Search Results (6,306)

Liquid biopsy has emerged as a transformative approach in modern oncology, offering minimally invasive access to tumor-derived biomarkers through the analysis of circulating tumor DNA, cell-free DNA, and extracellular vesicles such as exosomes. Unlike conventional tissue biopsies, liquid biopsy enables real-time monitoring of tumor dynamics, molecular heterogeneity, treatment response, and the development of therapeutic resistance. Recent advances in ultra-sensitive molecular technologies, including digital droplet polymerase chain reaction, next-generation sequencing, methylation profiling, and fragmentomic analysis, have substantially improved the sensitivity and specificity of circulating nucleic acid detection, facilitating their integration into precision cancer medicine. ctDNA analysis has demonstrated significant clinical utility across multiple malignancies, including lung, breast, colorectal, pancreatic, and prostate cancers, particularly in the identification of actionable genomic alterations, minimal residual disease, and mechanisms of acquired resistance. In parallel, cell-free DNA provides broader insights into tumor biology and systemic genomic alterations, while exosomes contribute additional layers of molecular information through the transport of nucleic acids, proteins, and signaling molecules involved in intercellular communication and tumor microenvironment modulation. The integration of artificial intelligence and machine learning approaches further enhances the interpretative power of liquid biopsy-derived datasets and supports the development of personalized therapeutic strategies. Despite these advances, important challenges remain, including low tumor fraction in early-stage disease, biological and technical variability, clonal hematopoiesis-associated false positives, assay standardization, and cost-effectiveness considerations. Nevertheless, the expanding clinical applicability of liquid biopsy technologies positions them as essential components of contemporary precision oncology. This review summarizes the biological foundations, analytical methodologies, current clinical applications, technological innovations, and future perspectives of circulating tumor DNA, cell-free DNA, and exosome-based liquid biopsies in cancer diagnosis, monitoring, and personalized treatment strategies. Full article

Background/Objectives: Oral lichen planus (OLP) is a chronic immune-mediated disease of the oral mucosa in which T-cell infiltration, epithelial injury, and stress-related neuroendocrine signaling appear to intersect. Catestatin (CST), a peptide generated from chromogranin A (CgA), modulates catecholamine release and inflammatory cell responses; however, its association with localized mucosal inflammation in OLP has not been clarified. This study aimed to compare serum CST levels between OLP patients and healthy controls and to examine their association with clinical subtype, disease severity, and vitamin D status. Methods: In this cross-sectional study, 51 patients with clinically and histopathologically confirmed OLP and 60 healthy controls were enrolled at the University Hospital of Split. Serum CST was quantified using ELISA. OLP severity was assessed with the REU score by a single experienced oral medicine examiner who was blinded to laboratory results, and pain and burning were recorded separately using 0–10 VASs. Results: Serum CST levels were significantly higher in OLP patients compared to healthy controls (p < 0.001). CST levels showed a strong positive correlation with the REU total score (r = 0.781, p < 0.001), as well as with VAS pain (r = 0.708, p < 0.001) and VAS burning (r = 0.729, p < 0.001). Erosive OLP exhibited significantly higher CST levels compared to the non-erosive subtype (p < 0.001). Furthermore, serum vitamin D levels were significantly lower in OLP patients (p < 0.001), with no significant correlation observed between CST and vitamin D levels. Conclusions: Serum CST levels were higher in OLP patients and showed close associations with REU scores and symptoms, with the strongest signal observed in erosive disease. CST may therefore contribute to the neuroendocrine-immune profile of OLP and may be useful as an adjunctive marker of clinically active or erosive disease. Larger prospective studies including salivary and tissue-based CST measurements are needed before CST can be used for routine monitoring. Full article

The house cricket (Acheta domesticus) is among the edible insect species authorized under the European Union novel food framework and is increasingly explored as a food ingredient. This review synthesizes current evidence on its nutritional composition, techno-functional properties, safety, sustainability, and application potential for human consumption. A. domesticus powder is a nutrient-dense ingredient, with reported protein contents ranging from 57.4 to 76.19 g/100 g dry material, together with a relevant amino acid profile, unsaturated lipid fraction, chitin-associated fiber, and selected minerals and vitamins. Its techno-functional profile, including water absorption capacity around 175%, oil absorption capacity around 140%, and emulsifying capacity around 77.7%, supports applications in bakery products, snacks, pasta, meat analogs, and protein-enriched foods. From a sustainability perspective, reported feed conversion ratios around 1.5–1.9 and controlled indoor rearing strengthen its relevance for scalable production, although outcomes depend on substrate choice, energy inputs, production scale, and processing conditions. Safety issues, especially allergenicity, cross-reactivity, microbial control, and contaminant monitoring, remain central to industrial use. Overall, A. domesticus powder has emerged and should be regarded as a nutritionally valuable, technologically versatile and promising functional ingredient for sustainable food innovation. Full article

