Detecting new or worsening hypomobility in acute ischemic cerebrovascular patients is challenging, especially when asleep or unattended. This study used a wearable movement acceleration monitoring system to identify changes in these patients, aiming to improve early detection. Continuous bilateral upper limb acceleration data, clinical characteristics, specific treatments, stroke etiology, and in-hospital outcomes were collected from patients. The primary outcome was newly emerging or worsening hypomobility during monitoring. An XGBoost model, trained with synthetic minority oversampling to address class imbalance and validated via 5-fold cross-validation, analyzed movement acceleration features to diagnose hypomobility timing. Model performance was evaluated through AUC and feature importance metrics. From April 2023 to February 2025, 85 patients with acute ischemic cerebrovascular events were enrolled; three were excluded due to data errors. A total of 82 patients were included in the analysis, comprising 76 (92.7%) with ischemic stroke and 6 (7.3%) with transient ischemic attack. Among them, 26 (31.7%) were women. The median monitoring duration was 26.9 hours (IQR: 24.6–46.5), with 15 patients (18.3%) developing hypomobility. The XGBoost model achieved an AUC of 0.975 (95% CI: 0.965–0.985) and a mean AUC of 0.975 (SD 0.003) across folds. Optimized with a learning rate of 0.1, maximum depth of 6, and 200 boosting rounds, the model, at a cutoff of 0.587, recorded an average sensitivity of 0.969 and specificity of 0.900, accurately detecting 96.9% of the hypomobility cases. The overall metrics included a sensitivity of 0.966, specificity of 0.900, positive predictive value of 0.896, negative predictive value of 0.968, and F1-score of 0.930. The SHAP (SHapley Additive exPlanations) analysis revealed the significant contributions of the interaction terms (mean |SHAP| = 3.475) and slope features for movement changes (e.g., 1-min RSMA and LSMA slopes), while elevating the importance of the ‘Likely weak side’ predictor (mean |SHAP| = 2.053) in orienting asymmetry. This wearable movement acceleration monitoring system, by continuously tracking upper limb acceleration data, effectively detects the onset of hypomobility in acute ischemic cerebrovascular patients, highlighting its substantial potential for clinical application in enabling timely interventions and improving patient outcomes.

This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employing algorithms with high initial data volume and computations time to increase the accuracy is an important problem in prediction issues. Consequently, reduction of calculations is met by applying only two effective EEG signal channels without manual removal of artifacts by visual inspection as the algorithm’s input. Autoregression (AR) modeling and Cepstrum detect changes due to IT period. We carry out the goal of higher accuracy by increasing sensitivity to interictal epileptiform discharges or artifacts and reduce errors caused by them, taking advantage of the discrete wavelet transform and the comparison of two channels epochs by applying the median filter. Averaging and positive envelope methods are introduced to patient-specific thresholds become more differentiated as soon as possible and can be lead to sooner prediction. We examined this method on a mathematical model of adult epilepsy as well as on 10 patients with EEG data. The results of our experiments confirm that performance of the proposed approach in accuracy and average false prediction rate is superior to other algorithms. Simulation results have been shown the robustness of our proposed method to artifacts and errors, which is a step towards the development of real-time alarm devices by non-invasive techniques.

