–pathogen interactions are central to microbial adhesion, immune escape, and virulence. This review highlights current advances in glycomics technologies, including mass spectrometry, lectin microarrays, and glycoengineering, which have enabled the high-resolution profiling of the glycome. It also highlights the emerging potential of single-cell glycomics and multi-omics integration in precision medicine. Understanding glycome and its dynamic regulation is essential for uncovering the molecular mechanisms of disease and translating glycomic insights into innovative diagnostic and therapeutic strategies. Full article

Over the past decade, patient-derived organoids (PDOs) have emerged as powerful in vitro models that closely recapitulate the histological, genetic, and functional features of their parental primary tissues, representing a ground-breaking tool for cancer research and precision medicine. This advancement has led to the development of living PDO biobanks, collections of organoids derived from a wide range of tumor types and patient populations, which serve as essential platforms for drug screening, biomarker discovery, and functional genomics. The classification and global distribution of these biobanks reflect a growing international effort to standardize protocols and broaden accessibility, supporting both basic and translational research. While their relevance to personalized medicine is increasingly recognized, the establishment and maintenance of PDO biobanks remain technically demanding, particularly in terms of optimizing long-term culture conditions, preserving sample viability, and mimicking the tumor microenvironment. In this context, this review provides an overview of the classification and worldwide distribution of tumor and paired healthy tissue-specific PDO biobanks, explores their translational applications, highlights recent advances in culture systems and media formulations, and discusses current challenges and future perspectives for their integration into clinical practice. Full article

Amyotrophic lateral sclerosis (ALS) is still a heterogeneous neurodegenerative disorder that can be identified clinically and biologically, without a strong set of biomarkers that can adequately measure its fast rate of progression and molecular heterogeneity. In this review, we intend to consolidate the most relevant and timely advances in ALS biomarker discovery, in order to begin to bring molecular, imaging, genetic, and digital areas together for potential integration into a precision medicine approach to ALS. Our goal is to begin to display how several biomarkers in development (e.g., neurofilament light chain (NfL), phosphorylated neurofilament heavy chain (pNfH), TDP-43 aggregates, mitochondrial stress markers, inflammatory markers, etc.) are changing our understanding of ALS and ALS dynamics. We will attempt to provide a framework for thinking about biomarkers in a systematic way where our candidates are not signals alone but part of a tethered pathophysiological cascade. We are particularly interested in the fast progressor phenotype, a devastating and under-characterized subset of ALS due to a rapid axonal degeneration, early respiratory failure, and very short life span. We will try to highlight the salient molecular features of this ALS subtype, including SOD1 A5V toxicity, C9orf72 repeats, FUS variants, mitochondrial collapse, and impaired autophagy mechanisms, and relate these features to measurable blood and CSF (biomarkers) and imaging platforms. We will elaborate on several interesting tools, for example, single-cell transcriptomics, CSF exosomal cargo analysis, MRI techniques, and wearable sensor outputs that are developing into high-resolution windows of disease progression and onset. Instead of providing a static catalog, we plan on providing a conceptual roadmap to integrate biomarker panels that will allow for earlier diagnosis, real-time disease monitoring, and adaptive therapeutic trial design. We hope this synthesis will make a meaningful contribution to the shift from observational neurology to proactive biologically informed clinical care in ALS. Although there are still considerable obstacles to overcome, the intersection of a precise molecular or genetic association approach, digital phenotyping, and systems-level understandings may ultimately redefine how we monitor, care for, and treat this challenging neurodegenerative disease. Full article

Mistletoe (Viscum album L.) has been used in complementary cancer therapy for decades, but its mechanisms remained poorly understood until recently. This review synthesizes transformative advances in mistletoe cancer research from 2020 to 2025, focusing on newly discovered molecular mechanisms, immunomodulatory properties, and clinical applications. We conducted a comprehensive analysis of controlled studies, mechanistic investigations, and real-world evidence published between 2020 and 2025. The discovery of mistletoe-induced immunogenic cell death (ICD) represents a paradigm shift in understanding its anticancer effects. Mistletoe extracts trigger endoplasmic reticulum stress, leading to calreticulin exposure in 18–51% of cancer cells and a 7-fold increase in adenosine triphosphate (ATP) release. Three-dimensional culture models revealed enhanced macrophage reprogramming effects, with a 15.8% increase in pro-inflammatory interleukin (IL)-6 and a 26.4% reduction in immunosuppressive IL-10. Real-world evidence from over 400 non-small-cell lung cancer patients shows that combining mistletoe with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors doubles median overall survival (6.8 to 13.8 months), with biomarker-selected populations experiencing up to a 91.2% reduction in death risk. The Johns Hopkins Phase I trial established intravenous administration safety at 600 mg three times weekly. Advanced analytical approaches including metabolomics, chronobiology, and machine learning are enabling precision medicine applications. These findings position mistletoe as a scientifically validated component of integrative oncology, bridging traditional medicine with evidence-based cancer care. Future research should focus on ferroptosis mechanisms, single-cell immune profiling, and standardized clinical protocols. 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

(1) Background: Following the discovery of the adipokine/hormone asprosin, a substantial amount of research has provided evidence for its role in the regulation of glucose homeostasis, as well as appetite, and insulin sensitivity. Its levels are dysregulated in certain disease states, including breast cancer. To date, little is known about its role in endometrial cancer (EC). The present study investigated the effects of asprosin on the transcriptome of the Ishikawa and NOU-1 EC cell lines, and assessed the expression of asprosin’s candidate receptors (TLR4, PTPRD, and OR4M1) in health and disease. (2) Methods: tissue culture, RNA extraction, RNA sequencing, reverse transcription-quantitative PCR, gene enrichment and in silico analyses were used for this study. (3) Results: TLR4 and PTPRD were significantly downregulated in EC when compared to healthy controls. TLR4 appeared to have a prognostic role in terms of overall survival (OS) in EC patients (i.e., higher expression, better OS). RNA sequencing revealed that asprosin affected 289 differentially expressed genes (DEGs) in Ishikawa cells and 307 DEGs in NOU-1 cells. Pathway enrichment included apoptosis, glycolysis, hypoxia, and PI3K/AKT/ mTOR/NOTCH signalling for Ishikawa-treated cells. In NOU-1, enriched processes included inflammatory response, epithelial-mesenchymal transition, reactive oxygen species pathways, and interferon gamma responses. Other signalling pathways included mTORC1, DNA repair, and p53, amongst others. (4) Conclusions: These findings underscore the importance of understanding receptor dynamics and signalling pathways in the context of asprosin’s role in EC, and provide evidence for a potential role of TLR4 as a diagnostic biomarker. Full article