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Abstract The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p

Color pattern plays a vital role in animal survival and communication. The type, distribution, and pigment state of pigment cells, and the reflective ability of iridophores determines body color. It varies adaptively in response to external environmental changes and physiological states. The skin pigmentation pattern reflects the number and arrangement of chromatophores. Some fish with rich color patterns, including egg spot patterns, blotch patterns, melanism, horizontal stripe patterns, and vertical bar patterns, have been studied increasingly. This study observed the formation, distribution, and main pattern of chromatophores in 1–60-day-old Misgurnus anguillicaudatus after hatching. Larval melanocytes were first observed in the yolk sac of loach larvae at 3 h post-hatching. From the larval to juvenile stage at 21 days, larval melanocytes appeared on the loach body surface. From the juvenile stage at 22 days to the adult stage, adult melanocytes appeared on the loach body surface. Iridocytes were first observed in the eyes of one-day-old larvae but not on the body surface until they were 12 days old. Xanthophores appeared on the body surface of seven-day-old juveniles. At 2–21 days post-hatching, the melanocytes in the loaches were larval, and their shape changed from star-to snowflake-shaped before forming a black spot. From 22 days, different morphological adult melanocytes formed on the body surface of the loaches with three types of black spots. Chrysanthemum-shaped melanocytes regularly aggregated into large black spots on large black spot loaches. Round and dendritic melanocytes gathered to form small black spots on small black spot loaches. Dendritic melanocytes were evenly distributed on non-black spot loaches. The pigmentation-related mitfa gene was obtained from M. anguillicaudatus using the rapid amplification of cDNA ends (RACE) approach with the SMARTer RACE 5´/3´ Kit User Manual according to the manufacturer's recommendations and was analyzed using bioinformatics and quantitative methods. The results showed that the mitfa gene encoded a protein with 408 amino acids with a calculated molecular mass of 45.68 kDa and an estimated isoelectric point of 7.16. MITFa contained MITF_TFEB_C_3_N, bHLH-Zip, and DUF 3371 domains. MITFa was well-conserved compared to MITF of various species with a higher degree of sequence similarity with other fishes (58.8%–83.2%). The qRT-PCR results showed that the mitfa mRNA was expressed at all stages of embryonic development and reached a peak value at the fertilization stage. mitfa expression was detected in all examined tissues of the three types of loaches, and the highest level of expression was detected in both muscle and dorsal skin (P < 0.01). This study explored pigmentation formation and mitfa expression, serving as a foundation for gaining further insight into the genetic mechanism of body color formation in M. anguillicaudatus.

Thyroidectomy scars, located on the exposed site, can cause distress in patients. Owing to the cosmetic importance of thyroidectomy scars, many studies have been conducted on its prevention and treatment. Scar formation factors mainly include inflammatory cell infiltration, angiogenesis, fibroblast proliferation, secretion of cytokines such as transforming growth factor (TGF)-β1, and mechanical tension on the wound edges. Anti-scar methods including topical anti-scar agents, skin tension-bearing devices, and local injections of botulinum toxin, as well as lasers and phototherapies, that target these scar formation factors have been developed. However, current studies remain fragmented, and there is a lack of a comprehensive evaluation of the impacts of these anti-scar methods on treating thyroidectomy scars. Early intervention is a crucial but often neglected key to control hyperplastic thyroidectomy scars. Therefore, we review the currently adopted early postoperative strategies for thyroidectomy scar reduction, aiming to illustrate the mechanism of these anti-scar methods and provide flexible and comprehensive treatment selections for clinical physicians to deal with thyroidectomy scars.

Flexible, wearable pressure sensors offer numerous benefits, including superior sensing capabilities, a lightweight and compact design, and exceptional conformal properties, making them highly sought after in various applications including medical monitoring, human–computer interactions, and electronic skins. Because of their excellent characteristics, such as simple fabrication, low power consumption, and short response time, capacitive pressure sensors have received widespread attention. As a flexible polymer material, polydimethylsiloxane (PDMS) is widely used in the preparation of dielectric layers for capacitive pressure sensors. The Young’s modulus of the flexible polymer can be effectively decreased through the synergistic application of sacrificial template and laser ablation techniques, thereby improving the functionality of capacitive pressure sensors. In this study, a novel sensor was introduced. Its dielectric layer was developed through a series of processes, including the use of a sacrificial template method using NaCl microparticles and subsequent CO2 laser ablation. This porous PDMS dielectric layer, featuring an array of holes, was then sandwiched between two flexible electrodes to create a capacitive pressure sensor. The sensor demonstrates a sensitivity of 0.694 kPa−1 within the pressure range of 0–1 kPa and can effectively detect pressures ranging from 3 Pa to 200 kPa. The sensor demonstrates stability for up to 500 cycles, with a rapid response time of 96 ms and a recovery time of 118 ms, coupled with a low hysteresis of 6.8%. Furthermore, our testing indicates that the sensor possesses limitless potential for use in detecting human physiological activities and delivering signals.

