Functional Integration Research Articles

Islet Cell Replacement and Regeneration for Type 1 Diabetes: Current Developments and Future Prospects.

Type 1 diabetes (T1D) is a chronic autoimmune disorder characterized by the destruction of insulin-producing beta cells in the pancreas, leading to insulin deficiency and chronic hyperglycemia. The main current therapeutic strategies for clinically overt T1D - primarily exogenous insulin administration combined with blood glucose monitoring - fail to fully mimic physiological insulin regulation, often resulting in suboptimal or insufficient glycemic control. Islet cell transplantation has emerged as a promising avenue for functionally replacing endogenous insulin production and achieving long-term glycemic stability. Here, we provide an overview of current islet replacement strategies, ranging from islet transplantation to stem cell-derived islet cell transplantation, and highlight emerging approaches such as immunoengineering. We examine the advancements in immunosuppressive protocols to enhance graft survival, innovative encapsulation, and immunomodulation techniques to protect transplanted islets, and the ongoing challenges in achieving durable and functional islet integration. Additionally, we discuss the latest clinical outcomes, the potential of gene editing technologies, and the emerging strategies for islet cell regeneration. This review aims to highlight the potential of these approaches to transform the management of T1D and improve the quality of life of individuals affected by this condition.

Switching the Multiple Function Channels in 2D Hofmann-Type Coordination Polymers.

The modulation of multifunctional molecular materials by utilizing the stimuli-responsive spin crossover (SCO) has attracted considerable research interest due to its potential applications in information storage and smart switching devices. However, the complexity of achieving the integration and interconnection of multiple functions constitutes a formidable challenge. Herein, we present a pair of 2D FeII-based Hofmann-type coordination polymers (HTCPs), namely {FeII(aep)2[AgI(CN)2]2}·0.3DMF (1) and {FeII(avp)2[AgI(CN)2]2} (2), using fluorescence ligands 4-[2-(9-anthracenyl)ethynyl]pyridine (aep) and 4-[2-(9-anthryl)vinyl]pyridine (avp), respectively. Both complexes exhibit one-step SCO, with transition temperatures of 216 K for complex 1 and 255 K for complex 2. Their dielectric properties align well with the observed magnetic behaviors, demonstrating the dielectric transition process caused by the change of spin state. A variable-temperature fluorescence study reveals the coexistence of SCO and luminescent properties in both complexes, with a remarkable synergistic coupling observed in complex 2 due to the shorter distance between the SCO centers and the fluorophore. These findings underscore the potential of HTCPs as a promising platform for modulating multiple functions. By manipulating their spin states through external stimuli, SCO materials will promisingly advance the development of next-generation molecule-based sensors and devices.

Size-Tunable Covalent Organic Framework Nanoparticles for Assembling One-Dimensional Photonic Crystals With Visual Sensing.

One-dimensional photonic crystals (1D PCs) emerged as superb sensing platforms due to their high sensitivity to environmental changes. However, effectively translating the microscopic interactions between covalent organic frameworks (COFs) and analytes into macroscopic optical responses via 1D PCs for intuitive detection presents a significant challenge. Here, we propose a stepwise-induced synthesis strategy that for the first time achieves a size-controlled synthesis of uniform nanoscale COFs (60-80nm), leading to a high-quality COF layer. This advancement enables COF-based 1D PCs to exhibit diverse color variations and controllable saturation. Notably, the excellent compatibility of the COF layer with inorganic oxides, organic polymers, and metal-organic frameworks (MOFs) results in these 1D PCs exhibiting bright colors. Crucially, introducing the mesoporous materials enables 1D PCs to achieve deep integration of adsorption and recognition functions for volatile organic compounds (VOCs). The fabricated COF/MOF 1D PC allows differentiation of 12 VOCs and visually detects VOCs at different concentrations (0-80gm- 3) through color changes, with a response time under 1 s. In particular, COF-based 1D PCs can be transferred to flexible substrates while retaining VOC visual sensing. These attributes highlight the potential of COF-based 1D PCs for real-time VOC monitoring in industrial environments.

FMRI Activation in Sensorimotor Regions at 6 Weeks After Anterior Cruciate Ligament Reconstruction.

