Human Skeletal Muscle Myoblast Culture in Aligned Bacterial Nanocellulose and Commercial Matrices.

Bacterial nanocellulose (BNC) is a durable, flexible, and dynamic biomaterial capable of serving a wide variety of fields, sectors, and applications within biotechnology, healthcare, electronics, agriculture, fashion, and others. BNC is produced spontaneously in carbohydrate-rich bacterial culture media, forming a cellulosic pellicle via a nanonetwork of fibrils extruded from certain genera. Herein, we demonstrate engineering BNC-based scaffolds with tunable physical and mechanical properties through postprocessing.

Wearable Multi-Sensor Positioning Prototype for Rowing Technique Evaluation.

The goal of this study is to determine the feasibility of a wearable multi-sensor positioning prototype to be used as a training tool to evaluate rowing technique and to determine the positioning accuracy using multiple mathematical models and estimation methods. The wearable device consists of an inertial measurement unit (IMU), an ultra-wideband (UWB) transceiver, and a global navigation satellite system (GNSS) receiver. An experiment on a rowing shell was conducted to evaluate the performance of the system on a rower's wrist, against a centimeter-level GNSS reference trajectory.

Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study.

Background: Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep learning models are capable of learning from data to accurately predict patient outcomes, but they typically require large amounts of data and computational resources. Transfer learning (TL) can help in scenarios where data and computational resources are scarce by leveraging pretrained models.

Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi.

Objective: The purpose of this study is to summarize the results from 3 experimental studies into the use of artificial intelligence to classify and segment colour fundus images with choroidal nevi.

Study Design: This study is based on a secondary analysis of colour fundus images taken of patients receiving usual clinical care from the Alberta Ocular Brachytherapy Program.

Methods: High-resolution colour fundus images were labeled by experienced ocular oncologists.

Precision improvement of robotic bioprinting via vision-based tool path compensation.

Robotic 3D bioprinting is a rapidly advancing technology with applications in organ fabrication, tissue restoration, and pharmaceutical testing. While the stepwise generation of organs characterizes bioprinting, challenges such as non-linear material behavior, layer shifting, and trajectory tracking are common in freeform reversible embedding of suspended hydrogels (FRESH) bioprinting, leading to imperfections in complex organ construction. To overcome these limitations, we propose a computer vision-based strategy to identify discrepancies between printed filaments and the reference robot path.

Predicting Aneurysmal Degeneration in Uncomplicated Residual Type B Aortic Dissection.

The formation of an aneurysm in the false lumen (FL) is a long-term complication in a significant percentage of type B aortic dissection (AD) patients. The ability to predict which patients are likely to progress to aneurysm formation is key to justifying the risks of interventional therapy. The investigation of patient-specific hemodynamics has the potential to enable a patient-tailored approach to improve prognosis by guiding disease management for type B dissection.

High-Resolution Peripheral Quantitative Computed Tomography in Rheumatic Diseases: A New Option for Knee Osteoarthritis.

High-resolution peripheral quantitative computed tomography (HR-pQCT) provides a new opportunity for the noninvasive measurement of bone microarchitecture in patient research of rheumatic disease in joints of the extremities. Rheumatoid arthritis of the hand and osteoarthritis of the knee are highly prevalent and there are no cures, so an improved understanding of the etiology of these diseases, particularly when combining HR-pQCT with other imaging modalities, is important to develop new disease management strategies. Analysis of HR-pQCT data is challenging due to the large size of the datasets, but the field continues to rapidly develop, and there is excellent opportunity to integrate sophisticated analyses for this novel research tool.

A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions.

Unlabelled: Prediction of well production from unconventional reservoirs is a complex problem given an incomplete understanding of physics despite large amounts of data. Recently, Data Analytics Techniques (DAT) have emerged as an effective approach for production forecasting for unconventional reservoirs. In some of these approaches, DAT are combined with physics-based models to capture the essential physical mechanisms of fluid flow in porous media, while leveraging the power of data-driven methods to account for uncertainties and heterogeneities.

Droplet Electrophoresis with Internal Free Ions: Effect of Permittivity Changes in the Electric Double Layer.

Droplet electrophoresis (EP) is of interest in biological systems, microfluidics, and separation techniques. We investigate EP of an oil droplet that contains free ions and is stabilized in an electrolyte solution through an amphoteric surfactant. The presence of mobile ions within the droplet leads to the creation of a distinct nonzero space charge density inside the droplet and consequently, formation of an inner EDL inside the droplet in addition to the traditionally considered outside EDL.

Blood vessel wall shear stress determines regions of liposome accumulation in angiogenic vasculature.