To enhance the efficiency and load-carrying capacity of the jumping robot’s transmission system, the transmission characteristics of the planthopper hip gear were analyzed. The analysis revealed that the gear pair exhibited low transmission errors, high transmission efficiency, and significant torque capacity, making it suitable for high-speed, high-precision, and high-power density transmissions. A bionic design study of the jumping robot’s gear was conducted, drawing inspiration from the planthopper hip gear’s tooth shape and profile. A logarithmic spiral was assumed as the tooth profile based on its consistent pitch angle and curvature gradient characteristics. Parameters of the tooth profile of the bionic gear were calculated, and equations for the tooth surface were derived. A three-dimensional model of the bionic gear was constructed, and both its transmission efficiency and error were calculated. Using the finite element method, theoretical formulas for contact stress and bending stress in the bionic gear were deduced. The distribution characteristics of both bending and contact stress were analyzed, demonstrating that the bionic gear used in high-performance jumping robots possesses low transmission error, high transmission efficiency, and excellent contact strength, providing a theoretical foundation for enhancing their jumping capabilities.

Abstract Background The phagocytosis and homeostasis of microglia play an important role in promoting blood clearance and improving prognosis after subarachnoid hemorrhage (SAH). LC3-assocaited phagocytosis (LAP) contributes to the microglial phagocytosis and homeostasis via autophagy-related components. With RNA-seq sequencing, we found potential signal pathways and genes which were important for the LAP of microglia. Methods We used an in vitro model of oxyhemoglobin exposure as SAH model in the study. RNA-seq sequencing was performed to seek critical signal pathways and genes in regulating LAP. Bioparticles were used to access the phagocytic ability of microglia. Western blot (WB), immunoprecipitation, quantitative polymerase chain reaction (qPCR) and immunofluorescence were performed to detect the expression change of LAP-related components and investigate the potential mechanisms. Results In vitro SAH model, there were increased inflammation and decreased phagocytosis in microglia. At the same time, we found that the LAP of microglia was inhibited in all stages. RNA-seq sequencing revealed the importance of P38 MAPK signal pathway and DAPK1 in regulating microglial LAP. P38 was found to regulate the expression of DAPK1, and P38-DAPK1 axis was identified to regulate the LAP and homeostasis of microglia after SAH. Finally, we found that P38-DAPK1 axis regulated expression of BECN1, which indicated the potential mechanism of P38-DAPK1 axis regulating microglial LAP. Conclusion P38-DAPK1 axis regulated the LAP of microglia via BECN1, affecting the phagocytosis and homeostasis of microglia in vitro SAH model. Video Abstract

The practical application of flexible pressure sensors, including electronic skins, wearable devices, human–machine interaction, etc., has attracted widespread attention. However, the linear response range of pressure sensors remains an issue. Ecoflex, as a silicone rubber, is a common material for flexible pressure sensors. Herein, we have innovatively designed and fabricated a pressure sensor with a gradient micro-cone architecture generated by CO2 laser ablation of MWCNT/Ecoflex dielectric layer film. In cooperation with the gradient micro-cone architecture and a dielectric layer of MWCNT/Ecoflex with a variable high dielectric constant under pressure, the pressure sensor exhibits linearity (R2 = 0.990) within the pressure range of 0–60 kPa, boasting a sensitivity of 0.75 kPa−1. Secondly, the sensor exhibits a rapid response time of 95 ms, a recovery time of 129 ms, hysteresis of 6.6%, and stability over 500 cycles. Moreover, the sensor effectively exhibited comprehensive detection of physiological signals, airflow detection, and Morse code communication, thereby demonstrating the potential for various applications.

Introduction Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity.Methods and analysis This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables.Ethics and dissemination This study was approved by the Ethics Committee of Shanghai Sixth People’s Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases.Trial registration number ChiCTR2300068786.

Summary: Background: Large language models (LLMs) are garnering wide interest due to their human-like and contextually relevant responses. However, LLMs’ accuracy across specific medical domains has yet been thoroughly evaluated. Myopia is a frequent topic which patients and parents commonly seek information online. Our study evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and Google Bard, in delivering accurate responses to common myopia-related queries. Methods: We curated thirty-one commonly asked myopia care-related questions, which were categorised into six domains—pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis. Each question was posed to the LLMs, and their responses were independently graded by three consultant-level paediatric ophthalmologists on a three-point accuracy scale (poor, borderline, good). A majority consensus approach was used to determine the final rating for each response. ‘Good’ rated responses were further evaluated for comprehensiveness on a five-point scale. Conversely, ‘poor’ rated responses were further prompted for self-correction and then re-evaluated for accuracy. Findings: ChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses rated as ‘good’, compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved after self-correction. The LLM-Chatbots performed consistently across domains, except for ‘treatment and prevention’. However, ChatGPT-4.0 still performed superiorly in this domain, receiving 70% ‘good’ ratings, compared to 40% in ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001). Interpretation: Our findings underscore the potential of LLMs, particularly ChatGPT-4.0, for delivering accurate and comprehensive responses to myopia-related queries. Continuous strategies and evaluations to improve LLMs’ accuracy remain crucial. Funding: Dr Yih-Chung Tham was supported by the National Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).

Objective: To evaluate the effect of salidroside on oxygen and glucose deprivation (OGD)-treated NT2 cells and its underlying mechanisms of action. Methods: Retinoic acid was used to induce the differentiation of NT2 cells into neurons. The effects of salidroside on survival, apoptosis, inflammatory response, and oxidative stress of neurons undergoing OGD were evaluated. Using precursor cells as controls, the effect of salidroside on the differentiation progression of OGD-treated cells was evaluated. In addition, the effect of erastin, a ferroptosis inducer, on NT2 cells was examined to investigate the underlying mechanisms of neuroprotective action of salidroside. Results: Salidroside alleviated the effects of OGD on neuronal survival, apoptosis, inflammation, and oxidative stress, and promoted NT2 cell differentiation. Moreover, salidroside prevented ferroptosis of OGD-treated cells, which was abolished following erastin treatment, indicating that ferroptosis mediated the regulatory pathway of salidroside. Conclusions: Salidroside attenuates OGD-induced neuronal injury by inhibiting ferroptosis and promotes neuronal differentiation.

