Search Results (5,977)

Biotinidase deficiency is an autosomal recessive disorder that disrupts biotin recycling and multiple carboxylase-dependent pathways. Early and continuous biotin therapy prevents major clinical manifestations, but its long-term biochemical effects remain unclear. This study applied untargeted metabolomic and lipidomic profiling in 54 pediatric patients with genetically confirmed BD receiving regular biotin supplementation and 30 age- and sex-matched controls. Multivariate analyses and pathway enrichment revealed distinct biochemical signatures involving amino acid, energy, and lipid metabolism. Reduced levels of serine, glycine, threonine, and tricarboxylic acid cycle intermediates suggested modified mitochondrial flux, while octopine, exhibiting an approximately 11-fold increase, was the metabolite best able to discriminate between the groups. Lipidomic profiling indicated elevations in sphingolipids, phosphatidylcholines, long-chain fatty acids, and acylcarnitines, consistent with systemic lipid remodeling. These coordinated alterations imply metabolic adaptations to sustained biotin exposure rather than ongoing pathology. Octopine and selected lipid species may represent biochemical indicators of this adaptive state. Overall, the findings highlight that clinically stable children with Biotinidase deficiency exhibit unique metabolic and lipidomic patterns reflecting long-term compensatory mechanisms, underscoring the value of combined omics approaches for understanding disease-specific homeostasis and informing personalized follow-up strategies. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

Mass spectrometry imaging (MSI) is an innovative analytical technique that integrates chemical analysis with spatial localization, enabling label-free, in situ detection and visualization of diverse biomolecules within tissue sections. This review summarizes the recent advances in the application of MSI to neurological disorders, with a focus on Parkinson’s disease, Alzheimer’s disease, schizophrenia, and traumatic brain injury. Studies have demonstrated that MSI can delineate the spatial heterogeneity of disease-related molecules—such as neurotransmitters, lipids, and metabolites—thereby providing new perspectives for understanding the pathological mechanisms of neurodegenerative and psychiatric diseases. Platforms including MALDI-MSI and DESI-MSI have been effectively employed for visualizing drug distribution, characterizing lipid metabolic pathways, and identifying spatial biomarkers. Although challenges remain in quantitative accuracy, spatial resolution, and the detection of low-abundance molecules, advances in high-resolution mass spectrometry, single-cell-level imaging, and multi-omics integration are expected to further enhance the utility of MSI in the investigation of brain diseases. Full article

Hydroxyprogesterone (HP) is a synthetic progestogen widely used in obstetric care, and its potential influence on breast cancer biology has become an emerging area of interest. Despite its clinical use, the molecular mechanisms by which HP affects tumor tissue remain insufficiently explored. In this study, transcriptomic profiling was performed to investigate gene expression changes associated with HP in operable breast cancer. Pre-operative 17α-HP caproate (17-OHPC) exposure was associated, in normal adjacent tissue (NAT), with activation of steroid-hormone and lipid/xenobiotic-metabolism programs and crosstalk to phosphoinositide 3-kinase (PI3K)–Akt and nuclear factor kappa B (NF-κB). In NAT, these pathways showed the largest absolute log₂ fold-change (|log₂FC|); significance is reported as false discovery rate (FDR) throughout (e.g., FKBP5↑ with HP). In tumor tissue, the dominant signal reflected tight-junction/apical-junction and extracellular matrix (ECM)-receptor remodeling (e.g., CLDN4↑). We prioritized FKBP5 (HP pharmacodynamics) and CLDN4 (tumor baseline) as the main candidates; TSPO and SGK1 are reported as exploratory. This discovery-level, hypothesis-generating analysis nominates candidate biomarkers and pathway signals for prioritization and evaluation in independent datasets and future studies. These findings provide mechanistic insight into HP’s molecular effects in breast cancer and suggest potential applications in biomarker perioperative management. Full article

Background: Multimodal AI integration across genomics, radiomics, and pathomics is rapidly evolving in oncology, but evidence remains heterogeneous and unevenly distributed across modalities. Objective: To map empirical studies integrating two or more -omic modalities, summarize integration and validation approaches, and identify gaps informing future directions toward surgomics. Methods: We conducted a scoping review in accordance with PRISMA-ScR, searching PubMed, Ovid, Wiley Online Library, and Google Scholar for English-language studies published from January 2020 to 5 March 2025. We charted study characteristics, modalities combined, fusion strategies, AI model categories, validation approaches, and reported performance metrics as presented by the original studies. Results: From 184 records, 11 studies met inclusion criteria (n = 1078 total participants across reported studies), most focusing on radiomics–pathomics integration; fewer incorporated genomics, and tri-modal fusion was uncommon. Studies varied widely in clinical tasks, endpoints, preprocessing, and validation, limiting direct comparability. Conclusions: The mapped evidence indicates growing methodological activity in radiopathomics and cross-scale association modeling, while tri-modal pipelines and clinically deployable multimodal workflows remain underdeveloped. Surgomics is presented as a conceptual, staged roadmap informed by these gaps rather than a current clinical capability. Full article

