Human OX40L-CAR-Tregs target activated antigen-presenting cells and control T cell alloreactivity

Regulatory T cells (Tregs) make major contributions to immune homeostasis. Because Treg dysfunction can lead to both allo- and autoimmunity, there is interest in correcting these disorders through Treg adoptive transfer. Two of the central challenges in clinically deploying Treg cellular therapies are ensuring phenotypic stability and maximizing potency. Here, we describe an approach to address both issues through the creation of OX40 ligand (OX40L)-specific chimeric antigen receptor (CAR)-Tregs under the control of a synthetic forkhead box P3 (FOXP3) promoter. The creation of these CAR-Tregs enabled selective Treg stimulation by engagement of OX40L, a key activation antigen in alloimmunity, including both graft-versus-host disease and solid organ transplant rejection, and autoimmunity, including rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus. We demonstrated that OX40L-CAR-Tregs were robustly activated in the presence of OX40L-expressing cells, leading to up-regulation of Treg suppressive proteins without induction of proinflammatory cytokine production. Compared with control Tregs, OX40L-CAR-Tregs more potently suppressed alloreactive T cell proliferation in vitro and were directly inhibitory toward activated monocyte-derived dendritic cells (DCs). We identified trogocytosis as one of the central mechanisms by which these CAR-Tregs effectively decrease extracellular display of OX40L, resulting in decreased DC stimulatory capacity. OX40L-CAR-Tregs demonstrated an enhanced ability to control xenogeneic graft-versus-host disease compared with control Tregs without abolishing the graft-versus-leukemia effect. These results suggest that OX40L-CAR-Tregs may have wide applicability as a potent cellular therapy to control both allo- and autoimmune diseases.

Deep plug-and-play MRI reconstruction based on multiple complementary priors

Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditional undersampling reconstruction techniques such as compressed sensing mainly rely on relatively single sparse or low-rank prior information to reconstruct the image, which has limitations in capturing the comprehensive features of images, resulting in the insufficient performance of the reconstructed image in terms of details and key information. In this paper, we propose a deep plug-and-play multiple complementary priors MRI reconstruction model, which combines traditional low-rank matrix recovery model methods and deep learning methods, and integrates global, local and nonlocal priors to improve reconstruction quality. Specifically, we capture the global features of the image through the matrix nuclear norm, and use the deep convolutional neural network denoiser Swin-Conv-UNet (SCUNet) and block-matching and 3-D filtering (BM3D) algorithm to preserve the local details and structural texture of the image, respectively. In addition, we utilize an efficient half-quadratic splitting (HQS) algorithm to solve the proposed model. The experimental results show that our proposed method has better reconstruction ability than the existing popular methods in terms of visual effects and numerical results.

Zombie diatoms: acoustically powered diatom frustule bio-templated microswimmers

Frustules, or the silica based cell walls of diatomaceous algae Aulacoseira granulata, provide large numbers of reliably cylindrical microstructures with an inner cavity and surface chemistry suitable for constructing bubble-based, acoustically-powered micro-swimmers. In this way, microswimmers can be made in a scalable, accessible and low-cost manner, enabling studies of their individual and collective behavior as active colloids.

Formation and biological activities of foreign body giant cells in response to biomaterials

The integration of biomaterials in medical applications triggers the foreign body response (FBR), a multi-stage immune reaction characterized by the formation of foreign body giant cells (FBGCs). Originating from the fusion of monocyte/macrophage lineage cells, FBGCs are pivotal participants during tissue-material interactions. This review provides an in-depth examination of the molecular processes during FBGC formation, highlighting signaling pathways and fusion mediators in response to both exogenous and endogenous stimuli. Moreover, a wide range of material-specific characteristics, such as surface chemical and physical properties, has been proven to influence the fusion of macrophages into FBGCs. Multifaceted biological activities of FBGCs are also explored, with emphasis on their phagocytic capabilities and extracellular secretory functions, which profoundly affect the vascularization, degradation, and encapsulation of the biomaterials. This review further elucidates the heterogeneity of FBGCs and their diverse roles during FBR, as demonstrated by their distinct behaviors in response to different materials. By presenting a comprehensive understanding of FBGCs, this review intends to provide strategies and insights into optimizing biocompatibility and the therapeutic potential of biomaterials for enhanced stability and efficacy in clinical applications. STATEMENT OF SIGNIFICANCE: As a hallmark of the foreign body response (FBR), foreign body giant cells (FBGCs) significantly impact the success of implantable biomaterials, potentially leading to complications such as chronic inflammation, fibrosis, and device failure. Understanding the role of FBGCs and modulating their responses are vital for successful material applications. This review provides a comprehensive overview of the molecules and signaling pathways guiding macrophage fusion into FBGCs. By elucidating the physical and chemical properties of materials inducing distinct levels of FBGCs, potential strategies of materials in modulating FBGC formation are investigated. Additionally, the biological activities of FBGCs and their heterogeneity in responses to different material categories in vivo are highlighted in this review, offering crucial insights for improving the biocompatibility and efficacy of biomaterials.

Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy

BACKGROUND AND OBJECTIVE

Transcranial focused ultrasound (tFUS) is an emerging non-invasive therapeutic technology that offers new brain stimulation modality. Precise localization of the acoustic focus to the desired brain target throughout the procedure is needed to ensure the safety and effectiveness of the treatment, but acoustic distortion caused by the skull poses a challenge. Although computational methods can provide the estimated location and shape of the focus, the computation has not reached sufficient speed for real-time inference, which is demanded in real-world clinical situations. Leveraging the advantages of deep learning, we propose multi-modal networks capable of generating intracranial pressure map in real-time.

METHODS

The dataset consisted of free-field pressure maps, intracranial pressure maps, medical images, and transducer placements was obtained from 11 human subjects. The free-field and intracranial pressure maps were computed using the k-space method. We developed network models based on convolutional neural networks and the Swin Transformer, featuring a multi-modal encoder and a decoder.

RESULTS

Evaluations on foreseen data achieved high focal volume conformity of approximately 93% for both computed tomography (CT) and magnetic resonance (MR) data. For unforeseen data, the networks achieved the focal volume conformity of 88% for CT and 82% for MR. The inference time of the proposed networks was under 0.02 s, indicating the feasibility for real-time simulation.

CONCLUSIONS

The results indicate that our networks can effectively and precisely perform real-time simulation of the intracranial pressure map during tFUS applications. Our work will enhance the safety and accuracy of treatments, representing significant progress for low-intensity focused ultrasound (LIFU) therapies.

An engineered model of metastatic colonization of human bone marrow reveals breast cancer cell remodeling of the hematopoietic niche

Incomplete understanding of metastatic disease mechanisms continues to hinder effective treatment of cancer. Despite remarkable advancements toward the identification of druggable targets, treatment options for patients in remission following primary tumor resection remain limited. Bioengineered human tissue models of metastatic sites capable of recreating the physiologically relevant milieu of metastatic colonization may strengthen our grasp of cancer progression and contribute to the development of effective therapeutic strategies. We report the use of an engineered tissue model of human bone marrow (eBM) to identify microenvironmental cues regulating cancer cell proliferation and to investigate how triple-negative breast cancer (TNBC) cell lines influence hematopoiesis. Notably, individual stromal components of the bone marrow niche (osteoblasts, endothelial cells, and mesenchymal stem/stromal cells) were each critical for regulating tumor cell quiescence and proliferation in the three-dimensional eBM niche. We found that hematopoietic stem and progenitor cells (HSPCs) impacted TNBC cell growth and responded to cancer cell presence with a shift of HSPCs (CD34+CD38-) to downstream myeloid lineages (CD11b+CD14+). To account for tumor heterogeneity and show proof-of-concept ability for patient-specific studies, we demonstrate that patient-derived tumor organoids survive and proliferate in the eBM, resulting in distinct shifts in myelopoiesis that are similar to those observed for aggressively metastatic cell lines. We envision that this human tissue model will facilitate studies of niche-specific metastatic progression and individualized responses to treatment.

