Search Results (336)

Paraburkholderia fungorum (P. fungorum) is an environmental bacterium with biotechnological applications, yet clinical isolations raise concerns about opportunistic infection risk. Genetically related pathogens exhibit substantial antibiotic resistance, motivating the investigation of alternative control strategies. This paper investigates P. fungorum photoinactivation across ultraviolet (222 nm, 254 nm, 313 nm, and 365 nm) and visible (400 nm and 464 nm) wavelengths including ROS (reactive oxygen species) quantification via DCFH-DA fluorescence assay. A two-way ANOVA analysis demonstrated that the wavelength is the dominant determinant of photoinactivation efficacy (F = 100.4, p < 0.001) with ROS generation as a more powerful predictor of inactivation than fluence dose alone (F = 60.6, p < 0.001) at 365 nm, 400 nm, and 464 nm. Ultraviolet irradiation at 254 nm achieved the highest efficiency (5.4 log reduction at 24 mJ/cm²), while 365 nm irradiation demonstrated a high efficacy of 5.2 log reduction at 122 J/cm² with extraordinary ROS production (12,642-fold fluorescence increase). Conversely, inactivation efficiency declined at 400 nm (4.8 log reduction at 378 J/cm² with 122-fold ROS increase) and 464 nm (3.4 log reduction at 3017 J/cm² with lesser ROS detection at 27-fold increase). Wavelength-dependent ROS production correlated directly with bacterial inactivation efficacy, explaining the approximately 500-fold ROS differential between 365 nm and 464 nm. The demonstrated photosensitivity of P. fungorum across multiple wavelengths, with the statistical validation of wavelength-dependent mechanisms, provides a foundation for developing practical, mechanism-based phototherapy protocols tailored to specific clinical and environmental decontamination scenarios. Full article

Monitoring peptide and protein self-association is essential for understanding biological function, formulation stability, and aggregation mechanisms. While size-exclusion chromatography (SEC) is routinely used to quantify protein-size variants under native conditions, its hyphenation to high-resolution mass spectrometry (HRMS) for simultaneous structural characterization remains limited. Here, we report the development and validation of a robust SEC-UV/HRMS method optimized for native-like analysis of bovine serum albumin (BSA) monomers and higher-order oligomers using standard-flow electrospray ionization. Systematic evaluation of source parameters, mobile-phase composition, and chromatographic conditions enabled retention of native BSA structure, minimized in-source unfolding, and enhanced MS sensitivity, allowing detection of oligomers up to the heptamer. A short, narrow-bore 200 Å UHPLC SEC separation column was used. Low-flow separations (~0.05 mL/min) enabled efficient ionization and 10 min run times. An accelerated 60 °C stress-testing protocol demonstrated that SEC-MS can semi-quantitatively monitor oligomerization dynamics, complementing UV-based quantification and revealing transient species not resolved by UV alone. The method showed acceptable linearity, precision, and sample stability, and comparison with SEC-RALS/LALS confirmed molecular-weight trends across aggregation states. Overall, the developed SEC-UV/HRMS workflow provides a rapid, sensitive, and widely accessible approach for UV-based quantification of monomer- and HRMS-based characterizing protein aggregation in research and quality control in pharmaceutical laboratories. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Background: Cartilage defects remain a clinical challenge due to the limited intrinsic repair capacity of hyaline cartilage, driving increasing interest in blood-derived products, including platelet-rich plasma (PRP). Variability in PRP preparation and activation protocols limits reproducibility and clinical translation, particularly in large animal models where species-specific differences are an additional cue. This study aimed to standardize and optimize in pigs a protocol for plasma rich in growth factors (PRGF), a leukocyte-poor PRP, aligned with current human clinical practice. Methods: Whole blood from six female pigs was processed via three centrifugation protocols and activated with varying CaCl₂ concentrations to evaluate gelation and morphology. PRGF was characterized through hematological analysis, ELISA-based quantification of soluble factors, and structural imaging of fibrin gel via histology and scanning electron microscopy. Data were further analyzed using protein–protein interaction networks, hierarchical clustering, and comparative human PRGF proteomic profiles. Results: Protocol with 400× g centrifugation followed by 13.3 mM CaCl₂ activation achieved the most favorable performance, yielding the highest platelet recovery, effective leukocyte clearance, and consistent formation of a well-organized fibrin network. Porcine activated PRGF showed substantial overlap in detected factors and concentration ranges with human activated PRGF prepared with the same protocol. Conclusions: These findings establish a robust, clinically aligned porcine PRGF protocol and support the pig as a relevant translational model for PRP-based regenerative strategies, providing a reliable platform for preclinical evaluation of cartilage therapies. Full article

