Search Results (5,021)

Dopamine (DA) is a crucial catecholamine neurotransmitter, and its abnormal levels are closely associated with neurological disorders such as Parkinson’s disease. Electrochemical sensing technology offers a rapid and cost-effective platform for DA detection; however, it often suffers from interference from coexisting biomolecules such as ascorbic acid (AA) and uric acid (UA). In this study, we report a novel electrochemical biosensor based on PdMo bimetallene, a nanomaterial synthesized via a facile wet-chemical approach, aiming to enhance the detection performance and selectivity for DA. PdMo bimetallene is a highly curved, atomically thin two-dimensional nanosheet featuring abundant strained sites and a high density of active centers, enabling the selective and sensitive detection of DA. The results demonstrate that the as-prepared PdMo bimetallene-modified glassy carbon electrode (GCE) exhibits excellent electrocatalytic activity toward the oxidation of DA. The sensor displays a good linear response over the concentration range from 10 nM to 200 µM, with an ultrahigh sensitivity of 80 µA·µM⁻¹ cm⁻² and a low detection limit of 0.14 µM (S/N = 3). Owing to the synergistic electronic effect between Pd and Mo, the high density of exposed active sites, and the unique strained lattice structure of the bimetallene, the sensor enables accurate determination of DA concentrations even in the presence of interfering species such as AA and UA. In summary, the successfully fabricated PdMo bimetallene-based sensor offers the advantages of low cost, facile synthesis, a wide linear range, and high sensitivity, positioning it as a promising candidate for neurotransmitter detection applications. Full article

The valorization of agri-food residues represents an attractive strategy within the circular economy for the development of bio-based materials. In this study, a PLA–cellulose biocomposite (PLACEL10) was developed using cellulose extracted from vine pruning residues (Vitis vinifera, Tintilla de Rota). Cellulose was isolated through sequential acid and alkaline treatments, and the extracted material was incorporated into PLA by melt blending to produce injection-molded specimens. FT-IR confirmed the progressive removal of non-cellulosic components during extraction, while SEM revealed a relatively homogeneous dispersion of cellulose within the polymer matrix. Mechanical characterization showed that PLACEL10 exhibited higher stiffness and tensile strength than the processed PLA and BCF10 controls, although with reduced elongation at break. Biocompatibility was evaluated using hFOB 1.19 osteoblasts by MTS assay, showing viability values above 95% and a proliferative response at 72 h. These results suggest that vine-pruning-derived cellulose can act as an effective reinforcement in PLA and support the potential of this agricultural residue as a feedstock for bio-based composites with possible biomedical and packaging applications. Although the current extraction route involves chemical treatments and cannot be considered fully green, the approach provides a promising route for agricultural waste valorization. Full article

This study uses a bismuth ferrite (BiFeO₃) and MXene (Ti₃C₂Tx) to design a surface plasmon resonance (SPR) biosensor for the sensitivity enhancement at a 633 nm wavelength. Here, MXene serves as a biorecognition element (BRE) layer to ensure stable and reliable biomolecule adsorption. The MXene is a family of two-dimensional (2D) materials with metallic-like conductivity, a large surface area that can attach biomolecules, and improve biocompatibility. The addition of a conductive 2D MXene layer and a high-index BiFeO₃ dielectric layer greatly improves light–matter interaction and evanescent field penetration at the sensing interface. Strong plasmonic coupling is indicated by the reflectance analysis, which shows a distinct and consistent shift in the resonance angle as analyte RI increases. This study examined the sensitivity at optimized Ag and BiFeO₃ layer thickness. At an Ag of 39 nm and BiFeO₃ of 3 nm thickness, the maximal sensitivity of 340.68°/RIU with a remarkable figure of merit (FoM) of 47.38/RIU is obtained. The overall detection accuracy (DA) and FoM are significantly improved by the large sensitivity enhancement, despite a slight increase in full width at half maximum (FWHM). Furthermore, the penetration depth (PD) of 198.50 nm (at RI:1.330) and 199.52 nm (at RI:1.335) is attained with the proposed structure. Due to its high sensitivity, reusability, and reproducibility, the SPR biosensor has the potential to be used in biochemical, environmental, and medical detection. Full article