Background/Objectives: Lactic acid bacteria (LAB) are essential drivers of food fermentation, yet systematic metabolic comparisons across different LAB strains remain underexplored. This study characterized and contrasted the metabolic fingerprints of Lactiplantibacillus plantarum and Lacticaseibacillus rhamnosus in vegetable fermentation and subsequently evaluated whether the same untargeted metabolomics pipeline could be applied to rapid foodborne pathogen detection. Methods: A multi-platform untargeted metabolomics strategy integrating GC-MS and UPLC-Q-TOF-MS was applied to profile four experimental conditions: non-fermented control, L. plantarum monoculture, L. rhamnosus monoculture, and mixed-culture fermentation. Multivariate statistical tools (PCA and PLS-DA) were used to identify differential metabolites and perturbed pathways. The identical analytical workflow was then applied to beef samples artificially contaminated with Escherichia coli O157:H7, Salmonella enterica, or Listeria monocytogenes. Results: Across all samples, 847 metabolites were annotated, of which 312 showed significant abundance changes upon fermentation. PLS-DA delivered robust group discrimination (R²X = 0.89, R²Y = 0.95, Q² = 0.91). Organic acids (32.0%) and amino acids (24.0%) dominated the metabolic landscape, with lactic acid, acetic acid, diacetyl, and acetoin as the most elevated compounds. KEGG analysis highlighted glycolysis/gluconeogenesis, pyruvate metabolism, and branched-chain amino acid degradation as the most heavily rewired pathways. When the same pipeline was applied to pathogen detection, it yielded AUC values of 0.89, 0.87, and 0.88 for E. coli O157:H7, S. enterica, and L. monocytogenes, respectively, with detection times of 18 h, 24 h, and 30 h. Conclusions: This work delivers a side-by-side metabolic atlas of two prominent LAB species and demonstrates the technical portability of an untargeted metabolomics pipeline from a fermentation model system to a pathogen detection scenario. The identified biomarker panels warrant further validation in diverse food matrices, and translation to routine monitoring will require matrix-specific model training and validation. Full article

Circulating tumor DNA (ctDNA) has emerged as a promising minimally invasive biomarker in multiple myeloma (MM), providing dynamic insight into tumor burden, clonal evolution and molecular heterogeneity. Unlike conventional bone marrow-based diagnostics, ctDNA analysis offers a systemic approach to disease assessment and may better reflect spatially heterogeneous and extramedullary disease. Recent advances in highly sensitive molecular techniques, including digital droplet polymerase chain reaction and next-generation sequencing, have improved the feasibility of ctDNA detection and longitudinal disease monitoring in MM. Increasing evidence demonstrates substantial concordance between ctDNA and bone marrow genomic profiles, while also highlighting the ability of ctDNA to identify resistant subclones, molecular relapse and genomic evolution during therapy. ctDNA has shown potential clinical utility in molecular profiling, therapeutic monitoring and minimal residual disease assessment. ctDNA may become increasingly relevant in the era of novel immunotherapies, including chimeric antigen receptor T-cell therapy and bispecific antibodies. Despite these promising applications, several biological and technical limitations still restrict routine clinical implementation, including low ctDNA concentration in patients with minimal disease burden and lack of methodological standardization. Overall, ctDNA represents a rapidly evolving tool with significant potential to improve personalized disease monitoring and therapeutic strategies in MM. Full article

