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Mesenchymal stem/stromal cell (MSC)-based therapeutics is already available for treatment of a range of diseases or medical conditions. Autologous or allogeneic MSCs obtained from self or donors have their own advantages and disadvantages in their medical practice. Therapeutic benefits of using autologous vs. allogeneic MSCs are inconclusive. Transplanted MSCs within the body interact with their physical microenvironment or niche, physiologically or pathologically, and such cells in a newly established tissue microenvironment may be impacted by the pathological harmful environmental factors to alter their unique biological behaviors. Meanwhile, a temporary microenvironment/niche may be also altered by the resident or niche-surrounding MSCs. Therefore, the functional plasticity and heterogeneity of MSCs caused by different donors and subpopulations of MSCs may result in potential uncertainty in their safe and efficacious medical practice. Acknowledging a connection between MSCs’ biology and their existing microenvironment, donor-controlled clinical practice for the long-term therapeutic benefit is suggested to further consider minimizing MSCs potential harm for MSC-based individual therapies. In this review, we summarize the advantages and disadvantages of autologous vs. allogeneic MSCs in their therapeutic applications. Among other issues, we highlight the importance of better understanding of the various microenvironments that may affect the properties of niche-surrounding MSCs and discuss the clinical applications of MSCs within different contexts for treatment of different diseases including cardiomyopathy, lupus and lupus nephritis, diabetes and diabetic complications, bone and cartilage repair, cancer and tissue fibrosis.

A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. The proposed intrusion detection model is evaluated based on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 datasets, and the experimental results show that the classification accuracy reaches 86.25%, 99.69%, 99.65%, which are 1.95%, 0.47% and 0.12% higher than that of the same type of CNN-GRU, and can solve the problems of low classification accuracy and class imbalance well.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients 1 – 4 . However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness 5 – 11 , and existing wearable cardiac devices can only capture signals on the skin 12 – 16 . Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments. Innovations in device design, material fabrication and deep learning are described, leading to a wearable ultrasound transducer capable of dynamic cardiac imaging in various environments and under different conditions.

Biohybrid is a newly emerging and promising approach to construct soft robotics and soft machines with novel functions, high energy efficiency, great adaptivity and intelligence. Despite many unique advantages of biohybrid systems, it is well known that most biohybrid systems have a relatively short lifetime, require complex fabrication process, and only remain functional with careful maintenance. Herein, we introduce a simple method to create a highly robust and power-free soft biohybrid mechanoluminescence, by encapsulating dinoflagellates, bioluminescent unicellular marine algae, into soft elastomeric chambers. The dinoflagellates retain their intrinsic bioluminescence, which is a near-instantaneous light response to mechanical forces. We demonstrate the robustness of various geometries of biohybrid mechanoluminescent devices, as well as potential applications such as visualizing external mechanical perturbations, deformation-induced illumination, and optical signaling in a dark environment. Our biohybrid mechanoluminescent devices are ultra-sensitive with fast response time and can maintain their light emission capability for weeks without special maintenance. Despite the advantages of biohybrid systems for soft robotics, most systems have short lifetime, require complex fabrication, and only remain functional with careful maintenance. Here, the authors report biohybrid mechanoluminescence in soft elastomer-encapsulated bioluminescent dinoflagellates.

LGR4 has been identified as a new receptor for RANKL in bone cells where it opposes RANK signaling to inhibit osteoclasts differentiation, and its therapeutic targeting promotes reduced bone loss in three mouse models of osteoporosis. Tumor necrosis factor (TNF) superfamily member 11 (TNFSF11, also known as RANKL) regulates multiple physiological or pathological functions, including osteoclast differentiation and osteoporosis. TNFRSF11A (also called RANK) is considered to be the sole receptor for RANKL. Herein we report that leucine-rich repeat-containing G-protein-coupled receptor 4 (LGR4, also called GPR48) is another receptor for RANKL. LGR4 competes with RANK to bind RANKL and suppresses canonical RANK signaling during osteoclast differentiation. RANKL binding to LGR4 activates the Gα q and GSK3-β signaling pathway, an action that suppresses the expression and activity of nuclear factor of activated T cells, cytoplasmic, calcineurin-dependent 1 (NFATC1) during osteoclastogenesis. Both whole-body ( Lgr4 −/− ) and monocyte conditional knockout mice of Lgr4 ( Lgr4 CKO) exhibit osteoclast hyperactivation (including elevation of osteoclast number, surface area, and size) and increased bone erosion. The soluble LGR4 extracellular domain (ECD) binds RANKL and inhibits osteoclast differentiation in vivo . Moreover, LGR4-ECD therapeutically abrogated RANKL-induced bone loss in three mouse models of osteoporosis. Therefore, LGR4 acts as a second RANKL receptor that negatively regulates osteoclast differentiation and bone resorption.

