Search Results (7,472)

Molecular diversity is a key component of overall biodiversity, playing a vital role in evolution. It results from the adaptation of organisms to various habitats, which impacts their survival. The Amaryllidoideae subfamily is a significant group of monocotyledonous plants known for producing an exclusive and still-expanding group of molecules with diverse biological activities. Galanthamine (Gal), the most renowned metabolite from Amaryllidoideae subfamily, has been marketed for the palliative treatment of Alzheimer’s disease since 2001 due to its ability to inhibit the acetylcholinesterase enzyme. Due to the high cost and low yield of its synthesis, pharmaceutical companies extract this drug from Amaryllidoideae plants, such as Narcissus pseudonarcissus cv. Carlton in Europe and Lycoris radiata in China. The aim of this study was to describe the alkaloid profile of fifteen different species of Narcissus L. (commonly known as daffodils) collected in Spain using gas chromatography coupled with mass spectrometry. Fifty-one alkaloids were identified and quantified within these species through our private library of Amaryllidaceae alkaloids (AA) built over the last four decades, while thirty structures remained not identified in thirteen of these species. The highest concentration of these nitrogenate metabolites was quantified in N. confusus, 541 μg Gal·100 mg⁻¹ DW, which also exhibited a notably high concentration of Gal, 301 μg Gal·100 mg⁻¹ DW, which represents about 55% of the alkaloids identified in this species. The species N. bujei was also found to contain a significant quantity of this compound, amounting to 103.2 μg Gal·100 mg⁻¹ DW. The plant N. assoanus harbored a total of seven unidentified compounds, indicating that this species could be a potentially important source of novel alkaloids. In conclusion, this study facilitates a direct comparison of alkaloid profiles for fifteen Narcissus plant species. This serves as a valuable tool for identifying possible new sources of galanthamine, as well as other novel medicinal alkaloids. Finally, this work presents the first alkaloid profile of the species N. minor and N. nevadensis. Full article

The stomach epithelium is a highly dynamic tissue that undergoes continuous self-renewal and responds robustly to injury through tightly regulated repair processes. Organoids have emerged as powerful tools for modelling gastrointestinal biology. This review focuses on the capacity of gastric organoids to model epithelial homeostasis, injury and repair in the stomach. We examine how organoid systems recapitulate key features of in vivo gastric architecture and stem cell dynamics, enabling detailed interrogation of lineage specification, proliferative hierarchies and regional identity. Gastric organoids have proven particularly useful for studying how environmental factors, such as Helicobacter pylori infection or inflammatory cytokines, disrupt epithelial equilibrium and drive metaplastic transformation. Furthermore, we discuss the emerging use of injury-mimicking conditions, co-cultures and bioengineered platforms to model regeneration and inflammatory responses in vitro. While organoids offer unparalleled accessibility and experimental manipulation, they remain limited by the absence of critical niche components such as immune, stromal and neural elements. Nevertheless, advances in multi-cellular and spatially resolved organoid models are closing this gap, making them increasingly relevant for disease modelling and regenerative medicine. Overall, gastric organoids represent a transformative approach to dissecting the cellular and molecular underpinnings of stomach homeostasis and repair. Full article

The application of microbial alkaline proteases holds significant potential for eco-sustainable industrial processes by reducing chemical usage and lowering the costs of effluent treatment. In the search for novel proteases with industrial relevance, several microbial strains were isolated from alkaline baths of the Portuguese tannery agroindustry. The most promising protease-producing strains were selected for identification and further study. Two isolates demonstrated the highest proteolytic activity, reaching 0.51 ± 0.01 U mL⁻¹ and 0.70 ± 0.01 U mL⁻¹ after 7.5 h of submerged cultivation in nutrient broth. Based on API biochemical tests, molecular biology techniques, and GC-FAME analysis of membrane lipids, the isolates were identified as Bacillus subtilis and incorporated into INIAV’s collection of industrial microbial cultures as B. subtilis CCMI 1253 (BMR2) and B. subtilis CCMI 1254 (BMR1). The most promising protease producer, B. subtilis CCMI 1253 (BMR2), exhibited a maximum specific growth rate of 0.88 ± 0.10 h⁻¹. The proteases produced exhibited good extracellular proteolytic activity, with adaptability to industrial conditions, indicating their suitability for agroindustry applications such as leather making, detergent formulations and the treatment of effluents and protein residues. The results support the potential of microbial proteases as valuable tools in the bioeconomy and green chemistry. Full article