Snow depth and persistence strongly influence ground–atmosphere energy exchange, meltwater input, and the thermal regime of rock glacier systems, yet high-resolution snow monitoring remains scarce in the Southern Carpathians. This study uses multi-temporal Unmanned Aerial Vehicle (UAV) Structure-from-Motion (SfM) photogrammetry to map snow-depth variability and microtopographic controls on the Galeșu Rock Glacier, Retezat Mountains. Eight UAV surveys were conducted between 2023 and 2025, including seven snow-covered acquisitions and one snow-free reference survey in August 2025. Snow depth was derived by DEM differencing and analyzed against morphometric indices, mainly profile curvature and relative topographic position. Results reveal strong spatial heterogeneity, with recurrent snow accumulation in furrowed, concave, and depressional sectors and reduced snow depth on local topographic highs. The 2024 surveys showed substantially deeper snow than 2025, with mean snow depths of 1.82 m in February and 1.62 m in March 2024, compared with 0.72 m and 0.83 m in February and March 2025. April 2023 displayed the deepest snowpack, with a mean snow depth of 2.27 m. Class-based analysis showed median contrasts of 2.30 m in 2024 and 1.20 m in 2025 between strong negative and strong positive curvature classes. These findings demonstrate that rock glacier microtopography exerts a first-order control on snow accumulation and persistence, providing a basis for future studies linking snow redistribution to ground thermal regimes, meltwater pathways, and ground-ice preservation in marginal periglacial environments. Full article

Catalyst deactivation shifts the optimal operating region of exothermic fixed-bed reactors, yet most reactor digital twins focus on monitoring rather than catalyst-state-aware operating decisions. This work presents a simulation-based self-optimizing digital-twin prototype integrating a physics-based reactor model, a moving-window constrained activity estimator, and a target-optimization layer for o-xylene oxidation to phthalic anhydride in a vanadia–titania heat-exchanged fixed-bed reactor. Sparse axial temperature and conversion measurements are reconciled to estimate an axial catalyst activity profile; gas and coolant inlet temperatures are then updated subject to a hot-spot safety constraint. The estimator achieved an activity-profile root mean square error (RMSE) of 0.075, an outlet-conversion RMSE of 0.99 percentage points, and an outlet-temperature RMSE of 1.85 K. Under the baseline noisy-measurement scenario, estimated activity optimization raised the mean phthalic anhydride yield from 46.3% under fixed targets to 61.9%, within 0.14 percentage points of the true-activity optimum, while maintaining the maximum reactor temperature below 730 K. In this matched-model simulation study, this corresponds to recovering approximately 99.1% of the yield improvement available with perfect catalyst-state knowledge. The policy remained superior to fixed-target operation across all tested noise levels, sensor configurations, and kinetic pre-exponential perturbations. All results are obtained from synthetic-measurement simulations rather than experimental or plant data, and plant validation is still required to quantify structural model error. The findings demonstrate the value of linking catalyst-state estimation to operating-target adaptation in a reproducible catalytic-reactor digital-twin workflow. Full article

This study explores current applications of exergaming in healthcare, with a focus on how the Otago Exercise Program—a structured, evidence-based program designed to improve strength and balance in older adults—integrated into the FallSensing exergames, contributes to improving older adults’ functioning. It also aims to generate evidence to support the optimization of sensor-based technologies for more personalized and adaptable exercise interventions. Community-dwelling older adults (≥60 years) were recruited from facilities in Coimbra, Portugal, and allocated into an exergames group (IG; n = 27) and a control group (CG; n = 34). The CG maintained usual daily activities, while the IG completed an 8-week (16-session) exergame-based program. After completing the program, the CG showed a decline in functional ability, whereas the IG demonstrated significant improvements in the Step Test (p = 0.001), Four-Stage Balance Modified Test (p = 0.001), Self-Efficacy for Exercise Scale (p = 0.009), and Activities and Participation Profile Related to Mobility questionnaire (p < 0.001). Exergaming was safe and effective in enhancing functional ability, participation, and self-efficacy in older adults. However, careful consideration of exercise frequency, intensity, and participants’ age is recommended when prescribing exergame-based interventions. These results also highlight another interesting topic among physiotherapists who prescribe and monitor exergames, that technology developers should consider exercise-time monitoring systems that integrate physical (e.g., eye, facial, and mouth features) and physiological signals to enhance fatigue detection accuracy. Full article

This paper presents a single-sensor solution for the detection and monitoring of power supply voltage unbalances in AC/DC/AC converters. By analyzing the DC-link input current, it is demonstrated that characteristic current pulse deformations directly indicate the presence and severity of grid voltage imbalances. Based on these pulse profile variations, a novel diagnostic metric termed the Current Ripple Voltage Unbalance Factor (CRVUF) is derived. The proposed methodology was comprehensively evaluated and validated through both simulation studies and experimental testing. The presented approach offers a computationally efficient, non-invasive alternative to conventional monitoring systems, effectively reducing the number of required voltage sensors from three to a single current sensor. Full article