Despite a thorough understanding of the structure of human papillomavirus (HPV) and its genotypic variations (high-risk and low-risk variants), the mechanisms underlying HPV-induced cervical cancer (CC) pathogenesis and the molecular signatures of drug resistance remain to be fully understood. Accumulating evidence has shown the involvement of kinase targets in the induction of drug resistance in high-risk (HR) HPV-CC. Molecularly, the genome of high-risk HPV is reported to control the expression of host kinases. In particular, Aurora kinases A, B, and C (ARKA, ARKB, and ARKC), phosphotidylinositol–trisphosphate kinase (PI3K)-Akt, and Glycogen synthase kinase3-α/β (GSK3 α/β) promote the transformation of infected cells, and also enhance the resistance of cells to various chemotherapeutic agents such as nelfinavir and cisplatin. However, the precise mechanisms through which HPV activates these kinases are yet to be fully elucidated. Furthermore, there is still ambiguity surrounding whether targeting HPV-induced kinases along with HPV-targeted therapies (such as phytopharmaceuticals and PROTAC/CRISPR-CAS-based systems) synergistically inhibit cervical tumor growth. Given the critical role of kinases in the pathogenesis and treatment of CC, a comprehensive review of current evidence is warranted. This review aims to provide key insights into the mechanisms of HPV-induced CC development, the involvement of kinases in drug resistance induction, and the rationale for combination therapies to improve clinical outcomes. Full article

Plant-based fermented foods are increasingly promoted for glycemic control, yet their mechanisms and clinical impact remain incompletely defined. This narrative review synthesizes mechanistic, preclinical, and human data for key matrices—kimchi and other fermented vegetables, tempeh/miso/natto, and related legume ferments, kombucha and fermented teas, plant-based kefir, and cereal/pulse sourdoughs. Across these systems, microbial β-glucosidases, esterases, tannases, and phenolic-acid decarboxylases remodel polyphenols toward more bioaccessible aglycones and phenolic acids, while lactic and acetic fermentations generate organic acids, exopolysaccharides, bacterial cellulose, γ-polyglutamic acid, γ-aminobutyric acid, and bioactive peptides. We map these postbiotic signatures onto proximal mechanisms—α-amylase/α-glucosidase inhibition, viscosity-driven slowing of starch digestion, gastric emptying and incretin signaling, intestinal-barrier reinforcement, and microbiota-dependent short-chain–fatty-acid and bile-acid pathways—and their downstream effects on AMPK/Nrf2 signaling and the gut–liver axis. Animal models consistently show improved glucose tolerance, insulin sensitivity, and hepatic steatosis under fermented vs. non-fermented diets. In humans, however, glycemic effects are modest and highly context-dependent: The most robust signal is early postprandial attenuation with γ-PGA-rich natto, strongly acidified or low-glycemic sourdough breads, and selected kombucha formulations, particularly in individuals with impaired glucose regulation. We identify major sources of heterogeneity (starters, process parameters, substrates, background diet) and safety considerations (sodium, ethanol, gastrointestinal symptoms) and propose minimum reporting standards and trial designs integrating metabolomics, microbiome, and host-omics. Overall, plant-based ferments appear best positioned as adjuncts within cardiometabolic dietary patterns and as candidates for “purpose-built” postbiotic products targeting early glycemic excursions and broader metabolic risk. Full article

The biomarker landscape in rheumatoid arthritis (RA) is evolving from reliance on traditional markers toward integrated, multimodal strategies enabling precision medicine approaches. To critically evaluate emerging biomarkers across serological, cellular, genetic, imaging, and multi-omic domains, distinguishing those approaching clinical readiness from those requiring further development. In this study, a narrative review of the literature published between 2000 and 2024 relevant to clinical decision-making in RA was conducted. Among novel serological markers, 14-3-3η protein and anti-carbamylated protein antibodies show the strongest validation for seronegative disease and prognostic stratification. Calprotectin demonstrates utility for disease activity monitoring and de-escalation decisions. Multi-biomarker disease activity scores provide an objective assessment but lack outcome trial validation. Musculoskeletal ultrasound offers accessible imaging biomarker capability, while MRI bone marrow edema remains the strongest structural progression predictor. Synovial tissue pathotyping has demonstrated proof-of-concept for treatment stratification. Genetic, epigenetic, and metabolomic approaches remain investigational. Key clinical implications include using 14-3-3η and calprotectin to inform seronegative diagnosis and de-escalation decisions, integrating ultrasound for remission verification, and recognizing that emerging biomarkers for extra-articular complications, including cardiovascular risk and venous thromboembolism, represent important unmet needs. Full article

