While electrical circuits are a fundamental part of neural activity, the soft microstructural extracellular environment plays a vital role in driving the morphogenesis of neurons. To leverage this, we combine a wireless electromagnetic neural stimulation system with anisotropic GelMA hydrogels to promote and guide neural morphogensis. We show that oriented GelMA hydrogels support anisotropic growth of dorsal root ganglion as potential peripheral neural guide conduit and promote the formation of hippocampal neurospheres networking along the fiber direction. The central nervous system has limited regeneration due to glial scar formation, the presence of the perineuronal network, and limited upregulation of growth-associated genes post traumatic injuries. The system was further tested on human cortical brain tissue where hydrogel microfilaments promoted neurite outgrowth in a white-matter-mimicking manner between two pieces of brain slices, opening the possibility of central neural tissue regeneration.

Current approaches for localized intravascular treatments rely on using solid implants, such as metallic coils for embolizing aneurysms, or on direct injection of a therapeutic agent that can disperse from the required site of action. Here, we present a fluid-based strategy for localizing intravascular therapeutics that leverages surface tension and immiscible fluid interactions, to allow confined and focal treatment at brain aneurysm sites. We first show, computationally and experimentally, that an immiscible phase can be robustly positioned at the neck of human aneurysm models to seal and isolate the aneurysm’s cavity for further treatment, including in wide-neck aneurysms. We then demonstrate localized delivery and confined treatment, by selective staining of cell nuclei within the aneurysm cavity as well as by hydrogel-based embolization in patient-specific aneurysm models. Altogether, our interfacial flow-driven strategy offers a potential approach for intravascular localized treatment of cardiovascular and other diseases.

Implantable cardiac pacemakers are crucial therapeutic tools for managing various cardiac conditions. For effective pacing, electrodes should exhibit flexibility, deformability, biocompatibility, and high conductivity/capacitance. Laser-induced graphene (LIG) shows promise due to its exceptional electrical and electrochemical properties. However, the fragility of LIG and the non-stretchability of polyimide substrates pose challenges when interfacing with the beating heart. Here, we present a simple method for fabricating robust, flexible, and stretchable bioelectronic interfaces by transferring LIG via water-responsive, nonswellable polyvinyl alcohol (PVA) gels. PVA solution penetrates the porous structure of LIG and solidifies into PVA xerogel as the solvent evaporates. The robust PVA xerogel enables the smooth transfer of LIG and prevents stretching of the LIG network during this process, which helps maintain its conductivity. When hydrated, the xerogel becomes a stable, nonswellable hydrogel. This gives the LIG-PVA hydrogel (LIG-PVA-H) composites with excellent conductivity (119.7 ± 4.3Ω sq⁻¹), high stretchability (up to 420%), reliability (cyclic stretch under 15% strain, with ∼ 1-time resistance increase), and good stability in phosphate buffered saline. The LIG-PVA-H composites were used as biointerfaces for electrocardiogram signal recording and electrical pacing on rat hearts ex vivo and in vivo, using commercial setups and a custom-built implantable wireless device. This work expands the application of LIG in bioelectronic interfaces and facilitates the development of electrotherapy for cardiac diseases.

Granular hydrogels, formed by jamming microgels suspension, are promising materials for three-dimensional bioprinting applications. Despite their extensive use as support materials for embedded bioprinting, the influence of the particle’s physical properties on the macroscale viscoelasticity on one hand and on the printing performance on the other hand remains unclear. Herein, we investigate the linear and nonlinear rheology of κ-carrageenan granular hydrogel through small- and large-amplitude oscillatory shear measurements. We tuned the granular hydrogel’s properties by changing the stiffness (soft, medium, stiff) and the packing density of the individual microgels. Characterizations in the linear viscoelasticity regime revealed that the storage modulus of granular hydrogels is not a simple function of microgel stiffness and depends on the microgel packing density. At larger strains, increasing the microgel stiffness reduced the energy dissipation of the granular beds and increased the solid–fluid transition point. To understand how the different rheological properties of granular support materials influence embedded bioprinting, we examined the printing fidelity and cellular filament shrinkage within the granular beds. We found that microgels with low packing density diminished the printing quality, while stiff microgels promoted filament roughness. In addition, we found that highly packed stiff microgels significantly reduced the postprinting contraction of cellular filaments. Overall, this work provides a comprehensive knowledge of the rheology of granular hydrogels that can be used to rationally design support beds for bioprinting applications with specific characteristics.

