Search Results (6,161)

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) represent a robust platform for modelling inherited cardiac disorders. Comparative analysis of ion channel activity in patient-specific and isogenic control lines provides critical insights into the molecular mechanisms underlying channelopathies and arrhythmias. Atrial-specific hiPSC-CMs (hiPSC-aCMs) exhibit distinct electrophysiological properties governed by unique ion channel expression profiles, underscoring the need for optimized methodologies to record atrial ionic currents accurately. Here, we characterized the electrophysiological features of hiPSC-aCMs using the Nanion Patchliner automated patch-clamp system. An optimized cell dissociation protocol was developed to enhance cell integrity and seal formation, while tailored intra- and extracellular solutions were employed to isolate specific ionic currents. Using this approach, we reliably recorded major atrial currents, including the sodium current (I_Na), L-type calcium current (I_CaL), transient outward potassium current (I_to), ultrarapid component of the delayed rectifier current (I_Kur), small-conductance calcium-activated potassium current (I_SK), and pacemaker funny current (I_f). The resulting current profiles were reproducible and consistent with those observed in native atrial cardiomyocytes. These findings establish the feasibility of the automated electrophysiological characterization of ion channels in hiPSC-aCMs. This platform enables more efficient investigation of pathogenic variants and facilitates the development of targeted therapeutics for atrial arrhythmias and related channelopathies. Full article

The convergence of Internet of Things (IoT), embedded microcontrollers, and robotics has significantly transformed industrial and service applications under the Industry 5.0 paradigm. IoT-enabled automation not only reduces human intervention but also improves system efficiency, safety, and adaptability across multiple domains. The growing integration of automation technologies in manufacturing lines has significantly reduced human intervention while improving productivity and operational safety. Robotic arms play a crucial role in modern industrial environments, particularly for repetitive, hazardous, or precision-demanding tasks. This study presents a cost-effective robotic arm system for product selection, sorting and processing in automated production lines. The system operates in both automatic and manual modes and utilizes an ESP32-based controller, radio frequency identification (RFID) modules, and low-cost sensors to identify and transport products on a conveyor. A mobile, IoT-enabled interface provides remote real-time monitoring and control, while integrated safety mechanisms, current-voltage protections, and emergency stop circuitry enhance operational reliability. Using cost-effective components to reduce total cost, the system has been successfully validated through experiments to reduce labor dependency and operational errors, proving its scalability and economic viability for industrial automation. Compared to similar systems, this study presents an Industry 5.0 approach for low-cost IoT-based automated production lines. Full article

Intrusion detection systems (IDSs) must operate under severe class imbalance, evolving attack behavior, and the need for calibrated decisions that integrate smoothly with security operations. We propose a human-in-the-loop IDS that combines a convolutional neural network and a long short-term memory network (CNN–LSTM) classifier with a variational autoencoder (VAE)-seeded conditional Wasserstein generative adversarial network with gradient penalty (cWGAN-GP) augmentation and entropy-based abstention. Minority classes are reinforced offline via conditional generative adversarial (GAN) sampling, whereas high-entropy predictions are escalated for analysts and are incorporated into a curated retraining set. On CIC-IDS2017, the resulting framework delivered well-calibrated binary performance (ACC = 98.0%, DR = 96.6%, precision = 92.1%, F1 = 94.3%; baseline ECE ≈ 0.04, Brier ≈ 0.11) and substantially improved minority recall (e.g., Infiltration from 0% to >80%, Web Attack–XSS +25 pp, and DoS Slowhttptest +15 pp, for an overall +11 pp macro-recall gain). The deployed model remained lightweight (~42 MB, <10 ms per batch; ≈32 k flows/s on RTX-3050 Ti), and only approximately 1% of the flows were routed for human review. Extensive evaluation, including ROC/PR sweeps, reliability diagrams, cross-domain tests on CIC-IoT2023, and FGSM/PGD adversarial stress, highlights both the strengths and remaining limitations, notably residual errors on rare web attacks and limited IoT transfer. Overall, the framework provides a practical, calibrated, and extensible machine learning (ML) tier for modern IDS deployment and motivates future research on domain alignment and adversarial defense. Full article

