Toolbox for creating three-dimensional liver models

Research within the hepato-biliary system and hepatic function is currently experiencing heightened interest, this is due to the high frequency of relapse rates observed in chronic conditions, as well as the imperative for the development of innovative therapeutic strategies to address both inherited and acquired diseases within this domain. The most commonly used sources for studying hepatocytes include primary human hepatocytes, human hepatic cancer cell lines, and hepatic-like cells derived from induced pluripotent stem cells. However, a significant challenge in primary hepatic cell culture is the rapid decline in their phenotypic characteristics, dedifferentiation and short cultivation time. This limitation creates various problems, including the inability to maintain long-term cell cultures, which can lead to failed experiments in drug development and the creation of relevant disease models for researchers' purposes. To address these issues, the creation of a powerful 3D cell model could play a pivotal role as a personalized disease model and help reduce the use of animal models during certain stages of research. Such a cell model could be used for disease modelling, genome editing, and drug discovery purposes. This review provides an overview of the main methods of 3D-culturing liver cells, including a discussion of their characteristics, advantages, and disadvantages.

HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data

Introduction: Structural Variants (SVs) are a type of variation that can significantly influence phenotypes and cause diseases. Thus, the accurate detection of SVs is a vital part of modern genetic analysis. The advent of long-read sequencing technology ushers in a new era of more accurate and comprehensive SV calling, and many tools have been developed to call SVs using long-read data. Haplotype-tagging is a procedure that can tag haplotype information on reads and can thus potentially improve the SV detection; nevertheless, few methods make use of this information. In this article, we introduce HapKled, a new SV detection tool that can accurately detect SVs from Oxford Nanopore Technologies (ONT) long-read alignment data. Methods: HapKled utilizes haplotype information underlying alignment data by conducting haplotype-tagging using Whatshap on the reads to improve the detection performance, with three unique calling mechanics including altering clustering conditions according to haplotype information of signatures, determination of similar SVs based on haplotype information, and slack filtering conditions based on haplotype quality. Results: In our evaluations, HapKled outperformed state-of-the-art tools and can deliver better SV detection results on both simulated and real sequencing data. The code and experiments of HapKled can be obtained from https://github.com/CoREse/HapKled. Discussion: With the superb SV detection performance that HapKled can deliver, HapKled could be useful in bioinformatics research, clinical diagnosis, and medical research and development.

Investigating mechanical deformation's role in cochlear implant durability

Platinum and platinum-based alloys are used as the electrode material in cochlear implants because of the biocompatibility and the favorable electrochemical properties. Still, these implants can fail over time. The present study was conducted to shed light on the effects of microstructure on the electrochemical degradation of platinum. After three days of stimulation with a square wave signal, corrosive attack appeared on the platinum surface. The influence of mechanical deformation, in particular rolling, on the corrosion resistance of platinum was also prominent. The cyclic voltammetry showed a clear dependence on the electrolyte used, which was interpreted as an influence of the buffer in the artificial perilymph used. In addition, the polarization curves showed a shift with grain size that was not expected. This could be attributed to the defects present on the surface. These findings are crucial for the manufacture of cochlear implants to ensure their long-term functionality.

Driving healthcare forward: The potential of mobile MRI and CT units in streamlining radiological services in Saudi Arabia - A narrative review

BACKGROUND AND PURPOSE

This narrative review focuses on the role of mobile MRI and CT units in addressing the challenges of healthcare accessibility and patient wait times in Saudi Arabia. It underscores the growing demand for diagnostic imaging amid infrastructural and geographical barriers, emphasizing mobile units as innovative solutions for enhancing radiological services across diverse Saudi landscapes. The purpose of this study is to assess how these mobile technologies can mitigate service delays, improve patient outcomes, and support healthcare delivery in remote or underserved areas, reflecting on global trends towards more dynamic, patient-centered healthcare models.

METHODS

This review utilizes an expanded database search and refined keywords to ensure comprehensive literature coverage. The study focused on peer-review articles and grey literatures that directly examined the impact of these mobile units on healthcare accessibility, wait times, and service delivery. A thematic analysis identified significant contributions to accessibility improvements, emergency responses, and rural healthcare, highlighting areas for further research and policy development.

DISCUSSION

Mobile units have advanced technical specifications with high-field magnets and multi-slice CT scanners on par with fixed facilities. They prioritize patient comfort and safety with examination areas, control rooms, and waiting areas. Telemedicine capabilities allow real-time image transmission to specialists. Strategic deployment can address workforce shortages by distributing services equitably. Mobile units represent cost-effective solutions to expand healthcare access without fixed infrastructure.

CONCLUSION

Integration of mobile MRI and CT units in Saudi Arabia can transform access to diagnostic imaging by decentralizing services and directly reaching patients, including rural areas. Evidence shows mobile units reduce diagnostic delays and optimize resource use. Despite challenges, strategic investments and collaborations can overcome obstacles to make radiological services more equitable, flexible and patient-focused in Saudi Arabia.

Moldable Plastics (Polycaprolactone) can be Acutely Toxic to Developing Zebrafish and Activate Nuclear Receptors in Mammalian Cells

Popularized on social media, hand-moldable plastics are formed by consumers into tools, trinkets, and dental prosthetics. Despite the anticipated dermal and oral contact, manufacturers share little information with consumers about these materials, which are typically sold as microplastic-sized resin pellets. Inherent to their function, moldable plastics pose a risk of dermal and oral exposure to unknown leachable substances. We analyzed 12 moldable plastics advertised for modeling and dental applications and determined them to be polycaprolactone (PCL) or thermoplastic polyurethane (TPU). The bioactivities of the most popular brands advertised for modeling applications of each type of polymer were evaluated using a zebrafish embryo bioassay. While water-borne exposure to the TPU pellets did not affect the targeted developmental end points at any concentration tested, the PCL pellets were acutely toxic above 1 pellet/mL. The aqueous leachates of the PCL pellets demonstrated similar toxicity. Methanolic extracts from the PCL pellets were assayed for their bioactivity using the Attagene FACTORIAL platform. Of the 69 measured end points, the extracts activated nuclear receptors and transcription factors for xenobiotic metabolism (pregnane X receptor, PXR), lipid metabolism (peroxisome proliferator-activated receptor γ, PPARγ), and oxidative stress (nuclear factor erythroid 2-related factor 2, NRF2). By nontargeted high-resolution comprehensive two-dimensional gas chromatography (GC × GC-HRT), we tentatively identified several compounds in the methanolic extracts, including PCL oligomers, a phenolic antioxidant, and residues of suspected antihydrolysis and cross-linking additives. In a follow-up zebrafish embryo bioassay, because of its stated high purity, biomedical grade PCL was tested to mitigate any confounding effects due to chemical additives in the PCL pellets; it elicited comparable acute toxicity. From these orthogonal and complementary experiments, we suggest that the toxicity was due to oligomers and nanoplastics released from the PCL rather than chemical additives. These results challenge the perceived and assumed inertness of plastics and highlight their multiple sources of toxicity.

Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review

PURPOSE

Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs.

METHODS

A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS).

RESULTS

Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73-100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs.

CONCLUSION

AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms.