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Current Scenario and Future Perspectives of Porcine Corneal Xenotransplantation

ABSTRACT

Corneal diseases represent a significant cause of blindness worldwide, with corneal transplantation being an effective treatment to prevent vision loss. Despite substantial advances in transplantation techniques, the demand for donor corneas exceeds the available supply, particularly in developing countries. Cornea xenotransplantation has emerged as a promising strategy to address the worldwide scarcity, notably using porcine corneas. In addition to the inherent immune privilege of the cornea, the low cost of porcine breeding and the anatomical and physiological similarities between humans and pigs have made porcine corneas a viable alternative. Nonetheless, ethical concerns, specifically the risk of xenozoonotic transmission and the necessity for stringent biosafety measures, remain significant obstacles. Moreover, the success of xenotransplantation is compromised by innate and adaptive immune responses, which requires meticulous consideration and further studies. Despite these challenges, recent breakthroughs have further contributed to reducing immunogenicity while preserving the corneal architecture. Advances in genetic engineering, such as the use of CRISPR-Cas9 to eliminate critical porcine antigens, have shown promise for mitigating immune reactions. Additionally, new immunosuppressive protocols, such as have techniques like decellularization and the use of porcine-derived acellular matrices, have greatly increased graft survival in preclinical models. Future research must focus on refining immunomodulatory strategies and improving graft preparation techniques to ensure the long-term survival and safety of porcine corneal xenotransplantation in clinical trials in humans.

Electrical stimulation for cartilage tissue engineering - A critical review from an engineer's perspective

Cartilage has a limited intrinsic healing capacity. Hence, cartilage degradation and lesions pose a huge clinical challenge, particularly in an ageing society. Osteoarthritis impacts a significant number of the population and requires the development of repair and tissue engineering methods for hyaline articular cartilage. In this context, electrical stimulation has been investigated for more than 50 years already. Yet, no well-established clinical therapy to treat osteoarthritis by means of electrical stimulation exists. We argue that one reason is the lack of replicability of electrical stimulation devices from a technical perspective together with lacking hypotheses of the biophysical mechanism. Hence, first, the electrical stimulation studies reported in the context of cartilage tissue engineering with a special focus on technical details are summarized. Then, an experimental and numerical approach is discussed to make the electrical stimulation experiments replicable. Finally, biophysical hypotheses have been reviewed on the interaction of electric fields and cells that are relevant for cartilage tissue engineering. With that, the aim is to inspire future research to enable clinical electrical stimulation therapies to fight osteoarthritis.

Dynamic Liver Chip Based on Well-Coupled Microfluidics: An Accurate NASH Model for Drug Evaluation

The convenient liver model in vitro recapitulating the hepatic functions, metabolism, and even steatohepatitis to perform the accurate drug evaluation is still challenging because of the unattainable hominine physiological microenvironment in vitro. Here, the progressed stages of nonalcoholic steatohepatitis (NASH) disease were precisely modeled to accurately evaluate the performance of antilipemic based on the dynamic liver chip adopting the well-coupled microfluidics, which well recapitulated the normal and steatohepatitis of liver in vitro. In brief, the mild nutrient flow and sufficient oxygen supply for parenchymal liver cells could be well supplied through the endothelial cells layer that mimicked the real physiological barrier of endothelium, while the loading of drugs might be obtained by directly adding drug into the running nutrient flow to mimic the intravenously administrable. The progressed degree of steatohepatitis could be directly reflected by the amount of intramyocellular lipid content (IMLC) of the HepG2 cell hepatocyte layer in wells that were induced by different concentrations of free fatty acids (FFA). To prove the concept of the liver chip in drug evaluation, an accurate assessment of the performance of firsocostat, the acetyl-CoA carboxylase (ACC) inhibitor of hepatic mitochondria of hepatocytes, was carried out. The subtle time dependence of firsocostat treatment to different progressed stages of NASH was clearly figured out. Therefore, we prospect the liver chip that adopted well-coupled microfluidics could be an accurate and standard liver model in vitro to carry out the antilipemic evaluation and screening, which significantly enlightens the drug evaluation by liver on chip in vitro.

Molecular Engineering of Virus Tropism

Engineered viral vectors designed to deliver genetic material to specific targets offer significant potential for disease treatment, safer vaccine development, and the creation of novel biochemical research tools. Viral tropism, the specificity of a virus for infecting a particular host, is often modified in recombinant viruses to achieve precise delivery, minimize off-target effects, enhance transduction efficiency, and improve safety. Key factors influencing tropism include surface protein interactions between the virus and host-cell, the availability of host-cell machinery for viral replication, and the host immune response. This review explores current strategies for modifying the tropism of recombinant viruses by altering their surface proteins. We provide an overview of recent advancements in targeting non-enveloped viruses (adenovirus and adeno-associated virus) and enveloped viruses (retro/lentivirus, Rabies, Vesicular Stomatitis Virus, and Herpesvirus) to specific cell types. Additionally, we discuss approaches, such as rational design, directed evolution, and in silico and machine learning-based methods, for generating novel AAV variants with the desired tropism and the use of chimeric envelope proteins for pseudotyping enveloped viruses. Finally, we highlight the applications of these advancements and discuss the challenges and future directions in engineering viral tropism.

MIDC: Medical image dataset cleaning framework based on deep learning

Deep learning technology is widely used in the field of medical imaging. Among them, Convolutional Neural Networks (CNNs) are the most widely used, and the quality of the dataset is crucial for the training of CNN diagnostic models, as mislabeled data can easily affect the accuracy of the diagnostic models. However, due to medical specialization, it is difficult for non-professional physicians to judge mislabeled medical image data. In this paper, we proposed a new framework named medical image dataset cleaning (MIDC), whose main contribution is to improve the quality of public datasets by automatically cleaning up mislabeled data. The main innovations of MIDC are: firstly, the framework innovatively utilizes multiple public datasets of the same disease, relying on different CNNs to automatically recognize images and remove mislabeled data to complete the data cleaning process. This process does not rely on annotations from professional physicians and does not require additional datasets with more reliable labels; Secondly, a novel grading rule is designed to divide the datasets into high-accuracy datasets and low-accuracy datasets, based on which the data cleaning process can be performed; Thirdly, a novel data cleaning module based on CNN is designed to identify and clean low-accuracy datasets by using high-accuracy datasets. In the experiments, the validity of the proposed framework was verified by using four kinds of datasets diabetic retinal, viral pneumonia, breast tumor, and skin cancer, with results showing an increase in the average diagnostic accuracy from 71.18 % to 85.13 %, 82.50 %to 93.79 %, 85.59 %to 93.45 %, and 84.55 %to 94.21 %, respectively. The proposed data cleaning framework MIDC could better help physicians diagnose diseases based on the dataset with mislabeled data.