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value. Full article

Large Language Models (LLMs) are emerging technologies and a growing research trend in Artificial General Intelligence (AGI), which envisions a future where machines can think and learn like humans across a wide range of tasks. Information generated by LLMs is essentially the prediction of next tokens in Natural Language Processing (NLP) tasks. However, the generated content is always subject to issues of truthfulness and hallucinations. The information and knowledge integrity of LLM-generated content therefore remains subjective. Exploring recent literature on the integrity of LLMs in a systematic manner is both timely and essential. Moreover, ensuring the reliability of LLMs in real-world applications is critical. Various approaches have been explored to promote information and knowledge integrity in LLMs, including adversarial training, data augmentation, and calibration methods. However, beyond these techniques, other strategies also contribute to maintaining knowledge integrity. This paper specifically focuses on three such approaches: knowledge distillation, semantic integrity, and provenance tracking, which play essential roles in ensuring that LLMs generate accurate, consistent, and trustworthy information. Knowledge distillation enhances model efficiency by transferring knowledge from larger models to smaller ones while preserving essential learning without compromising knowledge integrity. This reduces hallucinations. Semantic integrity safeguards consistency and strengthens the robustness of generated outputs. It is concurrently checking the meaningfulness of the outputs with the context. Provenance tracking improves transparency and trustworthiness through mechanisms such as data lineage and explainability, thereby ensuring the credibility of the LLM-generated responses. This review suggests that knowledge distillation, semantic integrity, and provenance tracking can enhance the reliability of LLM outputs, with prior studies reporting reductions in hallucination rates, improvements in robustness, and gains in factual consistency. Full article

Polymyxins, including polymyxin B (PMB), are last-resort antibiotics against multidrug-resistant Gram-negative infections in humans and livestock. Residual polymyxins from wastewater and manure can accumulate in soil, facilitating the emergence and spread of polymyxin resistance. Paenibacillus polymyxa, a natural polymyxin producer used in crop cultivation, may increase soil polymyxin burden. Since PMB strongly adsorbs to soil, its reliable quantification has been challenging. To address this, the extraction solvent and solid-phase extraction procedure were optimized to improve recovery and reduce matrix effects. We developed and validated a UPLC-MS/MS method to quantify PMB in soil. The method showed linearity (10–1000 ng/g), with a limit of detection of 0.86 ng/g and a limit of quantification of 2.12 ng/g. Method validation confirmed acceptable analytical performance. A 28-day monitoring of PMB in soil inoculated with varying P. polymyxa doses revealed a dose-dependent increase over the first 14 days, followed by a decline; PMB remained detectable on day 28. Ecological risk assessment using the risk quotient (RQ) indicated that PMB levels in the high-dose group (2 × 10⁸ CFU/100 g) approached the high-risk threshold (RQ ≥ 1) on day 14, while lower doses posed low to medium risk. This work provides a soil PMB quantification method and insight into the ecological risk of P. polymyxa application. Full article

Fluoride is a crucial inorganic anion found in drinking water, which may pose serious health hazards to human health if consumed in excess amounts. The quantification of fluoride in drinking water with high sensitivity, selectivity, and cross-sensitivity is critical. Given these factors, the present work proposes a spectroelectrochemical sensing platform for fluoride sensing using 5,10,15,20-tetraphenyl-21H,23H-porphine iron (III) chloride (FeTPP), and tetrabutylammonium perchlorate (TBAP) as the electrolyte. The proposed spectroelectrochemistry (SEC) is a hybrid platform that concurrently provides spectroscopic and electrochemical information about a system susceptible to oxidation and reduction. An ensemble–based multivariate prediction model was developed to simultaneously analyze electrochemical and spectroscopic data to predict fluoride concentration with enhanced reliability and precision. The prediction model provided promising results with a coefficient of determination of 0.9923 ± 0.0063 and a MSE of 0.369 ± 0.0596. These encouraging results demonstrate the promising performance of the proposed spectroelectrochemical platform in complex real-world applications. Full article