Background: Fluorescence-guided surgery based on near-infrared imaging, most often using indocyanine green (ICG), is increasingly used in colorectal cancer (CRC) surgery. This narrative review integrates current evidence across four clinically relevant domains-anastomotic perfusion, lymphatic mapping, tumor localization, and metastasis detection and emphasizes the technical and translational factors that will determine broader implementation. Methods: We performed a structured narrative review of clinical and translational studies identified through PubMed and citation tracking, with emphasis on ICG-based workflows and emerging targeted tracers. Because the literature spans heterogeneous interventions, imaging platforms, and endpoints, no de novo meta-analysis or formal risk-of-bias assessment was undertaken. Results: ICG fluorescence angiography is the most mature application and can refine transection-line selection, although its effect on anastomotic leak appears protocol dependent. In lymphatic mapping, ICG improves visualization of drainage pathways and nodal basins but does not reliably distinguish benign from metastatic nodes. For tumor localization, ICG supports lesion marking and dynamic tissue characterization, while targeted probes and contrast-free adjuncts may improve oncologic specificity. For metastatic disease, ICG is most useful for liver margin guidance and for excluding residual disease, whereas CEA-targeted and multimodal approaches appear particularly promising for peritoneal metastases. Conclusions: The added value of this review lies in linking current clinical maturity to the translational steps still required for routine adoption. In CRC surgery, fluorescence imaging is already useful in selected settings, but broader implementation will depend on standardized protocols, objective real-time quantification, and multicenter validation of targeted tracers against clinically meaningful outcomes. Full article

Background/Objectives: Standardizing measurement of circulating cell-free DNA (cfDNA) in healthy individuals is critical for its application as a reference in biomarker research, yet methodological variability remains poorly documented. Methods: We systematically reviewed 35 studies (n = 1250 healthy subjects) assessing how pre-analytical handling, extraction kits, and quantification methods influence plasma cfDNA levels. We identified quantification approaches (qPCR vs. fluorometry) and use of custom extraction kits as the strongest drivers of variability. Results: In qPCR studies, including ≥ 40 subjects reduced variability, underscoring the importance of adequate sample size. Commercial kits produced more consistent yields than in-house protocols; in our dataset, many studies used Qiagen’s QIAamp Circulating Nucleic Acid Kit, which has historically served as a widely used reference platform. Blood collection in EDTA tubes had minimal impact when commercial kits were used. Conclusions: Based on these findings, we recommend EDTA tubes, a standardized commercial extraction kit, and qPCR quantification to minimize cfDNA measurement variability in healthy cohorts. Finally, we provide expected cfDNA ranges for healthy individuals based on methodological flow, which can guide future benchmarking efforts and biomarker studies, improving comparability and early-detection research. Full article

Greek oregano (Origanum vulgare ssp. hirtum) is an important aromatic and medicinal crop grown in Greece, often on marginal lands. Effective weed management is essential for sustainable production, but the use of herbicides raises concerns about potential pesticide residues. Therefore, this study was conducted to evaluate the residue levels of metribuzin + pendimethalin applied and incorporated pre-planting, as well metribuzin + cycloxydim and glyphosate applied post-emergence in oregano crop grown over a three-year period in the Agrinio location in Greece. Herbicide residue analysis in the edible part of the oregano plants was performed using two validated protocols, i.e., QuEChERS and QuPPe coupled with LC-MS/MS. The analytical methods demonstrated high sensitivity, with limits of quantification (LOQ) at 0.01 mg/kg and recovery rates ranging from 71% to 102%. These results indicated that the application of the above herbicides in oregano crop grown under Greek field conditions resulted in no detectable residues above the established LOQs, strongly supporting the potential safe use of these herbicides in oregano crop and their possible use for regulatory assessments and consumer safety assurance. Full article

Deployed in distributed organizations and edge networks, contemporary intrusion detection increasingly requires high-performing models without centralizing sensitive traffic logs. This study presents a lightweight federated intrusion detection framework that integrates (i) unidirectional serialization to convert tabular flow records into short sequences, (ii) multi-scale one-dimensional convolutions to capture heterogeneous temporal–statistical patterns at different receptive fields, and (iii) an attention-based reweighting module that emphasizes informative feature channels prior to classification. A sample-size-weighted FedAvg aggregation protocol is used to train a global detector without transferring raw data. Experiments on three widely used benchmarks (UNSW-NB15, KDD Cup 99, and NSL-KDD) under multiple client configurations report consistently high detection effectiveness, with peak accuracies of 99.38% (UNSW-NB15), 99.86% (KDD Cup 99), and 99.02% (NSL-KDD), alongside strong precision, recall, and F1 scores. In addition, the proposed framework is quantitatively benchmarked on UNSW-NB15 against two recent federated intrusion detection baselines, FedMSP-SPEC and a multi-view federated CAE-NSVM model, demonstrating improvements of more than 10 percentage points in macro F1-score while retaining a compact architecture. The manuscript further specifies a concrete threat model, clarifies the client data partitioning strategy and Non-IID quantification, and provides a reproducibility protocol (hyperparameters, random seeds, and evaluation procedures) to facilitate independent verification. Full article