Printed biosensors have attracted increasing attention as platforms for rapid, low-cost, and portable diagnostics because they can be fabricated on flexible or rigid substrates using scalable printing techniques. Their performance is strongly influenced by both the printing process and the materials employed, since factors such as ink rheology, particle dispersion, interfacial behavior, and post-processing conditions directly affect device architecture, sensing performance, and manufacturing reliability. This review summarizes recent advances in printed biosensors from the combined perspectives of printing technologies and functional materials. Commonly employed printing techniques, including inkjet, screen, aerosol jet, and roll-to-roll gravure printing, are discussed with emphasis on their processing characteristics and material requirements. The review also examines key material platforms used in printed biosensors, including carbon-based nanomaterials, metal oxides, metal nanoparticles, conductive polymers, dielectric materials, and hybrid composites, highlighting their roles in electrical conductivity, catalytic activity, biomolecule immobilization, mechanical flexibility, and overall analytical performance. Finally, current challenges and emerging research directions are outlined with respect to ink stability, post-processing strategies, sensor reliability, manufacturability, and practical translation. Overall, this review emphasizes that the development of high-performance printed biosensors depends on the synergistic integration of rational material design with optimized printing strategies. Full article

Background/Objectives: Early detection through minimally invasive approaches is critical for timely patient stratification and optimal therapeutic decision-making in colorectal cancer (CRC). Liquid biopsy, based on the analysis of tumor-derived components in blood and other body fluids, has emerged as a promising strategy to overcome current limitations in CRC diagnosis and follow-up. This review evaluates the current landscape of liquid biopsy clinical trials in CRC, focusing on predictive biomarker detection, prognostic assessment, and disease monitoring. Methods: ClinicalTrials.gov was searched using the terms “colorectal cancer” and “liquid biopsy” yielding 153 registered trials. After manual screening, 44 trials were excluded for not using liquid biopsy for CRC management, leaving 109 trials for analysis. Of these, 25 were completed, and 13 had publicly available results related to liquid biopsy. Results: The included trials were conducted across 27 countries on four continents. Overall, 119 biomolecules assessments and 167 different endpoints were reported across 109 clinical trials. Because individual trials could evaluate multiple biomolecules and endpoints, counts exceed the total number of trials. Cell-free DNA (cfDNA) was evaluated in 92/109 trials (84%) and accounting for 77% of all biomolecule assessments. Circulatingtumor cells (CTCs) were analyzed in 9/109 trials (8%, representing 8% of all the biomolecules analyzed), and microRNAs (miRNAs) in 8/109 (7%, representing 7% of all the biomolecules analyzed). Treatment sensitivity was the most common endpoint (57/109, 52% of the clinical trials; representing 34% of all the 167 different endpoints analyzed), followed by disease progression (28/109, 26%; representing 17% of all the different endpoints analyzed) and diagnostic applications (21/109, 19%; representing 12% of all the different endpoints analyzed). Among the 25 completed studies, 10/25 (40%) were interventional and 15/25 (60%) observational, spanning 14 countries. The majority of completed trials (21/25, 84%) used cfDNA. Interventional studies were predominantly phase II (5/10), with fewer phase III trials (2/10), primarily evaluating treatment response, particularly in relation to EGFR inhibitors and RAS/BRAF mutation status. Four observational studies (4/15) investigated emerging biomarkers, including long noncoding RNAs and miRNAs. Conclusions: Current clinical trials highlight cfDNA as the dominant and most clinically advanced liquid biopsy biomarker in CRC, primarily used for treatment guidance and disease monitoring. In contrast, CTCs and RNA-based biomarkers remain underrepresented. The limited number of randomized late-phase trials, heterogeneity in study design, and technical challenges associated with emerging biomarkers underscore the need for standardized methodologies and robust validation before routine clinical implementation. Full article