Background: Generative AI and synthetic media have enabled realistic human Embodied Conversational Agents (ECAs) or avatars. A subset of this technology replicates faces and voices to create realistic likenesses. When combined with avatars, these methods enable the creation of “digital twins” of physicians, offering patients scalable, 24/7 clinical communication outside the immediate clinical environment. This study evaluated surgical patient perceptions of an AI-generated surgeon avatar for postoperative education. Methods: We conducted a pilot feasibility study with 30 plastic surgery patients at Mayo Clinic, USA (July–August 2025). A bespoke interactive surgeon avatar was developed in Python using the HeyGen IV model to reproduce the surgeon’s likeness. Patients interacted with the avatar through natural voice queries, which were mapped to predetermined, pre-recorded video responses covering ten common postoperative topics. Patient perceptions were assessed using validated scales of usability, engagement, trust, eeriness, and realism, supplemented by qualitative feedback. Results: The avatar system reliably answered 297 of 300 patient queries (99%). Usability was excellent (mean System Usability Scale score = 87.7 ± 11.5) and engagement high (mean 4.27 ± 0.23). Trust was the highest-rated domain, with all participants (100%) finding the avatar trustworthy and its information believable. Eeriness was minimal (mean = 1.57 ± 0.48), and 96.7% found the avatar visually pleasing. Most participants (86.6%) recognized the avatar as their surgeon, although many still identified it as artificial; voice resemblance was less convincing (70%). Interestingly, participants with prior exposure to deepfakes demonstrated consistently higher acceptance, rating usability, trust, and engagement 5–10% higher than those without prior exposure. Qualitative feedback highlighted clarity, efficiency, and convenience, while noting limitations in realism and conversational scope. Conclusions: The AI-generated physician avatar achieved high patient acceptance without triggering uncanny valley effects. Transparency about the synthetic nature of the technology enhanced, rather than diminished, trust. Familiarity with the physician and institutional credibility likely played a key role in the high trust scores observed. When implemented transparently and with appropriate safeguards, synthetic physician avatars may offer a scalable solution for postoperative education while preserving trust in clinical relationships. Full article

Total joint arthroplasty is among the most common surgical procedures performed worldwide, with frequency increasing due to demographic changes. Accelerating the diagnostic process using new techniques is crucial for effective therapy. This pilot study aims to test such innovative technology in the context of periprosthetic joint infection (PJI) using Scattered Light Integrating Collector (SLIC) technology. While we wish to evaluate whether SLIC can be used to reliably detect the status of infection within human tissue samples in the future, our current research focused on building its foundation by evaluating steps of sample preparation that allow for heightened growth depiction. It is, to our knowledge, the first study concerning the usage of solid human tissue samples using the SLIC device. Adult patients presenting with native or periprosthetic joint infections were included in this prospective study. Biopsies were obtained using sequential sampling, and bacterial density was optimized through titration series. Cryopreservation and agents influencing coagulation were investigated. Our study demonstrates that simple pretreatment could aid in detecting pathogen growth in infected tissue samples. Findings showed a clear advantage for no addition of agents affecting coagulation. Additionally, our protocols proved reliable after prolonged cryopreservation at −20 °C for up to 8 weeks, showing no significant difference compared to primary testing. AUC comparison showed comparable results for sample storage at −80 °C for up to 8 weeks. Similar outcomes were seen for samples ranging from 25 µL to 300 µL, with biological replicates displaying higher thresholds for larger volumes without significant differences. This study introduces a simple and quick diagnostic tool for detecting bacterial growth using tissue biopsies and develops an SOP for further research with this innovative technique. The suggested SOP enables SLIC to hint at an underlying bacterial infection within 5 h using joint tissue, offering a possible novel approach in diagnosing periprosthetic joint infections and septic arthritis. While not yet designed to compare sensitivity to other culture methods, it provides a solid basis for further clinical research. Full article

