Search Results (810)

Oxidative stress is caused by an imbalance between the formation of reactive oxygen species (ROS) and the activity of antioxidant defense system, which disrupts redox signaling and causes molecular damage. While there are numerous methods to measure oxidative stress, the complex and dynamic nature of ROS production and antioxidant reactions requires a multi-faceted approach. Direct methods such as electron spin resonance (ESR) and fluorescent probes measure ROS directly but are limited by the short lifespan of certain species. Indirect methods such as lipid peroxidation markers (e.g., malondialdehyde, MDA), protein oxidation (e.g., carbonyl content), and DNA damage (e.g., 8-oxo-dG) provide information on oxidative damage, but they do not capture the real-time dynamics of ROS. The antioxidant defense system, which includes enzymatic components such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), further complicates assessment, as it responds dynamically to oxidative challenges. Furthermore, the compartmentalized nature of ROS production in organelles and tissues coupled with the temporal variability of oxidative damage and repair underscores the need to integrate multiple assessment methods. This commentary highlights the limitations of using single assays and emphasizes the importance of combining complementary techniques to achieve a comprehensive assessment of oxidative stress. A multi-method approach ensures accurate identification of ROS dynamics, antioxidant responses, and the extent of oxidative damage, providing crucial insights into redox biology and its impact on health and disease. Full article

S-adenosylmethionine decarboxylase (AdoMetDC) is an essential enzyme in the polyamine biosynthesis pathway and plays a key role in the synthesis of the polyamines spermidine and spermine, polycationic alkylamines that are present in millimolar levels in mammalian cells. Polyamines are metabolic molecules that are involved in many fundamental processes, including regulation of protein and nucleic acid synthesis, stabilization of chromatin, differentiation, apoptosis, protection from oxidation, and regulation of ion channels. Multiple oncogenic pathways lead to dysregulation of polyamines, making polyamines a potential biomarker for cancer and polyamine biosynthesis a target for therapeutic intervention. This study uses multi-temperature crystallography to probe the structure and dynamics of AdoMetDC by collecting diffraction data at 100 K, 273 K, and 293 K. Differential loop behavior is observed across the collected datasets, with dramatic residue rearrangements. In the loop containing residues 20–28, the ambient temperature datasets show a large motion relative to the cryo structure. In a second loop containing residues 164–174, previous cryo structures do not report ordered positions. This loop is ordered in our 100 K structure, while assuming different conformations in the 273 K and 293 K data. These results further illustrate the usefulness of ambient data collection for understanding the structure and dynamics of proteins, especially in loop regions which are less restrained than protein cores. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

Background: Stiff-knee gait commonly involves rectus femoris spasticity in patients with central nervous system lesions. Diagnostic nerve blocks aid in predicting treatment outcomes; however, current techniques may overlook multiple nerve branches that innervate the rectus femoris muscle, potentially resulting in an incomplete assessment of treatment outcomes. Methods: We present an ultrasound-guided approach that we currently use in our practice, using anatomical landmarks, including the femoral artery, the sartorius muscle, and the rectus femoris’ characteristic “J-shaped” internal tendon. The technique employs an “elevator” scanning method to identify all motor nerve branches (typically 2–3) entering the proximal third of the rectus femoris muscle. Each branch is blocked using an in-plane needle approach with 1–2 mL of 2% lidocaine. Results: The technique enables the visualization of hyperechoic nerve branches entering the rectus femoris muscle from medial to lateral, sometimes accompanied by small vascular branches that are identifiable with a Doppler ultrasound. Optimal ultrasound settings include probes >8 MHz, appropriate focus positioning, and dynamic range < 60 dB. The multi-branch approach produces rapid-onset motor weakness (5–10 min). Conclusions: This comprehensive multi-branch rectus femoris nerve block technique may enhance diagnostic accuracy for spasticity assessment, potentially leading to more informed treatment selection for stiff-knee gait. Full article

Objectives: Point-of-care ultrasound (POCUS) has become an integral part of emergency, intensive care, and perioperative medicine. However, the training and subsequent evaluation of POCUS users are still not standardized. The aim of the study was to develop and validate an assessment tool for POCUS users. Methods: After reviewing the existing literature and a multi-stage expert survey (Delphi method), consensus on twelve items for the assessment tool was reached. To validate the assessment tool, a group of volunteer doctors and medical students performed a POCUS examination using simple linear probe and more complex sector probe techniques. The examination was evaluated by two independent assessors using the created assessment tool. Then, four experts evaluated anonymized recordings of the examinations. We tested the reliability and validity, including internal consistency. Results: A total of 70 examinations were included. Of these, 19 examinations were carried out by physicians and 51 by medical students. A high inter-rater reliability (Cohen’s kappa 0.78 (linear weighted; SEM 0.37; p < 0.001) and Krippendorff’s alpha 0.895) was shown for the evaluation tool. To improve discriminative power and strengthen reliability, the assessment tool was modified using Cronbach’s alpha. Modification resulted in the removal of three items (patient positioning, ultrasound mode selection, and probe selection) from the tool. The mean values of instrument and expert ratings were now 2.62% apart (46.90% instrument vs. 44.29% expert). Pearson’s correlation coefficient between tool and expert ratings showed moderate to high validity (r = 0.69; p < 0.001). Conclusions: The new assessment tool is highly reliable and a valid tool for assessing POCUS skills. It holds strong potential for integration into medical education and training to objectify ultrasound skills. Further studies are required to investigate discriminatory power and transferability to other POCUS algorithms. Full article

Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods: In this multi-reader, multi-case study, 14 clinicians of varying experience reviewed 374 retrospectively selected LUS scans (cine clips from the PLAPS point, obtained using three different probes) from 359 patients across six centers in the U.S. and Canada. In phase one, readers scored the likelihood (0–100) of pleural effusion and consolidation/atelectasis without AI. After a 4-week washout, they re-evaluated all scans with AI-generated bounding boxes. Performance metrics included area under the curve (AUC), sensitivity, specificity, and Fleiss’ Kappa. Subgroup analyses examined effects by reader experience. Results: For pleural effusion, AUC improved from 0.917 to 0.960, sensitivity from 77.3% to 89.1%, and specificity from 91.7% to 92.9%. Fleiss’ Kappa increased from 0.612 to 0.774. For consolidation/atelectasis, AUC rose from 0.870 to 0.941, sensitivity from 70.7% to 89.2%, and specificity from 85.8% to 89.5%. Kappa improved from 0.427 to 0.756. Conclusions: AI assistance enhanced clinician detection of pleural effusion and consolidation/atelectasis in LUS scans, particularly benefiting less experienced users. Full article

Cryptosporidium is a major waterborne parasite that causes gastrointestinal illness. Conventional assays, including microscopy and immunological identification, often suffer from false positives or negatives due to non-specific binding or morphological differences between Cryptosporidium species. We developed a novel qPCR assay based on a Cryptosporidium-specific Conserved Signature Protein (CSP) to address the limitations of testing complex samples, including those from recreational waters. The CSP (hypothetical protein (cgd2_3830)) was identified as taxonomically unique to Cryptosporidium species. The CSP sequence and designed qPCR assay primers/probe demonstrated high specificity for the targeted Cryptosporidium species when tested against NCBI RefSeq databases. qPCR assay efficiency was determined as 95% and an R² value of 0.99, with a slope and intercept of −3.4 and 40.1, respectively. Additionally, the Lower Limit of Detection (ALLOD) was determined as three gene copies, suggesting the potential to detect even a single oocyst. No non-specific amplification products or primer dimers were observed when the qPCR assay was evaluated using recreational water, fecal solution, and wastewater, while spike-in-control tests indicated minimal interference with the sensitivity of the assay, highlighting application for testing complex environmental DNA extracts. These findings highlight the application of the novel CSP-based qPCR assay for the rapid and sensitive detection of Cryptosporidium sp., thereby circumventing the sequence variability and multi-copy limitations associated with existing molecular markers. This proof-of-concept study presents a diagnostic framework utilizing CSP-based markers for developing water quality monitoring strategies, with scope for expansion to other microbial pathogens and potential applications in clinical and food safety settings. Full article

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe. Full article

Targeted sequencing, a pivotal branch of next-generation sequencing (NGS), enables the selective enrichment of specific genomic regions and has demonstrated significant advantages in the detection of animal pathogens. This review systematically explores the underlying principles of targeted sequencing, various enrichment strategies—including PCR amplification, probe hybridization, and CRISPR-Cas systems—and their key applications in veterinary pathogen diagnostics. Due to its high throughput, sensitivity, and cost-effectiveness, targeted sequencing has been successfully applied in the multiplex detection of pathogens in economically significant livestock, such as cattle, as well as in the surveillance of antimicrobial resistance (AMR) genes, pathogen typing, and source tracing. It is particularly effective in identifying mixed infections and low-abundance pathogens. Nonetheless, wide application is restricted by some factors, like incomprehensive reference databases, cost-effectiveness, and limited application in primary-level laboratories. Further development directions are AI-based panel design, multimodal diagnostic platform integration, standard workflow construction, and introduction of a multi-omics method. Such progress focuses on enhancing the targeted sequencing scalability and precision consistent with the “One Health” initiative objective. Full article