Neural network-enabled, all-electronic control of non-Newtonian fluid flow

Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min-1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups.

Hierarchical Nanostructures as Acoustically Manipulatable Multifunctional Agents in Dynamic Fluid Flow

Acoustic waves provide a biocompatible and deep-tissue-penetrating tool suitable for contactless manipulation in in vivo environments. Despite the prevalence of dynamic fluids within the body, previous studies have primarily focused on static fluids, and manipulatable agents in dynamic fluids are limited to gaseous core-shell particles. However, these gas-filled particles face challenges in fast-flow manipulation, complex setups, design versatility, and practical medical imaging, underscoring the need for effective alternatives. In this study, flower-like hierarchical nanostructures (HNS) into microparticles (MPs) are incorporated, and demonstrated that various materials fabricated as HNS-MPs exhibit effective and reproducible acoustic trapping within high-velocity fluid flows. Through simulations, it is validated that the HNS-MPs are drawn to the focal point by acoustic streaming and form a trap through secondary acoustic streaming at the tips of the nanosheets comprising the HNS-MPs. Furthermore, the wide range of materials and modification options for HNS, combined with their high surface area and biocompatibility, enable them to serve as acoustically manipulatable multimodal imaging contrast agents and microrobots. They can perform intravascular multi-trap maneuvering with real-time imaging, purification of wastewater flow, and highly-loaded drug delivery. Given the diverse HNS materials developed to date, this study extends their applications to acoustofluidic and biomedical fields.

Sclerostin Antibody-Loaded Dense Collagen Hydrogels Promote Critical-Size Bone Defect Repair

The management of extensive bone loss remains a clinical challenge. Numerous studies are underway to develop a combination of biomaterials, biomolecules, and stem cells to address this challenge. In particular, the systemic administration of antibodies against sclerostin, a regulator of bone formation, was recently shown to enhance the bone repair efficiency of dense collagen hydrogels (DCHs) hosting murine dental pulp stem cells (mDPSCs). The aim of the present study was to assess whether these antibodies, encapsulated and released from DCHs, could promote craniofacial bone repair by the local inhibition of sclerostin. In vitro studies showed that antibody loading modified neither the hydrogel structure nor the viability of seeded mDPSCs. When implanted in a mouse calvaria critical-size bone defect, antibody-loaded DCHs showed repair capabilities similar to those of acellular unloaded DCHs combined with antibody injections. Importantly, the addition of mDPSCs provided no further benefit. Altogether, the local delivery of antisclerostin antibodies from acellular dense collagen scaffolds is highly effective for bone repair. The drastic reduction in the required amount of antibody compared to systemic injection should reduce the cost of the procedure, making the strategy proposed here a promising therapeutic approach for large bone defect repair.

Self-Assembled Peptide with Morphological Structure for Bioapplication

Peptide materials, such as self-assembled peptide materials, are very important biomaterials. Driven by multiple interaction forces, peptide molecules can self-assemble into a variety of different macroscopic forms with different properties and functions. In recent years, the research on self-assembled peptides has made great progress from laboratory design to clinical application. This review focuses on the different morphologies, including nanoparticles, nanovesicles, nanotubes, nanofibers, and others, formed by self-assembled peptide. The mechanisms and applications of the morphology transformation are also discussed in this paper, and the future direction of self-assembled nanomaterials is envisioned.

Modifying Naturally Occurring, Nonmammalian-Sourced Biopolymers for Biomedical Applications

Natural biopolymers have a rich history, with many uses across the fields of healthcare and medicine, including formulations for wound dressings, surgical implants, tissue culture substrates, and drug delivery vehicles. Yet, synthetic-based materials have been more successful in translation due to precise control and regulation achievable during manufacturing. However, there is a renewed interest in natural biopolymers, which offer a diverse landscape of architecture, sustainable sourcing, functional groups, and properties that synthetic counterparts cannot fully replicate as processing and sourcing of these materials has improved. Proteins and polysaccharides derived from various sources (crustaceans, plants, insects, etc.) are highlighted in this review. We discuss the common types of polysaccharide and protein biopolymers used in healthcare and medicine, highlighting methods and strategies to alter structures and intra- and interchain interactions to engineer specific functions, products, or materials. We focus on biopolymers obtained from natural, nonmammalian sources, including silk fibroins, alginates, chitosans, chitins, mucins, keratins, and resilins, while discussing strategies to improve upon their innate properties and sourcing standardization to expand their clinical uses and relevance. Emphasis will be placed on methods that preserve the structural integrity and native biological functions of the biopolymers and their makers. We will conclude by discussing the untapped potential of new technologies to manipulate native biopolymers while controlling their secondary and tertiary structures, offering a perspective on advancing biopolymer utility in novel applications within biomedical engineering, advanced manufacturing, and tissue engineering.

Stimuli-Responsive Substrates to Control the Immunomodulatory Potential of Stromal Cells

Mesenchymal stromal cells (MSCs) have broad immunomodulatory properties that range from regulation, proliferation, differentiation, and immune cell activation to secreting bioactive molecules that inhibit inflammation and regulate immune response. These properties provide MSCs with high therapeutic potency that has been shown to be relevant to tissue engineering and regenerative medicine. Hence, researchers have explored diverse strategies to control the immunomodulatory potential of stromal cells using polymeric substrates or scaffolds. These substrates alter the immunomodulatory response of MSCs, especially through biophysical cues such as matrix mechanical properties. To leverage these cell-matrix interactions as a strategy for priming MSCs, emerging studies have explored the use of stimuli-responsive substrates to enhance the therapeutic value of stromal cells. This review highlights how stimuli-responsive materials, including chemo-responsive, microenvironment-responsive, magneto-responsive, mechano-responsive, and photo-responsive substrates, have specifically been used to promote the immunomodulatory potential of stromal cells by controlling their secretory activity.

A mathematical model simulating the adaptive immune response in various vaccines and vaccination strategies

Vaccination has been widely recognized as an effective measure for preventing infectious diseases. To facilitate quantitative research into the activation of adaptive immune responses in the human body by vaccines, it is important to develop an appropriate mathematical model, which can provide valuable guidance for vaccine development. In this study, we constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination, based on principles from immunology. Our model offers a concise and accurate representation of the kinetics of antibody response. We conducted a comparative analysis of antibody dynamics within the body after administering several common vaccines, including traditional inactivated vaccines, mRNA vaccines, and future attenuated vaccines based on defective interfering viral particles (DVG). Our findings suggest that booster shots play a crucial role in enhancing Immunoglobulin G (IgG) antibody levels, and we provide a detailed discussion on the advantages and disadvantages of different vaccine types. From a mathematical standpoint, our model proposes four essential approaches to guide vaccine design: enhancing antigenic T-cell immunogenicity, directing the production of high-affinity antibodies, reducing the rate of IgG decay, and lowering the peak level of vaccine antigen-antibody complexes. Our study contributes to the understanding of vaccine design and its application by explaining various phenomena and providing guidance in comprehending the interactions between antibodies and antigens during the immune process.

The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

BACKGROUND

Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.

PURPOSE

The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients.

METHODS

A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time.

RESULTS

COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.

CONCLUSION

The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.