Background: Tuberculosis (TB) remains a global health priority, with current interventions like the Bacille Calmette–Guérin (BCG) vaccine lacking efficacy against latent infection and drug-resistant strains. Novel vaccines targeting both latent and active TB are urgently needed. Objective: This study aims to design a multi-epitope vaccine (MEV) and evaluate its immunogenicity, structural stability, and interactions with toll-like receptor 2/4 (TLR-2/4) via computational biology approaches. Methods: We designed MEV using bioinformatics tools, prioritizing immunodominant epitopes from Mycobacterium tuberculosis antigens. Structural stability was optimized through disulfide engineering, and molecular docking/dynamics simulations were used to analyze interactions and conformational dynamics with TLR-2/4. Antigenicity, immunogenicity, population coverage, and immune responses were computationally assessed. Results: The MEV candidate, CP91110P, exhibited 86.18% predicted global human leukocyte antigen (HLA)-I/II coverage, high antigenicity (VaxiJen: 0.8789), and immunogenicity (IEDB: 4.40091), with favorable stability (instability index: 33.48) and solubility (0.485). Tertiary structure analysis indicated that 98.34% residues were located in favored regions. Molecular docking suggested strong TLR-2 (−1535.9 kcal/mol) and TLR-4 (−1672.5 kcal/mol) binding. Molecular dynamics simulations indicated stable TLR-2 interactions (RMSD: 6–8 Å; Rg: 38.50–39.50 Å) and flexible TLR-4 binding (RMSD: 2–6 Å; Rg: 33–36 Å). Principal component analysis, free energy landscapes, and dynamic cross-correlation matrix analyses highlighted TLR-2’s structural coherence versus TLR-4’s adaptive flexibility. Immune simulations predicted potential robust natural killer cell activation, T helper 1 polarization (interferon-gamma/interleukin-2 dominance), and elevated IgM/IgG levels. Conclusions: CP91110P is predicted to stably bind to TLR-2 and flexibly interact with TLR-4, with prediction of its high antigenicity and broad coverage across immune populations. However, this conclusion requires confirmation through experimental validation. Therefore, it may provide a promising candidate for experimental validation in the development of tuberculosis vaccines. Full article

Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination of conventional and advanced MRI techniques can overcome this issue. Methods: We retrospectively selected 78 patients with confirmed GBM (39) and single BM (39), with conventional MRI investigations, consisting of T2W FLAIR and CE T1W acquisitions. The MRI images (DICOM) were evaluated by an AI segmentation tool, comparatively evaluating tumor heterogeneity and peripheral edema. Results: We found that GBMs are less edematous than BMs (p = 0.04) but have more internal necrosis (p = 0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the highest grade of edema (p = 0.01). Compared with the ellipsoidal method of volume calculation, the AI machine obtained greater values when measuring lesions of the occipital and temporal lobes (p = 0.01). Conclusions: Although extremely useful in radiomics analysis, automated segmentation applied alone could effectively differentiate GBM and BM on a conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheral edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation. Full article

The construction of infectious clones (ICs) is essential for studying viral replication, pathogenesis, and host interactions. Milk vetch dwarf virus (MDV), a nanovirus with a multipartite, single-stranded DNA genome, presents unique challenges for IC development due to its segmented genome organization. To enable functional analysis of its genome, we constructed full-length tandem-dimer-based ICs for all eight MDV genomic segments. Each segment was cloned into a binary vector and co-delivered into Nicotiana benthamiana, Nicotiana tabacum, Vicia faba, and Vigna unguiculata plants via Agrobacterium-mediated inoculation. Systemic infection was successfully reconstituted in all host plants, with PCR-based detection confirming the presence of all viral segments in the infected leaves of nearly all tested plants. Segmental accumulation in infected plants was quantified using qPCR, revealing non-equimolar distribution across hosts. This study establishes the first complete IC system for MDV, enabling reproducible infection, replication analysis, and quantitative segment profiling. It provides a foundational tool for future molecular investigations into MDV replication, host interactions, and viral movement, advancing our understanding of nanovirus biology and transmission dynamics. Full article

