An interpenetrating polymer network (IPN) hydrogel was developed for the three-dimensional (3D) culture of multipotent mesenchymal stromal cells (MSCs) with the aim of independently controlling cell spreading and material modulus. Based on our previous studies, we formulated a semisynthetic material composed of two networks: a covalent network of poly(ethylene glycol) (PEG)-fibrinogen (PF) and a second guest–host (GH) network of hyaluronic acid (HA) coupled to β-cyclodextrin (CD) and adamantane (Ad). The PF network provided cell attachment, precise control over modulus through the incorporation of additional PEG-diacrylate (PEG-DA) cross-linking, and proteolytic degradability. The GH-HA network contributed to the hydrogel’s dynamic properties through enhanced viscoelasticity. This dynamic versatility enabled MSCs to better spread and grow in the IPN, even within highly cross-linked formulations. We also observed that the IPN facilitated significantly faster cell spreading kinetics, independent of the material modulus, when compared to single-network PF hydrogels. Hydrogel biodegradation was also characterized after subcutaneous implantation for up to 8 weeks by using MRI analysis. Increasing the PEG-DA cross-linking of the IPN significantly accelerated the in vivo bioresorption, whereas the biodegradation in single-network PF hydrogels was significantly delayed by the additional PEG-DA. We conclude that the covalent cross-links maintain the bulk structural integrity of the hydrogel, whereas the reversible GH interactions provide more localized adaptability for cell-mediated proteolysis and matrix remodeling, possibly through increased network heterogeneity. This design effectively mimics the ECM by providing a more supportive environment for encapsulated cells that allows them to adhere, spread, and proliferate, which may be useful in various MSC-based tissue engineering and regenerative medicine applications.

The classification of wound care products as form-stable dressings remains challenging due to the lack of objective and quantitative, material-science-based criteria. This study introduces a rheological testing framework to determine form stability of wound dressing materials. Using dynamic, oscillatory shear rheology, we evaluated the viscoelastic properties and responses of two tube-dispensed model dressings and compared them to those of honey, a high-viscosity liquid used in wound ointments. Measurements of the material storage modulus, loss modulus and phase angle demonstrated that both model dressings exhibit predominantly solid-like responses, confirming their classification as form-stable wound dressings despite being applied from a tube. The notably low phase angles of these dressings indicate structural integrity, which is essential for the mechanical protection of wounds; honey exhibited a liquid response and a high phase angle, without structural integrity. The reported laboratory method and findings support the implementation of rheological classification as a standardised, objective and quantitative approach for wound care product categorisation, independent of the packaging or application mode. Importantly, this study establishes a foundation for a material-science-driven classification framework, with implications for informed clinical decision-making and reimbursement policies.

Three-dimensional tissue models are considered a more comprehensive replica of the in vivo microenvironment than their traditional monolayer counterparts. Therefore, tissue engineering methods have the potential to revolutionize biomedical research by allowing researchers to shift away from animal models while improving model relevance. However, while state-of-the-art electrical devices can perturb the biophysical cell niche in 2D monolayers, the biomodulation “toolkit” available for 3D application does not meet the required level of complexity, specificity, and accuracy, limiting the ability to perform intracellular electrical modulation of cells inside 3D cellular constructs. In this work, a 3D e-scaffold impregnated with free-standing silicon nanowires was developed to enable local and leadless optoelectronic modulation at subcellular resolution. The versatility, simplicity, and biocompatibility of e-scaffolds, comprised of alginate and/or collagen, were demonstrated with a fibroblast cell line and primary cardiac cells. Their utility for bioelectrical modulation was demonstrated by optically stimulating intracellular nanowires and visualizing the calcium response using confocal microscopy. The e-scaffold was used to study the coupling between cardiac myofibroblasts and cardiomyocytes in a 3D context. The e-scaffold was found to enable straightforward 3D tissue culture as well as intracellular electrical modulation at subcellular resolution.

In sinoatrial node cells (SANC), stochastic Ca²⁺ release from the sarcoplasmic reticulum (SR) interacts with membrane channels, inducing their stochastic opening and closing, leading to inter-beat interval (IBI) variability (BIV). Additionally, stochastic neurotransmitter release activates cAMP/PKA signaling, influencing membrane channels and SR proteins, further contributing to BIV. Most computational models produce deterministic IBIs, lacking physiological BIV.

We tested three hypotheses: (i) incorporating stochastic behavior into intrinsic mechanisms mimics experimental BIV, (ii) increased neurotransmitter stimulation via β-adrenergic receptor (β-AR) activation, phosphodiesterase (PDE) inhibition, or enhanced SERCA activity shortens IBI and reduces BIV, and (iii) these interventions affect BIV parameters differently at short- and long-time scales.

To test this, we introduced stochastic behavior of Ca²⁺ channels, ryanodine receptor (RyR), funny channel, and autonomic nervous system (ANS) signaling via Carbachol (CCh) and Isoproterenol (ISO) stimulations into our mouse coupled-clock SANC model.