Background: Monocular depth estimation aims to predict the dense depth map from a single RGB image, which has important applications in 3D reconstruction, automatic driving, and augmented reality. However, existing methods directly feed the original RGB image into the model to extract depth features without avoiding the interference of depth-irrelevant information on depth estimation accuracy, which leads to inferior performance. To remove the influence of depth-irrelevant information and improve depth prediction accuracy, we propose RADepthNet, a novel reflectance-guided network fusing boundary features. Specifically, our method predicts depth maps using three steps: 1) Intrinsic Image Decomposition. We propose a Reflectance extraction module consisting of an encoder-decoder structure to extract depth-related reflectance. We demonstrate that the module can reduce the influence of illumination on depth estimation through an ablation study. 2) Boundary Detection. Boundary extraction module, consisting of an encoder, a refinement block, and an upsample block, is proposed to better predict depth at object boundaries utilizing gradient constraints. 3) Depth Prediction Module. Use a different encoder from 2) to obtain depth features from the reflectance map and fuse boundary features to predict depth. Besides, we proposed FIFADataset, a depth estimation dataset applied in soccer scenarios. Extensive experiments on the public dataset and our proposed FIFADataset show that our method achieves state-of-the-art performance.

Background: Exploring the correspondences across multi-view images is the basis of many computer vision tasks. However, most existing methods are limited on accuracy under challenging conditions. In order to learn more robust and accurate correspondences, we propose the DSD-MatchingNet for local feature matching in this paper. First, we develop a deformable feature extraction module to obtain multi-level feature maps, which harvests contextual information from dynamic receptive fields. The dynamic receptive fields provided by deformable convolution network ensures our method to obtain dense and robust correspondences. Second, we utilize the sparse-to-dense matching with the symmetry of correspondence to implement accurate pixel-level matching, which enables our method to produce more accurate correspondences. Experiments have shown that our proposed DSD-MatchingNet achieves a better performance on image matching benchmark, as well as on visual localization benchmark. Specifically, our method achieves 91.3% mean matching accuracy on HPatches dataset and 99.3% visual localization recalls on Aachen Day-Night dataset.

Abstract Pelvic injuries refer to the disruption of the inherent structural and mechanical integrity of the pelvic ring. Sacroiliac screw fixation technique is often applied for the treatment of posterior pelvic ring injury, which is prone to the iatrogenic injury. This study will compare the intraoperative and postoperative variables of patients underwent sacroiliac screw fixation with intraoperative CT and conventional imaging to evaluate the effect. Thirty-two patients with posterior pelvic ring injury treated by sacroiliac screw fixation from January 2019 to December 2020 were enrolled in this retrospective study. All patients were divided into two groups according to the different assistance of intraoperative imaging. Subsequently, the correlative data were compared and analysed statistically. Twelves cases were assigned to intraoperative CT group, and the remaining twenty cases were assigned to conventional group. There was no significant difference in duration of surgery, fracture healing time, time to ambulation, complications, and Matta radiological score. There was statistically significant difference (p

Virtual Reality (VR) has emerged as a new safe and efficient tool for the rehabilitation of many childhood and adulthood illnesses. VR-based therapies have the potential to improve both motor and functional skills in a wide range of age groups through cortical reorganization and the activation of various neuronal connections. Recently, the potential for using serious VR-based games that combine perceptual learning and dichoptic stimulation has been explored for the rehabilitation of ophthalmological and neurological disorders. In ophthalmology, several clinical studies have demonstrated the ability to use VR training to enhance stereopsis, contrast sensitivity, and visual acuity. The use of VR technology provides a significant advantage in training each eye individually without requiring occlusion or penalty. In neurological disorders, the majority of patients undergo recurrent episodes (relapses) of neurological impairment, however, in a few cases (60–80%), the illness progresses over time and becomes chronic, consequential in cumulated motor disability and cognitive deficits. Current research on memory restoration has been spurred by theories about brain plasticity and findings concerning the nervous system's capacity to reconstruct cellular synapses as a result of interaction with enriched environments. Therefore, the use of VR training can play an important role in the improvement of cognitive function and motor disability. Although there are several reviews in the community employing relevant Artificial Intelligence in healthcare, VR has not yet been thoroughly examined in this regard. In this systematic review, we examine the key ideas of VR-based training for prevention and control measurements in ocular diseases such as Myopia, Amblyopia, Presbyopia, and Age-related Macular Degeneration (AMD), and neurological disorders such as Alzheimer, Multiple Sclerosis (MS) Epilepsy and Autism spectrum disorder. This review highlights the fundamentals of VR technologies regarding their clinical research in healthcare. Moreover, these findings will raise community awareness of using VR training and help researchers to learn new techniques to prevent and cure different diseases. We further discuss the current challenges of using VR devices, as well as the future prospects of human training.