Common vetch (Vicia sativa L.) is a globally green manure and forage crop, cultivated extensively worldwide. Its seeds serve as an important concentrated feed. Due to the late release of the reference genome, few studies were conducted to analyze the genetic mechanisms of grain yield, which hindered the progress of common vetch breeding. Marker-assisted selection (MAS) is the best and most effective way to accelerate the genetic improvement of grain yield-related traits in common vetch. In this study, we performed a genome-wide association study (GWAS) using the high-density single nucleotide polymorphism (SNP) data obtained through re-sequencing to better understand the genetic basis of grain yield-related traits. In total, six grain yield-related traits were evaluated in 172 accessions mainly sourced from China and Russia, across four environments, including branches per plant (NB), pod length (PL), number of pods per plant (NP), number of grains per pod (NG), hundred-grain weight (HGW), and grain yield (GY). Population structure analysis of the 172 accessions revealed four distinct subpopulations, exhibiting strong geographical correlation. In total, 38 loci have been identified as significantly associated with six grain yield-related traits, accounting for 13.3–31.7% of the phenotypic variances. Among them, qGY1.1 and qNG1.1, qNG2.2 and qPL2.1, qNG3.2 and qGY3.2, qNG4.1 and qPL4.1, qGY4.1 and qHGW4.1, qNG6.1 and qPL6.1, and qNB6.2 and qGY6.2 exhibit overlapping regions, suggesting that these regions are pleiotropic and should be prioritized for further research and breeding. In total, 12 candidate genes encoding auxin response factor, F-box repeat protein, gibberellin receptor, serine/threonine-protein kinase-like protein, and cellulose synthase-like protein were identified. Furthermore, we successfully developed and verified a kompetitive allele-specific PCR (KASP) marker (Kasp-NB6.2) for the number of branches. These findings provide molecular insights into grain yield-related traits in common vetch and offer valuable loci and molecular tools for MAS breeding. Full article

Antibiotic resistance is a growing global health threat, with critical care settings representing one of the most vulnerable arenas due to the high burden of infection and frequent empirical antibiotic use. Rapid and precise diagnosis of infectious pathogens is crucial for initiating appropriate therapy, minimizing unnecessary antimicrobial exposure, and supporting effective stewardship programs. This review explores how innovative diagnostic technologies are reshaping infection management and antimicrobial stewardship in critical care. We examine the clinical utility of molecular assays, multiplex PCR, MALDI-TOF mass spectrometry, metagenomic sequencing, point-of-care (POC) diagnostics, and emerging tools like biosensors and AI-powered predictive models. These platforms enable earlier pathogen identification and resistance profiling, facilitating timely and targeted therapy while minimizing unnecessary broad-spectrum antibiotic use. By integrating diagnostics into stewardship frameworks, clinicians can optimize antimicrobial regimens, improve patient outcomes, and reduce resistance selection pressure. Despite their promise, adoption is challenged by cost, infrastructure, interpretation complexity, and inequitable access, particularly in low-resource settings. Future perspectives emphasize the need for scalable, AI-enhanced, and globally accessible diagnostic solutions. In bridging innovation with clinical application, diagnostic advancements can serve as pivotal tools in the global effort to curb antimicrobial resistance in critical care environments. Full article

Thanks to their multiple outstanding features—such as high fluorescence quantum yield, good photostability, and excellent sensitivity—conjugated polymers (CPs) have emerged as a pioneering class of fluorescent materials for sensing applications, particularly in environmental and biological fields, for the detection of a wide range of environmental pollutants and bioactive compounds. The presence of delocalized π-electrons in the CP backbone significantly enhances sensing performance through a unique phenomenon known as the “molecular wire effect.” As a result, CP-based fluorescent sensors have been extensively developed and employed as exceptional tools for monitoring various analytes in environmental and biological contexts. A deep understanding of their unique properties, fabrication techniques, and recent innovations is essential for guiding the strategic development of advanced CP-based fluorescent sensors, particularly for future point-of-care applications. This study presents a critical review of the key characteristics of fluorescent sensors and highlights several common types of conjugated polymers (CPs) used in their design and fabrication. It summarizes and discusses the main sensing mechanisms, state-of-the-art applications, and recent innovations of CP-based fluorescent sensors for detecting target compounds in environmental and biological fields. Furthermore, potential strategies and future perspectives for designing and developing high-performance CP-based fluorescent sensors are emphasized. By consolidating current scientific evidence, this review aims to support the advancement of highly sensitive fluorescent sensors based on various CP nanoparticles for environmental and biological applications. Full article