Kolmogorov–Smirnov tests showed that the IBI distribution of our stochastic model aligns significantly more closely with experimental data than that of the deterministic model. RyR noise maximized BIV, mainly increasing short-scale (1-10) entropy (IBI irregularity). Funny channel noise influenced short-scale entropy and low-frequency power, while ISO noise affected long-scale (11-20) entropy. Augmented β-AR activation or PDE inhibition shortened IBI and reduced its standard deviation, mirroring SERCA activity enhancement. β-AR activation reduced low-frequency power, PDE inhibition decreased entropy across scales, and SERCA activation reduced power in all frequency bands.

Thus, interactions among intrinsic stochastic mechanisms contribute to BIV complexity and drug effects have different response on BIV.

Localization microscopy techniques have overcome the diffraction limit, enabling nanoscale biological imaging by precisely determining the positions of individual emitters. However, the performance of deep learning methods commonly applied to these tasks often depends significantly on the quality of training data, typically generated through simulation. Creating simulations that perfectly replicate experimental conditions remains challenging, resulting in a persistent simulation-to-experiment (sim2exp) gap. To bridge this gap, we propose a physics-informed generative model leveraging self-supervised learning directly on experimental data. Our model extends the Deep Latent Particles (DLP) framework by incorporating a physical Point Spread Function (PSF) model into the decoder, enabling it to disentangle learned realistic environments from precise emitter properties. Trained directly on unlabeled experimental images, our model intrinsically captures realistic background, noise patterns, and emitter characteristics. The decoder thus acts as a high-fidelity generator, producing fully labeled, realistic training images with known emitter locations. Using these generated datasets significantly improves the performance of supervised localization algorithms, particularly in challenging scenarios such as complex backgrounds and low signal-to-noise ratios. Our results demonstrate substantial improvements in localization accuracy and emitter detection, underscoring the practical benefit of our approach for real-world microscopy applications. We will make our code publicly available.

Type 2 diabetes (T2D) is characterized by impaired glucose uptake in skeletal muscle and adipose tissues, which contributes to systemic hyperglycemia. GLUT4 is a crucial component in insulin-stimulated glucose uptake and its expression as well as translocation are impaired in T2D onset. This study explored the role of extracellular vesicles (EVs) derived from GLUT4-overexpressing engineered muscle constructs (G4OE-EMC) in glucose metabolism. G4OE-EMC-derived EVs enhanced glucose uptake and insulin sensitivity both in vitro, when tested on wild-type (WT) engineered muscle constructs, and in vivo using the diet-induced obesity (DIO) mouse model. Proteomic and transcriptomic analyses revealed that the EVs were enriched in IGF1 and contained reduced levels of miRNAs, such as miR-122-5p, miR-16-5p, and miR-486-5p, which target IGF1R. The multi-omic approach used here suggests a mechanism whereby G4OE-EMC-derived EVs enhance glucose metabolism via IGF1 signaling and miRNA-mediated regulation of IGF1R expression, offering a potential therapeutic strategy for T2D.

Measuring precise emotional tagging for taste information, with or without the use of words, is challenging. While affective taste valence and salience are core components of emotional experiences, traditional behavioral assays for taste preference, which often rely on cumulative consumption, lack the resolution to distinguish between different affective states, such as innate versus learned aversion, which are known to be mediated by distinct neural circuits. To overcome this limitation, we developed an open-source system for high-resolution microstructural analysis of licking behavior in freely moving mice. Our approach integrates traditional lick burst analysis with a proprietary software pipeline that utilizes interlick interval (ILI) distributions and principal component analysis (PCA) to create a multidimensional behavioral profile of the animal. Using this system, we characterized the licking patterns associated with innate appetitive, aversive, and neutral tastants. While conventional burst analysis failed to differentiate between two palatable stimuli (water and saccharin), our multidimensional approach revealed distinct and quantifiable behavioral signatures for each.Critically, this approach successfully dissociates innate and learned aversive taste valences, a distinction that cannot be achieved using standard metrics. By providing the designs for our custom-built setup and analysis software under an open-source license, this study offers a comprehensive and accessible methodology for examining hedonic responses in future studies. This powerful toolkit enhances our understanding of sensory valence processing and provides a robust platform for future investigations of the neurobiology of ingestive behavior.

A method for treatment of cardiac problems includes performing modulation of a cardiac rhythm of a patient by increasing a number of heart beats the patient during time interval with high pleural pressure relative to the number during low (negative) pleural pressure, wherein an amplitude of the modulation of the cardiac rhythm between these segments is determined by severity of a respiratory effort and lung congestion of the patient.

The present invention concerns compositions, kits and methods for genetic variation detection and classification. These methods combine the specificity of a ligation reaction with the single-molecule sensitivity of nanopore biosensors. This invention can achieve clinical detection of many diseases, including bacterial infections and cancer entities, by identification of specific genetic alterations.