Hemerocallis citrina Baroni with different maturity levels has different uses for food and medicine and has different economic benefits and sales value. However, the growth speed of Hemerocallis citrina Baroni is fast, the harvesting cycle is short, and the maturity identification is completely dependent on experience, so the harvesting efficiency is low, the dependence on manual labor is large, and the identification standard is not uniform. In this paper, we propose a GCB YOLOv7 Hemerocallis citrina Baroni maturity detection method based on a lightweight neural network and attention mechanism. First, lightweight Ghost convolution is introduced to reduce the difficulty of feature extraction and decrease the number of computations and parameters of the model. Second, between the feature extraction backbone network and the feature fusion network, the CBAM mechanism is added to perform the feature extraction independently in the channel and spatial dimensions, which improves the tendency of the feature extraction and enhances the expressive ability of the model. Last, in the feature fusion network, Bi FPN is used instead of the concatenate feature fusion method, which increases the information fusion channels while decreasing the number of edge nodes and realizing cross-channel information fusion. The experimental results show that the improved GCB YOLOv7 algorithm reduces the number of parameters and floating-point operations by about 2.03 million and 7.3 G, respectively. The training time is reduced by about 0.122 h, and the model volume is compressed from 74.8 M to 70.8 M. In addition, the average precision is improved from 91.3% to 92.2%, mAP@0.5 and mAP@0.5:0.95 are improved by about 1.38% and 0.20%, respectively, and the detection efficiency reaches 10 ms/frame, which meets the real-time performance requirements. It can be seen that the improved GCB YOLOv7 algorithm is not only lightweight but also effectively improves detection precision.

The diagnosis and clinical management of aneurysmal subarachnoid hemorrhage (aSAH) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of disease. We used microRNAs (miRNAs) as diagnostics to characterize plasma extracellular vesicles in aSAH. It is unclear whether they can diagnose and manage aSAH. Next-generation sequencing (NGS) was used to detect the miRNA profile of plasma extracellular vesicles (exosomes) in three patients with SAH and three healthy controls (HCs). We identified four differentially expressed miRNAs and validated the results using quantitative real-time polymerase chain reaction (RT-qPCR) with 113 aSAH patients, 40 HCs, 20 SAH model mice, and 20 sham mice. Exosomal miRNA NGS revealed that six circulating exosomal miRNAs were differentially expressed in patients with aSAH versus HCs and that the levels of four miRNAs (miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p) were differentially significant. After multivariate logistic regression analysis, only miR-369-3p, miR-486-3p, and miR-193b-3p enabled prediction of neurological outcomes. In a mouse model of SAH, greater expression of miR-193b-3p and miR-486-3p remained statistically significant relative to controls, whereas expression levels of miR-369-3p and miR-410-3p were lower. miRNA gene target prediction showed six genes associated with all four of these differentially expressed miRNAs. The circulating exosomes miR-369-3p, miR-410-3p, miR-193b-3p, and miR-486-3p may influence intercellular communication and have potential clinical utility as prognostic biomarkers for aSAH patients.

ObjectivePelvic injuries refer to the disruption of the inherent structural and mechanical integrity of the pelvic ring. Sacroiliac screw fixation technique is often used for the treatment of posterior pelvic ring injury, which is prone to the iatrogenic injury. Various attempts were proposed to avoid iatrogenic injuries, while the executing processes are usually too cumbersome. The patient-personalized guide template based on 3D printing technology has been considered as a promising method, which can achieve lower deviation and higher accuracy in a simple and convenient way. We reported the first case of posterior pelvic ring injury using 3D printing personalized guide template with the verification of intraoperative CT.MethodsThe subject was a 74-year-old female with posterior pelvic ring injury. Two patient-specific guide templates were customized based on 3D printing technology, one for S1 and the other for S2. We used the guide templates for navigation to place the sacroiliac screws. The placement of screws was verified by intraoperative CT. Intraoperative and postoperative variables were collected.ResultsThe technique helped us successfully insert the sacroiliac screws into the safe zone. The intraoperative blood loss was 23.03 ml, and the duration of operation was 62 min. The exposure dose during CT scanning was 7.025 mSv. The assessment of screws position was excellent. Furthermore, there was no sign of any functional impairment postoperatively.ConclusionSacroiliac screws fixation with the assistance of 3D printing personalized guide template under the verification of intraoperative CT may be a promising method to treat posterior pelvic ring injuries.

The significant potential of flexible sensors in various fields such as human health, soft robotics, human–machine interaction, and electronic skin has garnered considerable attention. Capacitive pressure sensor is popular given their mechanical flexibility, high sensitivity, and signal stability. Enhancing the performance of capacitive sensors can be achieved through the utilization of gradient structures and high dielectric constant media. This study introduced a novel dielectric layer, employing the BaTiO3-PDMS material with a gradient micro-cones architecture (GMCA). The capacitive sensor was constructed by incorporating a dielectric layer GMCA, which was fabricated using laser engraved acrylic (PMMA) molds and flexible copper-foil/polyimide-tape electrodes. To examine its functionality, the prepared sensor was subjected to a pressure range of 0–50 KPa. Consequently, this sensor exhibited a remarkable sensitivity of up to 1.69 KPa−1 within the pressure range of 0–50 KPa, while maintaining high pressure-resolution across the entire pressure spectrum. Additionally, the pressure sensor demonstrated a rapid response time of 50 ms, low hysteresis of 0.81%, recovery time of 160 ms, and excellent cycling stability over 1000 cycles. The findings indicated that the GMCA pressure sensor, which utilized a gradient structure and BaTiO3-PDMS material, exhibited notable sensitivity and a broad linear pressure range. These results underscore the adaptability and viability of this technology, thereby facilitating enhanced flexibility in pressure sensors and fostering advancements in laser manufacturing and flexible devices for a wider array of potential applications.

