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While stereo matching methods have great progress in recent years, they still face several formidable challenges. These challenges mainly include how to fully utilize global context and depth semantic information for depth feature extraction while maintaining low computational cost, and how to accurately predict disparity in object details and ill-posed regions of stereo images. In this paper, we propose GA-Stereo, an end-to-end stereo network aiming at fast and more accurate disparity regression from rectified stereo images. Firstly, we propose GA-Net, a depth feature extraction network for stereo networks; it utilizes a gradient flow shunting strategy and aggregated residual transformation to calculate the matching cost of stereo images. Secondly, we propose an Atrous Pyramid Network, which implements convolution kernels adaptive sampling to assist the initial disparity map optimize, on the basis of more feature information and larger receptive field. Finally, we propose a collaborative supervised learning strategy. By using the collaborative supervised learning strategy during training stage, our GA-Stereo can not only calculate loss from the Ground Truth, but also calculate the loss from perceptual reconstruction loss and edge-loss smoothing loss to obtain better training results and more accurate predicted disparity map than other stereo networks. Our GA-Stereo achieve highly competitive results in matching accuracy and computational efficiency on stereo benchmarks, including SceneFlow, KITTI2012, and KITTI2015. This advancement represents a significant step forward in real-time stereo matching methods.

BackgroundExtensive-stage small-cell lung cancer (ES-SCLC) is highly malignant, is highly prone to recurrence, and has a short survival period. It is very difficult to achieve long-term survival in ES-SCLC, which has not been significantly improved in the last 20 years. For a long time, platinum-based chemotherapy has occupied the core position in the treatment of small-cell lung cancer (SCLC), but there are few options for treatment drugs or regimens, and if disease progression occurs, the options for follow-up regimens are obviously limited. The advent of immunotherapy has changed this situation to some extent, and immunotherapy has shown some effects in improving efficiency and prolonging survival, whether in first- or third-line therapy, but it is still unsatisfactory.Case presentationA 57-year-old patient with ES-SCLC experienced disease progression after four lines of treatment including synchronous radiotherapy, chemotherapy, and antiangiogenesis. However, the patient still benefited when switching to the programmed cell death receptor-1 (PD-1) inhibitor toripalimab in combination with chemotherapy in the fifth line. Even after the development of immune resistance, the patient still benefited after switching to tislelizumab in combination with different chemotherapy regimens or alone in the sixth and seventh lines. Following the progression of tislelizumab in combination with chemotherapy, the patient again profited after switching to durvalumab in combination with anlotinib and again achieved a progressive-free survival (PFS) of 11 months. Overall, the patient achieved a total of 45 months of PFS and 50 months of overall survival (OS), with a shocking and exciting 30 months of PFS achieved in the immune combination phase alone.ConclusionWe report a patient with ES-SCLC who achieved long-term survival after at least eight lines of therapy including chemotherapy, antiangiogenesis, and different immune checkpoint inhibitors (ICIs). This suggests that long-term survival in SCLC is possible with aggressive, combined, and standardized treatment. Otherwise, immunotherapy postline enablement can still benefit patients, rechallenge after immune resistance is also possible in SCLC, and combination with chemotherapy or antiangiogenic therapy can improve the efficacy and prolong the survival. This will provide new ideas and options for the selection of treatment options for SCLC.

