A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

BACKGROUND

Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis.

METHOD

In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels-the pulmonary artery, aorta, and superior vena cava-in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images.

RESULTS

We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively.

CONCLUSIONS

Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Focused Ultrasound-Responsive Nanocomposite with Near-Infrared II Mechanoluminescence for Spatiotemporally Selective Immune Activation in Lymph Nodes

The immune regulation of the lymphatic system, especially the lymph node (LN), is of great significance for the treatment of diseases and the inhibition of pathogenic organisms spreading in the body. However, achieving precise spatiotemporal control of immune cell activation in LN in vivo remains a challenge due to tissue depth and off-target effects. Furthermore, minimally invasive and real-time feedback methods to monitor the regulation of the immune system in LN are lacking. Here, focused ultrasound responsive immunomodulator loaded nanoplatform (FURIN) with near-infrared II (NIR-II) luminescence is designed to achieve spatiotemporally controllable immune activation in LN in vivo. The NIR-II persistent luminescence of FURIN can track its delivery in LN through bioimaging. Under focused ultrasound (FUS) stimulation, the immunomodulator encapsulated in FURIN can be released locally in the LN to activate immune cells such as dendritic cells and the NIR-II mechanoluminescence of FURIN provides real-time optical feedback signals for immune activation. This work points to a FUS mediated, spatiotemporal selective immune activation strategy in vivo with the feedback control of luminescence signals via ultrasound responsive nanocomposite, which is of great significance in improving the efficacy and reducing the side effect of immune regulation for the development of potential immunotherapeutic methods in the future.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells

Engineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR-Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA-based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis-related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Using a probabilistic approach to derive a two-phase model of flow-induced cell migration

Interstitial fluid flow is a feature of many solid tumors. In vitro experiments have shown that such fluid flow can direct tumor cell movement upstream or downstream depending on the balance between the competing mechanisms of tensotaxis (cell migration up stress gradients) and autologous chemotaxis (downstream cell movement in response to flow-induced gradients of self-secreted chemoattractants). In this work we develop a probabilistic-continuum, two-phase model for cell migration in response to interstitial flow. We use a kinetic description for the cell velocity probability density function, and model the flow-dependent mechanical and chemical stimuli as forcing terms that bias cell migration upstream and downstream. Using velocity-space averaging, we reformulate the model as a system of continuum equations for the spatiotemporal evolution of the cell volume fraction and flux in response to forcing terms that depend on the local direction and magnitude of the mechanochemical cues. We specialize our model to describe a one-dimensional cell layer subject to fluid flow. Using a combination of numerical simulations and asymptotic analysis, we delineate the parameter regime where transitions from downstream to upstream cell migration occur. As has been observed experimentally, the model predicts downstream-oriented chemotactic migration at low cell volume fractions, and upstream-oriented tensotactic migration at larger volume fractions. We show that the locus of the critical volume fraction, at which the system transitions from downstream to upstream migration, is dominated by the ratio of the rate of chemokine secretion and advection. Our model also predicts that, because the tensotactic stimulus depends strongly on the cell volume fraction, upstream, tensotaxis-dominated migration occurs only transiently when the cells are initially seeded, and transitions to downstream, chemotaxis-dominated migration occur at later times due to the dispersive effect of cell diffusion.

An update on alternative therapy for Escherichia coli causing urinary tract infections; a narrative review

BACKGROUND

Urinary tract infections (UTIs) are the most common type of nosocomial infection and severe health issues because of the difficulties and frequent recurrence. Today, alternative methods such as sonodynamic therapy (SDT), photodynamic therapy (PDT) and herbal materials use for treating infections like UTI in many countries.

METHOD

We conducted searches of the biomedical databases (Google Scholar, Scopus, PubMed, and Web of sciences) to identify related studies from 2008 to 2023.

RESULT

SDT aims to use ultrasound to activate a sonosensitizer, which causes a biological effect by raising reactive oxygen species (ROS). When bacteria are exposed to ROS, several important effects occur: oxidative damage, DNA damage, protein dysfunction etc. SDT with herbal medicine significantly reduced the number of colony-forming units and bactericidal activity for Klebsiella pneumonia and E. coli. PDT is a promising treatment for cancer and microbial infections, combining a photosensitiser, light and tissue molecular oxygen. It involves a photosensitizer, light source, and oxygen, with variations affecting microbial binding and bactericidal activity. Factors affecting antibacterial properties include plant type, growing conditions, harvesting, and processing. This review highlights the recent advancements in sonodynamic, photodynamic, herbal, and bio-material-based approaches in the treatment of E. coli infections.

CONCLUSIONS

These alternative therapies offer exciting prospects for addressing UTIs, especially in cases where traditional antibiotic treatments may be less effective. Further research and clinical studies are warranted to fully explore the potential of these innovative treatment modalities in combating UTIs and improving patient outcomes.

Force-induced tail-autotomy mitochondrial fission and biogenesis of matrix-excluded mitochondrial-derived vesicles for quality control

Mitochondria constantly fuse and divide for mitochondrial inheritance and functions. Here, we identified a distinct type of naturally occurring fission, tail-autotomy fission, wherein a tail-like thin tubule protrudes from the mitochondrial body and disconnects, resembling autotomy. Next, utilizing an optogenetic mitochondria-specific mechanostimulator, we revealed that mechanical tensile force drives tail-autotomy fission. This force-induced fission involves DRP1/MFF and endoplasmic reticulum tubule wrapping. It redistributes mitochondrial DNA, producing mitochondrial fragments with or without mitochondrial DNA for different fates. Moreover, tensile force can decouple outer and inner mitochondrial membranes, pulling out matrix-excluded tubule segments. Subsequent tail-autotomy fission separates the matrix-excluded tubule segments into matrix-excluded mitochondrial-derived vesicles (MDVs) which recruit Parkin and LC3B, indicating the unique role of tail-autotomy fission in segregating only outer membrane components for mitophagy. Sustained force promotes fission and MDV biogenesis more effectively than transient one. Our results uncover a mechanistically and functionally distinct type of fission and unveil the role of tensile forces in modulating fission and MDV biogenesis for quality control, underscoring the heterogeneity of fission and mechanoregulation of mitochondrial dynamics.

Cellular response of advanced triple cultures of human osteocytes, osteoblasts and osteoclasts to high sulfated hyaluronan (sHA3)

Bone remodelling, important for homeostasis and regeneration involves the controlled action of osteoblasts, osteocytes and osteoclasts. The present study established a three-dimensional human in vitro bone model as triple culture with simultaneously differentiating osteocytes and osteoclasts, in the presence of osteoblasts. Since high sulfated hyaluronan (sHA3) was reported as a biomaterial to enhance osteogenesis as well as to dampen osteoclastogenesis, the triple culture was exposed to sHA3 to investigate cellular responses compared to the respective bone cell monocultures. Osteoclast formation and marker expression was stimulated by sHA3 only in triple culture. Osteoprotegerin (OPG) gene expression and protein secretion, but not receptor activator of NF-κB ligand (RANKL) or sclerostin (SOST), were strongly enhanced, suggesting an important role of sHA3 itself in osteoclastogenesis with other targets than indirect modulation of the RANKL/OPG ratio. Furthermore, sHA3 upregulated osteocalcin (BGLAP) in osteocytes and osteoblasts in triple culture, while alkaline phosphatase (ALP) was downregulated.