Fibrosis refers to the connective tissue deposition and stiffness usually as a result of injury. Fibrosis tissue-resident mesenchymal cells, including fibroblasts, myofibroblast, smooth muscle cells, and mesenchymal stem/stromal cells (MSCs), are major players in fibrogenic processes under certain contexts. Acknowledging differentiation potential of MSCs to the aforementioned other types of mesenchymal cell lineages is essential for better understanding of MSCs’ substantial contributions to progressive fibrogenesis. MSCs may represent a potential therapeutic option for fibrosis resolution owing to their unique pleiotropic functions and therapeutic properties. Currently, clinical trial efforts using MSCs and MSC-based products are underway but clinical data collected by the early phase trials are insufficient to offer better support for the MSC-based anti-fibrotic therapies. Given that MSCs are involved in the coagulation through releasing tissue factor, MSCs can retain procoagulant activity to be associated with fibrogenic disease development. Therefore, MSCs’ functional benefits in translational applications need to be carefully balanced with their potential risks.

Background Due to the inadequacy of published evidence, association of telomere length (TL), obesity and tobacco smoking with idiopathic pulmonary fibrosis (IPF) remains unclear. The aim of the study was to explore whether these exposures genetically affected the risk of the disease. Methods Genetic variants from genome-wide association studies for TL, body mass index (BMI), body fat percentage (BFP) and tobacco smoking (including maternal smoking) were used as instrumental variables. Inverse-variance weighted were mainly adopted to determine the genetic association of these exposures with IPF. All analyses were conducted by R-software (version 3.6.1). Results Firstly, longer TL was associated with the decreased risk of IPF (OR = 0.475 per SD increase in TL, 95%CI = 0.336 ~ 0.670, P<0.001). Secondly, higher levels of BMI and BFP were related to the increased risk of the disease (OR = 1.425 per SD increase in BMI level, 95%CI = 1.114 ~ 1.823, P = 0.005; OR = 1.702 per SD increase in BFP level, 95%CI = 1.202 ~ 2.409, P = 0.003). Thirdly, maternal smoking was implicated in the increased risk of the disease (OR = 13.183 per SD increase in the prevalence of maternal smoking, 95%CI = 1.820 ~ 95.484, P = 0.011). Conclusion TL should be a genetic risk factor for IPF. Obesity and exposure to tobacco smoking as a fetus might also contribute to the development of this fibrotic diseases. These findings should be verified by future studies.

Human mesenchymal stem cells (hMSCs) show promise for bone and cartilage regeneration. Our previous studies demonstrated that hMSCs with periodic mild heating had enhanced osteogenic and chondrogenic differentiation with significantly upregulated heat shock protein 70 (HSP70). However, the role of HSP70 in adult tissue regeneration is not well studied. Here, we revealed an essential regulatory mechanism of HSP70 in osteogenesis and chondrogenesis using adult hMSCs stably transfected with specific shRNAs to knockdown HSP70. Periodic heating at 39 °C was applied to hMSCs for up to 26 days. HSP70 knockdown resulted in significant reductions of alkaline phosphatase activity, calcium deposition, and gene expression of Runx2 and Osterix during osteogenesis. In addition, knockdown of HSP70 led to significant decreases of collagens II and X during chondrogenesis. Thus, downregulation of HSP70 impaired hMSC osteogenic and chondrogenic differentiation as well as the enhancement of these processes by thermal treatment. Taken together, these findings suggest a putative mechanism of thermal-enhanced bone and cartilage formation and underscore the importance of HSP70 in adult bone and cartilage differentiation.

Facial expressions, whether simple or complex, convey pheromones that can affect others. Plentiful sensory input delivered by marketing anchors’ facial expressions to audiences can stimulate consumers’ identification and influence decision-making, especially in live streaming media marketing. This paper proposes an efficient feature extraction network based on the YOLOv5 model for detecting anchors’ facial expressions. First, a two-step cascade classifier and recycler is established to filter invalid video frames to generate a facial expression dataset of anchors. Second, GhostNet and coordinate attention are fused in YOLOv5 to eliminate latency and improve accuracy. YOLOv5 modified with the proposed efficient feature extraction structure outperforms the original YOLOv5 on our self-built dataset in both speed and accuracy.

Background Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning. Materials and methods This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort ( n  = 122) and an internal test cohort ( n  = 46), and the data from the other center ( n  = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics–clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively. Results A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics–clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693–0.954) and external (AUC = 0.773, 95% CI 0.647–0.902) test cohorts, and the DCA also suggested the radiomics–clinical model had the highest net benefit. Conclusions The radiomics–clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.