Prediction of isometric forces from combined epidural spinal cord and neuromuscular electrical stimulation in the rat lower limb

Although epidural spinal cord and muscle stimulation have each been separately used for restoration of movement after spinal cord injury, their combined use has not been widely explored. Using both approaches in combination could provide more flexible control compared to using either approach alone, but whether responses evoked from such combined stimulation can be easily predicted is unknown. We evaluate whether responses evoked by combined spinal and muscle stimulation can be predicted simply, as the linear summation of responses produced by each type of stimulation individually. Should this be true, it would simplify the prediction of co-stimulation responses and the development of control schemes for spinal cord injury rehabilitation. In healthy anesthetized rats, we measured hindlimb isometric forces in response to spinal and muscle stimulation. Force prediction errors were calculated as the difference between predicted and observed co-stimulation forces. We found that spinal and muscle co-stimulation could be closely predicted as the linear summation of the individual spinal and muscle responses and that the errors were relatively low. We discuss the implications of these results to the use of combined muscle and spinal stimulation for the restoration of movement following spinal cord injury.

Advances in tumor microenvironment: Applications and challenges of 3D bioprinting

The tumor microenvironment (TME) assumes a pivotal role in the treatment of oncological diseases, given its intricate interplay of diverse cellular components and extracellular matrices. This dynamic ecosystem poses a serious challenge to traditional research methods in many ways, such as high research costs, inefficient translation, poor reproducibility, and low modeling success rates. These challenges require the search for more suitable research methods to accurately model the TME, and the emergence of 3D bioprinting technology is transformative and an important complement to these traditional methods to precisely control the distribution of cells, biomolecules, and matrix scaffolds within the TME. Leveraging digital design, the technology enables personalized studies with high precision, providing essential experimental flexibility. Serving as a critical bridge between in vitro and in vivo studies, 3D bioprinting facilitates the realistic 3D culturing of cancer cells. This comprehensive article delves into cutting-edge developments in 3D bioprinting, encompassing diverse methodologies, biomaterial choices, and various 3D tumor models. Exploration of current challenges, including limited biomaterial options, printing accuracy constraints, low reproducibility, and ethical considerations, contributes to a nuanced understanding. Despite these challenges, the technology holds immense potential for simulating tumor tissues, propelling personalized medicine, and constructing high-resolution organ models, marking a transformative trajectory in oncological research.

Toward a physiological model of vascular wall vibrations in the arteriovenous fistula

The mechanism behind hemodialysis arteriovenous fistula (AVF) failure remains poorly understood, despite previous efforts to correlate altered hemodynamics with vascular remodeling. We have recently demonstrated that transitional flow induces high-frequency vibrations in the AVF wall, albeit with a simplified model. This study addresses the key limitations of our original fluid-structure interaction (FSI) approach, aiming to evaluate the vibration response using a more realistic model. A 3D AVF geometry was generated from contrast-free MRI and high-fidelity FSI simulations were performed. Patient-specific inflow and pressure were incorporated, and a three-term Mooney-Rivlin model was fitted using experimental data. The viscoelastic effect of perivascular tissue was modeled with Robin boundary conditions. Prescribing pulsatile inflow and pressure resulted in a substantial increase in vein displacement ( + 400 %) and strain ( + 317 %), with a higher maximum spectral frequency becoming visible above -42 dB (from 200 to 500 Hz). Transitioning from Saint Venant-Kirchhoff to Mooney-Rivlin model led to displacement amplitudes exceeding 10 micrometers and had a substantial impact on strain ( + 116 %). Robin boundary conditions significantly damped high-frequency displacement ( - 60 %). Incorporating venous tissue properties increased vibrations by 91%, extending up to 700 Hz, with a maximum strain of 0.158. Notably, our results show localized, high levels of vibration at the inner curvature of the vein, a site known for experiencing pronounced remodeling. Our findings, consistent with experimental and clinical reports of bruits and thrills, underscore the significance of incorporating physiologically plausible modeling approaches to investigate the role of wall vibrations in AVF remodeling and failure.

Antifreeze Polysaccharides from Wheat Bran: The Structural Characterization and Antifreeze Mechanism

Exploring a novel natural cryoprotectant and understanding its antifreeze mechanism allows the rational design of future sustainable antifreeze analogues. In this study, various antifreeze polysaccharides were isolated from wheat bran, and the antifreeze activity was comparatively studied in relation to the molecular structure. The antifreeze mechanism was further revealed based on the interactions of polysaccharides and water molecules through dynamic simulation analysis. The antifreeze polysaccharides showed distinct ice recrystallization inhibition activity, and structural analysis suggested that the polysaccharides were arabinoxylan, featuring a xylan backbone with a majority of Araf and minor fractions of Manp, Galp, and Glcp involved in the side chain. The antifreeze arabinoxylan, characterized by lower molecular weight, less branching, and more flexible conformation, could weaken the hydrogen bonding of the surrounding water molecules more evidently, thus retarding the transformation of water molecules into the ordered ice structure.

Biological determinants of PSMA expression, regulation and heterogeneity in prostate cancer

Prostate-specific membrane antigen (PSMA) is an important cell-surface imaging biomarker and therapeutic target in prostate cancer. The PSMA-targeted theranostic 177Lu-PSMA-617 was approved in 2022 for men with PSMA-PET-positive metastatic castration-resistant prostate cancer. However, not all patients respond to PSMA-radioligand therapy, in part owing to the heterogeneity of PSMA expression in the tumour. The PSMA regulatory network is composed of a PSMA transcription complex, an upstream enhancer that loops to the FOLH1 (PSMA) gene promoter, intergenic enhancers and differentially methylated regions. Our understanding of the PSMA regulatory network and the mechanisms underlying PSMA suppression is evolving. Clinically, molecular imaging provides a unique window into PSMA dynamics that occur on therapy and with disease progression, although challenges arise owing to the limited resolution of PET. PSMA regulation and heterogeneity - including intertumoural and inter-patient heterogeneity, temporal changes, lineage dynamics and the tumour microenvironment - affect PSMA theranostics. PSMA response and resistance to radioligand therapy are mediated by a number of potential mechanisms, and complementary biomarkers beyond PSMA are under development. Understanding the biological determinants of cell surface target regulation and heterogeneity can inform precision medicine approaches to PSMA theranostics as well as other emerging therapies.

O-glycans Expand Lubricin and Attenuate Its Viscosity and Shear Thinning

Lubricin, an intrinsically disordered glycoprotein, plays a pivotal role in facilitating smooth movement and ensuring the enduring functionality of synovial joints. The central domain of this protein serves as a source of this excellent lubrication and is characterized by its highly glycosylated, negatively charged, and disordered structure. However, the influence of O-glycans on the viscosity of lubricin remains unclear. In this study, we employ molecular dynamics simulations in the absence and presence of shear, along with continuum simulations, to elucidate the intricate interplay between O-glycans and lubricin and the impact of O-glycans on lubricin's conformational properties and viscosity. We found the presence of O-glycans to induce a more extended conformation in fragments of the disordered region of lubricin. These O-glycans contribute to a reduction in solution viscosity but at the same time weaken shear thinning at high shear rates, compared to nonglycosylated systems with the same density. This effect is attributed to the steric and electrostatic repulsion between the fragments, which prevents their conglomeration and structuring. Our computational study yields a mechanistic mechanism underlying previous experimental observations of lubricin and paves the way to a more rational understanding of its function in the synovial fluid.

Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images

BACKGROUND

Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters.

METHOD

Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells.

RESULTS

Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells.

CONCLUSION

These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.