Brain activity during knee movements is altered throughout the sensorimotor network after anterior cruciate ligament reconstruction (ACLR). Patients at 2 to 5 years after surgery appear to require greater neural activity to perform basic knee movement patterns, but it is unclear if brain activity differences within sensorimotor regions are present early after surgery. It is also unknown whether uninvolved knee movements elicit similar or unique activity compared with involved knee movements. To examine brain activity in sensorimotor regions during involved and uninvolved knee movements in patients at 6 weeks after ACLR compared with control participants. Cohort study; Level of evidence, 2. A total of 15 patients who underwent ACLR (mean age, 21.9 ± 4.3 years [range, 17-29 years]; 8 female) and 15 control participants performed 30-second blocks of repeated knee flexion and extension, followed by 30 seconds of rest, during functional magnetic resonance imaging. Regions of interest included the right and left primary motor cortex (M1), right and left primary somatosensory cortex (S1), supplementary motor area (SMA), precuneus, and lingual gyrus. Activity from task-relevant voxels (move > rest) was extracted, and generalized estimating equations evaluated the main effect of group and group-by-limb interaction. Effect sizes were calculated using the Cohen d. Reduced brain activity during knee flexion and extension was observed in the ACLR group in the ipsilateral M1 and S1, contralateral S1, SMA, and precuneus during movements of the involved and uninvolved knees. There were no group-by-limb interaction effects, indicating no significant differences between the involved knee and uninvolved knee in the ACLR group. Medium to large effect sizes were identified for between-group differences in all regions. At 6 weeks after ACLR, patients exhibited bilateral reductions in brain activity during knee movements in multiple sensorimotor regions. These identified regions are associated with motor planning, motor execution, somatosensory function, and sensorimotor integration. These data indicate that ACLR affected sensorimotor brain activity in both limbs during the early postoperative phase of rehabilitation.

Refractive-index sensing based on terahertz cavity-modal confinement of Bragg metal grating structure

Abstract Integrated sensing platforms, which possess active layers of local electric-field oscillation in the terahertz (THz) frequency, can sensitively detect analytes via electromagnetic (EM) wave guidance, resonance, and spatial confinement in the system of THz time-domain spectroscopy. A Bragg grating structure of periodically perforated metal slits (PPMSs) is presented here as an active layer to generate the local electric-field oscillation of metal-cavity EM wave resonance that is coupled from 0.1–1 THz metal surface waves along the wavelength-scaled segments of metal slabs. The cavity modal confinement property, which is characterized from device transmittance or loss spectra, leads to the distribution of the metal-cavity wave resonance over a 35-pitch length and a 0.1–0.2 mm metal thickness of PPMSs. These distribution dimensions of metal-cavity wave resonance represent the longitudinal and transverse confinement performances, respectively. The PPMS-THz waves specified by 2D spatial confinement can realize the functional integration of a dielectric superstrate component and a sample adsorbing layer to adjust the dispersion responsivity and concentration of analytes, respectively. A refractive-index sensing application of glucose is presented by this integrated scheme for refractive-index sensing in a molecular density range of 0–24 μg/mm2, a resolution density of 1.6 μg/mm2, and a trace density of 2.61 μg/mm2. The PPMS-based THz integrated sensing platform facilitates the developments of specific test papers and relevant molecular surface modification to target molecules.

Development of an alginate-based bioink with enhanced hemostatic and antibacterial properties.

Alginate, a recognized biosafe material with inherent hemostatic properties, has been widely explored for emergency hemostatic applications and wound dressings. However, advancements in crosslinking strategies and the integration of additional functionalities are essential to expand its clinical versatility. In this study, we report the development of a novel alginate-based bioink designed to address urgent clinical challenges posed by incompressible and irregular site bleeding. Our bioink, characterized by rapid photocrosslinking and sustained antibacterial effects, offers a promising solution for hemorrhage control and wound repair. The developed bioink, AL@FM, is an injectable photopolymerized alginate-based hydrogel incorporating functional modifiers (PVP, CMC, glycerol and ε-PL) that enhance its rapid hemostasis, antibacterial action, and wound healing properties, demonstrating exceptional biocompatibility in extensive in vitro and in vivo evaluations. AL@FM exhibits superior hemostatic efficiency compared to commercial controls, effectively accelerating wound contraction and angiogenesis, while displaying potent antibacterial properties and improved injectability. The integration of these multifaceted features into an alginate-based platform extends its applicability, particularly in the context of in situ 3D bioprinting where rapid gelation and immediate functionality are critical.