In recent years, laser engraving has received widespread attention as a convenient, efficient, and programmable method which has enabled high-quality porous graphene to be obtained from various precursors. Laser engraving is often used to fabricate the dielectric layer with a microstructure for capacitive pressure sensors; however, the usual choice of electrodes remains poorly flexible metal electrodes, which greatly limit the overall flexibility of the sensors. In this work, we propose a flexible capacitive pressure sensor made entirely of thermoplastic polyurethane (TPU) and laser-induced graphene (LIG) derived from wood. The capacitive pressure sensor consisted of a flexible LIG/TPU electrode (LTE), an LIG/TPU electrode with a microhole array, and a dielectric layer of TPU with microcone array molded from a laser-engraved hole array on wood, which provided high sensitivity (0.11 kPa−1), an ultrawide pressure detection range (20 Pa to 1.4 MPa), a fast response (~300 ms), and good stability (>4000 cycles, at 0–35 kPa). We believe that our research makes a significant contribution to the literature, because the easy availability of the materials derived from wood and the overall consistent flexibility meet the requirements of flexible electronic devices.

Abstract Deubiquitinases (DUBs) have important biological functions, but their roles in breast cancer metastasis are not completely clear. In this study, through screening a series of DUBs related to breast cancer distant metastasis-free survival (DMFS) in the Kaplan-Meier Plotter database, we identified ubiquitin-specific protease 12 (USP12) as a key deubiquitinating enzyme for breast cancer metastasis. We confirmed this via an orthotopic mouse lung metastasis model. We revealed that the DMFS of breast cancer patients with high USP12 was worse than that of others. Knockdown of USP12 decreased the lung metastasis ability of 4T1 cells, while USP12 overexpression increased the lung metastasis ability of these cells in vivo. Furthermore, our results showed that the supernatant from USP12-overexpressing breast cancer cells could promote angiogenesis according to human umbilical vein endothelial cell (HUVEC) migration and tube formation assays. Subsequently, we identified midkine (MDK) as one of its substrates. USP12 could directly interact with MDK, decrease its polyubiquitination and increase its protein stability in cells. Overexpression of MDK rescued the loss of angiogenesis ability mediated by knockdown of USP12 in breast cancer cells in vitro and in vivo. There was a strong positive relationship between USP12 and MDK protein expression in clinical breast cancer samples. Consistent with the pattern for USP12, high MDK expression predicted lower DMFS and overall survival (OS) in breast cancer. Collectively, our study identified that USP12 is responsible for deubiquitinating and stabilizing MDK and leads to metastasis by promoting angiogenesis. Therefore, the USP12–MDK axis could serve as a potential target for the therapeutic treatment of breast cancer metastasis.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (

Abstract Background Heterotopic ossification (HO) represents pathological lesions that refer to the development of heterotopic bone in extraskeletal tissues around joints. This study investigates the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explores the potential pathways involved in this ailment. Methods Gene expression profiles (GSE94683) were obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for further analysis. Results In total, 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP) category, the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13, and WISP3, were intensively enriched in aspects of cell signal transmission, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation, and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4, and DKK2, were primarily involved in the mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway, and Wnt signaling pathway. Ten hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, and CALB2. Conclusions This study presented novel insight into the pathogenesis of HO. Ten hub genes and most of the DEGs intensively involved in enrichment analyses may be new candidate targets for the prevention and treatment of HO in the future.

Summary: We described a challenge named “Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge” in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis. The bigger picture: Diabetic retinopathy (DR) is the most common disease caused by diabetes. Challenges are held to address real-world issues encountered in the design of DR automated screening systems to advance the technology in this area. Thus, we described a challenge named ''Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge'' in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI 2020) for fundus image assessment and DR grading. The scientific community responded positively to the challenge. In the challenge, we provided a deep DR image dataset (DeepDRiD) containing regular DR images and ultra-widefield (UWF) DR images, both having image quality and DR grading diagnosis. We discussed details of the three best algorithms in each sub-challenges. The results by the top algorithms showed that image quality assessment can be used as a target for further exploration.

As the leading cause of vision loss in working-age adults, diabetic retinopathy requires routinely retinal screening. Here the authors develop a deep learning system that can facilitate the screening by providing real-time image quality assessment, lesions detection, and grades across the disease spectrum.

Abstract Background Myocardial ischemia–reperfusion injury (MIRI) is the main pathological manifestation of cardiovascular diseases such as myocardial infarction. The potential therapeutic effects of bone marrow-derived mesenchymal stem cells (BM-MSCs) and the participation of regulatory T cells (Tregs) in MIRI remains to be defined. Methods We used the experimental acute MIRI that was induced in mice by left ascending coronary ischemia, which were subsequently randomized to receive immunoglobulin G (IgG) or anti-CD25 antibody PC61 with or without intravenously injected BM-MSCs. The splenectomized mice underwent prior to experimental MIRI followed by intravenous administration of BM-MSCs. At 72 h post-MIRI, the hearts and spleens were harvested and subjected to cytometric and histologic analyses. Results CD25+Foxp3+ regulatory T cells were significantly elevated after MIRI in the hearts and spleens of mice receiving IgG + BM-MSCs and PC61 + BM-MSCs compared to the respective control mice (all p