A multi-scale numerical study of monoclonal antibodies uptake by initial lymphatics after subcutaneous injection

This paper studies the transport of monoclonal antibodies through skin tissue and initial lymphatics, which impacts the pharmacokinetics of monoclonal antibodies. Our model integrates a macroscale representation of the entire skin tissue with a mesoscale model that focuses on the papillary dermis layer. Our results indicate that it takes hours for the drugs to disperse from the injection site to the papillary dermis before entering the initial lymphatics. Additionally, we observe an inhomogeneous drug distribution in the interstitial space of the papillary dermis, with higher drug concentrations near initial lymphatics and lower concentrations near blood capillaries. To validate our model, we compare our numerical simulation results with experimental data, finding a good alignment. Our parametric studies on the drug molecule properties and injection parameters suggest that a higher diffusion coefficient increases the transport and uptake rate while binding slows down these processes. Furthermore, shallower injection depths lead to faster lymphatic uptake, whereas the size of the injection plume has a minor effect on the uptake rate. These findings advance our understanding of drug transport and lymphatic absorption after subcutaneous injection, offering valuable insights for optimizing drug delivery strategies and the design of biotherapeutics.

Assessment of subcutaneously administered insulins using in vitro release cartridge: Medium composition and albumin binding

Biotherapeutics is the fastest growing class of drugs administered by subcutaneous injection. In vitro release testing mimicking physiological conditions at the injection site may guide formulation development and improve biopredictive capabilities. Here, anin vitrorelease cartridge (IVR cartridge) comprising a porous agarose matrix emulating subcutaneous tissue was explored. The objective was to assess effects of medium composition and incorporation of human serum albumin into the matrix. Drug disappearance was assessed for solution, suspension and in situ precipitating insulin products (Actrapid, Levemir, Tresiba, Mixtard 30, Insulatard, Lantus) using the flow-based cartridge. UV-Vis imaging and light microscopy visualized dissolution, precipitation and albumin binding phenomena at the injection site. Divalent cations present in the release medium resulted in slower insulin disappearance for suspension-based and in situ precipitating insulins. Albumin-binding acylated insulin analogs exhibited rapid disappearance from the cartridge; however, sustained retention was achieved by coupling albumin to the matrix. An in vitro-in vivorelation was established for the non-albumin-binding insulins.The IVR cartridge is flexible with potential in formulation development as shown by the ability to accommodate solutions, suspensions, and in situ forming formulations while tailoring of the system to probe in vivo relevant medium effects and tissue constituent interactions.

Chemical Modification of Nanocrystalline Cellulose for Manufacturing of Osteoconductive Composite Materials

Cellulose is one of the main renewable polymers whose properties are very attractive in many fields, including biomedical applications. The modification of nanocrystalline cellulose (NCC) opens up the possibility of creating nanomaterials with properties of interest as well as combining them with other biomedical polymers. In this work, we proposed the covalent modification of NCC with amphiphilic polyanions such as modified heparin (Hep) and poly(αL-glutamic acid) (PGlu). The modification of NCC should overcome two drawbacks in the production of composite materials based on poly(ε-caprolactone) (PCL), namely, (1) to improve the distribution of modified NCC in the PCL matrix, and (2) to provide the composite material with osteoconductive properties. The obtained specimens of modified NCC were characterized by Fourier-transform infrared spectroscopy and solid-state 13C nuclear magnetic resonance spectroscopy, dynamic and electrophoretic light scattering, as well as thermogravimetric analysis. The morphology of PCL-based composites containing neat or modified NCC as filler was studied by optical and scanning electron microscopy. The mechanical properties of the obtained composites were examined in tensile tests. The homogeneity of filler distribution as well as the mechanical properties of the composites depended on the method of NCC modification and the amount of attached polyanion. In vitro biological evaluation showed improved adhesion of human fetal mesenchymal stem cells (FetMSCs) and human osteoblast-like cells (MG-63 osteosarcoma cell line) to PCL-based composites filled with NCC bearing Hep or PGlu derivatives compared to pure PCL. Furthermore, these composites demonstrated the osteoconductive properties in the experiment on the osteogenic differentiation of FetMSCs.

Clustering induces switching between phoretic and osmotic propulsion in active colloidal rafts

Active particles driven by chemical reactions are the subject of intense research to date due to their rich physics, being intrinsically far from equilibrium, and their multiple technological applications. Recent attention in this field is now shifting towards exploring the fascinating dynamics of active and passive mixtures. Here we realize active colloidal rafts, composed of a single catalytic particle encircled by several shells of passive microspheres, and assembled via light-activated chemophoresis. We show that the cluster propulsion mechanism transits from diffusiophoretic to diffusioosmotic as the number of colloidal shells increases. Using the Lorentz reciprocal theorem, we demonstrate that in large clusters self-propulsion emerges by considering the hydrodynamic flow via the diffusioosmotic response of the substrate. The dynamics in our active colloidal rafts are governed by the interplay between phoretic and osmotic effects. Thus, our work highlights their importance in understanding the rich physics of active catalytic systems.

Breast Tumor Cell Survival and Morphology in a Brain-like Extracellular Matrix Depends on Matrix Composition and Mechanical Properties

Triple-negative breast cancer (TNBC) is the most invasive type of breast cancer with high risk of brain metastasis. To better understand interactions between breast tumors with the brain extracellular matrix (ECM), a 3D cell culture model is implemented using a thiolated hyaluronic acid (HA-SH) based hydrogel. The latter is used as HA represents a major component of brain ECM. Melt-electrowritten (MEW) scaffolds of box- and triangular-shaped polycaprolactone (PCL) micro-fibers for hydrogel reinforcement are utilized. Two different molecular weight HA-SH materials (230 and 420 kDa) are used with elastic moduli of 148 ± 34 Pa (soft) and 1274 ± 440 Pa (stiff). Both hydrogels demonstrate similar porosities. The different molecular weight of HA-SH, however, significantly changes mechanical properties, e.g., stiffness, nonlinearity, and hysteresis. The breast tumor cell line MDA-MB-231 forms mainly multicellular aggregates in both HA-SH hydrogels but sustains high viability (75%). Supplementation of HA-SH hydrogels with ECM components does not affect gene expression but improves cell viability and impacts cellular distribution and morphology. The presence of other brain cell types further support numerous cell-cell interactions with tumor cells. In summary, the present 3D cell culture model represents a novel tool establishing a disease cell culture model in a systematic way.

Bioelectric pharmacology of cancer: A systematic review of ion channel drugs affecting the cancer phenotype

Cancer is a pernicious and pressing medical problem; moreover, it is a failure of multicellular morphogenesis that sheds much light on evolutionary developmental biology. Numerous classes of pharmacological agents have been considered as cancer therapeutics and evaluated as potential carcinogenic agents; however, these are spread throughout the primary literature. Here, we briefly review recent work on ion channel drugs as promising anti-cancer treatments and present a systematic review of the known cancer-relevant effects of 109 drugs targeting ion channels. The roles of ion channels in cancer are consistent with the importance of bioelectrical parameters in cell regulation and with the functions of bioelectric signaling in morphogenetic signals that act as cancer suppressors. We find that compounds that are well-known for having targets in the nervous system, such as voltage-gated ion channels, ligand-gated ion channels, proton pumps, and gap junctions are especially relevant to cancer. Our review suggests further opportunities for the repurposing of numerous promising candidates in the field of cancer electroceuticals.

DeepLoc 2.1: multi-label membrane protein type prediction using protein language models

DeepLoc 2.0 is a popular web server for the prediction of protein subcellular localization and sorting signals. Here, we introduce DeepLoc 2.1, which additionally classifies the input proteins into the membrane protein types Transmembrane, Peripheral, Lipid-anchored and Soluble. Leveraging pre-trained transformer-based protein language models, the server utilizes a three-stage architecture for sequence-based, multi-label predictions. Comparative evaluations with other established tools on a test set of 4933 eukaryotic protein sequences, constructed following stringent homology partitioning, demonstrate state-of-the-art performance. Notably, DeepLoc 2.1 outperforms existing models, with the larger ProtT5 model exhibiting a marginal advantage over the ESM-1B model. The web server is available at https://services.healthtech.dtu.dk/services/DeepLoc-2.1.