Iron-based nanoparticles, particularly iron oxide nanostructures (IONPs), have emerged as versatile and clinically relevant platforms for drug delivery and theranostic applications. Among these, superparamagnetic iron oxide nanoparticles (SPIONs), including magnetite (Fe₃O₄) and maghemite (γ-Fe₂O₃), are the most extensively investigated due to their biocompatibility, magnetic responsiveness, and established safety profiles. Their unique superparamagnetic behavior enables external magnetic-field-guided targeting, magnetic resonance imaging (MRI) contrast enhancement, and magnetically triggered hyperthermia, enabling simultaneous diagnosis and therapy. Surface functionalization with polymers, silica, lipids, peptides, and biomolecules further improves colloidal stability, circulation time, targeting specificity, and controlled drug release. Core–shell architectures and multifunctional hybrid systems have expanded the therapeutic scope of iron nanoparticles, integrating chemotherapy, gene delivery, photothermal therapy, and Fenton reaction–mediated catalytic therapy. Despite promising preclinical outcomes, challenges remain regarding long-term biosafety, oxidative stress induction, biodistribution, large-scale reproducibility, and regulatory translation. This review summarizes the physicochemical properties, synthesis strategies, surface-engineering approaches, drug-loading mechanisms, and biomedical applications of iron-based nanoparticles, highlighting recent advances in multifunctional and peptide-functionalized systems. Critical considerations for clinical translation and future perspectives in precision nanomedicine are also discussed. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article

Endophytic fungi (EF) inhabit internal plant tissue in a mutually beneficial symbiotic relationship with their host plant. EF synthesizes metabolites that are structurally similar or identical to those found in their host plants, which include alkaloids, flavonoids, terpenoids, phenolic compounds, polysaccharides, proteins, lipids, and organic acids. These molecules have promising therapeutic effects, such as antimicrobial, antioxidant, anti-inflammatory, and antitumor activities. Wound healing has earned attention in recent years because of its relation to chronic pathological diseases. This systematic review scanned the available scientific literature database about the wound-healing properties of EF biomolecules. Amongst 994 works, 24 were screened after abstract and full-text reading. The studies were published between 2014 and 2026, in twelve countries. In total, 16 studies presented in vivo assays, 11 studies presented in vitro assays, and 3 studies presented both assays. Most studies identified molecules, which include melanin, benzoic acid, terpenes, sesquiterpenes (purpurolide), extracellular polysaccharides, exopolysaccharides, carotenoids, fatty acids, proteins, pyrones, quinones, and hydrocarbon acids, among others. A meta-analysis was not conducted due to high heterogeneity across extracts, methodologies, and outcomes. All studies showed wound-healing properties from EF extracts. The findings suggest a positive effect of EF extracts on wound-healing properties and the need for standardized in vitro and in vivo protocols. Full article

Aging is one of the most complex biological processes, which leads to a gradual decline in the function of organs, tissues and cells, and significant increases in the risks of many age-associated diseases, including cancer, neurodegenerative disorders, and cardiovascular diseases. Protein biomarkers have attracted increasing attention in research on aging and age-related diseases. Considering the fact that proteins are large heterogenous biomolecules due to coding polymorphisms, alternative RNA splicing and post-translational modifications (PTMs), including glycosylation, phosphorylation, and methylation, mass spectrometry (MS)-based top-down proteomics (TDP) is a powerful technology that allows for measuring proteins without proteolysis, thus characterizing intact forms of proteins, which provides information on primary sequences, including their modifications. This review provides an overview of TDP technologies, with a particular focus on the separation, ionization, and fragmentation of intact proteins and introduces the most recent applications of TDP to the discovery of proteoform-resolved biomarkers associated with aging and age-related diseases. Full article