Towards a general computed tomography image segmentation model for anatomical structures and lesions

Numerous deep-learning models have been developed using task-specific data, but they ignore the inherent connections among different tasks. By jointly learning a wide range of segmentation tasks, we prove that a general medical image segmentation model can improve segmentation performance for computerized tomography (CT) volumes. The proposed general CT image segmentation (gCIS) model utilizes a common transformer-based encoder for all tasks and incorporates automatic pathway modules for task prompt-based decoding. It is trained on one of the largest datasets, comprising 36,419 CT scans and 83 tasks. gCIS can automatically perform various segmentation tasks using automatic pathway modules of decoding networks through text prompt inputs, achieving an average Dice coefficient of 82.84%. Furthermore, the proposed automatic pathway routing mechanism allows for parameter pruning of the network during deployment, and gCIS can also be quickly adapted to unseen tasks with minimal training samples while maintaining great performance.

Universal CAR cell therapy: Challenges and expanding applications

Chimeric Antigen Receptor (CAR) T cell therapy has gained success in adoptive cell therapy for hematological malignancies. Although most CAR cell therapies in clinical trials or markets remain autologous, their acceptance has been limited due to issues like lengthy manufacturing, poor cell quality, and demanding cost. Consequently, "Off-the-shelf", universal CAR (UCAR) cell therapy has emerged. Current concerns with UCAR therapies revolve around side effects such as graft versus host disease (GVHD) and host versus graft response (HVGR). Preclinical research on UCAR cell therapies aims to enhance efficacy and minimize these side effects. Common approaches involve gene editing techniques to knock out T cell receptor (TCR), human leukocyte antigen (HLA), and CD52 expression to mitigate GVHD and HVGR risks. However, these methods carry drawbacks including potential genotoxicity of the edited cells. Most recently, novel editing techniques, such as epigenetic editing and RNA writer systems, have been developed to reduce the risk of GVHD and HVGR, allowing for multiplex editing at different sites. Additionally, incorporating more cell types into UCAR cell therapies, like T-cell subtypes (DNT, γδT, virus-specific T cells) and NK cells, can efficiently target tumors without triggering side effects. In addition, the limited efficacy of T cells and NK cells against solid tumors is being addressed through CAR-Macrophages. In summary, CAR cell therapy has evolved to accommodate multiple cell types while expanding applications to various diseases, including hematologic malignancies and solid tumors, which holds tremendous growth potential and is promised to improve the lives of more patients in the future.

Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit

Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.

Integrative Omics Strategies for Understanding and Combating Brown Planthopper Virulence in Rice Production: A Review

The brown planthopper (Nilaparvata lugens, BPH) is a serious insect pest responsible for causing immense economic losses to rice growers around the globe. The development of high-throughput sequencing technologies has significantly improved the research on this pest, and its genome structure, gene expression profiles, and host-plant interactions are being unveiled. The integration of genomic sequencing, transcriptomics, proteomics, and metabolomics has greatly increased our understanding of the biological characteristics of planthoppers, which will benefit the identification of resistant rice varieties and strategies for their control. Strategies like more optimal genome assembly and single-cell RNA-seq help to update our knowledge of gene control structure and cell type-specific usage, shedding light on how planthoppers adjust as well. However, to date, a comprehensive genome-wide investigation of the genetic interactions and population dynamics of BPHs has yet to be exhaustively performed using these next-generation omics technologies. This review summarizes the recent advances and new perspectives regarding the use of omics data for the BPH, with specific emphasis on the integration of both fields to help develop more sustainable pest management strategies. These findings, in combination with those of post-transcriptional and translational modifications involving non-coding RNAs as well as epigenetic variations, further detail intricate host-brown planthopper interaction dynamics, especially regarding resistant rice varieties. Finally, the symbiogenesis of the symbiotic microbial community in a planthopper can be characterized through metagenomic approaches, and its importance in enhancing virulence traits would offer novel opportunities for plant protection by manipulating host-microbe interactions. The concerted diverse omics approaches collectively identified the holistic and complex mechanisms of virulence variation in BPHs, which enables efficient deployment into rice resistance breeding as well as sustainable pest management.

Acoustic black hole effect enhanced micro-manipulator

Microparticle manipulation is a critical concern across various fields including microfabrication, flexible electronics and tissue engineering. Acoustic-activated sharp structures have been designed as simple and flexible tools to manipulate microparticles with their good compatibility, fast response, and broad tunability. However, there still lacks rational acoustic-structure design for effective energy concentration at the acoustic-activated sharp structures for microparticle manipulation. Here, we present the acoustic black hole (ABH) effect as enhancement for the acoustic micro-manipulator. It provides great reliability, simplicity and ease of use, supporting custom design of high-throughput patterning modes. Moreover, compared to commonly used configurations, such as cylindrical or conical microneedles, those microneedles with ABH profile exhibit superior acoustic energy focusing at the tip and induce stronger acoustofluidic effects. The average acoustic flow velocity induced by the ABH microneedle is 154 times greater than that of the conical one and 45 times greater than that of the cylindrical microneedle. Besides, the average acoustic radiation force (ARF) produced by the ABH microneedle against acrylic microparticles is about 319 times greater than that of the cylindrical one and 16 times greater than that of the conical one. These results indicate that ABH design significantly enhances microparticle manipulation. We demonstrate this concept with ABH effect enhanced microparticle manipulation and study the parameters influencing its performance including operating frequency, operating voltage and particle diameter. Furthermore, considering the flexibility of this system, we employ it for various patterning and high-throughput microparticle manipulation. This work paves the way for controllable microparticle manipulation, holding great potential for applications in microfabrication and biomedicine.

Early regional cerebral grey matter damage predicts long-term cognitive impairment phenotypes in multiple sclerosis: a 20-year study

Despite grey matter atrophy in cortical and subcortical regions has been related to cognitive impairment in multiple sclerosis, only a few studies evaluated its predictive value for alterations in the long-term. We aimed to determine early predictors of cognitive status after 20 years of multiple sclerosis. In this longitudinal retrospective study, participants underwent a 1.5 T MRI scanning at diagnosis (T0) and after two years (T2), which included the evaluation of regional grey matter volume loss patterns. All individuals with multiple sclerosis underwent a comprehensive neuropsychological assessment at the end of the study and were classified considering their global and specific cognitive domains status (memory, attention/information processing speed, executive functioning). Clinical and MRI characteristics were assessed as predictors of long-term cognitive impairment. Analysis of covariance, t-test, unadjusted and adjusted (for age, sex, disease duration, volume of white matter lesions, volume of cortical lesions) logistic regression were conducted. One hundred seventy-five people with multiple sclerosis (118 females; mean ± SD age at the end of study = 47.7 ± 9.4 years) clinically followed for 20 years from onset (mean ± SD = 19.9 ± 5.1) were evaluated. At the end of the study, 81 (47%) were classified as cognitively impaired: 38 as mildly impaired (22%), and 43 as severely impaired (25%). In particular, 46 were impaired in memory (27%), 66 were impaired in attention/information processing speed (38%), and 71 were impaired in executive functioning (41%). Regression models identified precuneus (adjusted odds ratio = 3.37; P < 0.001), insula (adjusted odds ratio = 2.33; P = 0.036), parahippocampal gyrus (adjusted odds ratio = 2.07; P < 0.001) and cingulate (adjusted odds ratio = 1.81; P = 0.009) as the most associated regions with global cognitive impairment and domains-specific cognitive alterations after a mean of 20 years of multiple sclerosis, after adjusting for demographic and clinical variables as well as for focal white matter and grey matter damage. Early grey matter volume loss of specific cortical and deep grey matter regions predicts global and domain cognitive alterations after 20 years from multiple sclerosis diagnosis.