Surface Cleaning of Oil Contaminants Using Bulk Nanobubbles

The removal of oil from solid surfaces, such as textiles and plates, remains a challenge due to the strong binding affinity of the oil. Conventional methods for surface cleaning often require surfactants and mechanical abrasion to enhance the cleaning process. However, in excess, these can pose adverse effects on the environment and to the material. This study investigated how bulk nanobubble water can clean oil microdroplets deposited on surfaces like glass coverslips and dishes. Microscopy imaging and further image analysis clearly revealed that these microdroplets detached from both hydrophobic and hydrophilic surfaces when washed with bulk nanobubble water within a fluidic microchannel. Oil contaminant cleaning was also conducted in water as mobile phase to mimic the circumstances that occur in a dishwasher and washing machine. Cleaning on a larger scale also proved very successful in the removal of oil from a porcelain bowl. These results indicate that nanobubble water can easily remove oil contaminants from glass and porcelain surfaces without the assistance of surfactants. This is in stark contrast to negligible results obtained with a control solution without nanobubbles. This study indicates that nanobubble technology is an innovative, low-cost, eco-friendly approach for oil removal, demonstrating its potential for broad practical applications.

Microstimulation reveals anesthetic state-dependent effective connectivity of neurons in cerebral cortex

Introduction

Complex neuronal interactions underlie cortical information processing that can be compromised in altered states of consciousness. Here intracortical microstimulation was applied to investigate anesthetic state-dependent effective connectivity of neurons in rat visual cortex in vivo.

Methods

Extracellular activity was recorded at 32 sites in layers 5/6 while stimulating with charge-balanced discrete pulses at each electrode in random order. The same stimulation pattern was applied at three levels of anesthesia with desflurane and in wakefulness. Spikes were sorted and classified by their waveform features as putative excitatory and inhibitory neurons. Network motifs were identified in graphs of effective connectivity constructed from monosynaptic cross-correlograms.

Results

Microstimulation caused early (<10 ms) increase followed by prolonged (11-100 ms) decrease in spiking of all neurons throughout the electrode array. The early response of excitatory but not inhibitory neurons decayed rapidly with distance from the stimulation site over 1 mm. Effective connectivity of neurons with significant stimulus response was dense in wakefulness and sparse under anesthesia. The number of network motifs, especially those of higher order, increased rapidly as the anesthesia was withdrawn indicating a substantial increase in network connectivity as the animals woke up.

Conclusion

The results illuminate the impact of anesthesia on functional integrity of local cortical circuits affecting the state of consciousness.

Exploiting Matrix Stiffness to Overcome Drug Resistance

Drug resistance is arguably one of the biggest challenges facing cancer research today. Understanding the underlying mechanisms of drug resistance in tumor progression and metastasis are essential in developing better treatment modalities. Given the matrix stiffness affecting the mechanotransduction capabilities of cancer cells, characterization of the related signal transduction pathways can provide a better understanding for developing novel therapeutic strategies. In this review, we aimed to summarize the recent advancements in tumor matrix biology in parallel to therapeutic approaches targeting matrix stiffness and its consequences in cellular processes in tumor progression and metastasis. The cellular processes governed by signal transduction pathways and their aberrant activation may result in activating the epithelial-to-mesenchymal transition, cancer stemness, and autophagy, which can be attributed to drug resistance. Developing therapeutic strategies to target these cellular processes in cancer biology will offer novel therapeutic approaches to tailor better personalized treatment modalities for clinical studies.

Nanosized DNA Hydrogel Functionalized with a DNAzyme Tetrahedron for Highly Efficient Gene Silencing

DNAzymes are DNA oligonucleotides that have catalytic activity without the assistance of protein enzymes. In particular, RNA-cleaving DNAzymes were considered as ideal candidates for gene therapy due to their unique characteristics. Nevertheless, efforts to use DNAzyme as a gene therapeutic agent are limited by issues such as their low physiological stability in serum and intracellular delivery efficiency. In this study, we developed a nanosized synthetic DNA hydrogel functionalized with a DNAzyme tetrahedron (TDz Dgel) to overcome these limitations. We observed remarkable improvement in the gene-silencing effect as well as intracellular uptake without the support of gene transfection reagents using TDz Dgel. The improved catalytic activity of the DNAzyme resulted from the combination of the cell-penetrating DNA tetrahedron structure and high stability of DNA hydrogel. We envision that this approach will become a convenient and efficient strategy for gene-silencing therapy using DNAzyme in the future.

The Effects of Telerehabilitation in Patients with Voice Disorders: A Systematic Review and Meta-Analysis

OBJECTIVES

To compare the effects of telerehabilitation (TR) and face-to-face rehabilitation (FTF) methods on the outcomes of adults with voice disorders and to analyze the effectiveness of TR.

METHODS

Following Boolean Logic, a search strategy was devised, combining subject terms and keywords based on the interventions and populations outlined in the inclusion criteria. We searched PubMed, Cochrane Library, Embase, Web of Science, Scopus, CNKI, Wanfang, CQVIP databases, and manually screened academic conference papers, journal articles, and gray literature to identify eligible randomized controlled trials (RCTs) on remote voice therapy. Two researchers assessed the risk of bias in the included studies using the risk of bias assessment tool for RCTs outlined in the Cochrane Handbook for Systematic Reviews of Interventions version 5.1.0.

RESULTS

Five trials with a total of 233 patients with voice disorders were included in the study after screening. The results revealed a significant difference in Jitter change values (mean difference [MD]=-0.12, 95%CI [-0.23,-0.01], P = 0.04) between TR and FTF, maximum phonation time (MD=0.76, 95%CI [-0.60,2.13], P = 0.27), Shimmer (MD=-0.04, 95%CI [-0.11,0.03], P = 0.27), voice handicap index (MD=0.87, 95%CI [-1.77,3.50], P = 0.52), and GRBAS(G) (MD=-0.00, 95%CI [-0.01,0.01], P = 0.99) had no significant difference.

CONCLUSION

TR demonstrates comparable efficacy to FTF in voice treatment and is associated with higher levels of patient satisfaction, making it a viable and effective therapeutic modality. However, given the limited sample size analyzed in this study, further validation of this conclusion necessitates additional RCTs with larger sample sizes. Furthermore, researchers should remain cognizant of the constraints associated with TR and consistently refine treatment protocols to enhance the efficacy of voice therapy.

Multimodal surface coils for low field MR imaging

Low field MRI is safer and more cost effective than the high field MRI. One of the inherent problems of low field MRI is its low signal-to-noise ratio or sensitivity. In this work, we introduce a multimodal surface coil technique for signal excitation and reception to improve the RF magnetic field (B1) efficiency and potentially improve MR sensitivity. The proposed multimodal surface coil consists of multiple identical resonators that are electromagnetically coupled to form a multimodal resonator. The field distribution of its lowest frequency mode is suitable for MR imaging applications. The prototype multimodal surface coils are built, and the performance is investigated and validated through numerical simulation, standard RF measurements and tests, and comparison with the conventional surface coil at low fields. Our results show that the B1 efficiency of the multimodal surface coil outperforms that of the conventional surface coil which is known to offer the highest B1 efficiency among all coil categories, i.e., volume coil, half-volume coil and surface coil. In addition, in low-field MRI, the required low-frequency coils often use large value capacitance to achieve the low resonant frequency which makes frequency tuning difficult. The proposed multimodal surface coil can be conveniently tuned to the required low frequency for low-field MRI with significantly reduced capacitance value, demonstrating excellent low-frequency operation capability over the conventional surface coil.