Transcription factor 19 (TCF19) is a multifunctional biomolecule located within the major histocompatibility complex (MHC) class I region on chromosome 6p21.3. Structurally, TCF19 contains a plant homeodomain (PHD) finger that recognizes histone H3 lysine 4 trimethylation (H3K4me3) and a forkhead-associated (FHA) domain with yet-uncharacterized functions. Emerging evidence positions TCF19 as a multifunctional regulator associated with cell cycle progression, transcriptional regulation, cancer progression, and immune modulation through epigenetic and signaling mechanisms. This review provides the first systematic synthesis of TCF19’s structural domains, regulatory networks, and context-dependent functions across cancer and non-cancer diseases. We highlight critical knowledge gaps, including the unresolved function of its FHA domain and the lack of direct small-molecule inhibitors. In cancer, TCF19 drives proliferation, metastasis, immune evasion, and therapy resistance. Beyond cancer, TCF19 is involved in metabolic diseases, chronic infections, inflammatory disorders, and sensory deficits. TCF19 serves as a promising molecular biomarker for cancer diagnosis, prognosis, and treatment response monitoring, though direct targeting strategies remain unavailable. Full article

Nuclear transport is a vital system that mediates movement of essential biomolecules between the nucleus and cytoplasm. It is tightly regulated by the Importin (IMP) superfamily to maintain separation of cellular compartments. Cellular stress in various forms, particularly oxidative, can suspend nuclear transport and lead to cell death. Prolonged oxidative stress manifests in myriad conditions, including cancer, viral infection and metabolic disease. An IMP protein, Importin-13 (IMP13), retains function under stress, while all other IMP family members tested to date do not. Phylogenetic and structural analysis revealed Transportin-3 (TNPO3) as the closest homologue of IMP13, suggesting it may also retain its function under stress. Subcellular localisation studies indicated that TNPO3 maintained its typical subcellular localisation, even in the presence of stress, unlike most IMP family members. Also, fluorescence recovery after photobleaching (FRAP) demonstrated that TNPO3 shuttling is unaffected under stress. Co-immunoprecipitation studies examining cargo binding revealed the capacity of TNPO3 to bind its cargo in the presence of stress. This demonstrated for the first time that TNPO3 retains functionality under stress conditions, in contrast to other IMPs, but similar to IMP13. Furthermore, both IMP13 and TNPO3 appear to protect against the potentially critical mislocalisation of Ran, a key molecule involved in the maintenance of the nuclear transport system. Full article

The arbitrary-order hidden Markov model ($\alpha$-HMM) is a nontrivial generalization of the standard HMM, designed to model stochastic processes with higher-order dependences among arbitrarily distant random events. The $\alpha$-HMM admits an efficient Viterbi-style optimal decoding algorithm, making it feasible to discover higher-order dependences among data objects in observed sequential data. Because the $\alpha$-HMM exceeds the expressive power of standard HMMs, fixed $k\mathrm{th}$-order HMMs, and stochastic context-free grammars, effective probabilistic parameter estimation approaches are required to translate this theoretical expressiveness of the $\alpha$-HMM into practical utility. This paper introduces a principled methodology for effective estimation of probabilistic parameters of the $\alpha$-HMM from observed data. In large-scale sequential datasets, higher-order dependencies can vary widely across instances, so a single global parameter set may be inadequate. Instead, an amortized parameter inference approach is proposed for the $\alpha$-HMM, in which an input-conditioned parameter estimator is learned from data and used to infer instance-specific parameters for each input instance to the decoding algorithm. Specifically, the neural parameter estimator is trained using a composite learning objective that is partially enabled by the optimal decoding algorithm. The effectiveness of the proposed parameter estimation method is demonstrated through empirical results of the application of the $\alpha$-HMM in biomolecular structure modeling and prediction. Full article

The Escherichia genus includes both commensal and pathogenic species and is characterized by its diversity and adaptability to the mammalian gut and other environments. Among these species, E. coli has facilitated many scientific advances as a model organism. Recently, a new member of the Escherichia genus, Escherichia marmotae, has been described as a phylogenetically distinct clade that shows the greatest genetic divergence from E. coli. This review explores E. marmotae, its cryptic evolution, distinct characteristics, and ecological niches. E. marmotae has recently gained scientific prominence due to its association with animal feces, environmental occurrence, human clinical samples, and emerging as a potential pathogen. While its pathogenicity remains understudied, growing evidence from clinical, environmental, and animal sources suggests the need for heightened surveillance. This review highlights current knowledge gaps, underscores the need for improved diagnostic tools, and proposes future research directions to elucidate the clinical and ecological implications of this emerging pathogen. Full article