Integration of AI Functions for Automatic Identification of Morphological Parameters in Medical Images Using MATLAB

Introduction: Artificial intelligence (AI) is transforming medical imaging by automating complicated and challenging processes with remarkable accuracy. This paper examines the adoption of AI algorithms using MATLAB for the automated recognition of morphological structures in medical imaging. Methods: This research utilizes advanced machine learning (ML) and deep learning (DL) methods to address essential tasks like image segmentation, feature extraction, and parameter estimation. MATLAB's intuitive interface and powerful computational capabilities make it easy to create and test models for specific medical imaging tasks. Segmentation and parameter prediction are carried out with great accuracy in experiments carried out on publically available datasets, such as brain MRI and lung CT scans. In many instances, the Dice coefficients surpass 0.9. While tackling issues like data quality, model interpretability, and computational efficiency, this study emphasizes the benefits of MATLAB for developing and implementing AI solutions in healthcare. Results: The results emphasize MATLAB's capability to improve diagnostic accuracy and operational effectiveness in clinical environments, facilitating future advancements in customized medicine. There has to be effective software for automated analysis and interpretation of the ever-increasing amount of medical imaging data. Conclusions: The purpose of this research is to examine the feasibility of using MATLAB's AI features for the automated detection of morphological parameters in medical images. Here, we compare AI-driven algorithms to more conventional image processing approaches for typical morphological metrics like area, perimeter, shape descriptors, and object recognition, and we find that the former are more accurate and efficient. To verify the efficacy of the suggested AI model, statistical analysis is carried out on a dataset consisting of medical photographs.

More on unconstrained descriptions of higher spin massless particles

Here we suggest a new local action describing arbitrary integer spin-s massless particles in terms of only two symmetric fields φ and α of rank-s and (s−3) respectively. It is an unconstrained version of the Fronsdal theory where the double traceless constraint on the physical field is evaded via a rank-(s−4) Weyl like symmetry. The constrained higher spin diffeomorphism is enlarged to full diffeomorphism via the Stueckelberg field α through an appropriate field redefinition. After a partial gauge fixing where the Weyl symmetry is broken while preserving diffeomorphisms, the field equations reproduce, for arbitrary integer spin-s, diffeomorphism invariant equations of motion previously obtained via a truncation of the spectrum of the open bosonic string field theory in the tensionless limit. In the s=4 case we show that the functional integration over α leads to a unique nonlocal Weyl and diffeomorphism invariant action given only in terms of the physical field φ whose spectrum is confirmed via an analysis of the analytic structure of the spin-4 propagator for which we introduce a complete basis of projection and transition nonlocal differential operators. We also show that the elimination of α after the Weyl gauge fixing leads to a nonlocal diffeomorphism invariant action previously obtained in the literature. Published by the American Physical Society 2025

Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmys or MYBPC3 causing inheritable heart diseases by affecting the βmys/MYBPC3 complex. Muscle genetics encode instructions for contraction informing native protein construction, functional integration, and inheritable disease impairment. A digital model decodes these instructions and evolves by processing new information content from diverse data modalities using a human partner-driven virtuous cycle optimization. A general neural-network contraction model characterizes SRV impacts on human health. It rationalizes phenotype and pathogenicity assignment given the SRVs characteristics and, in this sense, decodes βmys/MYBPC3 complex genetics and implicitly captures ventricular muscle functionality. When an SRV modified domain locates to an inter-protein contact in βmys/MYBPC3 it affects complex coordination. Domains involved, one in βmys and the other in MYBPC3, form coordinated domains (co-domains). Bilateral co-domains imply potential for their SRV modification probabilities to respond jointly to a common perturbation revealing location. Human genetic diversity from the serial founder effect is the common systemic perturbation coupling co-domains subsequently mapped by a method called 2-dimensional correlation genetics (2D-CG). Interpreting general neural-network contraction model output involves 2D-CG co-domain mapping providing structural insights with natural language expression. It aligns machine-learned intelligence from the neural network model with human provided structural insight from the 2D-CG map, and other data from the literature, to form a neural-symbolic hybrid model integrating genetic and protein-interaction data into a nascent digital twin. The process forms a template for combining new information content from diverse data modalities into an evolving digital model. This nascent digital twin interprets SRV implications for disease mechanism discovery.