Advancements in preclinical human-relevant modeling of pulmonary vasculature on-chip

Preclinical human-relevant modeling of organ-specific vasculature offers a unique opportunity to recreate pathophysiological intercellular, tissue-tissue, and cell-matrix interactions for a broad range of applications. Lung vasculature is particularly important due to its involvement in genesis and progression of rare, debilitating disorders as well as common chronic pathologies. Here, we provide an overview of the latest advances in the development of pulmonary vascular (PV) models using emerging microfluidic tissue engineering technology Organs-on-Chips (so-called PV-Chips). We first review the currently reported PV-Chip systems and their key features, and then critically discuss their major limitations in reproducing in vivo-seen and disease-relevant cellularity, localization, and microstructure. We conclude by presenting latest efforts to overcome such technical and biological limitations and future directions.

The low-LET radiation contribution to the tumor dose in diffusing alpha-emitters radiation therapy

BACKGROUND

Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a new technique that enables the use of alpha particles for the treatment of solid tumors. Alpha DaRT employs interstitial sources carrying a few μ $\mu$ Ci of224 $^{224}$ Ra below their surface, designed to release a chain of short-lived atoms (progeny of224 $^{224}$ Ra) which emit alpha particles, along with beta, Auger, and conversion electrons, x- and gamma rays. These atoms diffuse around the source and create-primarily through their alpha decays-a lethal high-dose region measuring a few millimeters in diameter.

PURPOSE

While previous studies focused on the dose from the alpha emissions alone, this work addresses the electron and photon dose contributed by the diffusing atoms and by the atoms remaining on the source surface, for both a single Alpha DaRT source and multi-source lattices. This allows to evaluate the low-LET contribution to the tumor dose and tumor cell survival, and demonstrate the sparing of surrounding healthy tissue.

METHODS

The low-LET dose is calculated using the EGSnrc and FLUKA Monte Carlo (MC) codes. We compare the results of a simple line-source approximation with no diffusion to those of a full simulation, which implements a realistic source geometry and the spread of diffusing atoms. We consider two opposite scenarios: one with low diffusion and high212 $^{212}$ Pb leakage, and the other with high diffusion and low leakage. The low-LET dose in source lattices is calculated by superposition of single-source contributions. Its effect on cell survival is estimated with the linear quadratic model in the limit of low dose rate.

RESULTS

For sources carrying 3  μ $\umu$ Ci/cm224 $^{224}$ Ra arranged in a hexagonal lattice with 4 mm spacing, the minimal low-LET dose between sources is∼ 18 - 30 $\sim 18-30$  Gy for the two test cases and is dominated by the beta contribution. The low-LET dose drops below 5 Gy∼ 3 $\sim 3$  mm away from the outermost source in the lattice with an effective maximal dose rate of< 0.04 $<0.04$  Gy/h. The accuracy of the line-source/no-diffusion approximation is∼ 15 % $\sim 15\%$ for the total low-LET dose over clinically relevant distances (2-4 mm). The low-LET dose reduces tumor cell survival by a factor of∼ 2 - 200 $\sim 2-200$ .

CONCLUSIONS

The low-LET dose in Alpha DaRT can be modeled by conventional MC techniques with appropriate leakage corrections to the source activity. For 3  μ $\umu$ Ci/cm224 $^{224}$ Ra sources, the contribution of the low-LET dose can reduce cell survival inside the tumor by up to two orders of magnitude. The low-LET dose to surrounding healthy tissue is negligible. Increasing source activities by a factor of 5 can bring the low-LET dose itself to therapeutic levels, in addition to the high-LET dose contributed by alpha particles, leading to a "self-boosted" Alpha DaRT configuration, and potentially allowing to increase the lattice spacing.

Control of complement-induced inflammatory responses to SARS-CoV-2 infection by anti-SARS-CoV-2 antibodies

Dysregulated immune responses contribute to the excessive and uncontrolled inflammation observed in severe COVID-19. However, how immunity to SARS-CoV-2 is induced and regulated remains unclear. Here, we uncover the role of the complement system in the induction of innate and adaptive immunity to SARS-CoV-2. Complement rapidly opsonizes SARS-CoV-2 particles via the lectin pathway. Complement-opsonized SARS-CoV-2 efficiently induces type-I interferon and pro-inflammatory cytokine responses via activation of dendritic cells, which are inhibited by antibodies against the complement receptors (CR) 3 and 4. Serum from COVID-19 patients, or monoclonal antibodies against SARS-CoV-2, attenuate innate and adaptive immunity induced by complement-opsonized SARS-CoV-2. Blocking of CD32, the FcγRII antibody receptor of dendritic cells, restores complement-induced immunity. These results suggest that opsonization of SARS-CoV-2 by complement is involved in the induction of innate and adaptive immunity to SARS-CoV-2 in the acute phase of infection. Subsequent antibody responses limit inflammation and restore immune homeostasis. These findings suggest that dysregulation of the complement system and FcγRII signaling may contribute to severe COVID-19.

Skeletal interoception in osteoarthritis

The interoception maintains proper physiological conditions and metabolic homeostasis by releasing regulatory signals after perceving changes in the internal state of the organism. Among its various forms, skeletal interoception specifically regulates the metabolic homeostasis of bones. Osteoarthritis (OA) is a complex joint disorder involving cartilage, subchondral bone, and synovium. The subchondral bone undergoes continuous remodeling to adapt to dynamic joint loads. Recent findings highlight that skeletal interoception mediated by aberrant mechanical loads contributes to pathological remodeling of the subchondral bone, resulting in subchondral bone sclerosis in OA. The skeletal interoception is also a potential mechanism for chronic synovial inflammation in OA. In this review, we offer a general overview of interoception, specifically skeletal interoception, subchondral bone microenviroment and the aberrant subchondral remedeling. We also discuss the role of skeletal interoception in abnormal subchondral bone remodeling and synovial inflammation in OA, as well as the potential prospects and challenges in exploring novel OA therapies that target skeletal interoception.

Simultaneous photoacoustic and ultrasound imaging: A review

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that combines the advantages of optical and ultrasound imaging, enabling the generation of images with both optical resolution and acoustic penetration depth. By leveraging similar signal acquisition and processing methods, the integration of photoacoustic and ultrasound imaging has introduced a novel hybrid imaging modality suitable for clinical applications. Photoacoustic-ultrasound imaging allows for non-invasive, high-resolution, and deep-penetrating imaging, providing a wealth of image information. In recent years, with the deepening research and the expanding biomedical application scenarios of photoacoustic-ultrasound bimodal systems, the immense potential of photoacoustic-ultrasound bimodal imaging in basic research and clinical applications has been demonstrated, with some research achievements already commercialized. In this review, we introduce the principles, technical advantages, and biomedical applications of photoacoustic-ultrasound bimodal imaging techniques, specifically focusing on tomographic, microscopic, and endoscopic imaging modalities. Furthermore, we discuss the future directions of photoacoustic-ultrasound bimodal imaging technology.

