Search Results (4,242)

In recent years, diagnostic technologies that utilize noninvasively collected sweat have garnered significant interest. However, the concentration of components in sweat is lower than that in blood, making the introduction of a concentration step as a sample pretreatment crucial for achieving highly sensitive detection. In this study, we developed a PDMS-based microchannel filled with silica gel, a desiccant particle, to concentrate liquid samples at room temperature without requiring an external power source or heating. The evaluation of the basic characteristics of the fabricated microchannel confirmed that filling it with silica gel efficiently removed the solvent vapor from the liquid samples. In concentration tests using the fluorescent dye uranine as a model for sweat sugar, a maximum 1.4-fold concentration was achieved in DPBS solution and a 1.2-fold concentration in artificial sweat at room temperature. In contrast, no similar concentration effect was observed in microchannels without silica gel packing. The proposed silica-gel-packed PDMS microchannel features a simple structure and requires no external equipment, making it easily integrable with existing microfluidic devices as a sample pretreatment module. This method is considered useful as a passive and simple sample concentration technique for the analysis of low-molecular-weight components in sweat. Full article

The development of efficient bioelectrodes requires suitable fabrication strategies, starting with the electrode material, which affects the electron transfer between the biocatalyst and the electrode surface. Then, selection and adjustment of the enzyme immobilization conditions are essential to enhance the performance of the bioelectrodes for their desirable utility. In this study, we report the fabrication of a high-performance bioelectrode using a one-step crosslinking of FAD-dependent glucose dehydrogenase (FAD-GDH) and thionine acetate as a redox mediator, with genipin serving as a natural, biocompatible crosslinker. Electrodes were manufactured on flexible polyester substrates using a direct printing technique, enabling reproducible and low-cost production. Among the tested crosslinkers, genipin significantly enhanced the catalytic performance of bioelectrodes. Comparative studies on graphite, silver, and gold electrode materials identified graphite as the most suitable due to its extended electroactive surface area. The developed bioelectrodes applied to glucose biosensing demonstrated a linear amperometric response to glucose in the range of 0.02–2 mM and 0.048–30 mM, covering clinically relevant concentrations. The application of artificial sweat confirmed high detection accuracy. These findings highlight the potential integration of genipin-based enzyme–mediator networks for future non-invasive sweat glucose monitoring platforms. Full article

Background/Objectives: Inter-individual variability in injury severity represents a major barrier to reproducibility in neonatal hypoxia–ischemia (HI) models. Objective early postoperative stratification of animals is therefore essential for standardized group allocation and reliable assessment of experimental outcomes. This study aimed to evaluate whether laser speckle contrast imaging (LSCI) can be used as a rapid, noninvasive tool for early post hoc stratification of ischemic brain damage severity in neonatal mice following HI. Methods: Neonatal CD-1 mice (postnatal day 9; n = 60) underwent hypoxia–ischemia using a modified Rice–Vannucci protocol. Cerebral perfusion was assessed by laser speckle contrast imaging at baseline, 3 h, and 7 days after HI. The difference in mean perfusion between ipsilateral and contralateral hemispheres at 3 h (Δ perfusion) was used to stratify animals into severity groups. Brain injury was quantified by 2,3,5-triphenyltetrazolium chloride (TTC) staining at 24 h and 7 days. Survival was monitored for 7 days and analyzed using Kaplan–Meier curves and the log-rank (Mantel–Cox) test. Results: LSCI-derived Δ perfusion at 3 h enabled the formation of distinct injury-severity groups (no visible damage, mild, moderate, and severe) with significant between-group differences (p < 0.0001). TTC-based lesion area increased stepwise across severity groups, and Δ perfusion correlated with lesion size when all animals were analyzed together (r = 0.688, p = 0.0011). No significant correlations were observed within individual severity groups, indicating that the overall association was driven primarily by between-group differences. Survival analysis revealed 75% mortality in the severe injury group (p < 0.0001). Conclusions: LSCI represents a robust and practical approach for early, objective, group-level stratification of neonatal mice by HI injury severity, thereby improving reproducibility and statistical validity in preclinical studies. However, its ability to predict outcomes within individual severity categories is limited, and repeated long-term measurements may pose technical challenges. Full article