BackgroundMAR algorithms have not been productized in interventional imaging because they are too time-consuming. Application of a beam hardening filter can mitigate metal artifacts and doesn’t increase computational burden. We evaluate the ability to reduce metal artifacts of a 0.5 mm silver (Ag) additional filter in a Multidetector Computed Tomography (MDCT) scanner during CT-guided biopsy procedures.MethodsA biopsy needle was positioned inside the lung field of an anthropomorphic phantom (Lungman, Kyoto Kagaku, Kyoto, Japan). CT acquisitions were performed with beam energies of 100 kV, 120 kV, 135 kV, and 120 kV with the Ag filter and reconstructed using a filtered back projection algorithm. For each measurement, the CTDIvol was kept constant at 1 mGy. Quantitative profiles placed in three regions of the artifact (needle, needle tip, and trajectory artifacts) were used to obtain metrics (FWHM, FWTM, width at − 100 HU, and absolute error in HU) to evaluate the blooming artifact, artifact width, change in CT number, and artifact range. An image quality analysis was carried out through image noise measurement. A one-way analysis of variance (ANOVA) test was used to find significant differences between the conventional CT beam energies and the Ag filtered 120 kV beam.ResultsThe 120 kV-Ag is shown to have the shortest range of artifacts compared to the other beam energies. For needle tip and trajectory artifacts, a significant reduction of − 53.6% (p < 0.001) and − 48.7% (p < 0.001) in the drop of the CT number was found, respectively, in comparison with the reference beam of 120 kV as well as a significant decrease of up to − 34.7% in the artifact width (width at − 100 HU, p < 0.001). Also, a significant reduction in the blooming artifact of − 14.2% (FWHM, p < 0.001) and − 53.3% (FWTM, p < 0.001) was found in the needle artifact. No significant changes (p > 0.05) in image noise between the conventional energies and the 120 kV-Ag were found.ConclusionsA 0.5 mm Ag additional MDCT filter demonstrated consistent metal artifact reduction generated by the biopsy needle. This reduction may lead to a better depiction of the target and surrounding structures while maintaining image quality.

Neural interface devices interact with the central nervous system (CNS) to substitute for some sort of functional deficit and improve quality of life for persons with disabilities. Design of safe, biocompatible neural interface devices is a fast-emerging field of neuroscience research. Development of invasive implant materials designed to directly interface with brain or spinal cord tissue has focussed on mitigation of glial scar reactivity toward the implant itself, but little exists in the literature that directly documents the effects of electrical stimulation on glial cells. In this review, a survey of studies documenting such effects has been compiled and categorized based on the various types of stimulation paradigms used and their observed effects on glia. A hybrid neuroscience cell biology-engineering perspective is offered to highlight considerations that must be made in both disciplines in the development of a safe implant. To advance knowledge on how electrical stimulation affects glia, we also suggest experiments elucidating electrochemical reactions that may occur as a result of electrical stimulation and how such reactions may affect glia. Designing a biocompatible stimulation paradigm should be a forefront consideration in the development of a device with improved safety and longevity.

BackgroundGluten, a food allergen, is available in foods derived from wheat, rye and barley. It damages the small intestine and causes celiac disease. Herein, we designed a rapid immunochromatographic lateral flow test assay for detecting the gluten contents of raw materials. In this rapid test, the presence of gluten was screened through the capturing of gliadin (a toxic component of gluten) by two identical gliadin monoclonal antibodies. One of the antibodies was immobilized on the membrane in the test zone as a capture reagent. The other antibody was labeled with gold nanoparticles (AuNPs) as a detector reagent.ResultsGold nanoparticles with a size of about 20 nm were synthesized and conjugated to the gliadin monoclonal antibodies. The detection limit of the experimental assay was 20 ppm and positive results were visualized after 15 min using only 40 μL of the extracted sample for each test. Analysis of different flour samples identified the best sensitivity and specificity of the lateral flow test strip (LFTS).ConclusionThe experimental LFTS is an easy-to-use and rapid method for the screening of gluten level in raw materials. The LFTS may be employed to ensure the safety of foods.

BackgroundOrganoids are a reliable model used in the study of human brain development and under pathological conditions. However, current methods for brain organoid culture generate tissues that range from 0.5 to 2 mm of size, which need to be constantly agitated to allow proper oxygenation. The culture conditions are, therefore, not suitable for whole-brain organoid live imaging, required to study developmental processes and disease progression within physiologically relevant time frames (i.e. days, weeks, months).ResultsHere we designed 3D-printed microplate inserts adaptable to standard 24 multi-well plates, which allow the growth of multiple organoids in pre-defined and fixed XYZ coordinates. This innovation facilitates high-resolution imaging of whole-cerebral organoids, allowing precise assessment of organoid growth and morphology, as well as cell tracking within the organoids, over long periods. We applied this technology to track neocortex development through neuronal progenitors in brain organoids, as well as the movement of patient-derived glioblastoma stem cells within healthy brain organoids.ConclusionsThis new bioengineering platform constitutes a significant advance that permits long term detailed analysis of whole-brain organoids using multimodal inverted fluorescence microscopy.