BackgroundImmunotherapies represented by immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. A large part of the population has both cancer and psoriasis but is usually excluded from ICI clinical trials because of the dysregulated activation of the immune system. This is the first study to evaluate the safety and efficacy of ICI therapy in patients with cancer and preexisting psoriasis.MethodsPubMed, EMBASE, Cochrane, and MEDLINE databases were searched from inception through February 2022. Observational studies on patients with cancer and confirmed psoriasis before ICI initiation were included. Outcomes included the incidence of psoriasis flares, de novo immune-related adverse events (irAEs), discontinuation rate due to flare/de novo irAEs, and efficacy of ICI therapy. Clinical manifestations, management, and outcomes for adverse events (AEs) were systematically reviewed. All pooled analyses were based on a random-effects model using Stata software. Meta-regression and subgroup analyses were performed to identify sources of heterogeneity.ResultsTwelve studies involving 191 patients were included. The pooled incidence of psoriasis flares was 45.0% (95% CI: 31.1%-58.9%, I2 = 71.7%) and 44.9% (95% CI: 29.0%–60.7%, I2 = 71.8%) for de novo irAEs. The tumor type, psoriasis subtype, ICI class, and country were the main sources of heterogeneity. Grade 3–4 flares occurred in 10.8% (95% CI: 5.3%–16.3%) of patients, and about 16.6% (95% CI: 10.7%–22.5%) of patients experienced grade 3–4 de novo irAEs. The estimated incidence of ICI discontinuation due to AE was 18.5% (95% CI: 6.1%–30.8%, I2 = 68.7%). The median times to develop flare and de novo irAEs were 44 and 63 days, respectively. Endocrinopathies and colitis were the most common de novo irAEs. Conventional therapy is effective for most AEs. The estimated objective response rate (ORR) of ICIs was 38.1% (95% CI: 11.8%–64.3%, I2 = 81.7%), and the disease control rate (DCR) was 64.5% (95% CI: 55.3%–73.8%, I2 = 0).ConclusionsThe flare of patients with cancer and preexisting psoriasis treated with ICI therapy is frequent, but the incidence of de novo irAEs and the efficacy of ICI therapy are comparable to those of the general population. Most AEs are mild and manageable with conventional therapy, which required discontinuation of ICI therapy in 18.5%.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022320646

Mechanical wear failure is the main cause of device failure, and wear particles can be utilized to judge the degree and mechanism of mechanical wear. However, with the further study by researchers, the classes of wear particles are likely to increase. As the number of classes increases, it is necessary to reconstruct and retrain the previous automatic recognition model of wear particles. To solve this problem, a novel convolutional neural network (CNN) model based on class center vectors and distance comparison, called CDCNN, is proposed in this study. The model can not only accurately classify the particles that appear in the training set, but also identify the new classes that were not seen during training. This work can show its advantages when the classification criteria for wear particles have changed or new classes of particles have appeared. We applied the proposed model and the previous convolutional neural network model for the wear particle dataset, and compared the classification results of the models. The comparison results show that the model has strong classification ability and generalization ability.

How nanocrystals assemble into superlattices is poorly understood, given the difficulty of probing these processes in real time, in a controlled environment. Here, the authors use space- and time-resolved in situ small angle X-ray scattering to monitor the ordering of nanocrystal superlattices by electric field, which allows them to extract quantitative information about the assembly process.

Abstract Sepsis, a severe and life-threatening condition arising from a dysfunctional host response to infection, presents considerable challenges to the health care system and is characterized by high mortality rates and substantial economic costs. Exosomes have garnered attention as potential diagnostic markers because of their capacity to mirror the pathophysiological milieu of sepsis. This discourse reviews the progression of sepsis classification from Sepsis 1.0 to Sepsis 3.0, highlighting the imperative for sensitive and specific biomarkers to facilitate timely diagnosis and optimize patient outcomes. Existing biomarkers, such as procalcitonin (PCT) and C-reactive protein (CRP), exhibit certain limitations, thereby prompting the quest for more dependable diagnostic indicators. Exosomal cargoes, which encompass proteins and miRNAs, present a trove of biomarker candidates, attributable to their stability, pervasive presence, and indicative nature of the disease status. The potential of exosomal biomarkers in the identification of sepsis-induced organ damage, including cardiomyopathy, acute kidney injury, and acute lung injury, is emphasized, as they provide real-time insights into cardiac and renal impairments. Despite promising prospects, hurdles persist in the standardization of exosome extraction and the need for extensive clinical trials to validate their efficacy. The combination of biomarker development and sophisticated exosome detection techniques represents a pioneering strategy in the realm of sepsis diagnosis and management, underscoring the significance of further research and clinical validation. Graphical Abstract