Antimicrobial resistance (AMR) in livestock production systems has emerged as a major global health concern, threatening not only animal welfare and agricultural productivity but also food safety and public health. The widespread, and often poorly regulated, use of antimicrobials for growth promotion, prophylaxis, and metaphylaxis has accelerated the emergence and dissemination of resistant bacteria and resistance genes. These elements circulate across interconnected animal, environmental, and human ecosystems, driven by mobile genetic elements and amplified through the food production chain. It is estimated that more than two-thirds of medically important antimicrobials are used in animals, and AMR could cause millions of human deaths annually by mid-century if unchecked. In some livestock systems, multidrug-resistant E. coli prevalence already exceeds half of isolates, particularly in poultry and swine in low- and middle-income countries (LMICs). This narrative review provides a comprehensive overview of the molecular epidemiology, ecological drivers, and One Health implications of AMR in food-producing animals. We highlight key zoonotic and foodborne bacterial pathogens—including Escherichia coli, Salmonella enterica, and Staphylococcus aureus—as well as underappreciated reservoirs in commensal microbiota and livestock environments. Diagnostic platforms spanning phenotypic assays, PCR, MALDI-TOF MS, whole-genome sequencing, and CRISPR-based tools are examined for their roles in AMR detection, surveillance, and resistance gene characterization. We also evaluate current antimicrobial stewardship practices, global and regional surveillance initiatives, and policy frameworks, identifying critical implementation gaps, especially in low- and middle-income countries. Emerging sectors such as aquaculture and insect farming are considered for their potential role as future AMR hotspots. Finally, we outline future directions including real-time genomic surveillance, AI-assisted resistance prediction, and integrated One Health data platforms as essential innovations to combat AMR. Mitigating the threat of AMR in animal agriculture will require coordinated scientific, regulatory, and cross-sectoral responses to ensure the long-term efficacy of antimicrobial agents for both human and veterinary medicine. Full article

In this study, we present experimental investigations of the surface structure and water contact angles of magnetoactive elastomers (MAEs), which are controlled by an external magnetic field. Specifically, we examine how the polymer matrix architecture affects the surface roughness and wettability of MAEs in various magnetic fields. We performed a comparative analysis on MAEs based on a linear polysiloxane network and on a matrix of the same chemical nature containing side-grafted chains. We synthesized a series of magnetoactive elastomers containing 75 wt.% carbonyl iron and varying amounts of a low-molecular-weight plasticizer. Although the magnetorheological effect is higher for traditional linear MAEs, we found that the magnetic response in surface properties is higher for novel MAEs with side-grafted chains. The largest increase in water contact angle was observed in the side-chain MAEs with the highest 60 wt.% plasticizer content: rising from 112° in a zero field to 168° in a 490 mT magnetic field. Water contact angles exhibit greater stability over time for side-chain MAEs, and this stability further increases in the presence of a magnetic field. Our results demonstrate that the architecture of the polymer matrix serves as an effective tool for designing smart, magnetically responsive surfaces. Full article

Machine learning models and web-based tools have been developed for predicting key properties of imidazolium-based ionic liquids. Two high-quality datasets containing experimental density and viscosity values at 298 K were curated from the ILThermo database: one containing 434 systems for density and another with 293 systems for viscosity. Molecular structures were optimized using the GOAT procedure at the GFN-FF level to ensure chemically realistic geometries, and a diverse set of molecular descriptors, including electronic, topological, geometric, and thermodynamic properties, was calculated. Three support vector regression models were built: two for density (IonIL-IM-D1 and IonIL-IM-D2) and one for viscosity (IonIL-IM-V). IonIL-IM-D1 uses three simple descriptors, IonIL-IM-D2 improves accuracy with seven, and IonIL-IM-V employs nine descriptors, including DFT-based features. These models, designed to predict the mentioned properties at room temperature (298 K), are implemented as interactive applications on the atomistica.online platform, enabling property prediction without coding or retraining. The platform also includes a structure generator and searchable databases of optimized structures and descriptors. All tools and datasets are freely available for academic use via the official web site of the atomistica.online platform, supporting open science and data-driven research in molecular design. Full article