BackgroundEnd-stage heart failure is a major risk of mortality. The conductive super-aligned carbon nanotubes sheets (SA-CNTs) has been applied to restore the structure and function of injured myocardium through tissue engineering, and developed as efficient cardiac pacing electrodes. However, the interfacial interaction between SA-CNTs and the surface cells is unclear, and it remains challenge to restore the diminished contraction for a seriously damaged heart.ResultsA concept of a multifunctional power assist system (MPS) capable of multipoint pacing and contraction assisting is proposed. This device is designed to work with the host heart and does not contact blood, thus avoiding long-term anticoagulation required in current therapies. Pacing electrode constructed by SA-­CNTs promotes the epithelial-mesenchymal transition and directs the migration of pro-regenerative epicardial cells. Meanwhile, the power assist unit reveals an excellent frequency response to alternating voltage, with natural heart mimicked systolic/diastolic amplitudes. Moreover, this system exhibits an excellent pacing when attached to the surface of a rabbit heart, and presents nice biocompatibility in both in vitro and in vivo evaluation.ConclusionsThis MPS provides a promising non-blood contact strategy to restore in situ the normal blood-pumping function of a failed heart.

BackgroundBismuth compounds are known for their activity against multiple microorganisms; yet, the antibiotic properties of bismuth nanoparticles (BiNPs) remain poorly explored. The objective of this work is to further the research of BiNPs for nanomedicine-related applications. Stable Polyvinylpyrrolidone (PVP)-coated BiNPs were produced by a chemical reduction process, in less than 30 min.ResultsWe produced stable, small, spheroid PVP-coated BiNPs with a crystalline organization. The PVP-BiNPs showed potent antibacterial activity against the pathogenic bacterium Staphylococcus aureus and antifungal activity against the opportunistic pathogenic yeast Candida albicans, both under planktonic and biofilm growing conditions.ConclusionsOur results indicate that BiNPs represent promising antimicrobial nanomaterials, and this facile synthetic method may allow for further investigation of their activity against a variety of pathogenic microorganisms.

BackgroundExcessive extracellular matrix (ECM) deposition is a hallmark feature in fibrosis and tissue remodelling diseases. Typically, mesenchymal cells will produce collagens under standard 2D cell culture conditions, however these do not assemble into fibrils. Existing assays for measuring ECM production are often low throughput and not disease relevant. Here we describe a robust, high content, pseudo-3D phenotypic assay to quantify mature fibrillar collagen deposition which is both physiologically relevant and amenable to high throughput compound screening. Using pulmonary fibroblasts derived from patients with idiopathic pulmonary fibrosis (IPF), we developed the ‘scar-in-a-jar’ assay into a medium-throughput phenotypic assay to robustly quantify collagen type I deposition and other extracellular matrix (ECM) proteins over 72 h.ResultsThis assay utilises macromolecular crowding to induce an excluded volume effect and enhance enzyme activity, which in combination with TGF-β1 stimulation significantly accelerates ECM production. Collagen type I is upregulated approximately 5-fold with a negligible effect on cell number. We demonstrate the robustness of the assay achieving a Z prime of approximately 0.5, and % coefficient of variance (CV) of < 5 for the assay controls SB-525334 (ALK5 inhibitor) and CZ415 (mTOR inhibitor). This assay has been used to confirm the potency of a number of potential anti-fibrotic agents. Active compounds from the ‘scar-in-a-jar’ assay can be further validated for other markers of ECM deposition and fibroblast activation such as collagen type IV and α-smooth muscle actin exhibiting a 4-fold and 3-fold assay window respectively.ConclusionIn conclusion, we have developed ‘scar -in-a-jar is’ into a robust disease-relevant medium-throughput in vitro assay to accurately quantify ECM deposition. This assay may enable iterative compound profiling for IPF and other fibroproliferative and tissue remodelling diseases.

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.