The transition to renewable energy sources is critical for sustainable development, yet integrating these sources into existing power systems poses significant challenges. Energy Storage Systems (ESS) are essential in enhancing the reliability and efficiency of renewable energy systems. Despite growing research, a comprehensive scientometric analysis mapping development and trends in this field is lacking. This study addresses this gap by conducting a detailed scientometric analysis of power systems and new energy research from 2014 to 2023. The novelty of this study lies in its systematic use of advanced bibliometric tools to provide a thorough analysis of the research landscape. Utilizing 425 research articles from the Web of Science database, the study employs CiteSpace to visualize academic networks, identify research hotspots, and outline current trends. Specific methodologies include burst detection to identify significant shifts in research focus, centrality measurement to determine the influence of key studies, and heterogeneous network analysis to map the interconnectedness of various research themes. The analysis reveals extensive international collaborations, with China leading in publication volume (344 articles) and centrality (0.69), followed by the United States (29 articles, centrality 0.53). Significant contributions come from institutions like North China Electric Power University, China Electric Power Research Institute, and Tsinghua University. The findings underscore the importance of international cooperation and the need for broader geographical representation in this research field. This study provides valuable insights into the evolution and current state of power systems and renewable energy research, offering essential guidance for future research and development. The results highlight the critical role of ESS in the transition to renewable energy and suggest directions for future investigations, particularly focusing on enhancing ESS efficiency and reliability and expanding international research collaborations.

Exosome-derived microRNAs (miRNAs) are emerging as pivotal players in the pathophysiology of sepsis, representing a new frontier in both the diagnosis and treatment of this complex condition. Sepsis, a severe systemic response to infection, involves intricate immune and nonimmune mechanisms, where exosome-mediated communication can significantly influence disease progression and outcomes. During the progress of sepsis, the miRNA profile of exosomes undergoes notable alterations, is reflecting, and may affect the progression of the disease. This review comprehensively explores the biology of exosome-derived miRNAs, which originate from both immune cells (such as macrophages and dendritic cells) and nonimmune cells (such as endothelial and epithelial cells) and play a dynamic role in modulating pathways that affect the course of sepsis, including those related to inflammation, immune response, cell survival, and apoptosis. Taking into account these dynamic changes, we further discuss the potential of exosome-derived miRNAs as biomarkers for the early detection and prognosis of sepsis and advantages over traditional biomarkers due to their stability and specificity. Furthermore, this review evaluates exosome-based therapeutic miRNA delivery systems in sepsis, which may pave the way for targeted modulation of the septic response and personalized treatment options.

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.

Brain tumors have shown extreme mortality and increasing incidence during recent years, which bring enormous challenges for the timely diagnosis and effective treatment of brain tumors. Concretely, accurate brain tumor segmentation on multi-modal Magnetic Resonance Imaging (MRI) is essential and important since most normal tissues are unresectable in brain tumor surgery. In the past decade, with the explosive development of artificial intelligence technologies, a series of deep learning-based methods are presented for brain tumor segmentation and achieved excellent performance. Among them, vision transformers with non-local receptive fields show superior performance compared with the classical Convolutional Neural Networks (CNNs). In this review, we focus on the representative transformer-based works for brain tumor segmentation proposed in the last three years. Firstly, this review divides these transformer-based methods as the pure transformer methods and the hybrid transformer methods according to their transformer architectures. Then, we summarize the corresponding theoretical innovations, implementation schemes and superiorities to help readers better understand state-of-the-art transformer-based brain tumor segmentation methods. After that, we introduce the most commonly-used Brain Tumor Segmentation (BraTS) datasets, and comprehensively analyze and compare the performance of existing methods through multiple quantitative statistics. Finally, we discuss the current research challenges and describe the future research trends.