A functional RNA-origami as direct thrombin inhibitor with fast-acting and specific single-molecule reversal agents in vivo model

Injectable anticoagulants are widely used in medical procedures to prevent unwanted blood clotting. However, many lack safe, effective reversal agents. Here, we present new data on a previously described RNA origami-based, direct thrombin inhibitor (HEX01). We describe a new, fast-acting, specific, single-molecule reversal agent (antidote) and present in vivo data for the first time, including efficacy, reversibility, preliminary safety, and initial biodistribution studies. HEX01 contains multiple thrombin-binding aptamers appended on an RNA origami. It exhibits excellent anticoagulation activity in vitro and in vivo. The new single-molecule, DNA antidote (HEX02) reverses anticoagulation activity of HEX01 in human plasma within 30 s in vitro and functions effectively in a murine liver laceration model. Biodistribution studies of HEX01 in whole mice using ex vivo imaging show accumulation mainly in the liver over 24 h and with 10-fold lower concentrations in the kidneys. Additionally, we show that the HEX01/HEX02 system is non-cytotoxic to epithelial cell lines and non-hemolytic in vitro. Furthermore, we found no serum cytokine response to HEX01/HEX02 in a murine model. HEX01 and HEX02 represent a safe and effective coagulation control system with a fast-acting, specific reversal agent showing promise for potential drug development.

Rigid Cyclic Fluorinated Detergents: Fine-Tuning the Hydrophilic-Lipophilic Balance Controls Self-Assembling and Biochemical Properties

We report herein the synthesis of three detergents bearing a perfluorinated cyclohexyl group connected through a short, hydrogenated spacer (i.e., propyl, butyl, or pentyl) to a β-maltoside polar head that are, respectively, called FCymal-3, FCymal-4, and FCymal-5. Increasing the length of the spacer decreased the critical micellar concentration (CMC), as demonstrated by surface tension (SFT) and isothermal titration calorimetry (ITC), from 5 mM for FCymal-3 to 0.7 mM for FCymal-5. The morphology of the micelles was studied by dynamic light scattering (DLS), analytical ultracentrifugation (AUC), and small-angle X-ray scattering (SAXS), indicating heterogeneous rod-like shapes. While micelles of FCymal-3 and -4 have similar hydrodynamic diameters of ∼10 nm, those of FCymal-5 were twice as large. We also investigated the ability of the detergents to solubilize lipid membranes made of 1-palmitoyl-2-oleyl-sn-glycero-3-phosphocholine (POPC). Molecular modeling indicated that the FCymal detergents generate disorder in lipid bilayers, with FCymal-3 being inserted more deeply into bilayers than FCymal-4 and -5. This was experimentally confirmed using POPC vesicles that were completely solubilized within 2 h with FCymal-3, whereas FCymal-5 required >8 h. A similar trend was noticed for the direct extraction of membrane proteins from E. coli membranes, with FCymal-3 being more potent than FCymal-5. An opposite trend was observed in terms of stabilization of the two model membrane proteins bacteriorhodopsin (bR) and SpNOX. In all three FCymal detergents, bR was stable for at least 2 months with no signs of aggregation. However, while the structural integrity of bR was fully preserved in FCymal-4 and -5, minor bleaching was observed in FCymal-3. Similarly, SpNOX exhibited the least activity in FCymal-3 and the highest activity in FCymal-5. By combining solubilizing and stabilizing potency, FCymal detergents push forward our expectations of the usefulness of fluorinated detergents for handling and investigating membrane proteins.

Construction of multilayered small intestine-like tissue by reproducing interstitial flow

Recent advances have made modeling human small intestines in vitro possible, but it remains a challenge to recapitulate fully their structural and functional characteristics. We suspected interstitial flow within the intestine, powered by circulating blood plasma during embryonic organogenesis, to be a vital factor. We aimed to construct an in vivo-like multilayered small intestinal tissue by incorporating interstitial flow into the system and, in turn, developed the micro-small intestine system by differentiating definitive endoderm and mesoderm cells from human pluripotent stem cells simultaneously on a microfluidic device capable of replicating interstitial flow. This approach enhanced cell maturation and led to the development of a three-dimensional small intestine-like tissue with villi-like epithelium and an aligned mesenchymal layer. Our micro-small intestine system not only overcomes the limitations of conventional intestine models but also offers a unique opportunity to gain insights into the detailed mechanisms underlying intestinal tissue development.

Assessment of the Behaviors of an In Vitro Brain Model On-Chip under Shockwave Impacts

Herein we report the assessment of the effects of shockwave (SW) impacts on adult rat hippocampal progenitor cell (AHPC) neurospheres (NSs), which are used as in vitro brain models, for enhancing our understanding of the mechanisms of traumatic brain injury (TBI). The assessment has been achieved by using culture dishes and a new microchip. The microchip allows the chemicals released from the brain models cultured inside the cell culture chamber under SW impacts to diffuse to the nanosensors in adjacent sensor chambers through built-in diffusion barriers, which are used to prevent the cells from entering the sensor chambers, thereby mitigating the biofouling issues of the sensor surface. Experiments showed the negative impact of the SW on the viability, proliferation, and differentiation of the cells within the NSs. A qPCR gene expression analysis was performed and appeared to confirm some of the immunocytochemistry (ICC) results. Finally, we demonstrated that the microchip can be used to monitor lactate dehydrogenase (LDH) released from the AHPC-NSs subjected to SW impacts. As expected, LDH levels changed when AHPC-NSs were injured by SW impacts, verifying this chip can be used for assessing the degrees of injuries to AHPC-NSs by monitoring LDH levels. Taken together, these results suggest the feasibility of using the chip to better understand the interactions between SW impacts and in vitro brain models, paving the way for potentially establishing in vitro TBI models on a chip.

Essential dynamics of ubiquitin in water and in a natural deep eutectic solvent

Natural deep eutectic solvents (NADESs) comprised of osmolytes are of interest as potential biomolecular (cryo)protectants. However, the way these solvents influence the structure and dynamics of biomolecules as well as the role of water remains poorly understood. We carried out principal component analysis of various secondary structure elements of ubiquitin in water and a betaine : glycerol : water (1 : 2 : ζ; ζ = 0, 1, 2, 5, 10, 20, 45) NADES, from molecular dynamics trajectories, to gain insight into the protein dynamics as it undergoes a transition from a highly viscous anhydrous to an aqueous environment. A crossover of the protein's essential dynamics at ζ ∼ 5, induced by solvent-shell coupled fluctuations, is observed, indicating that ubiquitin might (re)fold in the NADES upon water addition at ζ > ∼5. Further, in contrast to water, the anhydrous NADES preserves ubiquitin's essential modes at high temperatures explaining the protein's seemingly enhanced thermal stability.