Estimating bone age is essential for accurate diagnoses, appropriate care based on biological age, and fairness in legal matters. In the Indian medicolegal context, determining age through a clinical approach involves analyzing multiple joints; however, the traditional method can be tedious and subjective, relying heavily on human expertise, which may lead to biased decisions in age-related legal disputes. Moreover, commonly used radiographs often exhibit pixel-level variations due to heterogeneous contrast, which complicate segmentation tasks and lead to inconsistencies and reduced model performance. The study presents a multifocal region-based symbolic segmentation technique to automatically retain the soft-tissue region that harbors a growth pattern of an ossification center. Experimental results demonstrate an 84.5% Jaccard similarity, an 81.4% Dice coefficient, an 88.3% precision, a 90.0% recall, and a 91.5% pixel accuracy on a novel multifocal dataset of Indian inhabitants. The proposed segmentation technique outperforms U-Net, Attention U-Net, TransU-Net, DeepLabV3+, Adaptive Otsu, and Watershed segmentation in terms of accuracy, indicating strong generalizability across joints and improving reliability. Compared with 86.4% without segmentation, the proposed integration of segmentation with VGG16 classification increases the overall accuracy to 93.8%, demonstrating that target-focused-region processing reduces unnecessary computations and improves feature discrimination without sacrificing accuracy. Full article

Haseki tick virus (HSTV) is a recently discovered virus detected in human serum following tick bites, yet its protein repertoire remains uncharacterized. In this study, we applied an integrative approach based first on membrane topology prediction, followed by AI-based structural prediction and experimental validation to annotate the structural part of the HSTV polyprotein. For the first time, we recombinantly expressed one of the putative HSTV structural protein (SP1) and determined its overall architecture using small-angle X-ray scattering (SAXS). Structural comparisons of the AI-predicted HSTV SP1 models revealed only a vague resemblance to the pestiviral Erns and Npro. The strong agreement between experimental SAXS data and the AI-predicted HSTV SP1 model supported the conclusion that HSTV SP1 adopts a distinct spatial architecture in solution, one that is not captured by existing pestiviral structures but is reliably represented by modern AI-based prediction. Our findings indicate that HSTV SP1 adopts a fold not previously observed among characterized members of the Flaviviridae family. This work establishes a methodological pipeline for characterizing highly divergent viral proteins and provides the first insights into HSTV SP1, a virus with emerging zoonotic potential. These results lay the foundation for future functional and structural studies, diagnostic development, and evolutionary analyses of atypical Flaviviridae family members. Full article

The Industry 5.0 paradigm places humans and the environment at the center. New communication methods based on virtual reality (VR) and brain–computer interfaces (BCIs) can improve system–operator interaction in multimedia communications, providing immersive environments where operators can more intuitively manage complex systems. The study was conducted through a systematic literature review combined with bibliometric and thematic analyses to map the current landscape of VR-BCI communication frameworks in IIoT environments. The methodology employed included structured resource selection, comparative assessment of interaction modalities, and cross-domain synthesis to identify patterns, gaps, and emerging technology trends. Key challenges identified include reliable signal processing, real-time integration of neural data with immersive interfaces, and the scalability of VR-BCI solutions in industrial applications. The study concludes by outlining future research directions focused on hybrid multimodal interfaces, adaptive cognition-based automation, and standardized protocols for evaluating human–cyber-physical system communication. VR interfaces enable operators to visualize and interact with network data in 3D, improving their monitoring and troubleshooting in real time. By integrating BCI technology, operators can control systems using neural signals, reducing the need for physical input devices and streamlining operation (including touchless technology). BCI-based protocols enable touchless control, which can be particularly useful in situations where operators must multitask, bypassing traditional input methods such as keyboards or mice. VR environments can simulate network conditions, allowing operators to practice and refine their responses to potential problems in a controlled, safe environment. Combining VR with BCI allows for the creation of adaptive interfaces that respond to the operator’s cognitive load, adjusting the complexity of the displayed information based on real-time neural feedback. This integration can lead to more personalized and effective training programs for operators, enhancing their skills and decision-making. VR and BCI-based solutions also have the potential to reduce operator fatigue by enabling more natural and intuitive interaction with complex systems. The use of these advanced technologies in multimedia telecommunications can translate into more efficient, precise, and user-friendly system management, ultimately improving service quality. Full article