Functional Graphene Fiber Materials for Advanced Wearable Applications

Abstract Graphene fiber materials have emerged as key enablers in the advancement of wearable electronics due to their outstanding electrical conductivity, mechanical strength and flexibility. This review explores the fabrication techniques of graphene fibers, including wet spinning, electrospinning and dry spinning, which have been refined to produce high-performance fibers tailored for various wearable applications. Graphene fibers demonstrate exceptional functionality in wearable sensing technologies, such as strain, pressure and humidity sensors, while also showing promises in flexible energy storage devices like supercapacitors and batteries. Moreover, fabrication techniques like weaving, spinning and additional encapsulations have enabled the integration of graphene fibers into smart textiles, enhancing flexibility and durability. These methods ensure seamless electronic integration into fabrics for applications in flexible displays and wearable systems. By summarizing all the advances of graphene fibers in wearable electronics, this review provides a roadmap for future research directions. Future developments will focus on enhancing structural performance, hybridization with other materials and scalable fabrication techniques to support commercialization. These advancements position graphene fibers as a critical material for next-generation wearable electronics, offering seamless integration of functionality, comfort and durability. Graphical Abstract

Principles and Applications of Two-Dimensional Semiconductor Material Devices for Reconfigurable Electronics

With the advances in edge computing and artificial intelligence, the demands of multifunctional electronics with large area efficiency are increased. As the scaling down of the conventional transistor is restricted by physical limits, reconfigurable electronics are developed to promote the functional integration of integrated circuits. Reconfigurable electronics refer to electronics with switchable functionalities, including reconfigurable logic operation functionalities and reconfigurable responses to electrical or optical signals. Reconfigurable electronics integrate data-processing capabilities with reduced size. Two-dimensional (2D) semiconductor materials exhibit excellent modulation capabilities through electrical and optical signals, and structural designs of 2D material devices achieve versatile and switchable functionalities. 2D semiconductors have great potential to develop advanced reconfigurable electronics. Recent years witnessed the rapid development of 2D material devices for reconfigurable electronics. This work focuses on the working principles of 2D material devices used for reconfigurable electronics, discusses applications of 2D-material-based reconfigurable electronics in logic operation and artificial intelligence, and further provides a future outlook for the development of reconfigurable electronics based on 2D material devices.

Sustainability Benefit Evaluation and Optimization of Rural Public Spaces Under Self-Organization Theory

Agriculture-oriented rural areas represent one of the forms of specialized agricultural practices and economic development. Public spaces serve as critical carriers within the rural spatial system. Rural public spaces are divided into two forms: explicit spaces and implicit spaces. The interaction between these forms significantly influences the morphological evolution of rural public spaces. This study takes the ancient village cluster in Anyi, Nanchang City, China as a case study. By collecting POI (Point of Interest) data and conducting surveys on visitors’ landscape preferences, it employs a life circle spatial division method and the VEISD (Village Evaluation Indicators for Sustainable Development) entropy model to evaluate the sustainability benefits of rural public spaces. Based on the evaluation results, the study proposes a control and guidance method for public spaces under self-organization theory. This method leverages the interference effects of explicit rural public spaces on implicit spaces to optimize rural public spaces. The study focuses on the planning and renovation of public space nodes in Luotian Village. By adjusting the sub-indicator “Village Public Environment Construction D22”, it validates the scientific robustness of the systems analysis theory and the VEISD framework. By adjusting the spatial layout and attributes of a critical spatial node—the Ancient Camphor Tree Square in Luotian Village—within rural public space planning, the study advances the guidance and control of public spaces during the self-organization evolution of rural areas. It enhances the openness of spatial forms and the functional integration of public space nodes. The results demonstrate that this method can analyze the vitality characteristics of factors within subsystems through the layout and indicator system of rural public spaces. It also validates the findings via correlation tests with the demands for POI and landscape preferences, ultimately constructing the VEISD framework for rural public spaces. This research provides theoretical support for optimizing the resource transformation and utilization of rural public spaces, offering a reference model for the sustainable development of rural areas.

