Mapping respiratory airflows and the transport mechanisms of inhaled aerosols characteristic of the deep regions of the lungs are of broad interest in assessing inhalation therapy outcomes. In the present talk, I will discuss our current understanding of such phenomena characterized by sub-millimeter 3D alveolated airspaces and low-Reynolds-number flows. I will exemplify advances brought forward by experimental efforts, in conjunction with numerical simulations, to revisit past mechanistic theories of respiratory airflow and particle transport, emphasizing the coupled roles of convection, diffusion, sedimentation and most recently electrostatics. I will highlight how microfluidics spanning the past decade have accelerated opportunities to deliver anatomically-inspired models that capture with sufficient realism and accuracy the leading mechanisms governing respiratory airflow and aerosol transport at true scale. Such efforts have provided previously unattainable in vitro quantifications on the local transport properties in the deep pulmonary acinar airways, with new paths to resolve mechanistic interactions between airborne particulate carriers and respiratory airflows at the pulmonary microscales.

Context: The rate of force development (RFD), defined as the ability to rapidly generate muscle force, is commonly tested using an electromechanical dynamometer in isometric mode. However, these devices are expensive and not readily available. Therefore, this study aims to evaluate the interrater reliability of a fixed handheld dynamometer to measure the knee extensors’ RFD and provide reference values using the proposed method. Design: This study used a cross-sectional study design. Methods: Using a fixed handheld dynamometer (microFET2) and a 3-dimensional-printed adapter, we evaluated the knee extensor muscles in participants seated at the edge of a treatment bed. Each participant performed a standardized warm-up, followed by 3 maximal isometric knee extension trials. The outcome measures were peak force and early and late phase RFD (0–100 and 0–200 ms, respectively). The study consisted of 3 sessions: Visit one comprised of an initial session (session 1A) followed by a second session (session 1B) after 30 minutes for intrasession reliability; and visit two, conducted on week later, comprised the third session (session 2) for intersession reliability. Results: Fifty-one participants were enrolled in the study. The in-session intraclass correlation coefficient for the early phase RFD was .87 (95% CI, .74–.92) and .91 to .92 (95% CI, .87–.94) for the late phase. The between-session intraclass correlation coefficient for the early phase RFD was .83 to .86 (95% CI, .74–.91) and .87 to .90 (95% CI, .80–.94) for the late phase. Finally, the peak force’s intraclass correlation coefficient was .95 (95% CI, .92–.97) for the in-session and .91 to .92 (95% CI, .86–.95) for the between-session reliability. Conclusions: Our approach provides a reliable, cost-effective, and quick method to evaluate the knee extensor muscles’ RFD and peak force.

Background: Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate. The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system. Methods: Long (>10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals' datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups. Results: Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity. Conclusion: In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

Expansion Microscopy is a super-resolution technique in which physically enlarging samples in an isotropic manner increases inter-molecular distances such that nano-scale structures can be resolved using light microscopy. This is particularly useful in neuroscience as many important structures are smaller than the diffraction limit. Since its invention in 2015, a variety of Expansion Microscopy protocols have been generated and applied to advance knowledge in many prominent organisms in neuroscience, including zebrafish, mice, Drosophila, and C. elegans. Here we review the last decade of Expansion Microscopy-enabled advances with a focus on neuroscience.