Administration of ROS-Scavenging Cerium Oxide Nanoparticles Simply Mixed with Autoantigenic Peptides Induce Antigen-Specific Immune Tolerance against Autoimmune Encephalomyelitis

The scavenging ability of cerium oxide nanoparticles (CeNPs) for reactive oxygen species has been intensively studied in the field of catalysis. However, the immunological impact of these particles has not yet been thoroughly investigated, despite intensive research indicating that modulation of the reactive oxygen species could potentially regulate cell fate and adaptive immune responses. In this study, we examined the intrinsic capability of CeNPs to induce tolerogenic dendritic cells via their reactive oxygen species-scavenging effect when the autoantigenic peptides were simply mixed with CeNPs. CeNPs effectively reduced the intracellular reactive oxygen species levels in dendritic cells in vitro, leading to the suppression of costimulatory molecules as well as NLRP3 inflammasome activation, even in the presence of pro-inflammatory stimuli. Subcutaneously administrated PEGylated CeNPs were predominantly taken up by antigen-presenting cells in lymph nodes and to suppress cell maturation in vivo. The administration of a mixture of PEGylated CeNPs and myelin oligodendrocyte glycoprotein peptides, a well-identified autoantigen associated with antimyelin autoimmunity, resulted in the generation of antigen-specific Foxp3+ regulatory T cells in mouse spleens. The induced peripheral regulatory T cells actively inhibited the infiltration of autoreactive T cells and antigen-presenting cells into the central nervous system, ultimately protecting animals from experimental autoimmune encephalomyelitis when tested using a mouse model mimicking human multiple sclerosis. Overall, our findings reveal the potential of CeNPs for generating antigen-specific immune tolerance to prevent multiple sclerosis, opening an avenue to restore immune tolerance against specific antigens by simply mixing the well-identified autoantigens with the immunosuppressive CeNPs.

Effect of dynamic exclusion and the use of FAIMS, DIA and MALDI-mass spectrometry imaging with ion mobility on amyloid protein identification

Amyloidosis is a disease characterized by local and systemic extracellular deposition of amyloid protein fibrils where its excessive accumulation in tissues and resistance to degradation can lead to organ failure. Diagnosis is challenging because of approximately 36 different amyloid protein subtypes. Imaging methods like immunohistochemistry and the use of Congo red staining of amyloid proteins for laser capture microdissection combined with liquid chromatography tandem mass spectrometry (LMD/LC-MS/MS) are two diagnostic methods currently used depending on the expertise of the pathology laboratory. Here, we demonstrate a streamlined in situ amyloid peptide spatial mapping by Matrix Assisted Laser Desorption Ionization-Mass Spectrometry Imaging (MALDI-MSI) combined with Trapped Ion Mobility Spectrometry for potential transthyretin (ATTR) amyloidosis subtyping. While we utilized the standard LMD/LC-MS/MS workflow for amyloid subtyping of 31 specimens from different organs, we also evaluated the potential introduction in the MS workflow variations in data acquisition parameters like dynamic exclusion, or testing Data Dependent Acquisition combined with High-Field Asymmetric Waveform Ion Mobility Spectrometry (DDA FAIMS) versus Data Independent Acquisition (DIA) for enhanced amyloid protein identification at shorter acquisition times. We also demonstrate the use of Mascot's Error Tolerant Search and PEAKS de novo sequencing for the sequence variant analysis of amyloidosis specimens.

Regenerative endodontic therapy: From laboratory bench to clinical practice

BACKGROUND

Maintaining the vitality and functionality of dental pulp is paramount for tooth integrity, longevity, and homeostasis. Aiming to treat irreversible pulpitis and necrosis, there has been a paradigm shift from conventional root canal treatment towards regenerative endodontic therapy.

AIM OF REVIEW

This extensive and multipart review presents crucial laboratory and practical issues related to pulp-dentin complex regeneration aimed towards advancing clinical translation of regenerative endodontic therapy and enhancing human life quality.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In this multipart review paper, we first present a panorama of emerging potential tissue engineering strategies for pulp-dentin complex regeneration from cell transplantation and cell homing perspectives, emphasizing the critical regenerative components of stem cells, biomaterials, and conducive microenvironments. Then, this review provides details about current clinically practiced pulp regenerative/reparative approaches, including direct pulp capping and root revascularization, with a specific focus on the remaining hurdles and bright prospects in developing such therapies. Next, special attention was devoted to discussing the innovative biomimetic perspectives opened in establishing functional tissues by employing exosomes and cell aggregates, which will benefit the clinical translation of dental pulp engineering protocols. Finally, we summarize careful consideration that should be given to basic research and clinical applications of regenerative endodontics. In particular, this review article highlights significant challenges associated with residual infection and inflammation and identifies future insightful directions in creating antibacterial and immunomodulatory microenvironments so that clinicians and researchers can comprehensively understand crucial clinical aspects of regenerative endodontic procedures.

TG-Net: Using text prompts for improved skin lesion segmentation

Automatic skin segmentation is an efficient method for the early diagnosis of skin cancer, which can minimize the missed detection rate and treat early skin cancer in time. However, significant variations in texture, size, shape, the position of lesions, and obscure boundaries in dermoscopy images make it extremely challenging to accurately locate and segment lesions. To address these challenges, we propose a novel framework named TG-Net, which exploits textual diagnostic information to guide the segmentation of dermoscopic images. Specifically, TG-Net adopts a dual-stream encoder-decoder architecture. The dual-stream encoder comprises Res2Net for extracting image features and our proposed text attention (TA) block for extracting textual features. Through hierarchical guidance, textual features are embedded into the process of image feature extraction. Additionally, we devise a multi-level fusion (MLF) module to merge higher-level features and generate a global feature map as guidance for subsequent steps. In the decoding stage of the network, local features and the global feature map are utilized in three multi-scale reverse attention modules (MSRA) to produce the final segmentation results. We conduct extensive experiments on three publicly accessible datasets, namely ISIC 2017, HAM10000, and PH2. Experimental results demonstrate that TG-Net outperforms state-of-the-art methods, validating the reliability of our method. Source code is available at https://github.com/ukeLin/TG-Net.

Harnessing the potential of monocytes/macrophages to regenerate tissue-engineered vascular grafts

Cell-free tissue-engineered vascular grafts provide a promising alternative to treat cardiovascular disease, but timely endothelialization is essential for ensuring patency and proper functioning post-implantation. Recent studies from our lab showed that blood cells like monocytes (MCs) and macrophages (Mϕ) may contribute directly to cellularization and regeneration of bioengineered arteries in small and large animal models. While MCs and Mϕ are leucocytes that are part of the innate immune response, they share common developmental origins with endothelial cells (ECs) and are known to play crucial roles during vessel formation (angiogenesis) and vessel repair after inflammation/injury. They are highly plastic cells that polarize into pro-inflammatory and anti-inflammatory phenotypes upon exposure to cytokines and differentiate into other cell types, including EC-like cells, in the presence of appropriate chemical and mechanical stimuli. This review focuses on the developmental origins of MCs and ECs; the role of MCs and Mϕ in vessel repair/regeneration during inflammation/injury; and the role of chemical signalling and mechanical forces in Mϕ inflammation that mediates vascular graft regeneration. We postulate that comprehensive understanding of these mechanisms will better inform the development of strategies to coax MCs/Mϕ into endothelializing the lumen and regenerate the smooth muscle layers of cell-free bioengineered arteries and veins that are designed to treat cardiovascular diseases and perhaps the native vasculature as well.