Tylorrhynchus heterochaetus is an aquatic food with both edible and medicinal value in China. With a protein-rich body wall, it has strong potential for producing bioactive peptides. To explore its potential as a source of immunomodulatory peptides, in this study, flavor enzymes were selected as the optimal hydrolases, and the hydrolyzed products were subjected to ultrafiltration fractionation. The <3000 Da portion exhibited the most effective immune-stimulating activity in RAW 264.7 macrophages, enhancing phagocytosis and promoting the secretion of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and nitric oxide (NO) in a concentration dependent manner. Peptide omics analysis, combined with the activity and safety screened by bioinformatics, identified 43 candidate peptides. Molecular docking predicts that three novel peptides, LPWDPL, DDFVFLR and LPVGPLFN, exhibit strong binding affinity with toll-like receptor 4/myeloid differentiation factor-2 (TLR4/MD-2) receptors through hydrogen bonding and hydrophobic/π stacking interactions. Synthetic verification confirmed that these peptides were not only non-toxic to cells at concentrations ranging from 62.5 to 1000 µg/mL, but also effective in activating macrophages and stimulating the release of immune mediators. This study successfully identified the specific immunomodulatory peptides of the Tylorrhynchus heterochaetus, supporting its high-value utilization as a natural source of raw materials for immunomodulatory functional foods. Full article

Background: Immune checkpoint inhibitors (ICIs), such as anti-programmed death (PD)-1 and anti-cytotoxic T-lymphocyte-associated protein (CTLA)-4 agents, have revolutionized oncology but are associated with immune-related adverse events (irAEs). Among these, ICI-associated myocarditis (ICI-M) is a rare but life-threatening complication, with mortality rates ranging from 27% to 50%. Objective: This narrative review summarizes the pathogenesis, epidemiology, clinical presentation, diagnostic methods, and management strategies for ICI-induced myocarditis, specifically highlighting emerging biomarkers and immunosuppressive therapeutic approaches. Results and Discussion: ICI-M typically presents within the first 65 days of treatment and is significantly more frequent with combination therapies. Pathologically, it is characterized by myocyte necrosis and massive infiltration of cluster of differentiation (CD)4+ and CD8+ T-cells, often overlapping with myositis (irM/M). Diagnosis relies on a multimodal approach. Management requires immediate ICI cessation and initiation of high-dose corticosteroids as first-line therapy. For steroid-refractory cases, second-line options include mycophenolate mofetil (MMF), intravenous immunoglobulin (IVIG), and emerging therapies like abatacept and ruxolitinib. Rechallenge with ICIs after high-grade ICI-M must be approached with extreme caution by the multidisciplinary team (MDT). Emerging biomarkers and omics techniques hold promise for earlier diagnosis and risk stratification. Conclusions: ICI-M is a rare yet highly lethal cardiac complication demanding high clinical vigilance and timely diagnosis. Management hinges on an aggressive multidisciplinary approach, aiming to minimize toxicity while balancing oncological efficacy. Full article

Drought stress severely restricts plant growth and substantially reduces crop productivity. Although drought-response mechanisms have been extensively characterized within individual plant species, the conserved metabolic strategies shared across species remain insufficiently understood. To elucidate both conserved and species-specific metabolic mechanisms underlying drought adaptation, we performed an integrated transcriptomic and metabolomic analysis in rice, maize, and tomato. Profiling of 543 annotated metabolites revealed strikingly divergent baseline metabolic landscapes: tomato leaves were enriched in triglycerides and anthocyanins, whereas maize and rice accumulated higher levels of glycerophospholipids, tricin-derived flavonoids, and B vitamins. Under drought conditions, these differences were further reflected in the distinct sets of differentially accumulated metabolites (DAMs) detected in tomato (121), rice (98), and maize (94). Despite these species-specific signatures, we identified a conserved drought-responsive module consisting of five phenolamides that were consistently induced across all three species. Reconstruction of the associated regulatory network uncovered divergent enzymatic control strategies governing phenolamide biosynthesis: the drought-induced BAHD acyltransferases OsPHT4 in rice and SlPHT3 in tomato exhibited broad-spectrum catalytic activities, whereas the maize homolog ZmPHT4 fulfilled a similar biosynthetic role through constitutive, non-drought-inducible activity. Together, this study provides a comprehensive metabolic framework for plant drought response and demonstrates that extensive species-specific metabolic architectures and transcriptional regulatory divergence coexist beneath a conserved core metabolomic response, offering promising targets for the precise genetic enhancement of crop drought tolerance. Full article