Red yeast rice (RYR) food supplements are widely used for cholesterol management owing to their content of monacolin K (MK), which, in its lactone form, is chemically identical to the prescription statin lovastatin. Despite their popularity, RYR products raise significant quality and safety concerns related to pronounced variability in MK content, frequent labeling non-compliance, contaminations with undeclared pharmaceutical statins, etc. The analytical differentiation between naturally produced MK and added synthetic lovastatin remains particularly challenging due to their identical chemical structures. This review provides a comprehensive overview of the chemical composition of RYR, with emphasis on monacolins, pigments, and relevant secondary metabolites, and critically summarizes current regulatory, safety, and quality issues associated with RYR-based food supplements. Furthermore, a practical, multi-level analytical strategy for product authentication is proposed. The approach integrates targeted quantification of MK and accompanying monacolins, identification of characteristic Monascus pigments as authenticity markers, gas chromatography–mass spectrometry for the detection of undeclared statins and other non-declared constituents, and proton nuclear magnetic resonance for global compositional fingerprinting. By combining complementary targeted and non-targeted techniques, this workflow enables more reliable authentication, detection of adulteration, and comprehensive quality assessment. The implementation of standardized analytical protocols is essential to improve transparency and enhance consumer safety in the rapidly expanding RYR supplement market. Full article

In this paper, we propose a standardized framework for automatic tuberculosis (TB) detection from cough audio and routinely collected clinical data using machine learning. While TB screening from audio has attracted growing interest, progress is difficult to measure because existing studies vary substantially in datasets, cohort definitions, feature representations, model families, validation protocols, and reported metrics. Consequently, reported gains are often not directly comparable, and it remains unclear whether improvements stem from modeling advances or from differences in data and evaluation. We address this gap by establishing a strong, well-documented baseline for TB prediction using cough recordings and accompanying clinical metadata from a recently compiled dataset from several countries. Our pipeline is reproducible end-to-end, covering feature extraction, multimodal fusion, cougher-independent evaluation, and uncertainty quantification, and it reports a consistent suite of clinically relevant metrics to enable fair comparison. We further quantify performance for cough audio-only and fused (audio + clinical metadata) models, and release the full experimental protocol to facilitate benchmarking. This baseline is intended to serve as a common reference point and to reduce methodological variance that currently holds back progress in the field. Full article

Modern Autonomous Vehicles generate large volumes of heterogeneous in-vehicle data, making cybersecurity a critical challenge as adversarial attacks become increasingly adaptive, stealthy, and multi-protocol. Traditional intrusion detection systems often fail under these conditions because of their limited contextual understanding, poor robustness to distribution shifts, and insufficient regulatory transparency. This study introduces LLM-Guardian, a hierarchical intrusion detection framework with decision-making mechanisms that integrates Large Language Models (LLMs) with classical statistical detection theory, optimal transport drift analysis, graph neural networks, and formal uncertainty quantification. LLM-Guardian uses semantic anomaly scoring, conformal prediction for distribution-free confidence calibration, adaptive cumulative sum (CUSUM) sequential testing for low-latency detection, and topology-aware GNN reasoning designed to identify coordinated attacks across CAN, Ethernet, and V2X interfaces. In this work, the framework is empirically evaluated on four heterogeneous CAN-bus datasets, while the Ethernet and V2X components are instantiated at the architectural level and left as directions for future multi-protocol experimentation. Full article