Multifunctional Nanomaterials for Advancing Neural Interfaces: Recording, Stimulation, and Beyond

ConspectusNeurotechnology has seen dramatic improvements in the last three decades. The major focus in the field has been to design electrical communication platforms with high spatial resolution, stability, and translatability for understanding and affecting neural pathways. The deployment of nanomaterials in bioelectronics has enhanced the capabilities of conventional approaches employing microelectrode arrays (MEAs) for electrical interfaces, allowing the construction of miniaturized, high-performance neuroelectronics (Garg, R.; et al. ACS Appl. Nano Mater. 2023, 6, 8495). While these advancements in the electrical neuronal interface have revolutionized neurotechnology both in scale and breadth, an in-depth understanding of neurons' interactions is challenging due to the complexity of the environments where the cells and tissues are laid. The activity of large, three-dimensional neuronal systems has proven difficult to accurately monitor and modulate, and chemical cell-cell communication is often completely neglected. Recent breakthroughs in nanotechnology have provided opportunities to use new nonelectric modes of communication with neurons and to significantly enhance electrical signal interface capabilities. The enhanced electrochemical activity and optical activity of nanomaterials owing to their nonbulk electronic properties and surface nanostructuring have seen extensive utilization. Nanomaterials' enhanced optical activity enables remote neural state modulation, whereas the defect-rich surfaces provide an enormous number of available electrocatalytic sites for neurochemical detection and electrochemical modulation of cell microenvironments through Faradaic processes. Such unique properties can allow multimodal neural interrogation toward generating closed-loop interfaces with access to more complete neural state descriptors. In this Account, we will review recent advances and our efforts spearheaded toward utilizing nanostructured electrodes for enhanced bidirectional interfaces with neurons, the application of unique hybrid nanomaterials for remote nongenetic optical stimulation of neurons, tunable nanomaterials for highly sensitive and selective neurotransmitter detection, and the utilization of nanomaterials as electrocatalysts toward electrochemically modulating cellular activity. We highlight applications of these technologies across cell types through nanomaterial engineering with a focus on multifunctional graphene nanostructures applied though several modes of neural modulation but also an exploration of broad material classes for maximizing the potency of closed-loop bioelectronics.

The estimation of pore size distribution of electroporated MCF-7 cell membrane

The size of the pores created by external electrical pulses is important for molecule delivery into the cell. The size of pores and their distribution on the cell membrane determine the efficiency of molecule transport into the cell. There are very few studies visualizing the presence of electropores. In this study, we aimed to investigate the size distribution of electropores that were created by high intensity and short duration electrical pulses on MCF-7 cell membrane. Scanning Electron Microscopy (SEM) was used to visualize and characterize the membrane pores created by the external electric field. Structural changes on the surface of the electroporated cell membrane was observed by Atomic Force Microscopy (AFM). The size distribution of pore sizes was obtained by measuring the radius of 500 electropores. SEM imaging showed non-uniform patterning. The average radius of the electropores was 12 nm, 51.60% of pores were distributed within the range of 5 to 10 nm, and 81% of pores had radius below 15 nm. These results showed that microsecond (µs) high intensity electrical pulses cause the creation of heterogeneous nanopores on the cell membrane.

Catalase-Powered Nanobots for Overcoming the Mucus Barrier

Biological barriers present a significant obstacle to treatment, especially when drugs are administered locally to increase their concentrations at the target site while minimizing unintended off-target effects. Among these barriers, mucus presents a challenge, as it serves as a protective layer in the respiratory, urogenital, and gastrointestinal tracts. Its role is to shield the underlying epithelial cells from pathogens and toxic compounds but also impedes the efficient delivery of drugs. Despite the exploration of mucolytic agents to improve drug delivery, overcoming this protective barrier remains a significant hurdle. In our study, we investigate an alternative approach involving the use of catalase-powered nanobots. We use an in vitro model that simulates intestinal mucus secretion to demonstrate the dual functionality of our nanobots. This includes their ability to disrupt mucus, which we confirmed through in vitro and ex vivo validation, as well as their self-propulsion to overcome the mucus barrier, resulting in a 60-fold increase compared with passive nanoparticles. Therefore, our findings highlight the potential utility of catalase-powered nanobots as carriers for therapeutic agents since they could enhance drug delivery efficiency by penetrating the mucus barrier.

Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks

Identifying constitutive parameters in engineering and biological materials, particularly those with intricate geometries and mechanical behaviors, remains a longstanding challenge. The recent advent of physics-informed neural networks (PINNs) offers promising solutions, but current frameworks are often limited to basic constitutive laws and encounter practical constraints when combined with experimental data. In this paper, we introduce a robust PINN-based framework designed to identify material parameters for soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions. Distinctively, our model emphasizes training PINNs with multi-modal synthetic experimental datasets consisting of full-field deformation and loading history, ensuring algorithm robustness even with noisy data. Our results reveal that the PINNs framework can accurately identify constitutive parameters of the incompressible Arruda-Boyce model for samples with intricate geometries, maintaining an error below 5%, even with an experimental noise level of 5%. We believe our framework provides a robust modulus identification approach for complex solids, especially for those with geometrical and constitutive complexity.

Radiology of fibrosis. Part I: Thoracic organs

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

Separation of Activated T Cells Using Multidimensional Double Spiral (MDDS) Inertial Microfluidics for High-Efficiency CAR T Cell Manufacturing

This study introduces a T cell enrichment process, capitalizing on the size differences between activated and unactivated T cells to facilitate the isolation of activated, transducible T cells. By employing multidimensional double spiral (MDDS) inertial sorting, our approach aims to remove unactivated or not fully activated T cells post-activation, consequently enhancing the efficiency of chimeric antigen receptor (CAR) T cell manufacturing. Our findings reveal that incorporating a simple, label-free, and continuous MDDS sorting step yields a purer T cell population, exhibiting significantly enhanced viability and CAR-transducibility (with up to 85% removal of unactivated T cells and approximately 80% recovery of activated T cells); we found approximately 2-fold increase in CAR transduction efficiency for a specific sample, escalating from ∼10% to ∼20%, but this efficiency highly depends on the original T cell sample as MDDS sorting would be more effective for samples possessing a higher proportion of unactivated T cells. This new cell separation process could augment the efficiency, yield, and cost-effectiveness of CAR T cell manufacturing, potentially broadening the accessibility of this transformative therapy and contributing to improved patient outcomes.

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials

BACKGROUND AND OBJECTIVE

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.

METHODS

The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.

RESULTS

ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determinationR 2 . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.

CONCLUSION

ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve highR 2 values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

Measurement of covalent bond formation in light-curing hydrogels predicts physical stability under flow

Photocrosslinking hydrogels are promising for tissue engineering and regenerative medicine, but challenges in reaction monitoring often leave their optimization subject to trial and error. The stability of crosslinked gels under fluid flow, as in the case of a microfluidic device, is particularly challenging to predict, both because of obstacles inherent to solid-state macromolecular analysis that prevent accurate chemical monitoring, and because stability is dependent on size of the patterned features. To solve both problems, we obtained 1H NMR spectra of cured hydrogels which were enzymatically degraded. This allowed us to take advantage of the high-resolution that solution NMR provides. This unique approach enabled the measurement of degree of crosslinking (DoC) and prediction of material stability under physiological fluid flow. We showed that NMR spectra of enzyme-digested gels successfully reported on DoC as a function of light exposure and wavelength within two classes of photocrosslinkable hydrogels: methacryloyl-modified gelatin and a composite of thiol-modified gelatin and norbornene-terminated polyethylene glycol. This approach revealed that a threshold DoC was required for patterned features in each material to become stable, and that smaller features required a higher DoC for stability. Finally, we demonstrated that DoC was predictive of the stability of architecturally complex features when photopatterning, underscoring the value of monitoring DoC when using light-reactive gels. We anticipate that the ability to quantify chemical crosslinks will accelerate the design of advanced hydrogel materials for structurally demanding applications such as photopatterning and bioprinting.