In recent years, the development of new diagnostic technologies, such as tandem mass spectrometry (MS/MS) and next-generation sequencing (NGS), has caused a veritable revolution in the diagnosis of genetic diseases, reducing time, cost, and invasiveness associated with prior diagnostic techniques. While MS/MS laid the foundation for the development of numerous, usually institutionally based, neonatal screening programs, NGS has gained traction in newborn screening (NBS), primarily through pilot projects and private funding across different countries. As a result, the traditional Wilson and Jungner criteria have been supplemented by additional criteria, including considerations of equity and access, in response to emerging technologies. This review aims to provide an up-to-date overview of the global landscape of metabolic screening panels, highlight the major ongoing genomic screening projects, and outline the current models for integrating these two screening systems. Substantial differences exist across countries in the numbers and types of diseases included in national NBS programmes. In this context, Italy represents a prominent case, as its neonatal screening framework has seen significant expansion and development in recent years, reaching a particularly comprehensive metabolic screening panel. Nonetheless, a number of initiatives to incorporate genomic technologies into the NBS pathway are currently underway, primarily involving high-income countries. Nonetheless, unlike metabolomic-based NBS programs, no country has a government-mandated NGS program as first-tier testing for newborns. New evidence is emerging from ongoing models of integration of multi-omics approaches into NBS, including the use of AI and machine learning. Identifying the most appropriate system for this integration to reduce the false-positive and false-negative rates associated with both screening types, ensure more equitable access to screening, and facilitate faster access to treatment may represent a useful and foresightful way to conceptualize NBS in the future. This transitional phase should promote rigorous improvements before full-scale adoption. Full article

Gastrointestinal nematode (GIN) infections are the most prevalent parasitic diseases in grazing sheep worldwide, causing significant productivity losses, high mortality and, as a result, economic losses and emerging animal welfare concerns. Conventional control strategies, primarily relying on anthelmintic treatments, face limitations due to rising drug resistance and environmental concerns, underscoring the need for sustainable alternatives. Selective breeding for host genetic resistance has emerged as a promising strategy, while recent advances in transcriptomics and integrative omics research are providing deeper insights into the immune pathways and molecular and genetic mechanisms that underpin host–parasite interactions. This review summarizes current evidence on transcriptomic signatures associated with resistance and susceptibility to H. contortus and T. circumcincta GIN infections, highlighting candidate genes, functional genetic markers, key immune pathways, and regulatory networks. Furthermore, we discuss how other omics approaches, including genomics, proteomics, metabolomics, microbiome, and multi-omics integrations, provide perspectives that enhance the understanding of the complexity of the GIN resistance trait. Transcriptomic studies, particularly using RNA-Sequencing technology, have revealed differential gene expression, functional genetic variants, such as SNPs and INDELs, in expressed regions and splice junctions, and regulatory long non-coding RNAs that distinguish resistance from susceptible sheep, highlighting pathways related to Th2 immunity, antigen presentation, tissue repair, and stress signaling. Genomic analyses have identified SNPs, QTL, and candidate genes linked to immune regulation and parasite resistance. Proteomic and metabolomic profiling further elucidates breed- and tissue-specific alterations in protein abundance and metabolic pathways, while microbiome studies demonstrate distinct microbial signatures in resistant sheep, suggesting a role in modulating host immunity. In conclusion, emerging multi-omics approaches and their integration strategies provide a comprehensive framework for understanding the complex host–parasite interactions that govern GIN resistance, offering potential candidate biomarkers for genomic selection and breeding programs aimed at developing sustainable, parasite-resistant sheep populations. Full article

Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R² = 0.961; Q² = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks. Full article

Extracellular vesicles (EVs) are emerging as key factors in maintaining cellular homeostasis, critical mediators of intercellular communication, potential biomarkers, and therapeutic tools. While small EVs have been extensively characterized, the molecular signatures of large EVs (including those generated during regulated cell death pathways) remain poorly defined. Here, we investigated the characteristics of large EVs released during apoptosis and pyroptosis by human monocytic cell lines (THP-1 and U937). Apoptosis was induced by staurosporine and blocked using the pan-caspase inhibitor Q-VD-OPh, whereas pyroptosis was triggered by LPS/nigericin and inhibited with a selective NLRP3 inhibitor. We found that both forms of regulated cell death markedly enhanced the release of large EVs. Both apoptotic and pyroptotic large EVs showed increased Annexin V binding and decreased CD9 expression compared with those released by healthy cells. Large EVs derived from apoptotic and pyroptotic cells exhibited distinct proteomic profiles. Pyroptotic large EVs carried interacting protein networks of RNA-binding proteins and chromatin-associated proteins many of which are known damage-associated molecular patterns or alarmins. In contrast, we found that a subpopulation of apoptotic large EVs was characterized by the presence of dsDNA, and active caspase-3/7. Together, our data shed light on the specific protein cargo of large EVs released by cells during apoptosis and pyroptosis. This study identifies candidate markers of large EVs released by dying cells and may enhance our understanding of the role of EVs in regulated cell death. Full article