Depth-Dependent Contributions of Various Vascular Zones to Cerebral Autoregulation and Functional Hyperemia: An In-Silico Analysis

Autoregulation and neurogliavascular coupling are key mechanisms that modulate myogenic tone (MT) in vessels to regulate cerebral blood flow (CBF) during resting state and periods of increased neural activity, respectively. To determine relative contributions of distinct vascular zones across different cortical depths in CBF regulation, we developed a simplified yet detailed and computationally efficient model of the mouse cerebrovasculature. The model integrates multiple simplifications and generalizations regarding vascular morphology, the hierarchical organization of mural cells, and potentiation/inhibition of MT in vessels. Our analysis showed that autoregulation is the result of the synergy between these factors, but achieving an optimal balance across all cortical depths and throughout the autoregulation range is a complex task. This complexity explains the non-uniformity observed experimentally in capillary blood flow at different cortical depths. In silico simulations of cerebral autoregulation support the idea that the cerebral vasculature does not maintain a plateau of blood flow throughout the autoregulatory range and consists of both flat and sloped phases. We learned that small-diameter vessels with large contractility, such as penetrating arterioles and precapillary arterioles, have major control over intravascular pressure at the entry points of capillaries and play a significant role in CBF regulation. However, temporal alterations in capillary diameter contribute moderately to cerebral autoregulation and minimally to functional hyperemia. In addition, hemodynamic analysis shows that while hemodynamics within capillaries remain relatively stable across all cortical depths throughout the entire autoregulation range, significant variability in hemodynamics can be observed within the first few branch orders of precapillary arterioles or transitional zone vessels. The computationally efficient cerebrovasculature model, proposed in this study, provides a novel framework for analyzing dynamics of the CBF regulation where hemodynamic and vasodynamic interactions are the foundation on which more sophisticated models can be developed.

Evaluation of fabric-based pneumatic actuator enclosure and anchoring configurations in a pediatric soft robotic exosuit

Introduction

Soft robotics play an increasing role in the development of exosuits that assist, and in some cases enhance human motion. While most existing efforts have focused on the adult population, devices targeting infants are on the rise. This work investigated how different configurations pertaining to fabric-based pneumatic shoulder and elbow actuator embedding on the passive substrate of an exosuit for pediatric upper extremity motion assistance can affect key performance metrics.

Methods

The configurations varied based on actuator anchoring points onto the substrate and the type of fabric used to fabricate the enclosures housing the actuators. Shoulder adduction/abduction and elbow flexion/extension were treated separately. Two different variants (for each case) of similar but distinct actuators were considered. The employed metrics were grouped into two categories; reachable workspace, which includes joint range of motion and end-effector path length; and motion smoothness, which includes end-effector path straightness index and jerk. The former category aimed to capture first-order terms (i.e., rotations and displacements) that capture overall gross motion, while the latter category aimed to shed light on differential terms that correlate with the quality of the attained motion. Extensive experimentation was conducted for each individual considered configuration, and statistical analyses were used to establish distinctive strengths, weaknesses, and trade-offs among those configurations.

Results

The main findings from experiments confirm that the performance of the actuators can be significantly impacted by variations in the anchoring and fabric properties of the enclosures while establishing interesting trade-offs. Specifically, the most appropriate anchoring point was not necessarily the same for all actuator variants. In addition, highly stretchable fabrics not only maintained but even enhanced actuator capabilities, in comparison to the less stretchable materials which turned out to hinder actuator performance.

Conclusion

The established trade-offs can serve as guiding principles for other researchers and practitioners developing upper extremity exosuits.

Ligament reconstruction for distal radioulnar joint instability with the biomechanical analysis: A case report

RATIONALE

The aim of the study is to evaluate the clinical effects and feasibility on chronic distal radioulnar unstable joint (DRUJ) under wrist arthroscope triangular fibrocartilage complex (TFCC) repair and reconstruction. The biomechanical characteristics of the recovery process after treatment were analyzed using finite element modeling.

PATIENT CONCERNS

A patient with chronic DRUJ instability was treated with reconstruction of the distal radioulnar ligament using the Adams Berger method. Piano sign and forearm rotation tests were used to evaluate the function of the DRUJ. Grip power, range of motion, and visual analog scale scores were recorded at the last follow-up. Joint function was evaluated by the disability of the arm, shoulder, and hand score. A geometric model for the surgical repair of TFCC damage, meshing in finite element simulation, and stress distribution of the repaired ligament during forearm rotation were analyzed by finite element simulation.

DIAGNOSES

The diagnosis was confirmed as chronic DRUJ instability.

INTERVENTIONS AND OUTCOMES

The patient had no postoperative complications and was followed-up for 6 months. Stability was achieved in all patients. The results of the stress and forearm rotation tests are negative. At the last follow-up, the grip power, disability of the arm, shoulder, and hand score, visual analog scale score, and range of motion of the wrist were significantly improved compared with the preoperative values (P < .05).

LESSONS

TFCC repair and reconstruction under wrist arthroscopy can effectively treat chronic DRUJ and improve wrist function. Our study established a three-dimensional finite element model of the entire DRUJ, which provided a digital visual platform for simulating the biomechanical features of the TFCC, DRUJ, and other structures in different states. This study demonstrated that the rotation angle of the wrist joint should be confined within 75° for a short period after surgery to avoid tearing the tendon as a result of excessive force. It also provides an intuitive simulation tool for the design of TFCC repair and the evaluation of curative effects.

Thermoelectric Materials and Devices for Advanced Biomedical Applications

Thermoelectrics (TEs), enabling the direct conversion between heat and electrical energy, have demonstrated extensive application potential in biomedical fields. Herein, the mechanism of the TE effect, recent developments in TE materials, and the biocompatibility assessment of TE materials are provided. In addition to the fundamentals of TEs, a timely and comprehensive review of the recent progress of advanced TE materials and their applications is presented, including wearable power generation, personal thermal management, and biosensing. In addition, the new-emerged medical applications of TE materials in wound healing, disease treatment, antimicrobial therapy, and anti-cancer therapy are thoroughly reviewed. Finally, the main challenges and future possibilities are outlined for TEs in biomedical fields, as well as their material selection criteria for specific application scenarios. Together, these advancements can provide innovative insights into the development of TEs for broader applications in biomedical fields.

Estimates of natural frequencies for nuclear vibration, and an assessment of the feasibility of selective ultrasound ablation of cancer cells

Selective ablation of cancer cells by ultrasound would be transformative for cancer therapy, but has not yet been possible. A key challenge is that cancerous and non-cancerous cells typically have similar acoustic impedance and are thus indistinguishable as materials in their responses to ultrasound. However, in certain cancers, cytoskeletal and nuclear lamin structures differ between healthy and malignant cells, opening the possibility of exploiting structural differences that manifest as different vibrational responses. To assess the possibility that the nuclei of certain cancerous cells might vibrate at different frequencies, we measured sizes and effective indentation moduli of a range of cancerous and non-cancerous cells from several cell lines and regions of the brain, and estimated the natural frequencies for nuclear vibration. Results suggest a potential difference in natural frequency for nuclear vibration between certain cancerous and non-cancerous cells, on the order of tens of kHz. This gap is potentially sufficient for selective ablation and motivates future experimentation on these specific cell types.