Amid a policy mandate to foster national identity in Hong Kong’s early childhood education sector, this study explores the complex intersection of pedagogy, home–school collaboration, and technology integration. Navigating this value-laden topic depends fundamentally on a strong partnership between parents and educators, yet the rapid push for artificial intelligence (AI) creates additional pressures. This qualitative study investigates the shared and conflicting perspectives of these key stakeholders. Eight focus groups were conducted with 21 parents and 26 educators from four diverse Hong Kong kindergartens. Data were analyzed using a novel human–AI collaborative thematic analysis to ensure analytical depth and reliability. The findings reveal a paradoxical consensus: while parents and educators agree on an experiential, play-based pedagogy, they hold divergent views on the division of responsibility. A further misalignment exists in communication ideals, with parents prioritizing efficiency and educators prioritizing relational nuance. Critically, a technology paradox emerges, pitting parents’ aspirational hopes for AI against educators’ pragmatic concerns over inadequate resources, training, and pedagogical suitability. The study concludes that a significant perception gap strains the home–school partnership. Simply introducing technology without first addressing these foundational human and resource-based misalignments risks widening, rather than bridging, the divide, offering important lessons for education systems globally. Full article

Objective: The objective of this study is to present contemporary findings regarding the relationship between the application of assisted reproduction methods and their impact on the incidence of multiple sclerosis. Design: This study adopts a narrative review design. Text: Assisted reproductive technology (ART) is increasingly used to treat human infertility. Due to the massive use of these techniques, it is increasingly important to record not only the course of fertilization and embryonic and fetal development of the individual but also the overall health status of the children born and their mothers. The incidence of autoimmune diseases continues to rise for reasons that remain unclear. One of the factors considered in connection with autoimmune disorders is ART. Opinions on the safety and reliability of ART methods are not consistent. Recently, extensive studies focusing on this issue have been presented and have not found a connection between infertility treatment with assisted reproductive techniques and the development of multiple sclerosis (MS). Conclusions: Current evidence suggests that, in adherence to the principles of evidence-based medicine and modern approaches to multiple sclerosis therapy, assisted reproduction in women with this disease is effective and does not pose a serious health risk. Therefore, it is necessary to always individualize therapy with regard to future pregnancy. Interdisciplinary cooperation on the timing of IVF therapy and minimizing the risk of MS exacerbation is also important. Full article

Large Language Models (LLMs) are increasingly used for rubric-based assessment, yet reliability is limited by instability, bias, and weak diagnostics. We present EvalCouncil, a committee-and-chief framework for rubric-guided grading with auditable traces and a human adjudication baseline. Our objectives are to (i) characterize domain structure in Human–LLM alignment, (ii) assess robustness to concordance tolerance and panel composition, and (iii) derive a domain-adaptive audit policy grounded in dispersion and chief–panel differences. Authentic student responses from two domains–Computer Networks (CNs) and Machine Learning (ML)–are graded by multiple heterogeneous LLM evaluators using identical rubric prompts. A designated chief arbitrator operates within a tolerance band and issues the final grade. We quantify within-panel dispersion via MPAD (mean pairwise absolute deviation), measure chief–panel concordance (e.g., absolute error and bias), and compute Human–LLM deviation. Robustness is examined by sweeping the tolerance and performing leave-one-out perturbations of panel composition. All outputs and reasoning traces are stored in a graph database for full provenance. Human–LLM alignment exhibits systematic domain dependence: ML shows tighter central tendency and shorter upper tails, whereas CN displays broader dispersion with heavier upper tails and larger extreme spreads. Disagreement increases with item difficulty as captured by MPAD, concentrating misalignment on a relatively small subset of items. These patterns are stable to tolerance variation and single-grader removals. The signals support a practical triage policy: accept low-dispersion, small-gap items; apply a brief check to borderline cases; and adjudicate high-dispersion or large-gap items with targeted rubric clarification. EvalCouncil instantiates a committee-and-chief, rubric-guided grading workflow with committee arbitration, a human adjudication baseline, and graph-based auditability in a real classroom deployment. By linking domain-aware dispersion (MPAD), a policy tolerance dial, and chief–panel discrepancy, the study shows how these elements can be combined into a replicable, auditable, and capacity-aware approach for organizing LLM-assisted grading and identifying instability and systematic misalignment, while maintaining pedagogical interpretability. Full article