Oxidative Stress Promotes Liver Cancer Metastasis via RNF25-Mediated E-Cadherin Protein Degradation

Loss of E-cadherin (ECAD) is required in tumor metastasis. Protein degradation of ECAD in response to oxidative stress is found in metastasis of hepatocellular carcinoma (HCC) and is independent of transcriptional repression as usually known. Mechanistically, protein kinase A (PKA) senses oxidative stress by redox modification in its β catalytic subunit (PRKACB) at Cys200 and Cys344. The activation of PKA kinase activity subsequently induces RNF25 phosphorylation at Ser450 to initiate RNF25-catalyzed degradation of ECAD. Functionally, RNF25 repression induces ECAD protein expression and inhibits HCC metastasis in vitro and in vivo. Altogether, these results indicate that RNF25 is a critical regulator of ECAD protein turnover, and PKA is a necessary redox sensor to enable this process. This study provides some mechanistic insight into how oxidative stress-induced ECAD degradation promotes tumor metastasis of HCC.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

The evolving landscape of gene therapy strategies for the treatment of osteoarthritis

OBJECTIVES

Significant advances have been made in our understanding of osteoarthritis (OA) pathogenesis; however, no disease-modifying therapies have been identified. This review will summarize the gene therapy landscape, its initial successes for OA, and possible challenges using recent studies and examples of gene therapies in clinical trials.

DESIGN

This narrative review has three major sections: 1) vector systems for OA gene therapy, 2) current and emerging targets for OA gene therapy, and 3) considerations and future directions.

RESULTS

Gene therapy is the strategy by which nucleic acids are delivered to treat and reverse disease progression. Specificity and prolonged expression of these nucleic acids are achieved by manipulating promoters, genes, and vector systems. Certain vector systems also allow for the development of combinatorial nucleic acid strategies that can be delivered in a single intraarticular injection - an approach likely required to treat the complexity of OA pathogenesis. Several viral and non-viral vector-based gene therapies are in clinical trials for OA, and many more are being evaluated in the preclinical arena.

CONCLUSIONS

In a post-coronavirus disease 2019 (COVID-19) era, the future of gene therapy for OA is certainly promising; however, the majority of preclinical validation continues to focus heavily on post-traumatic models and changes in only cartilage and subchondral bone. To ensure successful translation, new candidates in the preclinical arena should be examined against all joint tissues as well as pain using diverse models of injury-, obesity-, and age-induced disease. Lastly, consideration must be given to strategies for repeat administration and the cost of treatment owing to the chronic nature of OA.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

Effects of Chemical Fixatives on Kinetic Measurements of Biomolecular Interaction on Cell Membrane

Understanding the interaction between ligands and membrane proteins is important for drug design and optimization. Although investigation using live cells is desirable, it is not feasible in some circumstances and cell fixation is performed to reduce cell motion and degradation. This study compared the effects of five fixatives, i.e., formaldehyde vapor (FV), paraformaldehyde (PFA), acetone, methanol, and ethanol, on kinetic measurements via the LigandTracer method. We found that all five fixatives exerted insignificant effects on lectin-glycan interaction. However, antibody-receptor interaction is markedly perturbed by coagulant fixatives. The acetone fixation changed the binding of the anti-human epidermal growth factor receptor 2 (HER2) antibody to HER2 on the cell membrane from a 1:2 to a 1:1 binding model, while methanol and ethanol abolished the antibody binding possibly by removal of the HER2 receptors on the cell membrane. The capability of binding was retained when methanol fixation was performed at lower temperatures, albeit with a binding model of 1:1 instead. Moreover, whereas cell morphology does not exert a substantial impact on lectin-glycan interaction, it can indeed modify the binding model of antibody-receptor interaction. Our results provided insights into the selection of fixatives for cell-based kinetic studies.

Development of mechanosensitive synthetic cells for biomedical applications

The ability of cells to sense and respond to their physical environment plays a fundamental role in a broad spectrum of biological processes. As one of the most essential molecular force sensors and transducers found in cell membranes, mechanosensitive (MS) ion channels can convert mechanical inputs into biochemical or electrical signals to mediate a variety of sensations. The bottom-up construction of cell-sized compartments displaying cell-like organization, behaviors, and complexity, also known as synthetic cells, has gained popularity as an experimental platform to characterize biological functions in isolation. By reconstituting MS channels in the synthetic lipid bilayers, we envision using mechanosensitive synthetic cells for several medical applications. Here, we describe three different concepts for using ultrasound, shear stress, and compressive stress as mechanical stimuli to activate drug release from mechanosensitive synthetic cells for disease treatments.

Engineering Copper-Containing Nanoparticles-Loaded Silicene Nanosheets with Triple Enzyme Mimicry Activities and Photothermal Effect for Promoting Bacteria-Infected Wound Healing

Skin wounds accompanied by bacterial infections threaten human health, and conventional antibiotic treatments are ineffective for drug-resistant bacterial infections and chronically infected wounds. The development of non-antibiotic-dependent therapeutics is highly desired but remains a challenging issue. Recently, 2D silicene nanosheets with considerable biocompatibility, biodegradability, and photothermal-conversion performance have received increasing attention in biomedical fields. Herein, copper-containing nanoparticles-loaded silicene (Cu2.8O@silicene-BSA) nanosheets with triple enzyme mimicry catalytic (peroxidase, catalase, and oxidase-like) activities and photothermal function are rationally designed and fabricated for efficient bacterial elimination, angiogenesis promotion, and accelerated wound healing. Cu2.8O@silicene-BSA nanosheets display excellent antibacterial activity through synergistic effects of reactive oxygen species generated from multiple catalytic reactions, intrinsic bactericidal activity of released Cu2+ ions, and photothermal effects, achieving high antibacterial efficiencies on methicillin-resistant Staphylococcus aureus (MRSA) of 99.1 ± 0.7% in vitro and 97.2 ± 1.6% in vivo. In addition, Cu2.8O@silicene-BSA nanosheets exhibit high biocompatibility for promoting human umbilical vein endothelial cell (HUVEC) proliferation and angiogenic differentiation. In vivo experiments reveal that Cu2.8O@silicene-BSA nanosheets with synergistic photothermal/chemodynamic therapeutics effectively accelerate MRSA-infected wound healing by eliminating bacteria, alleviating inflammation, boosting collagen deposition, and promoting angiogenesis. This research presents a promising strategy to engineer photothermal-assisted nanozyme catalysis for bacteria-invaded wound healing.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Automated Quantification of Total Cerebral Blood Flow from Phase-Contrast MRI and Deep Learning