Abstract For controlling recent COVID-19 outbreaks around the world, many countries have implemented suppression and mitigation interventions. This work aims to conduct a feasibility study for accessing the effect of multiple interventions to control the COVID-19 breakouts in the UK and other European countries, accounting for balance of healthcare demand. The model is to infer the impact of mitigation, suppression and multiple rolling interventions for controlling COVID-19 outbreaks in the UK, with two features considered: direct link between exposed and recovered population, and practical healthcare demand by separation of infections. We combined the calibrated model with COVID-19 data in London and non-London regions in the UK during February and April 2020. Our finding suggests that rolling intervention is an optimal strategy to effectively control COVID-19 outbreaks in the UK for balancing healthcare demand and morality ratio. It is better to implement regional based interventions with varied intensities and maintenance periods. We suggest an intervention strategy named as “Besieged and rolling interventions” to the UK that take a consistent suppression in London for 100 days and 3 weeks rolling intervention in other regions. This strategy would reduce the overall infections and deaths of COVID-19 outbreaks, and balance healthcare demand in the UK.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a severe neurological event with limited treatment options, and little is known about its pathophysiology. There are few objective tools for predicting outcomes of aSAH patients and further aiding in directing clinical therapeutic programs. This study aimed to determine whether an elevated serum D-dimer/albumin ratio (DAR) reflects disease severity and predicts aSAH outcomes. Methods: We included 178 patients with aSAH. Data included demographics; clinical severity of aSAH (World Federation of Neurological Societies (WFNS) grade and Hunt–Hess grade); levels of D-dimer, albumin, and c-reactive protein (CRP); leukocyte counts on admission; and three-month outcomes. The outcomes were dichotomized into good and poor. The predictive ability of DAR for outcomes was determined using receiver operating characteristic (ROC) curve analysis. Results: Serum DAR showed a positive correlation with disease severity. Univariate analysis revealed that DAR, WFNS grade, Hunt–Hess grade, delayed cerebral infarction (DCI), age, neutrophil-to-lymphocyte ratio (NLR), and CRP/albumin ratio (CAR) were associated with unfavorable outcomes. Multivariate regression analysis further revealed that elevated DAR predicted poor outcomes after adjusting for WFNS grade, Hunt–Hess grade, DCI, age, NLR, and CRP/albumin ratio. Receiver operating characteristic curve analysis revealed that DAR predicted outcomes at a level comparable with NLR and CAR and had superior predictivity than D-dimer alone. Conclusion: DAR is a promising objective tool for aSAH outcome prediction. A high content DAR was associated with disease severity and unfavorable short-term outcomes.

Background: Hand gesture interfaces are dedicated programs that principally perform hand tracking and hand gesture prediction to provide alternative controls and interaction methods. They take advantage of one of the most natural ways of interaction and communication, proposing novel input and showing great potential in the field of the human-computer interaction. Developing a flexible and rich hand gesture interface is known to be a time-consuming and arduous task. Previously published studies have demonstrated the significance of the finite-state-machine (FSM) approach when mapping detected gestures to GUI actions. Methods: In our hand gesture interface, we broadened the FSM approach by utilizing gesture-specific attributes, such as distance between hands, distance from the camera, and time of occurrences, to enable users to perform unique GUI actions. These attributes are obtained from hand gestures detected by the RealSense SDK employed in our hand gesture interface. By means of these gesture-specific attributes, users can activate static gestures and perform them as dynamic gestures. We also provided supplementary features to enhance the efficiency, convenience, and user-friendliness of our hand gesture interface. Moreover, we developed a complementary application for recording hand gestures by capturing hand keypoints in depth and color images to facilitate the generation of hand gesture datasets. Results: We conducted a small-scale user survey with fifteen subjects to test and evaluate our hand gesture interface. Anonymous feedback obtained from the users indicates that our hand gesture interface is adequately facile and self-explanatory to use. In addition, we received constructive feedback about minor flaws regarding the responsiveness of the interface. Conclusions: We proposed a hand gesture interface along with key concepts to attain user-friendliness and effectiveness in the control of existing GUIs.

Deep Neural Network methods have been used to a variety of challenges in automatic 3D recognition. Although discovered techniques provide many advantages in comparison with conventional methods, they still suffer from different drawbacks, e.g., a large number of pre-processing stages and time-consuming training. In this paper, an innovative approach has been suggested for recognizing 3D models. It contains encoding 3D point clouds, surface normal, and surface curvature, merge them to provide more effective input data, and train it via a deep convolutional neural network on Shapenetcore dataset. We also proposed a similar method for 3D segmentation using Octree coding method. Finally, comparing the accuracy with some of the state-of-the-art demonstrates the effectiveness of our proposed method.

For satellite communication applications, this paper presents an image rejection Ku-band low noise amplifier (LNA) in a 65-nm CMOS process for the Hartley receiver architecture. To achieve high input/output linearity performance, the inductive source degenerated cascode and the capacitive neutralized common-source (CS) amplifier topologies are used in the LNA input and output stages, respectively. To achieve wideband input return loss, the minimum noise figure and high gain performance simultaneously, the inductive source degenerated cascode amplifier is co-designed with the fourth order input impedance matching network. For the image rejection purpose, a bridged-tee band-stop filter is proposed. The measurements show the LNA achieves 14.3-to-18.3 GHz 3 dB bandwidth with 17-to-20 dB power gain, 3.5-to-4 dB noise figure, 17-to-37 dB image rejection and -5 dBm IIP3. Including all pads, the chip occupies a silicon area of 1360 × 450 μm2 and consumes 72 mW DC power.