Nanoparticles used for drug delivery often require intravenous administration exposing them to fluid forces within the vasculature, yet the impact of blood flow on nanoparticle delivery remains incompletely understood. Here, we utilized transgenic zebrafish embryos to investigate the relationship between the accumulation of fluorescently labeled PEGylated liposomes and various hemodynamic factors (such as flow velocity, wall shear stress (WSS), and flow pattern) across a wide range of angiogenic blood vessels. We reconstructed 3D models of vascular structures from confocal images and used computational fluid dynamics to calculate local WSS, velocities, and define flow patterns.

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis.

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases.

Pipeline for Planning and Execution of Transcranial Ultrasound Neuromodulation Experiments in Humans.

Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique capable of manipulating both cortical and subcortical structures with high precision. Conducting experiments involving humans necessitates careful planning of acoustic and thermal simulations. This planning is essential to adjust for bone interference with the ultrasound beam's shape and trajectory and to ensure TUS parameters meet safety requirements.

Monthly treatment with romosozumab for 1 year increases bone mineral at the hip, but not the knee, in women with chronic spinal cord injury.

Bone loss below the level of neurological lesion is a well-known complication of spinal cord injury (SCI). To date, most research has focused on pharmaceutical intervention using antiresorptives to prevent bone loss during the acute phase of SCI; however, limited research has investigated treatments for established osteoporosis during chronic SCI. Romosozumab, a monoclonal antibody with both antiresorptive and anabolic effects, has demonstrated significant increases in BMD for women with established PMO.

Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear.

Introduction: Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans.

Status of breast cancer detection in young women and potential of liquid biopsy.

Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes.

Measurements and PR EoS Modeling of Thermophysical Properties at Vapor-Liquid Equilibrium Conditions for Natural Gas Condensate/Live Bitumen Mixtures.

In response to the need for less energy-intensive and greener bitumen recovery techniques, the use of multicomponent diluents through the expanding solvent steam-assisted gravity drainage (ES-SAGD) technique has garnered significant interest in recent years. In this work, we report new comprehensive measurements and Peng-Robinson equation of state (PR EoS) modeling of thermophysical properties (saturation pressure, density, viscosity, and -values) of multicomponent mixtures of methane-bitumen-solvent. The multicomponent solvent is a natural gas condensate comprised of C, -C, -C, -C, -C, C, and C.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data.

Extracellular Vesicles Generated by Mesenchymal Stem Cells in Stirred Suspension Bioreactors Promote Angiogenesis in Human-Brain-Derived Endothelial Cells.

Interrupted blood flow in the brain due to ischemic injuries such as ischemic stroke or traumatic brain injury results in irreversible brain damage, leading to cognitive impairment associated with inflammation, disruption of the blood-brain barrier (BBB), and cell death. Since the BBB only allows entry to a small class of drugs, many drugs used to treat ischemia in other tissues have failed in brain-related disorders. The administration of mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) has shown promise in improving the functional recovery of the brain following cerebral ischemia by inducing blood vessel formation.

A novel synthesis method of magnetic Janus particles for wastewater applications.

Hypothesis: Magnetic particles are widely used in many adsorption and removal processes. Among the many types of magnetic colloids, magnetic Janus particles offer significant possibilities for the effective removal of several components from aqueous solutions. Nevertheless, the synthesis of structures integrating different types of materials requires scalable fabrication processes to overcome the limitations of the available methodologies.

Measuring the prediction difficulty of individual cases in a dataset using machine learning.

Different levels of prediction difficulty are one of the key factors that researchers encounter when applying machine learning to data. Although previous studies have introduced various metrics for assessing the prediction difficulty of individual cases, these metrics require specific dataset preconditions. In this paper, we propose three novel metrics for measuring the prediction difficulty of individual cases using fully-connected feedforward neural networks.

Evolution of Hybrid Hydrogels: Next-Generation Biomaterials for Drug Delivery and Tissue Engineering.

Hydrogels, being hydrophilic polymer networks capable of absorbing and retaining aqueous fluids, hold significant promise in biomedical applications owing to their high water content, permeability, and structural similarity to the extracellular matrix. Recent chemical advancements have bolstered their versatility, facilitating the integration of the molecules guiding cellular activities and enabling their controlled activation under time constraints. However, conventional synthetic hydrogels suffer from inherent weaknesses such as heterogeneity and network imperfections, which adversely affect their mechanical properties, diffusion rates, and biological activity.

Super-strong and high-performance electrical film heater derived from silver nanowire/aligned bacterial cellulose film.

High-performance electrical Joule heaters with high mechanical properties, low driving voltage, rapid response, and flexibility are highly desirable for portable thermal management. Herein, by using aligned bacterial cellulose (BC) and silver nanowire (AgNW), we fabricated a novel film heater based on Joule heating phenomena. The aligned BC film prepared by stretching BC hydrogel and hot-pressing drying technology showed outstanding mechanical properties and flexibility.