Knowledge of input blood to the brain, which is represented as total cerebral blood flow (tCBF), is important in evaluating brain health. Phase-contrast (PC) magnetic resonance imaging (MRI) enables blood velocity mapping, allowing for noninvasive measurements of tCBF. In the procedure, manual selection of brain-feeding arteries is an essential step, but is time-consuming and often subjective. Thus, the purpose of this work was to develop and validate a deep learning (DL)-based technique for automated tCBF quantifications. To enhance the DL segmentation performance on arterial blood vessels, in the preprocessing step magnitude and phase images of PC MRI were multiplied several times. Thereafter, a U-Net was trained on 218 images for three-class segmentation. Network performance was evaluated in terms of the Dice coefficient and the intersection-over-union (IoU) on 40 test images, and additionally, on externally acquired 20 datasets. Finally, tCBF was calculated from the DL-predicted vessel segmentation maps, and its accuracy was statistically assessed with the correlation of determination (R2), the intraclass correlation coefficient (ICC), paired t-tests, and Bland-Altman analysis, in comparison to manually derived values. Overall, the DL segmentation network provided accurate labeling of arterial blood vessels for both internal (Dice=0.92, IoU=0.86) and external (Dice=0.90, IoU=0.82) tests. Furthermore, statistical analyses for tCBF estimates revealed good agreement between automated versus manual quantifications in both internal (R2=0.85, ICC=0.91, p=0.52) and external (R2=0.88, ICC=0.93, p=0.88) test groups. The results suggest feasibility of a simple and automated protocol for quantifying tCBF from neck PC MRI and deep learning.

Engineered platelet-based immune engager for tumor post-surgery treatment

Tumor metastasis and recurrence are principal reasons for the high mortality and poor prognosis of cancers. Inefficient engagement between T cell and tumor cell, as well as the universal existence of immune checkpoints, are important factors to the limited immunological surveillance of the immune systems to tumor cells. Herein, an immune engager based on engineered platelets with CD3 antibody modification (P-aCD3) was constructed to facilitate the contact between T cell and tumor cell via providing the anchoring sites of above two cells. Combined with the immune checkpoint blockade strategy, P-aCD3 effectively enhanced T cell mediated cytotoxicity and inhibited tumor recurrence and metastasis in mice melanoma postoperative model and breast cancer model, resulting in significantly prolonged survival of mice.

Assessment of the tolerance angle for pedicle screw insertion

Cannulation process intervenes before implantation of pedicle screw and depends on the surgeon's experience. A reliable experimental protocol has been developed for the characterization of the slipping behavior of the surgical tool on the cortical shell simulated by synthetic materials. Three types of synthetic foam samples with three different densities were tested using an MTS Acumen 3 A/T electrodynamic device with a tri-axis 3 kN Kistler load cell mounted on a surgical tool, moving at a constant rotational speed of 10° mm-1 and performing a three-step cannulation test. Cannulation angle varied between 10° and 30°. Synthetic samples were scanned after each tests, and cannulation coefficient associated to each perforation section was computed. Reproducibility tests resulted in an ICC for Sawbone samples of 0.979 (p < 0.001) and of 0.909 (p < 0.001) for Creaplast and Sawbone samples. Cannulation coefficient and maximum force in Z-axis are found the best descriptors of the perforation. Angular threshold for perforation prediction was found to be 17.5° with an area under the curve of the Receiver Operating Characteristic of 89.5%. This protocol characterizes the cannulation process before pedicle screw insertion and identifies the perforation tool angle until which the surgical tool slips on the cortical shell depending on bone quality.

Nanocarriers address intracellular barriers for efficient drug delivery, overcoming drug resistance, subcellular targeting and controlled release

The cellular barriers are major bottlenecks for bioactive compounds entering into cells to accomplish their biological functions, which limits their biomedical applications. Nanocarriers have demonstrated high potential and benefits for encapsulating bioactive compounds and efficiently delivering them into target cells by overcoming a cascade of intracellular barriers to achieve desirable therapeutic and diagnostic effects. In this review, we introduce the cellular barriers ahead of drug delivery and nanocarriers, as well as summarize recent advances and strategies of nanocarriers for increasing internalization with cells, promoting intracellular trafficking, overcoming drug resistance, targeting subcellular locations and controlled drug release. Lastly, the future perspectives of nanocarriers for intracellular drug delivery are discussed, which mainly focus on potential challenges and future directions. Our review presents an overview of intracellular drug delivery by nanocarriers, which may encourage the future development of nanocarriers for efficient and precision drug delivery into a wide range of cells and subcellular targets.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Synthesis, characterization, pharmacological and computational evaluation of hyaluronic acid modified chebulinic acid encapsulated chitosan nanocomposite for cancer therapy

The purpose of this study was to produce hyaluronic acid customized nanoparticles with chitosan for the delivery of chebulinic acid (CLA) to enhance its anticancer potential against breast cancer. A significant portion of CLA was encapsulated (89.72 ± 4.38 %) and loaded (43.15 ± 5.61 %) within hybrid nanoparticles. The colloidal hybrid nanoparticles demonstrated a polydispersity index (PDI) of about 0.379 ± 0.112, with zeta capacitance of 32.69 ± 5.12 (mV), and an average size of 115 ± 8 (nm). It was found that CLA-CT-HA-NPs had stronger anticancer effects on MCF-7 cells (IC50 = 8.18 ± 3.02 μM) than pure CLA (IC50 = 17.15 ± 5.11 μM). The initial cytotoxicity findings were supported by additional investigations based on comet assay and flow cytometry analysis. Tumor remission and survival were evaluated in five separate groups of mice. When juxtaposed with pure CLA (3.17 ± 0.419 %), CLA-CT-HA-NPs improved survival rates and reduced tumor burden by 3.76 ± 0.811(%). Furthermore, in-silico molecular docking investigations revealed that various biodegradable polymers had several levels of compatibility with CLA. The outcomes of this study might potentially served as an effective strategy for delivering drugs in the context of breast cancer therapy.

Artificial cell synthesis using biocatalytic polymerization-induced self-assembly

Artificial cells are biomimetic microstructures that mimic functions of natural cells, can be applied as building blocks for molecular systems engineering, and host synthetic biology pathways. Here we report enzymatically synthesized polymer-based artificial cells with the ability to express proteins. Artificial cells were synthesized using biocatalytic atom transfer radical polymerization-induced self-assembly, in which myoglobin synthesizes amphiphilic block co-polymers that self-assemble into structures such as micelles, worm-like micelles, polymersomes and giant unilamellar vesicles (GUVs). The GUVs encapsulate cargo during the polymerization, including enzymes, nanoparticles, microparticles, plasmids and cell lysate. The resulting artificial cells act as microreactors for enzymatic reactions and for osteoblast-inspired biomineralization. Moreover, they can express proteins such as a fluorescent protein and actin when fed with amino acids. Actin polymerizes in the vesicles and alters the artificial cells' internal structure by creating internal compartments. Thus, biocatalytic atom transfer radical polymerization-induced self-assembly-derived GUVs can mimic bacteria as they are composed of a microscopic reaction compartment that contains genetic information for protein expression upon induction.