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.

Wearable fabric sensors have attracted enormous attention due to their huge potential in human health and activity monitoring, human–machine interaction and the Internet of Things (IoT). Among natural fabrics, bast fabric has the advantage of high strength, good resilience and excellent permeability. Laser engraving, as a high throughput, patternable and mask-free method, was demonstrated to fabricate fabric sensors. In this work, we developed a simplified, cost-effective and environmentally friendly method for engraving ramie fabric (a kind of bast fabric) directly by laser under an ambient atmosphere to prepare strain and humidity sensors. We used carboxymethylcellulose (CMC) to pretreat ramie fabric before laser engraving and gained laser-carbonized ramie fabrics (LCRF) with high conductivity (65 Ω sq−1) and good permeability. The strain and humidity sensors had high sensitivity and good flexibility, which can be used for human health and activity monitoring.

Recent outbreaks of coronavirus disease 2019 (COVID-19) has led a global pandemic cross the world. Most countries took two main interventions: suppression like immediate lockdown cities at epicenter or mitigation that slows down but not stopping epidemic for reducing peak healthcare demand. Both strategies have their apparent merits and limitations; it becomes extremely hard to conduct one intervention as the most feasible way to all countries. Targeting at this problem, this paper conducted a feasibility study by defining a mathematical model named SEMCR, it extended traditional SEIR (Susceptible-Exposed-Infectious-Recovered) model by adding two key features: a direct connection between Exposed and Recovered populations, and separating infections into mild and critical cases. It defined parameters to classify two stages of COVID-19 control: active contain by isolation of cases and contacts, passive contain by suppression or mitigation. The model was fitted and evaluated with public dataset containing daily number of confirmed active cases including Wuhan and London during January 2020 and March 2020. The simulated results showed that 1) Immediate suppression taken in Wuhan significantly reduced the total exposed and infectious populations, but it has to be consistently maintained at least 90 days (by the middle of April 2020). Without taking this intervention, we predict the number of infections would have been 73 folders higher by the middle of April 2020. Its success requires efficient government initiatives and effective collaborative governance for mobilizing of corporate resources to provide essential goods. This mode may be not suitable to other countries without efficient collaborative governance and sufficient health resources. 2) In London, it is possible to take a hybrid intervention of suppression and mitigation for every 2 or 3 weeks over a longer period to balance the total infections and economic loss. While the total infectious populations in this scenario would be possibly 2 times than the one taking suppression, economic loss and recovery of London would be less affected. 3) Both in Wuhan and London cases, one important issue of fitting practical data was that there were a portion (probably 62.9% in Wuhan) of self-recovered populations that were asymptomatic or mild symptomatic. This finding has been recently confirmed by other studies that the seroprevalence in Wuhan varied between 3.2% and 3.8% in different sub-regions. It highlights that the epidemic is far from coming to an end by means of herd immunity. Early release of intervention intensity potentially increased a risk of the second outbreak.

Angular precision of 0.1° for the blaze angle of echelle gratings is an important technical requirement for spectral instruments with echelle gratings. This paper proposes a precise adjustment method for the blaze angle of echelle gratings that are etched or deposited by self-shadowing rotating mask. The blaze angle will decrease or increase when the grating is rotated in beams for etching or deposition, respectively. The blaze angle can be controlled by adjusting the etching or deposition conditions. For the echelle grating with nominal blaze angle of 63.0° and groove density of 52.7 g/mm, the blaze angle was decreased by 0.12° with a maximum etching depth of 19 nm on the blaze facet. Additionally, the blaze angle adjustment precision will be better than 0.02° if the maximum etch depth on the blaze facet is less than 3 nm.

FGF21 has a number of beneficial metabolic effects. Here, Li et al. show that FGF21 promotes the healthy expansion of subcutaneous white adipose tissue, promoting the healthy expansion of fat tissue as a regulatory mechanism to maintain systemic insulin sensitivity during nutrient excess.

This paper describes a new method for fabrication of tailorable elastomeric gratings with tunable groove density gradients based on stretching of the grating sample in a direction perpendicular to the grating line within the grating surface. The advantage of this method is that the tunable groove density gradient can be achieved by tailoring of the shape of a grating sample with uniform thickness. The groove density gradient changes as a specific function of its spatial position on the tailored surface of the elastomeric grating. The resulting tailorable elastomeric gratings can be used in the fabrication of devices such as optical position sensors and tunable optical filters.

Objective: To investigate the hemodynamic features before and after embolization of paraclinoidal aneurysms using hemodynamic numerical simulation and the influence of embolization on recurrence after embolization.Methods: From January 2016 to December 2017, we enrolled a total of 113 paraclinoidal aneurysms treated with embolization. They were divided into recurrent group and stable group depending on follow-up results. An aneurysm model was generated based on 3D-DSA before and after embolization. The hemodynamic characteristics were analyzed between two groups using Computational fluid dynamic (CFD).Results: In the recurrent group, the peak systolic WSS, OSI and velocity around the aneurysm neck areas prior to embolization were 20.47 ± 3.04 Pa, 0.06 ± 0.02 and 0.07 ± 0.03 m/s, respectively. These values were 23.50 ± 4.11 Pa, 0.06 ± 0.01 and 0.11 ± 0.02 m/s, respectively in the stable group (P > 0.05). The WSS, OSI, velocity around the same areas in the recurrent group after embolization were 35.59 ± 8.75 Pa, 0.07 ± 0.02 and 0.12 ± 0.03 m/s, respectively (P < 0.01). In the stable group, the WSS, OSI and velocity were 13.08 ± 2.89 Pa, 0.04 ± 0.01 and 0.07 ± 0.02 m/s, respectively (P < 0.01). After embolization, the WSS, OSI and velocity around the aneurysm neck areas in the recurrent group were significantly higher than those in the stable group.Conclusions: High peak systolic WSS, OSI and velocity around aneurysm neck areas after embolization of paraclinoidal aneurysms may be important factors leading to recurrence.