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals. Full article

Following the COVID-19 pandemic, researchers have increasingly focused on monitoring the spread of the virus and improving methods to detect changes in the SARS-CoV-2 genome. Although clinical surveillance provides direct and reliable results, it has limited applicability. Wastewater-based epidemiology (WBE) has therefore emerged as a valuable, non-invasive complementary tool for disease surveillance. It provides a comprehensive picture of virus circulation in a population, including asymptomatic individuals and those who do not seek healthcare. In addition, it facilitates early detection of outbreaks and the collection of epidemiologic data at the community level. However, WBE also presents technical challenges, including variations in sampling and testing protocols, the presence of inhibitors that affect viral RNA extraction, and the need for standardised procedures between studies. These challenges should be addressed for possible future infectious disease outbreaks. One of the challenges facing researchers was to develop efficient methods that could overcome the extraction and detection problems related to inhibitors present in wastewater. To this aim, this systematic review highlights the potential use of WBE, the variety of techniques, and the most effective methods for the detection and quantification of SARS-CoV-2 in wastewater samples. A reproducible electronic search of the literature was conducted in the Web of Science (WoS) and PubMed databases for articles published between 2020 and 2024. Our search revealed that the majority of observed WBE applications emphasised a correlation between SARS-CoV-2 RNA concentration trends in wastewater and epidemiological data. Another relevant issue that the articles often discussed and compared was the techniques used in different steps of sample processing, such as sample collection, concentration and detection, hence the lack of standardised procedures. This paper provides a framework regarding previous research on WBE to gain a better understanding that will lead to functional solutions. Full article

Microplastics spread through the environment in various ways. Inland waters are an ideal medium for their dispersal, as they collect pollutants from various sources and transport them over long distances. From there, microplastics can enter the marine environment, break down into smaller particles or end up in drinking water treatment plants. However, the fate, transport and potential health effects of microplastics after ingestion of drinking water and water in food are not yet fully understood. It is therefore necessary to evaluate the quantification and identification of microplastics in drinking water by analysing real samples in order to assess the potential impact on human health. To this end, microplastic contamination in 32 treated drinking water samples from a surface water treatment plant in north-eastern Italy were analysed using micro-Fourier transform infrared spectroscopy (µ-FT-IR). The results indicated low levels of contamination, with all the samples containing less than 170 microplastics per litre, which is in line with European drinking water levels. Polyolefins with size 50–500 µm, such as polypropylene and polyethylene, were the predominant polymers detected (50.2%), while surprisingly polyethylene terephthalate was scarcely present (0.1%, size range 10–50 µm). Statistical analysis revealed a significant negative correlation between microplastic concentration and sampling volume, with larger volumes yielding fewer particles. This inconsistency likely results from the lack of bottle rinsing when only a fraction of the sampling volume is filtered. It was also found that rinsing the sampling bottles with ethanol alone prior to analysis was sufficient to ensure accurate quantification. These results highlight the challenges in standardising the detection of microplastics in drinking water and underline the need for optimised sampling protocols. Full article

Mucus plugs are airway-obstructing accumulations of inspissated secretions frequently observed in obstructive lung diseases (OLDs), including chronic obstructive pulmonary disease (COPD), severe asthma, and cystic fibrosis. Their presence on chest CT is strongly associated with airflow limitation, reduced lung function, and increased mortality, making them emerging imaging biomarkers of disease burden and treatment response. However, manual delineation of mucus plugs is labor-intensive, subjective, and impractical for large cohorts, leading most prior studies to rely on coarse segment-level scoring systems that overlook lesion-level characteristics such as size, extent, and location. Automated plug-level quantification remains challenging due to substantial heterogeneity in plug morphology, overlap in attenuation with adjacent vessels and airway walls on non-contrast CT, and pronounced size imbalance in clinical datasets, which are typically dominated by small distal plugs. To address these challenges, we developed and validated a simulation-driven, annotation-free deep learning framework for automated detection and segmentation of airway mucus plugs on non-contrast chest CT. A total of 200 COPD CT scans were analyzed (98 plug-positive, 83 plug-negative, and 19 uncertain). Synthetic mucus plugs were generated within segmented airways by transferring voxel-intensity statistics from adjacent intrapulmonary vessels, preserving realistic morphology and texture while enabling controlled sampling of plug phenotypes. An nnU-Net trained exclusively on synthetic data (S-Model) was evaluated on an independent, expert-annotated test set and compared with an nnU-Net trained on manual annotations using 10-fold cross-validation (M-Model). The S-Model achieved significantly higher detection performance than the M-Model (sensitivity 0.837 [95% CI: 0.818–0.854] vs. 0.757 [95% CI: 0.737–0.776]; 1.91 false positives per scan vs. 3.68; p < 0.001), with performance gains most pronounced for medium-to-large plugs (≥6 mm). This simulation-driven approach enables accurate, scalable quantification of mucus plugs without voxel-wise manual annotation in thin-slice (<1.5 mm) non-contrast chest CT scans. While the framework could, in principle, be extended to other annotation-limited medical imaging tasks, its generalizability beyond this COPD cohort and imaging protocol has not yet been established, and future work is required to validate performance across diverse populations and scanning conditions. Full article