Background: Precision medicine targets clinical remission, but traditional symptom-based frameworks often ignore the biological pathways driving the disease. Adequate T2 biomarker interpretation is crucial to identify phenotypes where FeNO reflects epithelial barrier distress. We investigated the “clinical-biological gap” through the analysis of six groups to distinguish profiles of symptomatic resilience from biological insensitivity to corticosteroids. Methods: Real-world study of 292 patients stratified into six groups based on BEC (≥250 cells/microL), FeNO (≥25 ppb), and IgE (≥100 kU/L). ACT, BMI, FEV1, comorbidities, and therapeutic burden were analyzed. Results: The EoS+ IgE+ group showed “symptomatic resilience”: apparent clinical control (54.5% ACT ≥ 20) supported by a critical pharmacological burden (90% salbutamol, 80% OCS ≥ 7.5 mg). The Triple Positive and EoS+ FeNO+ groups depicted “Maximum Steroid Insensitivity,” characterized by poor control despite maximal ICS/OCS regimens. FeNO could appear like the primary driver of instability, reflecting a trajectory of oxidative stress not altered by steroids. Conclusions: T2 biomarkers provide a unique prognostic value unidentifiable by standard clinical assessment. FeNO monitoring unmasks silent distress in resilient patients and identifies the “therapeutic wall” in refractory profiles. Therefore biological remission could represents the only effective target to modify the natural history of the disease. Full article

This study develops and applies a three-factor matrix model for assessing counterparty risk within Anti-Money Laundering (AML) measures. The research problem arises from the need to transform qualitative features related to ownership, effective control, economic logic of operations, geographical links, documents and review behavior into a comparable and documented risk score. The model uses three factors: counterparty exposure, transaction impact and control vulnerability. Each factor is assessed on a five-point ordered scale, and the overall risk score is obtained by multiplying the three values. The methodology includes scoring rules, rating interpretation, an application algorithm and a link between the profile, due diligence, ongoing monitoring and updating. The model was applied in 2026 to 25 anonymized counterparty profiles selected for methodological demonstration across different risk configurations. Identifying data, personal information and confidential business information were removed, while the relevant risk characteristics were preserved. The results demonstrate how the model distinguishes between low, moderate, increased, high and critical risk and how it creates a traceable link between established facts, numerical assessment and follow-up control actions. The study does not claim predictive accuracy, detection of money laundering or empirical validation against external AML outcomes. Its contribution is methodological and applied: it provides a transparent and explainable framework for structured, documented and proportionate management of counterparty AML risk, including potential use in AI-assisted monitoring under human oversight. Full article

High-throughput firewall telemetry increasingly demands real-time interpretation from short, dense observation windows rather than long historical baselines. We operationalize behavioral slicing, a privacy-preserving approach that extracts interpretable micro-behavioral signals—timing irregularities, destination dispersion (entropy), and intensity cues—from sub-minute traffic segments. This study does not claim confirmed intrusion detection. Instead, it evaluates whether short-window firewall telemetry can support privacy-preserving candidate triage under high-throughput conditions and quantifies the limits, feature stability, detector complementarity, and real-time feasibility of such a pipeline. Our case study uses a real operational firewall telemetry slice that was irreversibly anonymized prior to analysis. Using unsupervised outlier detection with explainable summaries, the slice-level pipeline produces investigation-oriented anomaly candidates for triage, without making longitudinal routine or habit claims. Synthetic anomaly injection is used only for relative validation, while behavioral findings are derived from the real telemetry slice. Scenario-level ablations show strong separation for scanning and exfiltration but weak performance for low-variance C2 beaconing, indicating that behavioral slicing should be treated as a triage pre-filter rather than a universal detector. In the context of IoT and edge-network monitoring, firewall telemetry can be treated as a high-rate network sensing stream that reflects device-level communication behavior under operational constraints. Overall, results support behavioral slicing as an operationally feasible pre-filtering and triage method under extreme network load, with clear trade-offs and limits. Full article