Many intrinsically disordered proteins (IDPs) are known to undergo liquid–liquid phase separation (LLPS), which is a physical process that drives the formation of biomolecular condensates and membraneless organelles in biological cells. Molecular dynamics (MD) simulations provide valuable tools to explore both the molecular mechanisms of LLPS and the physical properties of biomolecular condensates. However, a direct comparison of MD simulation results with phase diagrams obtained experimentally is normally prevented not only by the high computational costs of simulating large biomacromolecular systems on sufficient timescales but also by conceptual challenges. Specifically, there currently seems to be no standard or unambiguous method of defining and determining volumes occupied by coexisting phases at the nanoscale, with typically no more than a few hundred biomacromolecules in the simulation box. The goal of this work is to fill in this gap in the methodology. Focusing on $\alpha$-synuclein as a model IDP, we test and compare three methods for determining the molecular density of protein nanodroplets, or clusters, generated in MD simulations or using other molecular modeling approaches. Two of the methods are based on approximating nanodroplets with homogeneous spheres and ellipsoids, respectively. The third method, which is expected to yield the most physically accurate results, is based on the SPACEBALL algorithm, with optimized, cluster-specific radii for volume probes. Our results contribute to the construction of accurate phase diagrams on the basis of MD simulations of IDP systems. Full article

The introduction of alien insect species is increasingly facilitated by global plant trade, particularly through the movement of ornamental plants. Takecallis nigroantennatus, a host-specific aphid associated with cold-hardy Fargesia bamboo, has recently expanded its range in Europe. To examine its invasion dynamics, we conducted a population-level survey across 13 locations in six countries, sampling individuals from botanic and private gardens, specialized bamboo nurseries, garden centers, and urban horticultural environments in the UK, Belgium, The Netherlands, Germany, Poland, and Norway. A total of 117 specimens were analyzed using mitochondrial COI sequences, revealing a single dominant haplotype without geographic structure based on Bayesian and Maximum Likelihood phylogenetic analyses. This striking genetic uniformity indicates a narrow introduction bottleneck, suggesting a single or highly restricted introduction event followed by clonal spread. Despite the species’ ability for sexual reproduction, the data support a founder effect and rapid recent expansion closely linked to the introduction history of Fargesia in Europe. The results are also consistent with a possible time lag between the arrival of ornamental bamboo and the subsequent establishment of its associated herbivore, a scenario that warrants further investigation. Importantly, our study provides a practical framework for applied monitoring and early detection in bamboo nurseries, botanical gardens, and other high-risk introduction sites, illustrating how molecular tools can inform biosecurity and the management of emerging invasive species. Full article

Eel populations are globally threatened by overfishing and illegal trade, making accurate species identification essential for resource conservation and regulatory enforcement. Conventional molecular identification methods are generally applied in the laboratory, with limited rapid on-site application. This study developed a field-deployable assay to identify the Japanese eel (Anguilla japonica), by incorporating multienzyme isothermal rapid amplification (MIRA) technology with a visually readable lateral flow assay (LFA). Species-specific primers targeting a 286 bp region within the mitochondrial genome of A. japonica were designed and labeled with fluorescein amidite and biotin, respectively. The performance of the MIRA-LFA was validated by assessing its specificity against four other major eel species and its analytical sensitivity, i.e., limit of detection (LoD), under optimized temperature and reaction-time conditions. The MIRA-LFA demonstrated 100% specificity, generating a positive signal only for A. japonica, with no cross-reactivity. A clear visual result was obtained within 10 min at the optimal reaction temperature of 39 °C. Under these optimal conditions, the assay showed a high sensitivity, with an LoD of 0.1 ng/μL of genomic DNA. The proposed assay is an effective tool for the rapid, specific, and sensitive identification of A. japonica. The ability to obtain fast, equipment-free visual results makes this assay an ideal point-of-care testing solution to combat seafood fraud and support the sustainable management of this economically important and vulnerable species. Full article