Benzene, a well-established human carcinogen and major industrial pollutant, poses significant health risks through occupational exposure due to its no-threshold effect, leading to multi-system damage involving the hematopoietic, nervous, and immune systems. This makes the investigation of its toxic mechanisms crucial for precise prevention and control of its health impacts. Programmed cell death (PCD), an orderly and regulated form of cellular demise controlled by specific intracellular genes in response to various stimuli, has emerged as a key pathway where dysfunction may underlie benzene-induced toxicity. This review systematically integrates evidence linking benzene toxicity to PCD dysregulation, revealing that benzene and its metabolites induce abnormal subtypes of PCD (apoptosis, autophagy, ferroptosis) in hematopoietic cells. This occurs through mechanisms including activation of Caspase pathways, regulation of long non-coding RNAs, and epigenetic modifications, with recent research highlighting the IRP1-DHODH-ALOX12 ferroptosis axis and oxidative stress–epigenetic interactions as pivotal. Additionally, this review describes a comprehensive monitoring system for early toxic effects comprising benzene exposure biomarkers (urinary t,t-muconic acid (t,t-MA), S-phenylmercapturic acid (S-PMA)), PCD-related molecules (Caspase-3, let-7e-5p, ACSL1), oxidative stress indicators (8-OHdG), and genetic damage markers (micronuclei, p14ARF methylation), with correlative analyses between PCD mechanisms and benzene toxicity elaborated to underscore their integrative roles in risk assessment. Furthermore, the review details analytical techniques for these biomarkers, including direct benzene detection methods—direct headspace gas chromatography with flame ionization detection (DHGC-FID), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and portable headspace sampling (Portable HS)—alongside molecular imprinting and fluorescence probe technologies, as well as methodologies for toxic effect markers such as live-cell imaging, electrochemical techniques, methylation-specific PCR (MSP), and Western blotting, providing technical frameworks for mechanistic studies and translational applications. By synthesizing current evidence and mechanistic insights, this work offers novel perspectives on benzene toxicity through the PCD lens, identifies potential therapeutic targets associated with PCD dysregulation, and ultimately establishes a theoretical foundation for developing interventional strategies against benzene-induced toxicity while emphasizing the translational value of mechanistic research in occupational and environmental health. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

In recent years, the generation of circularly polarized attosecond pulses has garnered significant attention due to their potential applications in ultrafast spectroscopy and, notably, in chiral-sensitive molecular detection. The traditional methods for generating such pulses often involve complex laser configurations or specially engineered targets, limiting their experimental feasibility. In this study, we present a streamlined and effective approach to producing circularly polarized attosecond pulses by employing a linearly polarized laser field in conjunction with a stereosymmetric linear molecule, 1-butyne (C₄H₆). The generation of high-order harmonics by this molecular system reveals a distinct plateau in the perpendicular polarization component, which facilitates the generation of isolated attosecond pulses with circular polarization. Through a detailed analysis of the time-dependent charge density dynamics across atomic sites, we identify the atoms primarily responsible for the emission of circularly polarized harmonics in the plane orthogonal to the driving field. Moreover, we explore the role of multi-orbital contributions in shaping the polarization properties of the harmonic spectra. Our findings underscore the importance of molecular symmetry and the electronic structure in tailoring the harmonic polarization, and they demonstrate a viable pathway for using circularly polarized attosecond pulses to probe molecular chirality. This method offers a balance between simplicity and performance, opening new avenues for practical applications in chiral recognition and ultrafast stereochemical analysis. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

The error motion of the precision rotary stage, particularly the tilt error motion, significantly influences the accuracy of machining and measuring equipment. Nonetheless, reliable and effective in situ measurement methods for tilt error motion are still limited. Based on the analysis of the conventional three-probe measurement method, this paper proposes a multi-probe measurement method using an ultra-precision reference plate with high-resolution displacement sensors. This method employs principles and methods to avoid harmonic suppression issues through optimal probe designs, enabling simultaneous quantification of tilt and axial error motions via error separation. Error separation techniques can effectively decouple motion errors from artifact form error, making them widely applicable in precision measurement data processing. Experimental validation confirmed that the synchronous measurement error is not greater than 4.69%, consequently affirming the metrological efficacy and reliability of the method. This study provides an effective method for real-time error characterization of rotary stages. Full article

The investigations examined a potential reduction in discrepancies between the values of the unperturbed radiofrequency (RF) electromagnetic field (EMF) and values of the EMF measured by wearable equipment (personal exposure meters) impacted by the proximity of the human body. This was done by modelling distributed wearable (multi-location, with up to seven simultaneously locations) measurements. The performed numerical simulations mimicked distributed measurements in 24 environmental exposure scenarios (recognized as virtual measurements) covered: the horizontal or vertical propagation of the EMF and electric field vector polarization corresponding to typical conditions of far-field exposure from wireless communication systems (at a frequency of 100–3600 MHz). Physical tests using three EMF probes for simultaneous measurements have been also performed. Studies showed that the discrepancy in assessing EMF exposure by an on-body equipment and the parameters of the unperturbed EMF in the location under inspection (mimicking the contribution to measurement uncertainty from the human body proximity) may be significantly reduced by the appropriate use of a distributed measurement system. The use of averaged values, from at least three simultaneous measurements at relevant locations on the body, may reduce the uncertainty approximately threefold. Full article