This paper addresses the geomorphic response of an engineered Chicago beach to a >1.5 m rise in Lake Michigan’s base water level, from 2013 to 2020. Topographic monitoring data acquired since 2012 and subsurface geophysical data collected in 2022 are integrated to explore the roles of lakefront infrastructure and beach topographic development on sedimentary dynamics, beach morphologic development, and stratigraphic architecture. While conceptual models of coastal geomorphology infer upward and landward beach-profile translation with lake-level rise, a high degree of along-shore variance occurs within pocket beaches. This stems from infrastructure-related modifications of storm hydrodynamics and scour patterns, close to shore, and backshore terrain physiography. The studied beach evolved contrary to how regional littoral drift patterns would have suggested, with erosion most severe along the embayment’s downdrift end, a product of infrastructure-induced scour and the reduced capacity for sediment retention through overwash accretion. Documented geomorphic patterns with lake-level rise are manifested in subsurface imaging data from the lake-level highstand, accordingly, providing a guide to more regional paleo-reconstruction. Great Lakes urban pocket beaches buffer shoreline infrastructure, offer recreational terrains, and support dune ecosystems of value to migrating shorebirds. Understanding their geomorphology benefits coastal managers looking to mitigate impacts of future climate and lake-level change. Full article

Background/Objectives: Breast cancer (BC) remains the most frequently diagnosed malignancy among women worldwide, and patient outcome is strongly influenced by disease stage at diagnosis. Although imaging-based screening has improved early detection, its performance may be reduced in dense breast tissue and is associated with false-positive findings. In addition, tissue biopsy is invasive and unsuitable for longitudinal disease monitoring. Liquid biopsy (LB) has emerged as a minimally invasive approach for detecting tumor-derived material in peripheral blood. However, early-stage tumors typically exhibit low tumor burden and limited biomarker shedding, generating weak systemic signals that challenge reliable detection. This review examines current and emerging LB biomarkers for early BC detection. Methods: A comprehensive review of recent literature was conducted focusing on circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating RNAs, proteins, and other blood-based biomarkers associated with early BC. Studies addressing biomarker biology, detection technologies, clinical applications, and methodological limitations were critically evaluated. Results: ctDNA, CTCs, EVs, circulating RNAs, proteins, and additional blood-based biomarkers capture distinct aspects of tumor biology and disease evolution. ctDNA enables the analysis of tumor-specific mutations, methylation patterns, and fragmentation profiles, whereas CTCs provide direct cellular and phenotypic information despite their rarity and marked epithelial–mesenchymal plasticity. EVs offer increased molecular stability and actively participate in tumor progression, immune modulation, and metastatic niche formation. Nevertheless, low biomarker abundance, biological heterogeneity, technical variability, and background biological noise continue to limit analytical performance, particularly in early-stage disease. Current evidence further suggests that no single biomarker consistently provides sufficient sensitivity and specificity for reliable early BC detection. Conclusions: LB represents a promising strategy for non-invasive early BC detection. Future clinical implementation will likely depend on integrated multi-analyte approaches that combine complementary genomic, transcriptomic, proteomic, and cellular information, supported by multi-omics technologies and artificial intelligence-based analytical frameworks. Full article

Background and Objectives: Renal sinus fat accumulation affects many diabetic processes, including blood pressure. The aim of this study was to find out if there is a link between the amount of fat in the renal sinus and the blood pressure that changes throughout the day in adults with non-alcoholic fatty liver disease (NAFLD). Materials and Methods: This retrospective analysis involved 55 adult patients diagnosed with non-alcoholic fatty liver disease (NAFLD) based on abdominal computed tomography (CT) and who had undergone 24 h ambulatory blood pressure monitoring (ABPM). According to standard ABPM criteria, individuals whose night-time systolic blood pressure decreased by ≥10% compared to daytime values were classified as “dippers,” whereas those with a reduction of <10% were classified as “non-dippers”. Results: The non-dipper group had a significantly higher leukocyte count (p = 0.012). Renal sinus fat volume (RSFV) was significantly larger in the non-dipper group (6.41 ± 3 mL) than the dipper group (4.12 ± 2.26 mL; p = 0.005). In the ROC curve analysis showing the role of RSFV in predicting the non-dipper blood pressure pattern, the threshold value for RSFV in our cohort was determined as ≥3.7 mL with a positive predictive value of 72.2%. Conclusions: Our findings indicated that RSFV plays a significant role in predicting the non-dipper blood pressure profile among individuals diagnosed with NAFLD. Participants with an RSFV of 3.7 mL or more in our cohort were more likely to exhibit a non-dipping blood pressure pattern. Future studies are needed to validate these findings in larger cohorts. Full article