The Validity and Reliability of the Turkish Version of the Telerehabilitation Acceptance Scale (Health Care Professionals' Form)

Purpose: This study was conducted to examine the Turkish validity and reliability of the Telerehabilitation Acceptance Scale Health Care Professionals' Form (TRAS-HP). Methods: Health care professionals between the ages of 18 and 65 years were included. TRAS-HP was used to assess study participants' acceptance of telerehabilitation. Turkish translation of the scale was followed by confirmatory and explanatory factor analyses. Internal consistency and test-retest reliability were calculated. Results: Of the participants, 158 (65.83%) were female and 82 (34.17%) were male. Explanatory factor analysis revealed a three-factor structure explaining 71.87% of the total variation with one item removed. Confirmatory factor analysis determined that the model fit indices (the root mean square error of approximation = 0.080, adjusted goodness of fit index = 0.857, goodness of fit index = 0.899, and chi-square/degrees of freedom = 2.516) were satisfactory. The subdimensions' factor loads ranged from 0.78 to 0.82. After confirmatory and explanatory factor analysis, the 16-item TRAS-HP was reduced to 14 items. Internal consistency (cronbach alpha = 0.947) and test-retest reliability (intraclass correlation coefficient: 0.927) were extremely high. Conclusions: With this study, the Turkish validity and reliability of TRAS-HP were demonstrated, and it was revealed that they are a suitable tool for determining the acceptance and awareness of telerehabilitation of health care professionals working in rehabilitation.

Accuracy of formula-based volume and image segmentation-based volume in calculation of preoperative cystic jaw lesions' volume

OBJECTIVE

The aim of this study was to assess the accuracy of formula-based volume measurements and the 3D volume analysis with different software packages in the calculation of preoperative cystic jaw lesions' volume. The secondary aim was to assess the reliability and the accuracy of 3 imaging software programs for measuring the cystic jaw lesions' volume in CBCT images.

MATERIALS AND METHODS

This study consisted of two parts: an in vitro part using 2 dry human mandibles that were used to create simulated osteolytic lesions to assess the accuracy of the volumetric analysis and formula-based volume. As a gold standard, the volume of each bone defect was determined by taking an impression using rapid soft silicone (Vinylight) and then quantifying the volume of the replica. Afterward, each tooth socket was scanned using a high-resolution CBCT. A retrospective part using archived CBCT radiographs that were taken from the database of the outpatient clinic of the oral and maxillofacial radiology department, Faculty of Dentistry, Minia University to assess the reliability of the 3 software packages. The volumetric data set was exported for volume quantification using the 3 software packages (MIMICS-OnDemand and InVesalius software). Also, the three greatest orthogonal diameters of the lesions were calculated, and the volume was assessed using the ellipsoid formula. Dunn's test was used for pair-wise comparisons when Friedman's test was significant. The inter-examiner agreement was assessed using Cronbach's alpha reliability coefficient and intra-class correlation coefficient.

RESULTS

Regarding the results of the retrospective part, there was a statistically significant difference between volumetric measurements by equation and different software (P value < 0.001, Effect size = 0.513). The inter-observer reliability of the measurements of the cystic lesions using the different software packages was very good. The highest inter-examiner agreement for volume measurement was found with InVesalius (Cronbach's alpha = 0.992). On the other hand, there was a statistically significant difference between dry mandible volumetric measurements and Gold Standard. All software showed statistically significantly lower dry mandible volumetric measurements than the gold standard.

CONCLUSION

Computer-aided assessment of cystic lesion volume using InVesalius, OnDemand, and MIMICS is a readily available, easy to use, non-invasive option. It confers an advantage over formula-based volume as it gives the exact morphology of the lesion so that potential problems can be detected before surgery. Volume analysis with InVesalius software was accurate in determining the volume of simulated periapical defects in a human cadaver mandible as compared to true volume. InVesalius software proved that open-source software can be robust yet user-friendly with the advantage of minimal cost to use.

High-resolution local trabecular strain within trabecular structure under cyclic loading

Trabecular bone structure is a complex microstructure consisting of rods and plates, which poses challenges for its mechanical characterization. Digital image correlation (DIC) offers the possibility to characterize the strain response on the surface of trabecular bone. This study employed DIC equipped with a telecentric lens to investigate the strain state of individual trabeculae within their trabecular structure by assessing the longitudinal strain of the trabeculae at both the middle and near the edges of the trabeculae. Due to the high-resolution of the used DIC system, local surface strain of trabeculae was analyzed too. Lastly, the correlation between longitudinal trabecular strain and the orientation and slenderness of the trabeculae was investigated. The results showed that the strain magnification close to the edge of the trabeculae was higher and reached up to 8-folds the strain along the middle of the trabeculae. On the contrary, no strain magnification was found for most of the trabeculae between the longitudinal trabecular strain along the middle of the trabeculae and the globally applied strain. High-resolution full-field strain maps were obtained on the surface of trabeculae showing heterogeneous strain distribution with increasing load. No significant correlation was found between longitudinal trabecular strain and its orientation or slenderness. These findings and the applied methodology can be used to broaden our understanding of the deformation mechanisms of trabeculae within the trabecular network.

Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction

Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging phenotype classification of rare diseases is challenging due to the severe shortage of training examples. Few-shot learning (FSL) methods tackle this challenge by extracting generalizable prior knowledge from a large base dataset of common diseases and normal controls and transferring the knowledge to rare diseases. Yet, most existing methods require the base dataset to be labeled and do not make full use of the precious examples of rare diseases. In addition, the extremely small size of the training samples may result in inter-class performance imbalance due to insufficient sampling of the true distributions. To this end, we propose in this work a novel hybrid approach to rare disease imaging phenotype classification, featuring three key novelties targeted at the above drawbacks. First, we adopt the unsupervised representation learning (URL) based on self-supervising contrastive loss, whereby to eliminate the overhead in labeling the base dataset. Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantage of both unsupervised and (pseudo-) supervised learning on the base dataset. Third, we use the feature dispersion to assess the intra-class diversity of training samples, to alleviate the inter-class performance imbalance via dispersion-aware correction. Experimental results of imaging phenotype classification of both simulated (skin lesions and cervical smears) and real clinical rare diseases (retinal diseases) show that our hybrid approach substantially outperforms existing FSL methods (including those using a fully supervised base dataset) via effective integration of the URL, pseudo-label driven self-distillation, and dispersion-aware imbalance correction, thus establishing a new state of the art.

Platelet-derived drug delivery systems: Pioneering treatment for cancer, cardiovascular diseases, infectious diseases, and beyond

Platelets play a critical role as circulating cells in the human body and contribute to essential physiological processes such as blood clotting, hemostasis, vascular repair, and thrombus formation. Currently, platelets are extensively employed in the development of innovative biomimetic drug delivery systems, offering significant enhancements in circulation time, biocompatibility, and targeted delivery efficiency compared to conventional drug delivery approaches. Leveraging the unique physiological functions of platelets, these platelet-derived drug delivery systems (DDSs) hold great promise for the treatment of diverse diseases, including cancer, cardiovascular diseases, infectious diseases, wound healing and other diseases. This review primarily focuses on the design and characteristics of existing platelet-derived DDSs, including their preparation and characterization methods. Furthermore, this review comprehensively outlines the applications of these materials across various diseases, offering a holistic understanding of their therapeutic potential. This study aimed to provide a comprehensive overview of the potential value of these materials in clinical treatment, serving as a valuable reference for the advancement of novel platelet-derived DDSs and their broader utilization in the field of disease treatment.