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

BACKGROUND

Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease. This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance.

METHODS

Metal exposure history, whole blood metal, MRI R1 (1/T1) and R2* (1/T2*) metrics (estimates of brain Mn and Fe, respectively), and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume or cortical thickness) and diffusion tensor imaging [mean (MD), axial (AxD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, effects of mixed metal exposure on MTL structural and neuropsychological metrics were examined.

RESULTS

Compared to controls, exposed subjects displayed higher MD, AxD, and RD throughout all MTL ROIs (p's<0.001) with no morphological differences. They also had poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Long-term mixed metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p's<0.023). Higher entorhinal R1 and whole blood Mn and Cu levels predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007).

DISCUSSION

Mixed metal exposure predicted certain MTL structural and neuropsychological features that are similar to those detected in Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate a potential path for environmental exposure to brain changes associated with Alzheimer's disease-related health outcomes.

Clathrin-Mediated Endocytosis of Multiple Nanoparticles Tends to Be Less Cooperative: A Computational Study

The internalization of nanoparticles is of great significance for their biological applications. Clathrin-mediated endocytosis (CME) is one of the main endocytic pathways. However, there is still a lack of a fundamental understanding regarding the internalization of multiple nanoparticles via CME. Therefore, in this study, we conducted computational investigations to uncover detailed molecular mechanisms and kinetic pathways for differently shaped nanoparticles in the presence of clathrin. Particular focus is given to understanding the CME of multiple-nanoparticle systems. We found that unlike receptor-mediated endocytosis, multiple nanoparticles did not get cooperatively wrapped by the membrane but tended to undergo independent endocytosis in the presence of clathrin. To further investigate the endocytosis mechanism, we studied the effects of clathrins, nanoparticle shape, nanoparticle size, nanoparticle arrangement, and membrane surface tension. The self-assembly of clathrin prefers independent endocytosis for multiple nanoparticles. Besides, the cooperative behavior is weak with increasing nanoparticle-shape anisotropy. However, when the membrane tension is reduced, the endocytosis pathway for multiple nanoparticles is cooperative endocytosis. Moreover, we found that the self-assembly of clathrins reduces the critical size of nanoparticles to undergo cooperative wrapping by the cell membrane. Our results provide valuable insights into the molecular mechanisms of multiple nanoparticles through CME and offer useful guidance for the design of nanoparticles as drug/gene delivery carriers.

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance Ees with a 1% error, which suggests that this parameter is unique. The estimated Ees is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

Osteochondral Regeneration With Anatomical Scaffold 3D-Printing-Design Considerations for Interface Integration

There is a clinical need for osteochondral scaffolds with complex geometries for restoring articulating joint surfaces. To address that need, 3D-printing has enabled scaffolds to be created with anatomically shaped geometries and interconnected internal architectures, going beyond simple plug-shaped scaffolds that are limited to small, cylindrical, focal defects. A key challenge for restoring articulating joint surfaces with 3D-printed constructs is the mechanical loading environment, particularly to withstand delamination or mechanical failure. Although the mechanical performance of interfacial scaffolds is essential, interface strength testing has rarely been emphasized in prior studies with stratified scaffolds. In the pioneering studies where interface strength was assessed, varying methods were employed, which has made direct comparisons difficult. Therefore, the current review focused on 3D-printed scaffolds for osteochondral applications with an emphasis on interface integration and biomechanical evaluation. This 3D-printing focus included both multiphasic cylindrical scaffolds and anatomically shaped scaffolds. Combinations of different 3D-printing methods (e.g., fused deposition modeling, stereolithography, bioprinting with pneumatic extrusion of cell-laden hydrogels) have been employed in a handful of studies to integrate osteoinductive and chondroinductive regions into a single scaffold. Most 3D-printed multiphasic structures utilized either an interdigitating or a mechanical interlocking design to strengthen the construct interface and to prevent delamination during function. The most effective approach to combine phases may be to infill a robust 3D-printed osteal polymer with an interlocking chondral phase hydrogel. Mechanical interlocking is therefore recommended for scaling up multiphasic scaffold applications to larger anatomically shaped joint surface regeneration. For the evaluation of layer integration, the interface shear test is recommended to avoid artifacts or variability that may be associated with alternative approaches that require adhesives or mechanical grips. The 3D-printing literature with interfacial scaffolds provides a compelling foundation for continued work toward successful regeneration of injured or diseased osteochondral tissues in load-bearing joints such as the knee, hip, or temporomandibular joint.

Clinical insights into tooth extraction via torsion method: a biomechanical analysis of the tooth-periodontal ligament complex

Traditionally, extracting single, flat- or curved-rooted teeth through twisting is unfeasible. However, our clinical practice suggests that such teeth can be extracted efficiently through moderate twisting in a minimally invasive manner. Given the lack of studies on biomechanics of the tooth-periodontal ligament (PDL) complex during torsion, which has further constrained its application, we assessed the feasibility of the torsion method for extracting single-rooted teeth and evaluated its minimally invasive potential. Using three-dimensional finite element analysis, we examined the stress distribution of the tooth and PDL during torsion. Then, we examined changes in the optimal torsion angle (OTA) and stress distribution across various anatomical scenarios. During torsion loading, stress concentration was primarily observed on the sing-rooted tooth surface near the alveolar crest, whereas molars at the root furcation. The OTA was found to increase under conditions such as narrowing of root width, decrease in the root apical curvature, change from type I to IV bone, alveolar bone loss, and shortening of root length. Moreover, the clinically validated model demonstrated that 74% of outcomes fell within the standard OTA range. In conclusion, the decrease in PDL area necessitated a larger angle for complete PDL tearing. Single-rooted teeth with root width-to-thickness ratios of ≥0.42 and apical curvatures of ≤30°are suitable for extraction using the torsion method. This study confirms the feasibility of the torsion method for minimally invasive tooth extraction and expands its indications, laying the theoretical foundation and essential insights for its clinical application.

From ex ovo to in vitro: xenotransplantation and vascularization of mouse embryonic kidneys in a microfluidic chip

Organoids are emerging as a powerful tool to investigate complex biological structures in vitro. Vascularization of organoids is crucial to recapitulate the morphology and function of the represented human organ, especially in the case of the kidney, whose primary function of blood filtration is closely associated with blood circulation. Current in vitro microfluidic approaches have only provided initial vascularization of kidney organoids, whereas in vivo transplantation to animal models is problematic due to ethical problems, with the exception of xenotransplantation onto a chicken chorioallantoic membrane (CAM). Although CAM can serve as a good environment for vascularization, it can only be used for a fixed length of time, limited by development of the embryo. Here, we propose a novel lab on a chip design that allows organoids of different origin to be cultured and vascularized on a CAM, as well as to be transferred to in vitro conditions when required. Mouse embryonic kidneys cultured on the CAM showed enhanced vascularization by intrinsic endothelial cells, and made connections with the chicken vasculature, as evidenced by blood flowing through them. After the chips were transferred to in vitro conditions, the vasculature inside the organoids was successfully maintained. To our knowledge, this is the first demonstration of the combination of in vivo and in vitro approaches applied to microfluidic chip design.

Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing

Large field-of-view (FOV) microscopic imaging (over 100 mm2) with high lateral resolution (1-2 μm) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime. Lensless digital holographic microscopy (LDHM) is promising in this context but ensuring accurate quantitative phase imaging (QPI) in large FOV LDHM is challenging. While phantoms, 3D printed by two-photon polymerization (TPP), have facilitated testing small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector's edges. Here, we propose an application of TPP over large area to examine phase consistency in LDHM. Our research involves fabricating widefield phase test targets with galvo and piezo scanning, scrutinizing them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By measuring the structures near the detector's edges, we verified LDHM phase imaging errors across the entire FOV, with less than 12% phase value difference between areas. Our findings indicate that TPP, followed by LDHM and Linnik interferometry cross-verification, requires new design considerations for precise large-area photonic manufacturing. This research paves the way for quantitative benchmarking of large FOV lensless phase imaging, enhancing understanding and further development of LDHM technique.

Early screening of miliary tuberculosis with tuberculous meningitis based on few-shot learning with multiple windows and feature granularities

Tuberculous meningitis (TBM) is a fatal tuberculosis caused by a large number of Mycobacterium tuberculosis (M. tuberculosis) spread by blood flow, with a case fatality rate of more than 50%. It is one of the most serious complications of miliary tuberculosis (MT), whose incidence is closely related to MT. If doctors can provide early diagnosis and active treatment for TBM, the case fatality rate will be significantly reduced. At present, there is a lack of methods to predict the progression of MT to TBM in clinic. To explore whether MT cases will experience TBM, we propose an early screening model of miliary tuberculosis with tuberculous meningitis (MT-TBM) based on few-shot learning with multiple windows and feature granularities (MWFG). This model aims to screen potential TBM cases through chest computerized tomography (CT) images of MT cases. Chest CT is a routine examination for MT cases. The MWFG module can extract more comprehensive features from a set of CT images of each MT case. The softmax classifier with adaptive regularization is trained on the cooperation of support set and query set, which can effectively prevent overfitting. Experiments on a dataset of 40 MT cases with chest CT images established by the medical records demonstrate that our proposed model achieves state-of-the-art performance in the early screening of MT-TBM. It can establish the connection between MT and MT-TBM through chest CT images of MT cases. The early screening model of MT-TBM based on few-shot learning with MWFG fills the research gap in computer-aided predicting TBM and has certain clinical effects. This research can provide some reference for clinicians in early diagnosis of MT-TBM and help clinicians in the early prevention and treatment of TBM for MT patients.

Mahmoud T, Rajasekaran A, Bhatia B. Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization's ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy.

Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism

Objectives

The purpose of this study was to develop and validate a new feature fusion algorithm to improve the classification performance of benign and malignant ground-glass nodules (GGNs) based on deep learning.

Methods

We retrospectively collected 385 cases of GGNs confirmed by surgical pathology from three hospitals. We utilized 239 GGNs from Hospital 1 as the training and internal validation set, and 115 and 31 GGNs from Hospital 2 and Hospital 3, respectively, as external test sets 1 and 2. Among these GGNs, 172 were benign and 203 were malignant. First, we evaluated clinical and morphological features of GGNs at baseline chest CT and simultaneously extracted whole-lung radiomics features. Then, deep convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs) were applied to extract deep features from whole-lung CT images, clinical, morphological features, and whole-lung radiomics features separately. Finally, we integrated these four types of deep features using an attention mechanism. Multiple metrics were employed to evaluate the predictive performance of the model.

Results

The deep learning model integrating clinical, morphological, radiomics and whole lung CT image features with attention mechanism (CMRI-AM) achieved the best performance, with area under the curve (AUC) values of 0.941 (95% CI: 0.898-0.972), 0.861 (95% CI: 0.823-0.882), and 0.906 (95% CI: 0.878-0.932) on the internal validation set, external test set 1, and external test set 2, respectively. The AUC differences between the CMRI-AM model and other feature combination models were statistically significant in all three groups (all p<0.05).

Conclusion

Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.

Silencing endomucin in bone marrow sinusoids improves hematopoietic stem and progenitor cell homing during transplantation

Efficient homing of infused hematopoietic stem and progenitor cells (HSPCs) into the bone marrow (BM) is the prerequisite for successful hematopoietic stem cell transplantation. However, only a small part of infused HSPCs find their way to the BM niche. A better understanding of the mechanisms that facilitate HSPC homing will help to develop strategies to improve the initial HSPC engraftment and subsequent hematopoietic regeneration. Here, we show that irradiation upregulates the endomucin expression of endothelial cells in vivo and in vitro. Furthermore, depletion of endomucin in irradiated endothelial cells with short-interfering RNA (siRNA) increases the HSPC-endothelial cell adhesion in vitro. To abrogate the endomucin of BM sinusoidal endothelial cells (BM-SECs) in vivo, we develop a siRNA-loaded bovine serum albumin nanoparticle for targeted delivery. Nanoparticle-mediated siRNA delivery successfully silences endomucin expression in BM-SECs and improves HSPC homing during transplantation. These results reveal that endomucin plays a critical role in HSPC homing during transplantation and that gene-based manipulation of BM-SEC endomucin in vivo can be exploited to improve the efficacy of HSPC transplantation.

Shattering the Water Window: Comprehensive Mapping of Faradaic Reactions on Bioelectronics Electrodes

It is generally accepted that for safe use of neural interface electrodes, irreversible faradaic reactions should be avoided in favor of capacitive charge injection. However, in some cases, faradaic reactions can be desirable for controlling specific (electro)physiological outcomes or for biosensing purposes. This study aims to systematically map the basic faradaic reactions occurring at bioelectronic electrode interfaces. We analyze archetypical platinum-iridium (PtIr), the most commonly used electrode material in biomedical implants. By providing a detailed guide to these reactions and the factors that influence them, we offer a valuable resource for researchers seeking to suppress or exploit faradaic reactions in various electrode materials. We employed a combination of electrochemical techniques and direct quantification methods, including amperometric, potentiometric, and spectrophotometric assays, to measure O2, H2, pH, H2O2, Cl2/OCl-, and soluble platinum and iridium ions. We compared phosphate-buffered saline (PBS) with an unbuffered electrolyte and complex cell culture media containing proteins. Our results reveal that the "water window"─the potential range without significant water electrolysis─varies depending on the electrolyte used. In the culture medium that is rich with redox-active species, a window of potentials where no faradaic process occurs essentially does not exist. Under cathodic polarizations, significant pH increases (alkalization) were observed, while anodic water splitting competes with other processes in media, preventing prevalent acidification. We quantified the oxygen reduction reaction and accumulation of H2O2 as a byproduct. PtIr efficiently deoxygenates the electrolyte under low cathodic polarizations, generating local hypoxia. Under anodic polarizations, chloride oxidation competes with oxygen evolution, producing relatively high and cytotoxic concentrations of hypochlorite (OCl-) under certain conditions. These oxidative processes occur alongside PtIr dissolution through the formation of soluble salts. Our findings indicate that the conventional understanding of the water window is an oversimplification. Important faradaic reactions, such as oxygen reduction and chloride oxidation, occur within or near the edges of the water window. Furthermore, the definition of the water window significantly depends on the electrolyte composition, with PBS yielding different results compared with culture media.