Optimizing the Functions of Immigration Intelligence and Immigration Assisted Villages in the Prevention of Trafficking in Persons: An Integrative Approach to Enhancing Community Resilience in Indonesia - A Systematic Literature Review

Trafficking in Persons (TP) is a serious threat to national security and human rights in Indonesia. The complexity of this crime has increased over time, with an evolving modus operandi, especially in border areas and vulnerable communities. This research aims to analyze the optimization of the immigration intelligence function and the Immigration Assisted Village program in preventing TP, with an integrative approach based on technology and community empowerment. The research uses the systematic literature review (SLR) method to compile a comprehensive synthesis of related literature in the 2010-2024 period. The results of this research show that the integration of immigration intelligence functions, Early Warning Systems (EWS), and the Immigration Assisted Village program is proven to be able to increase the effectiveness of preventing TP. Big data technology and artificial intelligence play a key role in analyzing TP patterns and trends, while community empowerment through education and job training increases community resilience to this crime. However, the implementation of these strategies faces challenges, such as limited resources and inter-agency coordination. Implications This research recommends a multidisciplinary approach involving technology investment, community empowerment, and continuous evaluation to strengthen Indonesia's position in combating TP as well as increase community resilience to this threat.

Cover Image

The cover image is based on the article Porous structural battery composite for coordinated integration of mechanical and electrical functionalities by Yongxi He et al., https://doi.org/10.1002/pc.29060. image

Neural Density Functional Theory of Liquid-Gas Phase Coexistence

We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes. Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function g(r) obtained from automatic differentiation and the Ornstein-Zernike route and determine (i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of g(r) from monotonic to oscillatory, (ii) the (Widom) line of maximal correlation length, (iii) the line of maximal isothermal compressibility, and (iv) the spinodal by calculating the poles of the structure factor in the complex plane. The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation. Published by the American Physical Society 2025

Electrochemical Linked to Mechanical Simulation for an Assessment of State-of-Health of Thin-Flexible Li-Ion Batteries on Dynamic Flexing and Calendar Aging

Abstract Flexible hybrid electronics featuring wearable electronics offer numerous advantages such as function integration, light-weighting, and flexibility. However, the dynamic flexing of the flexible power sources during usage, along with flex-to-install, presents challenges for their durability. While previous research has focused on thick block batteries, the effects of daily motion-induced stresses on the state of health (SOH) degradation of thin-flexible batteries, in conjunction with usage parameters, are not well understood. Factors such as storage duration, operating temperature, flexing frequency, interval, and flex radius may vary, making it impractical and expensive to measure the battery response in every condition. Therefore, electrochemical simulation methods are necessary to predict the SOH degradation of the battery under various environmental conditions, which can assess conditions not previously measured. However, the degradation of the flexible battery is not only due to electrochemical aging but also due to mechanical aging. While electrochemical simulation is well-known, the effect of mechanical factors on degradation is relatively unknown. In this regard, this research seeks to make multiphysics simulations of SOH deterioration during charging/discharging of a flexible battery under dynamic folding, twisting, and static folding using a calendar-aged battery at elevated temperatures. Additionally, the method, which is to link the mechanical simulation to electrochemical simulation, was studied, which may be helpful in further understanding of unknown effects required for future study. The paper thoroughly discusses the developed model's capability to predict SOH degradation caused by mechanical stress and calendar aging. It also explores how accurately the model can illustrate degradation trends under various environmental conditions. The detailed results and their significance are presented comprehensively, providing a clear understanding of the model's effectiveness within the context of the study.

Hydrogen-Bonding-Driven Design of Organic-Inorganic Hybrid Ferroelastics with Reversible Photoisomerization.