Patient-derived tumor spheroid-induced angiogenesis preclinical platform for exploring therapeutic vulnerabilities in cancer

This study addresses the demand for research models that can support patient-treatment decisions and clarify the complexities of a tumor microenvironment by developing an advanced non-animal preclinical cancer model. Based on patient-derived tumor spheroids (PDTS), the proposed model reconstructs the tumor microenvironment with emphasis on tumor spheroid-driven angiogenesis. The resulting microfluidic chip system mirrors angiogenic responses elicited by PDTS, recapitulating patient-specific tumor conditions and providing robust, easily quantifiable outcomes. Vascularized PDTS exhibited marked angiogenesis and tumor proliferation on the microfluidic chip. Furthermore, a drug that targets the vascular endothelial growth factor receptor 2 (VEGFR2, ramucirumab) was deployed, which effectively inhibited angiogenesis and impeded tumor invasion. This innovative preclinical model was used for investigating distinct responses for various drug combinations, encompassing HER2 inhibitors and angiogenesis inhibitors, within the context of PDTS. This integrated platform could potentially advance precision medicine by harmonizing diverse data points within the tumor microenvironment with a focus on the interplay between cancer and the vascular system.

Mechanobiological stimulation in organ-on-a-chip systems reduces hepatic drug metabolic capacity in favor of regenerative specialization

Hepatic physiology depends on the liver's complex structural composition which among others, provides high oxygen supply rates, locally differential oxygen tension, endothelial paracrine signaling, as well as residual hemodynamic shear stress to resident hepatocytes. While functional improvements were shown by implementing these factors into hepatic culture systems, direct cause-effect relationships are often not well characterized-obfuscating their individual contribution in more complex microphysiological systems. By comparing increasingly complex hepatic in vitro culture systems that gradually implement these parameters, we investigate the influence of the cellular microenvironment to overall hepatic functionality in pharmacological applications. Here, hepatocytes were modulated in terms of oxygen tension and supplementation, endothelial coculture, and exposure to fluid shear stress delineated from oxygen influx. Results from transcriptomic and metabolomic evaluation indicate that particularly oxygen supply rates are critical to enhance cellular functionality-with cellular drug metabolism remaining comparable to physiological conditions after prolonged static culture. Endothelial signaling was found to be a major contributor to differential phenotype formation known as metabolic zonation, indicated by WNT pathway activity. Lastly, oxygen-delineated shear stress was identified to direct cellular fate towards increased hepatic plasticity and regenerative phenotypes at the cost of drug metabolic functionality - in line with regenerative effects observed in vivo. With these results, we provide a systematic evaluation of critical parameters and their impact in hepatic systems. Given their adherence to physiological effects in vivo, this highlights the importance of their implementation in biomimetic devices, such as organ-on-a-chip systems. Considering recent advances in basic liver biology, direct translation of physiological structures into in vitro models is a promising strategy to expand the capabilities of pharmacological models.

Advances of Layered Double Hydroxide-Based Materials for Tumor Imaging and Therapy

Layered double hydroxides (LDH) are a class of functional anionic clays that typically consist of orthorhombic arrays of metal hydroxides with anions sandwiched between the layers. Due to their unique properties, including high chemical stability, good biocompatibility, controlled drug loading, and enhanced drug bioavailability, LDHs have many potential applications in the medical field. Especially in the fields of bioimaging and tumor therapy. This paper reviews the research progress of LDHs and their nanocomposites in the field of tumor imaging and therapy. First, the structure and advantages of LDH are discussed. Then, several commonly used methods for the preparation of LDH are presented, including co-precipitation, hydrothermal and ion exchange methods. Subsequently, recent advances in layered hydroxides and their nanocomposites for cancer imaging and therapy are highlighted. Finally, based on current research, we summaries the prospects and challenges of layered hydroxides and nanocomposites for cancer diagnosis and therapy.

Advancing fetal ultrasound diagnostics: Innovative methodologies for improved accuracy in detecting down syndrome

This research work explores the integration of medical and information technology, particularly focusing on the use of data analytics and deep learning techniques in medical image processing. Specifically, it addresses the diagnosis and prediction of fetal conditions, including Down Syndrome (DS), through the analysis of ultrasound images. Despite existing methods in image segmentation, feature extraction, and classification, there is a pressing need to enhance diagnostic accuracy. Our research delves into a comprehensive literature review and presents advanced methodologies, incorporating sophisticated deep learning architectures and data augmentation techniques to improve fetal diagnosis. Moreover, the study emphasizes the clinical significance of accurate diagnostics, detailing the training and validation process of the AI model, ensuring ethical considerations, and highlighting the potential of the model in real-world clinical settings. By pushing the boundaries of current diagnostic capabilities and emphasizing rigorous clinical validation, this research work aims to contribute significantly to medical imaging and pave the way for more precise and reliable fetal health assessments.

The role of circular RNA during the urological cancer metastasis: exploring regulatory mechanisms and potential therapeutic targets

Metastasis is a major contributor to treatment failure and death in urological cancers, representing an important biomedical challenge at present. Metastases form as a result of cancer cells leaving the primary site, entering the vasculature and lymphatic vessels, and colonizing clones elsewhere in the body. However, the specific regulatory mechanisms of action underlying the metastatic process of urological cancers remain incompletely elucidated. With the deepening of research, circular RNAs (circRNAs) have been found to not only play a significant role in tumor progression and prognosis but also show aberrant expression in various tumor metastases, consequently impacting tumor metastasis through multiple pathways. Therefore, circRNAs are emerging as potential tumor markers and treatment targets. This review summarizes the research progress on elucidating how circRNAs regulate the urological cancer invasion-metastasis cascade response and related processes, as well as their role in immune microenvironment remodeling and circRNA vaccines. This body of work highlights circRNA regulation as an emerging therapeutic target for urological cancers, which should motivate further specific research in this regard.

Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study

BACKGROUND

2D angiographic parametric imaging (API) quantitatively extracts imaging biomarkers related to contrast flow and is conventionally applied to 2D digitally subtracted angiograms (DSA's). In the interventional suite, API is typically performed using 1-2 projection views and is limited by vessel overlap, foreshortening, and depth-integration of contrast motion.

PURPOSE

This work explores the use of a pathlength-correction metric to overcome the limitations of 2D-API: the primary objective was to study the effect of converting 3D contrast flow to projected contrast flow using a simulated angiographic framework created with computational fluid dynamics (CFD) simulations, thereby removing acquisition variability.

METHODS

The pathlength-correction framework was applied to in-silico angiograms, generating a reference (i.e., ground-truth) volumetric contrast distribution in four patient-specific intracranial aneurysm geometries. Biplane projections of contrast flow were created from the reference volumetric contrast distributions, assuming a cone-beam geometry. A Parker-weighted reconstruction was performed to obtain a binary representation of the vessel structure in 3D. Standard ray tracing techniques were then used to track the intersection of a ray from the focal spot with each voxel of the reconstructed vessel wall to a pixel in the detector plane. The lengths of each ray through the 3D vessel lumen were then projected along each ray-path to create a pathlength-correction map, where the pixel intensity in the detector plane corresponds to the vessel width along each source-detector ray. By dividing the projection sequences with this correction map, 2D pathlength-corrected in-silico angiograms were obtained. We then performed voxel-wise (3D) API on the ground-truth contrast distribution and compared it to pixel-wise (2D) API, both with and without pathlength correction for each biplane view. The percentage difference (PD) between the resultant API biomarkers in each dataset were calculated within the aneurysm region of interest (ROI).