Polyurethanes (PUs) are a highly adaptable class of biomaterials that are among some of the most researched materials for various biomedical applications. However, engineered tissue scaffolds composed of PU have not found their way into clinical application, mainly due to the difficulty of balancing the control of material properties with the desired cellular response. A simple method for the synthesis of tunable bioactive poly(ethylene glycol) diacrylate (PEGDA) hydrogels containing photocurable PU is described. These hydrogels may be modified with PEGylated peptides or proteins to impart variable biological functions, and the mechanical properties of the hydrogels can be tuned based on the ratios of PU and PEGDA. Studies with human cells revealed that PU–PEG blended hydrogels support cell adhesion and viability when cell adhesion peptides are crosslinked within the hydrogel matrix. These hydrogels represent a unique and highly tailorable system for synthesizing PU-based synthetic extracellular matrices for tissue engineering applications.

Infertility is a global problem that affects many couples. Sperm analysis plays an essential role in the clinical diagnosis of human fertility. The examination of sperm morphology is an essential technique because sperm morphology is a proven indicator of biological functions. At present, the morphological classification of human sperm is conducted manually by medical experts. However, manual classification is laborious and highly dependent on the experience and capability of clinicians. To address these limitations, we propose a transfer learning method based on AlexNet to automatically classify the sperms into four different categories in terms of the World Health Organization (WHO) standards by analyzing their morphology. We adopt the feature extraction architecture of AlexNet as well as its pre-training parameters. Besides, we redesign the classification network by adding the Batch Normalization layers to improve the performance. The proposed method achieves an average accuracy of 96.0% and an average precision of 96.4% in the freely-available HuSHeM dataset, which exceeds the performance of previous algorithms. Our method shows that automatic sperm classification has great potential to replace manual sperm classification in the future.

Background As an important part of the general practice team, assistant general practitioners undertake a lot of grassroots general practice work, but at present, there is no recognized national level assistant general practitioners post competency model to provide scientific basis for training qualified assistant general practitioners. Objective The competency of assistant general practitioners for China was constructed to provide support for the design of national assistant physician qualification examination, and provide theoretical basis for the training and evaluation of assistant general practitioners. Methods Based on the research group's previous literature research and behavioral event interviews, from September 2019 to January 2020, the entry pool was established, and 30 experts engaged in general practice were consulted by Delphi method for 2 rounds. The index and weight coefficient of the Chinese assistant general practitioner post competency model were determined by AHP. Results Six first-level indicators [the basic medical and health services (0.244 8), medical knowledge and life-long learning (0.244 8), and professional quality (0.244 8), interpersonal communication and team work (0.138 9), the basic public health services (0.085 8), the information utilization ability and management capacity (0.041 0) ] and 63 second-level indicators were finalized, and the weight coefficients of each indicator were defined. Conclusion This study constructs the post competency of assistant general practitioners for China for the first time, which has a high degree of expert representativeness and authority, and a more comprehensive and reasonable index selection, laying a foundation for the empirical study of the index system.

Background and purpose: Chimeric antigen receptor T (CAR-T) cell therapy has shown remarkable efficacy in treating hematological and lymphatic system tumors, but its effectiveness in solid tumors is relatively poor, which is partly attributed to target selection. For Ewing sarcoma (ES), CD99 can be a potential target for CAR-T cells. However, due to T cells’ endogenous expression of CD99 protein, CAR-T cells targeting CD99 face limitations in their expansion capacity in vitro. This study aimed to identify the optimal conditions for preparing CD99 CAR-T cells by incorporating CD99 knockdown short hairpin RNA (shRNA), optimizing the multiplicity of infection (MOI) for lentiviral transduction, and screening for the best culture medium and container for CAR-T cell expansion. Methods: shRNA sequences were screened to enhance the expansion capacity of CD99 CAR-T cells. Different MOI, culture media, and containers were used to assess CAR-T cell transduction efficiency, cell viability, proliferation capacity, specific killing ability, and interferon-γ (IFN-γ) release levels under various conditions, in order to identify the optimal cell preparation conditions. Results: The expansion level of KO-CD99 CAR-T cells obtained through shRNA knockdown was significantly higher than that of CD99 CAR-T cells [(16.40±0.40) vs (6.33±1.53), P