Pregnancies complicated by diabetes, including pregestational and gestational diabetes mellitus, are associated with increased maternal and fetal morbidity. Early identification of at-risk pregnancies is crucial for timely intervention and improved outcomes. Emerging evidence highlights the interplay of genetic predisposition, epigenetic modifications, and non-invasive biomarkers in the early detection of diabetic pregnancies. Genetic factors influencing insulin signaling, glucose metabolism, and pancreatic β-cell function may contribute to susceptibility to gestational hyperglycemia. Concurrently, epigenetic alterations, such as DNA methylation and histone modifications in maternal and placental tissues, have been linked to dysregulated metabolic pathways and adverse pregnancy outcomes. Non-invasive biomarkers, including circulating cell-free DNA and microRNAs in maternal blood, show promise for early diagnosis by offering a safer and more practical alternative to invasive testing. Integrating genetic, epigenetic, and molecular marker data could enhance risk stratification and enable personalized monitoring and management strategies. This review synthesizes current knowledge on the molecular underpinnings of diabetic pregnancies, evaluates the potential of emerging biomarkers for early diagnosis, and discusses the challenges and future perspectives for translating these findings into clinical practice. Understanding these mechanisms may pave the way for precision medicine approaches, ultimately improving maternal and neonatal outcomes in pregnancies affected by diabetes. Full article

This study aimed to develop machine learning models based on conventional MRI features to classify HER2 expression levels in invasive breast cancer and explore their association with disease-free survival (DFS). A total of 678 patients from two centers were included, with Center 1 divided into training and internal test sets and Center 2 serving as an external test set. Random Forest models were trained to distinguish HER2-positive vs. HER2-negative (Task 1) and HER2-low vs. HER2-zero tumors (Task 2) using BI-RADS–based MRI features. SHapley Additive exPlanations were applied to rank feature importance, assist feature selection, and enhance model interpretability. DFS was analyzed using Kaplan–Meier curves and log-rank tests. In Task 1, key features included tumor size, axillary lymph nodes, fibroglandular tissue, peritumoral edema, and multifocal, achieving AUCs of 0.75 and 0.73 in the internal and external test sets, respectively. In Task 2, tumor size, peritumoral edema, and multifocal yielded AUCs of 0.73 and 0.72, respectively. Higher task-specific model scores were associated with shorter DFS in Task 1 (p = 0.037) and longer DFS in Task 2 (p = 0.046). MRI-based machine learning models can noninvasively stratify HER2 expression levels, with potential for prognostic stratification and clinical application. Full article

Objective: Alzheimer’s disease (AD) and other forms of dementia are a heterogeneous group of neurodegenerative diseases characterized by progressive cognitive decline. Differential diagnosis between AD and other dementias is crucial for choosing the optimal treatment strategy. Currently, cerebrospinal fluid (CSF) analysis remains the most accurate diagnostic method, but its invasiveness limits its use. In this regard, the search for reliable biomarkers in the blood is an urgent task. Methods: The study included 31 dementia patients (23 women and 8 men) diagnosed via interdisciplinary consultations and neuropsychological testing (MMSE ≤ 24). CSF and blood plasma samples were collected and analyzed using Luminex technology. Biomarker concentrations were measured, and statistical analyses (ANOVA, Kruskal–Wallis, and Pearson correlation) were performed to compare groups and assess correlations. Results: Levels of Aβ40 and Aβ42 in CSF were significantly lower in patients with AD compared with non-AD dementia (p = 0.02 and p < 0.001, respectively). The Aβ42/40 ratio in CSF was higher in patients with non-AD dementia (p = 0.048). The concentration of Aβ42 in blood plasma was increased in patients with AD (p = 0.001). Positive correlations were found between Aβ42 in CSF and TDP-43 in plasma in non-AD dementia (r = 0.97, p < 0.001), as well as between neurogranin and TDP-43 in plasma in AD (r = 0.845, p < 0.001). Conclusions: The study demonstrates the potential of blood biomarkers, in particular Aβ42, for the differential diagnosis of AD and other forms of dementia. The discovered correlations between CSF and plasma biomarkers deepen the understanding of neurodegenerative processes and contribute to the development of noninvasive diagnostic methods. Full article