RESULTS

Intensity-based API parameters, such as the area under the curve (AUC) and peak height (PH), exhibited notable changes in magnitude and spatial distribution following pathlength correction: these now accurately represent conservation of mass of injected contrast media within each arterial geometry and accurately reflect regions of stagnation and recirculation in each aneurysm ROI. Improved agreement was observed between these biomarkers in the pathlength-corrected biplane maps: the maximum PD within the aneurysm ROI is 3.3% with pathlength correction and 47.7% without pathlength correction. As expected, improved agreement with ROI-averaged ground-truth 3D counterparts was observed for all aneurysm geometries, particularly large aneurysms: the maximum PD for both AUC and PH was 5.8%. Temporal parameters (mean transit time, MTT, time-to-peak, TTP, time-to-arrival, TTA) remained unaffected after pathlength correction.

CONCLUSIONS

This study indicates that the values of intensity-based API parameters obtained with conventional 2D-API, without pathlength correction, are highly dependent on the projection orientation, and uncorrected API should be avoided for hemodynamic analysis. The proposed metric can standardize 2D API-derived biomarkers independent of projection orientation, potentially improving the diagnostic value of all acquired 2D-DSA's. Integration of a pathlength correction map into the imaging process can allow for improved interpretation of biomarkers in 2D space, which may lead to improved diagnostic accuracy during procedures involving the cerebral vasculature.

NeuroBus - Architecture for an Ultra-Flexible Neural Interface

This article presents the system architecture for an implant concept called NeuroBus. Tiny distributed direct digitizing neural recorder ASICs on an ultra-flexible polyimide substrate are connected in a bus-like structure, allowing short connections between electrode and recording front-end with low wiring effort and high customizability. The small size (344 μm × 294 μm) of the ASICs and the ultraflexible substrate allow a low bending stiffness, enabling the implant to adapt to the curvature of the brain and achieving high structural biocompatibility. We introduce the architecture, the integrated building blocks, and the post-CMOS processes required to realize a NeuroBus, and we characterize the prototyped direct digitizing neural recorder front-end as well as polyimide-based ECoG brain interface. A rodent animal model is further used to validate the joint capability of the recording front-end and thin-film electrode array.

Transdermal Microneedles Alleviated Rheumatoid Arthritis by Inducing Immune Tolerance via Skin-Resident Antigen Presenting Cells

Restoring immune tolerance is the ultimate goal for rheumatoid arthritis (RA) treatment. The most reported oral or intravenous injection routes for the immunization of autoantigens cause gastrointestinal side effects, low patient compliance, and unsatisfied immune tolerance induction. Herein, the use of a transdermal microneedle patch is for the first time investigated to codeliver CII peptide autoantigen and rapamycin for reversing immune disorders of RA. The immunized microneedles efficiently recruit antigen-presenting cells particularly Langerhans cells, and induce tolerogenic dendritic cells at the administration skin site. The tolerogenic dendritic cells further homing to lymph nodes to activate systemic Treg cell differentiation, which upregulates the expression of anti-inflammatory mediators while inhibiting the polarization of Th1/2 and Th17 T cell phenotypes and the expression of inflammatory profiles. As a result, the optimized microneedles nearly completely eliminate RA symptoms and inflammatory infiltrations. Furthermore, it is demonstrated that a low dose of rapamycin is crucial for the successful induction of immune tolerance. The results indicate that a rationally designed microneedle patch is a promising strategy for immune balance restoration with increased immune tolerance induction efficiency and patient compliance.

Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks

This work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using convolutional neural networks (CNNs), seeking to enhance and fasten clinician diagnosis. We implemented three single-view 2D Attention U-Nets with 3D view integration and trained them to automatically segment the aortic root and coronary arteries of 124 computed tomography angiographies (CTAs), with normal coronaries or AAOCA. Furthermore, we automatically classified the segmented geometries as normal or AAOCA using a decision tree model. For CTAs in the test set (n = 13), we obtained median Dice score coefficients of 0.95 and 0.84 for the aortic root and the coronary arteries, respectively. Moreover, the classification between normal and AAOCA showed excellent performance with accuracy, precision, and recall all equal to 1 in the test set. We developed a deep learning-based method to automatically segment and classify normal coronary and AAOCA. Our results represent a step towards an automatic screening and risk profiling of patients with AAOCA, based on CTA.

Glutamate Chemical Exchange Saturation Transfer (GluCEST) MRI to Evaluate the Rapid Antidepressant Effects of Ketamine in the Hippocampus of Rat Depression Model

BACKGROUND

Ketamine is a quick acting antidepressant drug, and an accurate detection method is lacking. Ketamine's effects in a rat depression model have not previously been well explored using glutamate chemical exchange saturation transfer (GluCEST).

PURPOSE

To investigate the GluCEST changes of chronic unpredictable mild stress (CUMS) rats after receiving either ketamine or saline injection.

STUDY TYPE

Randomized animal model trial.

ANIMAL MODEL

12 CUMS and 6 Sprague-Dawley rats. Divided into three groups: ketamine (N = 6), saline (N = 6), and control (N = 6).

FIELD STRENGTH/SEQUENCE

7.0 T/the sequence is GluCEST and 1 H MR spectroscopy (MRS).

ASSESSMENT

The CUMS rats were exposed to different stress factors for 8 weeks. The glutamate concentration in the hippocampus was assessed by the GluCEST,1 H MRS, and the high-performance liquid chromatography (HPLC).

STATISTICAL TESTS

The t-test, Mann-Whitney U test, and Pearson's correlation.

RESULTS

In depression conditions, GluCEST signals were lower in the bilateral hippocampus than in control group. Thirty minutes after ketamine injection, the GluCEST signals in the bilateral hippocampus were higher compared with the saline group (left: 2.99 ± 0.34 [Control] vs. 2.44 ± 0.20 [Saline] vs. 2.85 ± 0.11 [Ketamine]; right: 2.97 ± 0.28 [Control] vs. 2.49 ± 0.25 [Saline] vs. 2.86 ± 0.19 [Ketamine]). In 1 H MRS, significant changes were only observed in the left hippocampus (2.00 ± 0.16 [Control] vs. 1.81 ± 0.09 [Saline] vs. 2.04 ± 0.14 [Ketamine]). Furthermore, HPLC results showed similar trends to those observed in the GluCEST results (left: 2.32 ± 0.22 [Control] vs. 1.96 ± 0.11 [Saline] vs. 2.18 ± 0.11 [Ketamine]; right: 2.35 ± 0.18 [Control] vs. 1.87 ± 0.16 [Saline] vs. 2.09 ± 0.08 [Ketamine]).