Building RNA-Mediated Artificial Signaling Pathways between Endogenous Genes

ConspectusSophisticated genetic networks play a pivotal role in orchestrating cellular responses through intricate signaling pathways across diverse environmental conditions. Beyond the inherent complexity of natural cellular signaling networks, the construction of artificial signaling pathways (ASPs) introduces a vast array of possibilities for reshaping cellular responses, enabling programmable control of living organisms. ASPs can be integrated with existing cellular networks and redirect output responses as desired, allowing seamless communication and coordination with other cellular processes, thereby achieving designable transduction within cells. Among diversified ASPs, establishing connections between originally independent endogenous genes is of particular significance in modifying the genetic networks, so that cells can be endowed with new capabilities to sense and deal with abnormal factors related to differentiated gene expression (i.e., solve the issues of the aberrant gene expression induced by either external or internal stimuli). In a typical scenario, the two genes X and Y in the cell are originally expressed independently. After the introduction of an ASP, changes in the expression of gene X may exert a designed impact on gene Y, subsequently inducing the cellular response related to gene Y. If X represents a disease signal and Y serves as a therapeutic module, the introduction of the ASP empowers cells with a new spontaneous defense system to handle potential risks, which holds great potential for both fundamental and translational studies.In this Account, we primarily review our endeavors in the construction of RNA-mediated ASPs between endogenous genes that can respond to differentiated RNA expression. In contrast to other molecules that may be restricted to specific pathways, synthetic RNA circuits can be easily utilized and expanded as a general platform for constructing ASPs with a high degree of programmability and tunability for diversified functionalities through predictable Watson-Crick base pairing. We first provide an overview of recent advancements in RNA-based genetic circuits, encompassing but not limited to utilization of RNA toehold switches, siRNA and CRISPR systems. Despite notable progress, most reported RNA circuits have to contain at least one exogenous RNA X as input or one engineered RNA Y as a target, which is not suitable for establishing endogenous gene connections. While exogenous RNAs can be engineered and controlled as desired, constructing a general and efficient platform for manipulation of naturally occurring RNAs poses a formidable challenge, especially for the mammalian system. With a focus on this goal, we are devoted to developing efficient strategies to manipulate cell responses by establishing RNA-mediated ASPs between endogenous genes, particularly in mammalian cells. Our step-by-step progress in engineering customized cell signaling circuits, from bacterial cells to mammalian cells, from gene expression regulation to phenotype control, and from small RNA to long mRNA of low abundance and more complex secondary structures, is systematically described. Finally, future perspectives and potential applications of these RNA-mediated ASPs between endogenous genes are also discussed.

FimH-mannose noncovalent bonds survive minutes to hours under force

The adhesin FimH is expressed by commensal Escherichia coli and is implicated in urinary tract infections, where it mediates adhesion to mannosylated glycoproteins on urinary and intestinal epithelial cells in the presence of a high-shear fluid environment. The FimH-mannose bond exhibits catch behavior in which bond lifetime increases with force, because tensile force induces a transition in FimH from a compact native to an elongated activated conformation with a higher affinity to mannose. However, the lifetime of the activated state of FimH has not been measured under force. Here we apply multiplexed magnetic tweezers to apply a preload force to activate FimH bonds with yeast mannan, then we measure the lifetime of these activated bonds under a wide range of forces above and below the preload force. A higher fraction of FimH-mannan bonds were activated above than below a critical preload force, confirming the FimH catch bond behavior. Once activated, FimH detached from mannose with multi-state kinetics, suggesting the existence of two bound states with a 20-fold difference in dissociation rates. The average lifetime of activated FimH-mannose bonds was 1000 to 10,000 s at forces of 30-70 pN. Structural explanations of the two bound states and the high force resistance provide insights into structural mechanisms for long-lived, force-resistant biomolecular interactions.

Potential Role of Dipeptidyl Peptidase-4 in Regulating Mitochondria and Oxidative Stress in Cardiomyocytes

Oxidative stress causes mitochondrial damage and bioenergetic dysfunction and inhibits adenosine triphosphate production, contributing to the pathogenesis of cardiac diseases. Dipeptidyl peptidase 4 (DPP4) is primarily a membrane-bound extracellular peptidase that cleaves Xaa-Pro or Xaa-Ala dipeptides from the N terminus of polypeptides. DPP4 inhibitors have been used in patients with diabetes and heart failure; however, they have led to inconsistent results. Although the enzymatic properties of DPP4 have been well studied, the substrate-independent functions of DPP4 have not. In the present study, we knocked down DPP4 in cultured cardiomyocytes to exclude the effects of differential alteration in the substrates and metabolites of DPP4 then compared the response between the knocked-down and wild-type cardiomyocytes during exposure to oxidative stress. H2O2 exposure induced DPP4 expression in both types of cardiomyocytes. However, knocking down DPP4 substantially reduced the loss of cell viability by preserving mitochondrial bioenergy, reducing intracellular reactive oxygen species production, and reducing apoptosis-associated protein expression. These findings demonstrate that inhibiting DPP4 improves the body's defense against oxidative stress by enhancing Nrf2 and PGC-1α signaling and increasing superoxide dismutase and catalase activity. Our results indicate that DPP4 mediates the body's response to oxidative stress in individuals with heart disease.

Protein Gas Vesicles of Bacillus megaterium as Enhancers of Ultrasound-Induced Transcriptional Regulation

Gas vesicles (GVs) are large cylindrical gas-filled protein assemblies found in diverse aquatic bacteria that enable their adaptation of buoyancy. GVs have already been used as ultrasound contrasting agents. Here, we investigate GVs derived from Bacillus megaterium, aiming to minimize the number of accessory Gvps within the GV gene cluster and demonstrate the use of GVs as enhancers of acoustic radiation force administered by ultrasound. Three (GvpR, GvpT, and GvpU) out of 11 genes in the cluster were found to be dispensable for functional GV formation, and their omission resulted in narrower GVs. Two essential proteins GvpJ and GvpN were absent from recently determined GV structures, but GvpJ was nevertheless found to be tightly bound to the cylindrical part of GVs in this study. Additionally, the N-terminus of GvpN was observed to play an important role in the formation of mature GVs. The binding of engineered GvpC fromAnabaena flos-aquae to HEK293 cells via integrins enhanced the acoustic force delivered by ultrasound and resulted in an increased Ca2+ influx into cells. Coupling with a synthetic Ca2+-dependent signaling pathway GVs efficiently enhanced cell stimulation by ultrasound, which expands the potentials of noninvasive sonogenetics cell stimulation.

Exosomes based strategies for cardiovascular diseases: Opportunities and challenges

Exosomes, as nanoscale extracellular vesicles (EVs), are secreted by all types of cells to facilitate intercellular communication in living organisms. After being taken up by neighboring or distant cells, exosomes can alter the expression levels of target genes in recipient cells and thereby affect their pathophysiological outcomes depending on payloads encapsulated therein. The functions and mechanisms of exosomes in cardiovascular diseases have attracted much attention in recent years and are thought to have cardioprotective and regenerative potential. This review summarizes the biogenesis and molecular contents of exosomes and details the roles played by exosomes released from various cells in the progression and recovery of cardiovascular disease. The review also discusses the current status of traditional exosomes in cardiovascular tissue engineering and regenerative medicine, pointing out several limitations in their application. It emphasizes that some of the existing emerging industrial or bioengineering technologies are promising to compensate for these shortcomings, and the combined application of exosomes and biomaterials provides an opportunity for mutual enhancement of their performance. The integration of exosome-based cell-free diagnostic and therapeutic options will contribute to the further development of cardiovascular regenerative medicine.