Background: Urine cytology remains widely used for surveillance of non-muscle-invasive bladder cancer despite well-known limitations in sensitivity, especially for low-grade tumors. Uromonitor^®, a molecular assay detecting TERT promoter, FGFR3, and KRAS mutations in voided urine, has emerged as a promising adjunct. To evaluate its suitability for routine use, a consolidated assessment of diagnostic performance and a direct comparison with urine cytology are needed. Methods: We conducted a prospectively registered systematic review (PROSPERO CRD420251173244), synthesizing all available studies that evaluated Uromonitor^® for the detection of urothelial carcinoma of the bladder (UCB). Methodological quality was assessed using the QUADAS-2 framework, and certainty of evidence was evaluated following GRADE for diagnostic tests. Sensitivity was prespecified as the primary endpoint. Comparative datasets were identified, and random-effects meta-analyses were performed for sensitivity, specificity, accuracy, and predictive values (PVs). Results: Across eight cohorts evaluating Uromonitor^®, 832 of 3196 patients (26.0%) had histologically confirmed UCB. Aggregated sensitivity was 0.55 (95% CI 0.52–0.58). Specificity was 0.95 (0.94–0.96). Accuracy was 0.85 (0.83–0.86). PPV was 0.79 (0.76–0.82), and NPV was 0.86 (0.84–0.87). Across seven paired datasets, urine cytology demonstrated a sensitivity of 0.42, a specificity of 0.91, an accuracy of 0.78, a PPV of 0.64, and an NPV of 0.81. Pooled odds ratio for sensitivity was 3.16 (0.73–13.76), while diagnostic accuracy yielded 1.71 (1.01–2.90). Differences in specificity and NPV were not statistically significant, whereas the PPV favored Uromonitor^®, reaching statistical significance in pooled analyses. Conclusions: Uromonitor^® demonstrates higher sensitivity and improved accuracy compared with urine cytology, although current performance remains insufficient for stand-alone surveillance. The sensitivity estimate showed very low certainty due to pronounced heterogeneity, underscoring the need for careful interpretation. With advancing DNA recovery methods, incorporation of droplet digital PCR, and rigorous evaluations in prospective multicenter studies, Uromonitor^® may become an integral element of risk-adapted follow-up strategies. Full article

Background/Objectives: Previous studies have demonstrated that the cessation of liver damage after HCV cure can improve liver function, histological necroinflammation, and portal hypertension. However, scarce data about fibrosis stage or cirrhosis regression have been reported during follow-up. Methods: A prospective study evaluating hepatic biopsies and liver stiffness measurement by vibration-controlled transient elastography (VCTE-LSM) after the end of treatment (EOT) in patients with compensated advanced chronic liver disease (cACLD). Fibrosis was evaluated according to two semi-quantitative grading systems (METAVIR and Laennec) at 6 years after EOT (LB6) and compared with biopsies at 3 years (LB3). Results: Fifty-four patients with LB6 (34 with paired LB3–LB6) were included. Median (IQR) age was 53.9 (48.5–59.3), 38 (70.4%) were men, and 13 (24.1%) were HIV-coinfected. The VCTE-LSM was >15 kPa in 30 (55.6%). The LB6 (81.4 months after EOT) showed non-advanced fibrosis (F1–F2) in 12 (22.4%) patients, bridging (F3) in 26 (48.2%), and cirrhosis (F4) in 16 (29.6%): F4A in 7 (13.0%), F4B in 4 (7.4%), and F4C in 5 (9.3%). The 1-year post-EOT follow-up VCTE-LSM ≤ 8.6 kPa identifies patients without advanced fibrosis (AUROC = 0.929), with a negative predictive value of 88.9% and a positive predictive value of 95.2%. Paired biopsies showed regression in 9 (47.4%) out of 19 patients with cirrhosis: 8 (61.5%) of 13 with F4A but only 1 (16.7%) of 6 with F4B–F4C. Conclusions: Advanced fibrosis persists in most patients with advanced chronic liver disease after HCV eradication. Regression is possible in mild cirrhosis. However, it is a limited and slow event. Full article