DATA CONCLUSION

GluCEST can sensitively evaluate the ketamine's antidepressant effects by detecting the fast increase in glutamate concentration.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Biomimetic microfluidic chips for toxicity assessment of environmental pollutants

Various types of pollutants widely present in environmental media, including synthetic and natural chemicals, physical pollutants such as radioactive substances, ultraviolet rays, and noise, as well as biological organisms, pose a huge threat to public health. Therefore, it is crucial to accurately and effectively explore the human physiological responses and toxicity mechanisms of pollutants to prevent diseases caused by pollutants. The emerging toxicological testing method biomimetic microfluidic chips (BMCs) exhibit great potential in environmental pollutant toxicity assessment due to their superior biomimetic properties. The BMCs are divided into cell-on-chips and organ-on-chips based on the distinctions in bionic simulation levels. Herein, we first summarize the characteristics, emergence and development history, composition and structure, and application fields of BMCs. Then, with a focus on the toxicity mechanisms of pollutants, we review the applications and advances of the BMCs in the toxicity assessment of physical, chemical, and biological pollutants, respectively, highlighting its potential and development prospects in environmental toxicology testing. Finally, the opportunities and challenges for further use of BMCs are discussed.

Experimental and biophysical modeling of transcription and translation dynamics in bacterial- and mammalian-based cell-free expression systems

Cell-free expression (CFE) systems have been used extensively in systems and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitro protein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized for both bacterial- and mammalian-based CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian- based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

Using an ER-specific optogenetic mechanostimulator to understand the mechanosensitivity of the endoplasmic reticulum

The ability of cells to perceive and respond to mechanical cues is essential for numerous biological activities. Emerging evidence indicates important contributions of organelles to cellular mechanosensitivity and mechanotransduction. However, whether and how the endoplasmic reticulum (ER) senses and reacts to mechanical forces remains elusive. To fill the knowledge gap, after developing a light-inducible ER-specific mechanostimulator (LIMER), we identify that mechanostimulation of ER elicits a transient, rapid efflux of Ca2+ from ER in monkey kidney COS-7 cells, which is dependent on the cation channels transient receptor potential cation channel, subfamily V, member 1 (TRPV1) and polycystin-2 (PKD2) in an additive manner. This ER Ca2+ release can be repeatedly stimulated and tuned by varying the intensity and duration of force application. Moreover, ER-specific mechanostimulation inhibits ER-to-Golgi trafficking. Sustained mechanostimuli increase the levels of binding-immunoglobulin protein (BiP) expression and phosphorylated eIF2α, two markers for ER stress. Our results provide direct evidence for ER mechanosensitivity and tight mechanoregulation of ER functions, placing ER as an important player on the intricate map of cellular mechanotransduction.

A novel discrete linkage-type electrode for radiofrequency-induced intestinal anastomosis

INTRODUCTION

For decades, radiofrequency (RF)-induced tissue fusion has garnered great attention due to its potential to replace sutures and staples for anastomosis of tissue reconstruction. However, the complexities of achieving high bonding strength and reducing excessive thermal damage present substantial limitations of existing fusion devices.

MATERIALS AND METHODS

This study proposed a discrete linkage-type electrode to carry out ex vivo RF-induced intestinal anastomosis experiments. The anastomotic strength was examined by burst pressure and shear strength test. The degree of thermal damage was monitored through an infrared thermal imager. And the anastomotic stoma fused by the electrode was further investigated through histopathological and ultrastructural observation.

RESULTS

The burst pressure and shear strength of anastomotic tissue can reach 62.2 ± 3.08 mmHg and 8.73 ± 1.11N, respectively, when the pressure, power and duration are 995 kPa, 160 W and 13 s, and the thermal damage can be controlled within limits. Histopathological and ultrastructural observation indicate that an intact and fully fused stomas with collagenic crosslink can be formed.

CONCLUSION

The discrete linkage-type electrode presents favorable efficiency and security in RF-induced tissue fusion, and these results are informative to the design of electrosurgical medical devices with controllable pressure and energy delivery.

Specifically targeting antimicrobial peptides for inhibition of Candidatus Liberibacter asiaticus

AIMS

Huanglongbing (citrus greening) is a plant disease putatively caused by the unculturable Gram-negative bacterium Candidatus Liberibacter asiaticus (CLas), and it has caused severe damage to citrus plantations worldwide. There are no definitive treatments for this disease, and conventional disease control techniques have shown limited efficacy. This work presents an in silico evaluation of using specifically targeting anti-microbial peptides (STAMPs) consisting of a targeting segment and an antimicrobial segment to inhibit citrus greening by inhibiting the BamA protein of CLas, which is an outer membrane protein crucial for bacterial viability.

METHODS AND RESULTS

Initially, a set of peptides with a high affinity toward BamA protein were screened and evaluated via molecular docking and molecular dynamics simulations and were verified in vitro via bio-layer interferometry (BLI). In silico studies and BLI experiments indicated that two peptides, HASP2 and HASP3, showed stable binding to BamA. Protein structures for STAMPs were created by fusing known anti-microbial peptides (AMPs) with the selected short peptides. The binding of STAMPs to BamA was assessed using molecular docking and binding energy calculations. The attachment of high-affinity short peptides significantly reduced the free energy of binding for AMPs, suggesting that it would make it easier for the STAMPs to bind to BamA. Efficacy testing in vitro using a closely related CLas surrogate bacterium showed that STAMPs had greater inhibitory activity than AMP alone.

CONCLUSIONS

In silico and in vitro results indicate that the STAMPs can inhibit CLas surrogate Rhizobium grahamii more effectively compared to AMPs, suggesting that STAMPs can achieve better inhibition of CLas, potentially via enhancing the site specificity of AMPs.

The Future of Durable Mechanical Circulatory Support: Emerging Technological Innovations and Considerations to Enable Evolution of the Field

The field of durable mechanical circulatory support (MCS) has undergone an incredible evolution over the past few decades, resulting in significant improvements in longevity and quality of life for patients with advanced heart failure. Despite these successes, substantial opportunities for further improvements remain, including in pump design and ancillary technology, perioperative and postoperative management, and the overall patient experience. Ideally, durable MCS devices would be fully implantable, automatically controlled, and minimize the need for anticoagulation. Reliable and long-term total artificial hearts for biventricular support would be available; and surgical, perioperative, and postoperative management would be informed by the individual patient phenotype along with computational simulations. In this review, we summarize emerging technological innovations in these areas, focusing primarily on innovations in late preclinical or early clinical phases of study. We highlight important considerations that the MCS community of clinicians, engineers, industry partners, and venture capital investors should consider to sustain the evolution of the field.

Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia

BACKGROUND AND OBJECTIVES

Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.

METHODS

A 27-year-old right-handed man with AIS-B (motor-complete, sensory-incomplete) C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of using a brain-machine interface to read from and write to the brain for restoring motor and sensory functions of the participant's own arm and hand.

RESULTS

Multiunit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions, resulting in functional movements that the participant was able to command under brain control to perform virtual and actual arm and hand movements. The system was well tolerated with no operative complications.

CONCLUSION

The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to create bidirectional restoration of motor and sensory functions of the arm and hand after neurological injury.