The development of photoresponsive ferroelastics, which couple light-induced macroscopic mechanical and microscopic domain properties, represents a frontier in materials science with profound implications for advanced functional applications. In this study, we report the rational design and synthesis of two new organic-inorganic hybrid ferroelastic crystals, (MA)(Me4N)[Fe(CN)5(NO)] (MA = methylammonium) (1) and (MA)(Me3NOH)[Fe(CN)5(NO)] (2), using a dual-organic molecular design strategy that exploits hydrogen-bonding interactions for tailoring ferroelastic properties. Specifically, 1 exhibits a two-step phase transition at 138 and 242 K, while the introduction of a hydroxyl group in 2 stabilizes its ferroelastic phase to a significantly higher temperature, achieving a phase transition at 328 K, 86 K above that of 1. This enhancement is attributed to hydrogen bonding between the hydroxyl group of Me3NOH+ and the nitroprusside anion, which suppresses lattice dynamics and reinforces structural stability. Remarkably, 2 demonstrates a large spontaneous strain of 0.153, vastly exceeding the 0.021 of 1, and undergoes an 11% size change along the b-axis in response to thermal stimuli. Both compounds exhibit reversible, photoinduced nitrosyl-linkage isomerization, as confirmed by IR spectroscopy, transitioning between the ground state (N-bound nitrosyl) and the metastable state (O-bound nitrosyl). This integration of photoresponsive functionality with ferroelastic properties establishes a versatile platform for energy-efficient actuation, adaptive devices, and multifunctional sensing applications. These findings offer an innovative pathway for designing next-generation hybrid materials with enhanced tunable properties.

Ketamine administration during adolescence impairs synaptic integration and inhibitory synaptic transmission in the adult dentate gyrus.

Ketamine administration during adolescence affects cognitive performance; however, its long-term impact on synaptic function and neuronal integration in the hippocampus a brain region critical for cognition remains unclear. Using functional and molecular analyses, we found that chronic ketamine administration during adolescence exerts long-term effects on synaptic integration, expanding the temporal window in an input-specific manner affecting the inner molecular layer but not the medial perforant path inputs in the adult mouse dorsal hippocampal dentate gyrus. Ketamine also alters the excitatory/inhibitory balance by reducing the efficacy of inhibitory inputs likely due to a reduction in parvalbumin-positive interneurons number and function. These findings indicate that during adolescence, ketamine exerts a strong effect on inhibitory synaptic function mediated by parvalbumin-positive neurons that ultimately impact synaptic integration in the dorsal adult dentate gyrus, which could help to understand the neurobiological and functional bases that confer greater vulnerability to the adolescent brain.

Steering Innovation: Leadership's Role in Tech Companies

It is necessary to have effective leaders in determining the enclosure of new products in technology organizations because of increased competition. This is why leaders also have the major responsibility of fostering organizational creativeness and flexibility. They contribute towards innovation by implementing the policies that encourage teamwork, support taken of risks in new ideas and ensure resources are placed in a way that supports innovation. The present paper explores the crucial roles of leadership in managing technological development issues, and possibly nurturing innovations. Some of the issues discussed include the delegation of authority to teams, cross functional integration and sustaining mechanisms which provide organizations with long term direction. Drawing from examples of tech leaders, the paper demonstrates how these leaders have influenced change and the need to be forward-looking. Keywords Leadership and Innovation, Adaptive Strategies, Product Innovation Strategies, Innovation in Tech Companies, Leadership in Technology, Driving Innovation, Innovation Leadership Roles, Creative Leadership in Tech.

Engineering Highly Aligned and Densely Populated Cardiac Muscle Bundles via Fibrin Remodeling in 3D-Printed Anisotropic Microfibrous Lattices.

Replicating the structural and functional features of native myocardium, particularly its high-density cellular alignment and efficient electrical connectivity, is essential for engineering functional cardiac tissues. Here, novel electrohydrodynamically printed InterPore microfibrous lattices with anisotropic architectures are introduced to promote high-density cellular alignment and enhanced tissue interconnectivity. The interconnected pores in the microfibrous lattice enable dynamic, cell-mediated remodeling of fibrous hydrogels, resulting in continuous, mechanically stable tissue bundles. Compared to lattices with isolated pores, the engineered aligned cardiac tissues from neonatal rat cardiomyocytes exhibit improved electrophysiological properties and synchronous contractions. Using a multiseeding strategy, an equivalent cell seeding density of 8 × 107 cellsmL-1, facilitating the formation of multicellular, vascularized cardiac structures with maintained tissue viability and integrity, is achieved. As a demonstration, human-induced pluripotent stem cell-derived cardiac tissues are engineered with progressive maturation and functional integration over time. These findings underscore the potential of InterPore microfibrous lattices for applications in cardiac tissue engineering, drug discovery, and therapeutic development.