Development and Biocompatibility Assessment of Decellularized Porcine Uterine Extracellular Matrix-Derived Grafts

Biomaterials derived from biological matrices have been widely investigated due to their great therapeutic potential in regenerative medicine, since they are able to induce cell proliferation, tissue remodeling, and angiogenesis in situ. In this context, highly vascularized and proliferative tissues, such as the uterine wall, present an interesting source to produce acellular matrices that can be used as bioactive materials to induce tissue regeneration. Therefore, this study aimed to establish an optimized protocol to generate decellularized uterine scaffolds (dUT), characterizing their structural, compositional, and biomechanical properties. In addition, in vitro performance and in vivo biocompatibility were also evaluated to verify their potential applications for tissue repair. Results showed that the protocol was efficient to promote cell removal, and dUT general structure and extracellular matrix composition remained preserved compared with native tissue. In addition, the scaffolds were cytocompatible, allowing cell growth and survival. In terms of biocompatibility, the matrices did not induce any signs of immune rejection in vivo in a model of subcutaneous implantation in immunocompetent rats, demonstrating an indication of tissue integration after 30 days of implantation. In summary, these findings suggest that dUT scaffolds could be explored as a biomaterial for regenerative purposes, which is beyond the studies in the reproductive field.

Enhancing radiotherapy for melanoma: the promise of high-Z metal nanoparticles in radiosensitization

Melanoma is a type of skin cancer that can be challenging to treat, especially in advanced stages. Radiotherapy is one of the main treatment modalities for melanoma, but its efficacy can be limited due to the radioresistance of melanoma cells. Recently, there has been growing interest in using high-Z metal nanoparticles (NPs) to enhance the effectiveness of radiotherapy for melanoma. This review provides an overview of the current state of radiotherapy for melanoma and discusses the physical and biological mechanisms of radiosensitization through high-Z metal NPs. Additionally, it summarizes the latest research on using high-Z metal NPs to sensitize melanoma cells to radiation, both in vitro and in vivo. By examining the available evidence, this review aims to shed light on the potential of high-Z metal NPs in improving radiotherapy outcomes for patients with melanoma.

Recent Advances in Lipid Nanoparticles and Their Safety Concerns for mRNA Delivery

INTRODUCTION

The advent of lipid nanoparticles (LNPs) as a delivery platform for mRNA therapeutics has revolutionized the biomedical field, particularly in treating infectious diseases, cancer, genetic disorders, and metabolic diseases. Recent Advances in Therapeutic LNPs: LNPs, composed of ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, facilitate efficient cellular uptake and cytosolic release of mRNA while mitigating degradation by nucleases. However, as synthetic entities, LNPs face challenges that alter their therapeutic efficacy and safety concerns. Toxicity/Reactogenicity/Immunogenicity: This review provides a comprehensive overview of the latest advancements in LNP research, focusing on preclinical safety assessments encompassing toxicity, reactogenicity, and immunogenicity. Summary and Outlook: Additionally, it outlines potential strategies for addressing these challenges and offers insights into future research directions for enhancing the application of LNPs in mRNA therapeutics.

Neuroimaging studies in major depressive disorder with suicidal ideation or behaviour among Chinese patients: implications for neural mechanisms and imaging signatures

Major depressive disorder (MDD) with suicidal ideation or behaviour (MDSI) is associated with an increased risk of future suicide. The timely identification of suicide risk in patients with MDD and the subsequent implementation of interventions are crucially important to reduce their suffering and save lives. However, the early diagnosis of MDSI remains challenging across the world, as no objective diagnostic method is currently available. In China, the challenge is greater due to the social stigma associated with mental health problems, leading many patients to avoid reporting their suicidal ideation. Additionally, the neural mechanisms underlying MDSI are still unclear, which may hamper the development of effective interventions. We thus conducted this narrative review to summarise the existing neuroimaging studies of MDSI in Chinese patients, including those involving structural magnetic resonance imaging (MRI), functional MRI, neuronal electrophysiological source imaging of the brain dynamics with electroencephalography and magnetoencephalography. By synthesising the current research efforts in neuroimaging studies of Chinese patients with MDSI, we identified potential objective neuroimaging biomarkers, which may aid in the early identification of patients with MDSI who are at high suicide-related risk. Our findings also offer insights into the complex neural mechanisms underlying MDSI and suggest promising therapeutic targets. Furthermore, we propose future directions to discover novel imaging signatures, improve patient care, as well as help psychiatrists and clinical investigators plan their future research.

Theoretical Insight into the Fluorescence Spectral Tuning Mechanism: A Case Study of Flavin-Dependent Bacterial Luciferase

Bioluminescence of bacteria is widely applied in biological imaging, environmental toxicant detection, and many other situations. Understanding the spectral tuning mechanism not only helps explain the diversity of colors observed in nature but also provides principles for bioengineering new color variants for practical applications. In this study, time-dependent density functional theory (TD-DFT) and quantum mechanics and molecular mechanics (QM/MM) calculations have been employed to understand the fluorescence spectral tuning mechanism of bacterial luciferase with a focus on the electrostatic effect. The spectrum can be tuned by both a homogeneous dielectric environment and oriented external electric fields (OEEFs). Increasing the solvent polarity leads to a redshift of the fluorescence emission maximum, λF, accompanied by a substantial increase in density. In contrast, applying an OEEF along the long axis of the isoalloxazine ring (X-axis) leads to a significant red- or blue-shift in λF, depending on the direction of the OEEF, yet with much smaller changes in intensity. The effect of polar solvents is directionless, and the red-shifts can be attributed to the larger dipole moment of the S1 state compared with that of the S0 state. However, the effect of OEEFs directly correlates with the difference dipole moment between the S1 and S0 states, which is directional and is determined by the charge redistribution upon deexcitation. Moreover, the electrostatic effect of bacterial luciferase is in line with the presence of an internal electric field (IEF) pointing in the negative X direction. Finally, the key residues that contribute to this IEF and strategies for modulating the spectrum through site-directed point mutations are discussed.

Exploring the Feasibility and Acceptability of a Telehealth Platform for Older Adults with Noncommunicable Diseases and Chronic Viral Hepatitis

Background: Telehealth improves access to health care and potentially leads to better clinical outcomes. However, digital competence could be an essential factor in ensuring its adoption, particularly among older adults. This study evaluated the acceptability and perception of a mobile application platform among Thai older patients with chronic diseases according to their demographics and digital skills. Methods: The demographic information and internet usage profiles of patients with nonmalignant chronic diseases, including chronic viral hepatitis, were collected. Participants were grouped based on their self-perceived digital familiarity. The chi-square test was used to evaluate the associations between the parameters. Results: Among 710 participants (61.7% women, mean age: 66.2 years), digital familiarity was significantly higher among individuals aged <70 years, men, those with a bachelor's degree or higher, those with higher incomes, and Bangkok residents (p < 0.001). In this study, regular use of smartphones and the internet, but not messaging applications, was associated with self-perceived digital familiarity. Of these, 100 participants completed a survey evaluating their satisfaction with and perceptions of telehealth. Participants with greater digital familiarity demonstrated significantly higher satisfaction with telemedicine compared with those with limited ability or relied on caretakers (χ2 = 70.145, p < 0.001). Conclusion: Our data indicated that a user-friendly mobile application is feasible and acceptable for the management of chronic diseases in older patients. Digital familiarity is an important factor associated with satisfaction with the platform, underscoring the need to bridge digital skill gaps and ensure equitable health care delivery.