Summary: Cells adopt distinct signaling pathways to optimize cell locomotion in different physical microenvironments. However, the underlying mechanism that enables cells to sense and respond to physical confinement is unknown. Using microfabricated devices and substrate-printing methods along with FRET-based biosensors, we report that, as cells transition from unconfined to confined spaces, intracellular Ca2+ level is increased, leading to phosphodiesterase 1 (PDE1)-dependent suppression of PKA activity. This Ca2+ elevation requires Piezo1, a stretch-activated cation channel. Moreover, differential regulation of PKA and cell stiffness in unconfined versus confined cells is abrogated by dual, but not individual, inhibition of Piezo1 and myosin II, indicating that these proteins can independently mediate confinement sensing. Signals activated by Piezo1 and myosin II in response to confinement both feed into a signaling circuit that optimizes cell motility. This study provides a mechanism by which confinement-induced signaling enables cells to sense and adapt to different physical microenvironments. : Hung et al. demonstrate that a Piezo1-dependent intracellular calcium increase negatively regulates protein kinase A (PKA) as cells transit from unconfined to confined spaces. The Piezo1/PKA and myosin II signaling modules constitute two confinement-sensing mechanisms. This study provides a paradigm by which signaling enables cells to sense and adapt to different microenvironments.

The precise role of nucleus pulposus cell proliferation in the pathogenesis of intervertebral disc degeneration remains to be elucidated. Recent findings have revealed that microRNAs, a class of small noncoding RNAs, may regulate cell proliferation in many pathological conditions. Here, we showed that miR-21 was significantly upregulated in degenerative nucleus pulposus tissues when compared with nucleus pulposus tissues that were isolated from patients with idiopathic scoliosis and that miR-10b levels were associated with disc degeneration grade. Moreover, bioinformatics target prediction identified PTEN as a putative target of miR-21. miR-21 inhibited PTEN expression by directly targeting the 3'UTR, and this inhibition was abolished through miR-21 binding site mutations. miR-21 overexpression stimulated cell proliferation and AKT signaling pathway activation, which led to cyclin D1 translation. Additionally, the increase in proliferation and cyclin D1 expression induced by miR-21 overexpression was almost completely blocked by Ly294002, an AKT inhibitor. Taken together, aberrant miR-21 upregulation in intervertebral disc degeneration could target PTEN, which would contribute to abnormal nucleus pulposus cell proliferation through derepressing the Akt pathway. Our study also underscores the potential of miR-21 and the PTEN/Akt pathway as novel therapeutic targets in intervertebral disc degeneration.

A stretchable optical sensor can quantify the strain generated by human movement, which has been widely studied in the development of health monitoring systems, human−machine interfaces and wearable devices. This paper reports a graphene-added polydimethylsiloxane (PDMS) fiber, which has high tensile properties and good light transmittance suitable for detecting human movement. When the graphene-added PDMS fiber is stretched, the concentration of graphene per unit volume is constant, and the sensor uses the optical loss of the beam through the graphene PDMS fiber to detect the tensile strain. The fiber has excellent strain-sensing performance, outstanding sensitivity, a tensile property of 150%, and an excellent waterproofing performance. The linear response and repeated response in large dynamic range could reach 100% stability. The results show that the sensor can be used to detect human motion detection. These excellent properties indicate that the fiber has potential applications in wearable devices, soft robots and electronic skin.

In this paper we introduce a polydimethylsiloxane (PDMS) composite fabricated using a simple production process and demonstrate the optical transmittance properties of this composite in the 300−1000 nm wavelength region. We control the material’s transmittance by varying the microcrystalline graphite powder concentration or the composite film’s thickness. In addition, we tailor the specimens into various trapezoidal shapes and load these specimens by mechanically stretching them in the direction perpendicular to both their base lines and their top lines. The advantage of this method is that a wide range of transmittance properties can be obtained for a given specimen. Furthermore, samples with different trapezoidal shapes have different transmittance tuning capabilities.

We report a facile method to prepare gradient wrinkles using a controlled-gradient light field. Because of the gradient distance between the ultraviolet (UV) lamp and polydimethylsiloxane (PDMS) substrate during UV/ozone treatment, the irradiance reaching the substrate continuously changed, which was transferred into the resulting SiOx film with a varying thickness. Therefore, wrinkles with continuously changing wavelength were fabricated using this approach. It was found that the wrinkle wavelength decreased as the distance increased. We fabricated 1-D wrinkle patterns and ring wrinkles with a gradient wavelength. The ring wrinkles were prepared using radial stresses, which were achieved by pulling the center of a freely hanging PDMS film. The resulting wrinkles with changing wavelength can be used in fluid handling systems, biological templates, and optical devices.