Duchenne muscular dystrophy (DMD) is caused by the absence of the full form of the dystrophin protein, which is essential for maintaining the structural integrity of muscle cells, including those in the heart and respiratory system. Despite progress in understanding the molecular mechanisms associated with DMD, myocardial insufficiency persists as the primary cause of mortality, and existing therapeutic strategies remain limited. This study investigates the hypothesis that a dysregulation of the biological communication between infiltrating macrophages (MPs) and neurocardiac junctions exists in dystrophic cardiac tissue. In a mouse model of DMD (mdx), this phenomenon is influenced by the over-release of chondroitin sulfate proteoglycan-4 (CSPG4), a key inhibitor of nerve sprouting and a modulator of the neural function, by MPs infiltrating the cardiac tissue and associated with dilated cardiomyopathy, a hallmark of DMD. Givinostat, the histone deacetylase inhibitor under current development as a clinical treatment for DMD, is effective at both restoring a physiological microenvironment at the neuro-cardiac junction and cardiac function in mdx mice in addition to a reduction in cardiac fibrosis, MP-mediated inflammation, and tissue CSPG4 content. This study provides novel insight into the pathophysiology of DMD in the heart, identifying potential new biological targets. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Single-molecule localization microscopy (SMLM) is a powerful tool for observing structures beyond the diffraction limit of light. Combining SMLM with engineered point spread functions (PSFs) enables 3D imaging over an extended axial range, as has been demonstrated for super-resolution imaging of various cellular structures. However, super-resolving structures in 3D in thick samples, such as whole mammalian cells, remains challenging as it typically requires acquisition and postprocessing stitching of multiple slices to cover the entire sample volume or more complex analysis of the data. Here, we demonstrate how the imaging and analysis workflows can be simplified by 3D single-molecule super-resolution imaging with long-axial-range double-helix (DH)-PSFs. First, we experimentally benchmark the localization precisions of short- and long-axial-range DH-PSFs at different signal-to-background ratios by imaging fluorescent beads. The performance of the DH-PSFs in terms of achievable resolution and imaging speed was then quantified for 3D single-molecule super-resolution imaging of mammalian cells by DNA-PAINT imaging of nuclear lamina protein lamin B1 in U-2 OS cells. Furthermore, we demonstrate how the use of a deep-learning-based algorithm allows the localization of dense emitters, drastically improving the achievable imaging speed and resolution. Our data demonstrate that using long-axial-range DH-PSFs offers stitching-free, 3D super-resolution imaging of whole mammalian cells, simplifying the experimental and analysis procedures for obtaining volumetric nanoscale structural information.

An ongoing thrombosis on a ruptured atherosclerotic plaque in the carotid may cause stroke. The primary treatment for patients with tandem lesion is stenting. Dual-layer stents have been introduced as an alternative to single-layer stents for elective and emergent carotid artery stenting. While the dual-layer structure shows promise in reducing plaque prolapse through the stent struts and with it the occurrence of post-procedural embolism, there are early signs that this newer generation of stents is more thrombogenic. We investigate a single- and a dual-layer stent design to assess their influence on a set of thrombosis-related flow factors in a novel setup of combined experiments and simulations. The in vitro results reveal that both stents reduce thrombus formation by approximately 50% when human anticoagulated whole blood was perfused through macrofluidic flow chambers coated with either collagen or human atherosclerotic plaque homogenates. Simulations predict that the primary cause is reduced platelet presence in the vicinity of the wall, due to the influence of stents on flow and cellular transport. Both stents significantly alter the near-wall flow conditions, modifying shear rate, shear gradient, cell-free zones, and platelet availability. Additionally, the dual-layer stent has further increased local shear rates on the inner struts. It also displays increased stagnation zones and reduced recirculation between the outer-layer struts. Finally, the dual-layer stent shows further reduced adhesion over an atherosclerotic plaque coating. The novel approach presented here can be used to improve the design optimization process of cardiovascular stents in the future by allowing an in-depth study of the emerging flow characteristics and agonist transport.

The neurovascular unit (NVU) is a critical interface in the central nervous system that links vascular interactions with glial and neural tissue. Disruption of the NVU has been linked to the onset and progression of neurodegenerative diseases. Despite its significance the NVU remains challenging to study in a physiologically relevant manner. Here, a 3D cell triculture model of the NVU is developed that incorporates human primary brain microvascular endothelial cells, astrocytes, and pericytes into a tissue system that can be sustained in vitro for several weeks. This tissue model helps recapitulate the complexity of the NVU and can be used to interrogate the mechanisms of disease and cell–cell interactions. The NVU tissue model displays elevated cell death and inflammatory responses following mechanical damage, to emulate traumatic brain injury (TBI) under controlled laboratory conditions, including lactate dehydrogenase (LDH) release, elevated inflammatory markers TNF-α and monocyte chemoattractant cytokines MCP-2 and MCP-3 and reduced expression of the tight junction marker ZO-1. This 3D tissue model serves as a tool for deciphering mechanisms of TBIs and immune responses associated with the NVU.

The invention is directed to a method for producing an edible composition, comprising incubating a three-dimensional porous scaffold and a plurality of cell types comprising: myoblasts or progenitor cells thereof, at least one type of extracellular (ECM)-secreting cell and endothelial cells or progenitor cells thereof, and inducing myoblasts differentiation into myotubes.

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.