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

The invasive hornet Vespa velutina nigrithorax was first recorded in Spain in 2010 and in Italy in 2012. Control strategies to reduce V. v. nigrithorax infestation level in apiaries include nest neutralization and trapping of adult hornets. Trapping methods are simpler, more cost-effective, and can be implemented directly by beekeepers without the use of insecticides; however, they are usually poorly effective or selective. While assessing trap effectiveness is essential for reducing V. v. nigrithorax pressure on hives, evaluating trap selectivity is equally crucial to minimize the capture of non-target insects, such as honey bees and native hornets like Vespa crabro, which exist in a delicate balance with the honey bees. During autumn 2024, five combinations of commercially available mechanical traps, tested with both a homemade and a commercial bait, were evaluated in Spain and Italy to determine the most effective and selective option against V. v. nigrithorax. The mean daily capture rate was significantly lower in Italy (0.19 ± 0.07) than in Spain (1.82 ± 0.39). Significant differences were observed among the five trap–bait combinations (p < 0.0001), with the VelutinaTrap^® (BeeVital GmbH, Vienna, Austria) associated with a homemade bait (sugar, yeast, and water) being the most effective. When trap design was considered independently of bait, VelutinaTrap^® remained the most effective option (p < 0.0001). In contrast, no significant differences were detected between bait types when analyzed irrespective of trap design (p = 0.524). Concerning selectivity, even though all tested traps showed positive results against A. mellifera, the combination VelutinaTrap^® associated with the homemade bait significantly outperformed in V. crabro selectivity. Further research is needed to develop more effective traps for capturing V. v. nigrithorax and to investigate environmental factors that influence variations in the attractiveness of the same trap and bait combinations across different seasons and geographical areas. Full article

Listeria monocytogenes (LM) is a lethal foodborne intracellular pathogen. Internalins A and B (inlA and inlB) are critical virulence factors that promote LM’s adhesion and invasion into host cells. InlA is covalently anchored to the cell wall by LM SortaseA (SrtA), while inlB is anchored to the cell wall via non-covalent bonds. Therefore, inhibiting SrtA and inlB is expected to suppress LM’s adhesion and invasion of host cells, enabling the prevention and control of infections. This study demonstrated that Luteolin inhibited the activity of purified LM SrtA protein in vitro. Interactive mechanism analysis indicated that Luteolin generates interaction with the critical active sites of SrtA, which may affect its binding to its natural substrates, thereby reducing the anchoring of inlA on the cell wall and achieving the inhibition of bacterial adhesion and invasion. In addition, Luteolin binds to the groove at the binding interface between inlB and its host receptor. The key residues in inlB that interact with the host receptor form weak interactions (Hydrogen bonds and van der Waals interactions) with Luteolin, this binding may inhibit their binding, suppressing LM’s adhesion and invasion of host cells. At the tested concentrations, Luteolin did not affect the growth of LM, but remarkably reduced the mortality and alleviated the infection symptoms of LM-infected Galleria mellonella. These results provide additional theoretical evidence for the application of Luteolin in the prevention and control of LM infections, which is expected to accelerate its application progress. Full article

Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust. Full article

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management. Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness. Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors. Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management. Full article

The structural analysis of paper fibers is vital for the noninvasive classification and conservation of traditional handmade paper in cultural heritage. Although digital still cameras (DSCs) offer a low-cost and noninvasive imaging solution, their inferior image quality compared to white-light confocal microscopy (WCM) limits their effectiveness in fiber classification. To address this modality gap, we propose an unpaired image-to-image translation approach using cycle-consistent adversarial networks (CycleGANs). Our study targets a multifiber setting involving kozo, mitsumata, and gampi, using publicly available domain-specific datasets. Generated WCM-style images were quantitatively evaluated using peak signal-to-noise ratio, structural similarity index measure, mean absolute error, and Fréchet inception distance, achieving 8.24 dB, 0.28, 172.50, and 197.39, respectively. Classification performance was tested using EfficientNet-B0 and Inception-ResNet-v2, with F1-scores reaching 94.66% and 98.61%, respectively, approaching the performance of real WCM images (99.50% and 98.86%) and surpassing previous results obtained directly from DSC inputs (80.76% and 84.19%). Furthermore, Grad-CAM visualization confirmed that the translated images retained class-discriminative features aligned with those of the actual WCM inputs. Thus, the proposed CycleGAN-based image conversion effectively bridges the modality gap, enabling DSC images to approximate WCM characteristics and support high-accuracy paper fiber classification, which is a practical alternative for noninvasive material analysis. Full article