Background: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults. However, for children, algorithm development is hindered by the limited availability of datasets, with the Childhood Adenotonsillectomy Trial (CHAT) being the only substantial source, comprising recordings from children aged 5-10. This limitation constrains the evaluation of algorithmic generalization performance. Methods: We employed a deep learning model for sleep staging from PPG, initially trained using a large dataset of adult sleep recordings, and fine-tuned it on 80% of the CHAT dataset (CHAT-train) for the task of three-class sleep staging (wake, REM, non-REM). The resulting algorithm performance was compared to the same model architecture but trained from scratch on CHAT-train (benchmark). The algorithms are evaluated on the local test set, denoted CHAT-test, as well as on a newly introduced independent dataset. Results: Our deep learning algorithm achieved a Cohen's Kappa of 0.88 on CHAT-test (versus 0.65), and demonstrated generalization capabilities with a Kappa of 0.72 on the external Ichilov dataset for children above 5 years old (versus 0.64) and 0.64 for those below 5 (versus 0.53). Significance: This research establishes a new state-of-the-art performance for the task of sleep staging in children using raw PPG. The findings underscore the value of transfer learning from the adults to children domain. However, the reduced performance in children under 5 suggests the need for further research and additional datasets covering a broader pediatric age range to fully address generalization limitations.

Whole Slide Images (WSIs) are critical for various clinical applications, including histopathological analysis. However, current deep learning approaches in this field predominantly focus on individual tumor types, limiting model generalization and scalability. This relatively narrow focus ultimately stems from the inherent heterogeneity in histopathology and the diverse morphological and molecular characteristics of different tumors. To this end, we propose a novel approach for multi-cohort WSI analysis, designed to leverage the diversity of different tumor types. We introduce a Cohort-Aware Attention module, enabling the capture of both shared and tumor-specific pathological patterns, enhancing cross-tumor generalization. Furthermore, we construct an adversarial cohort regularization mechanism to minimize cohort-specific biases through mutual information minimization. Additionally, we develop a hierarchical sample balancing strategy to mitigate cohort imbalances and promote unbiased learning. Together, these form a cohesive framework for unbiased multi-cohort WSI analysis. Extensive experiments on a uniquely constructed multi-cancer dataset demonstrate significant improvements in generalization, providing a scalable solution for WSI classification across diverse cancer types. Our code for the experiments is publicly available at .

Significance Rapid acquisition of large imaging volumes with microscopic resolution is an essential unmet need in biological research, especially for monitoring rapid dynamical processes such as fast activity in distributed neural systems. Aim We present a multifocal strategy for fast, volumetric, diffraction-limited resolution imaging over relatively large and scalable fields of view (FOV) using single-camera exposures. Approach Our multifocal microscopy approach leverages diffraction to image multiple focal depths simultaneously. It is based on a custom-designed diffractive optical element suited to low magnification and large FOV applications and customized prisms for chromatic correction, allowing for wide bandwidth fluorescence imaging. We integrate this system within a conventional microscope and demonstrate that our design can be used flexibly with a variety of magnification/numerical aperture (NA) objectives. Results We first experimentally and numerically validate this system for large FOV microscope imaging (three orders-of-magnitude larger volumes than previously shown) at resolutions compatible with cellular imaging. We then demonstrate the utility of this approach by visualizing high resolution three-dimensional (3D) distributed neural network at volume rates up to 100 Hz. These demonstrations use genetically encoded Ca²⁺ indicators to measure functional neural imaging both in vitro and in vivo. Finally, we explore its potential in other important applications, including blood flow visualization and real-time, microscopic, volumetric rendering. Conclusions Our study demonstrates the advantage of diffraction-based multifocal imaging techniques for 3D imaging of mm-scale objects from a single-camera exposure, with important applications in functional neural imaging and other areas benefiting from volumetric imaging.

Intervertebral disc (IVD) degeneration is a leading cause of lower back pain (LBP). Current treatments primarily address symptoms without halting the degenerative process. Cell transplantation offers a promising approach for early-stage IVD degeneration, but challenges such as cell viability, retention, and harsh host environments limit its efficacy. This study aimed to compare the injectability and biocompatibility of human nucleus pulposus cells (hNPC) attached to two types of microscaffolds designed for minimally invasive delivery to IVD. Microscaffolds are developed from poly(lactic-co-glycolic acid) (PLGA) using electrospinning and femtosecond laser structuration. These microscaffolds are tested for their physical properties, injectability, and biocompatibility. This study evaluates cell adhesion, proliferation, and survival in vitro and ex vivo within a hydrogel-based nucleus pulposus model. The microscaffolds demonstrate enhanced surface architecture, facilitating cell adhesion and proliferation. Laser structuration improved porosity, supporting cell attachment and extracellular matrix deposition. Injectability tests show that microscaffolds can be delivered through small-gauge needles with minimal force, maintaining high cell viability. The findings suggest that laser-structured PLGA microscaffolds are viable for minimally invasive cell delivery. These microscaffolds enhance cell viability and retention, offering potential improvements in the therapeutic efficiency of cell-based treatments for discogenic LBP.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes’ genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data.

The invention provides compositions, kits and methods for depositing a composition comprising a plurality of magnetic particles, to a target site of a subject by delivering the composition via controlled inhalation to a region of the subject's respiratory tract and applying a magnetic field to a target site within the region of the subject's respiratory tract to capture the particles in said target site.