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Abstract A highly efficient and multi-functional butterfly polarization conversion metasurface is proposed for the Ku-Ka frequency range, designed to reduce the radar cross-section. The suggested converter enables dual frequency bands linear-to-cross (LX) and linear-to-circular (LC) polarization transformations. The efficiency of cross-polarization conversion exceeds 90% over the frequency ranges of 14.57–16.30 GHz and 25.70–37.03 GHz, with relative bandwidths of 11% and 36%, respectively. Reflections of left-hand (LHCP) and right-hand circularly polarized (RHCP) waves are realized within the frequency ranges of 17.78–25.31 GHz and 37.38–37.73 GHz, with relative bandwidths of 35% and 0.9%. The oblique incidence significantly affects the metasurface conversion performance, and the proposed model manifests angular robustness up to 60°. Additionally, the butterfly metasurfaces are deployed in a triangular chessboard configuration to achieve radar cross section (RCS) reduction spanning the frequency bands of 14–15.17 GHz, 24.60–35.20 GHz, and 36.30–38 GHz. Drawing from simulation results and test-based validation data, the proposed converter holds promising applications in wireless communication, antenna engineering, and radar stealth.

The healing of large skin defects remains a significant challenge in clinical settings. The lack of epidermal sources, such as autologous skin grafting, limits full-thickness skin defect repair and leads to excessive scar formation. Skin organoids have the potential to generate a complete skin layer, supporting in-situ skin regeneration in the defect area. In this study, skin organoid spheres, created with human keratinocytes, fibroblasts, and endothelial cells, showed a specific structure with a stromal core surrounded by surface keratinocytes. We selected an appropriate bioink and innovatively combined an extrusion-based bioprinting technique with dual-photo source cross-linking technology to ensure the overall mechanical properties of the 3D bioprinted skin organoid. Moreover, the 3D bioprinted skin organoid was customized to match the size and shape of the wound site, facilitating convenient implantation. When applied to full-thickness skin defects in immunodeficient mice, the 3D bioprinted human-derived skin organoid significantly accelerated wound healing through in-situ regeneration, epithelialization, vascularization, and inhibition of excessive inflammation. The combination of skin organoid and 3D bioprinting technology can overcome the limitations of current skin substitutes, offering a novel treatment strategy to address large-area skin defects.

Abstract This study proposes and experimentally validates a multifunctional, ultra-wideband polarization conversion metasurface. The design integrates polarization conversion and electromagnetic scattering functions into a single structure, enabling applications in polarization conversion, beam control, and effective reduction of the radar cross-section (RCS). The metasurface achieves linear-to-circular polarization conversion with an axial ratio (AR) of less than 3 dB across dual-band ranges of 14.6–26.8 GHz and 31–33.5 GHz. Additionally, by adjusting metallic resonant rings within the unit structure, cross-polarization conversion with a polarization conversion ratio (PCR) greater than 0.9 is realized in the 13.6–29.8 GHz frequency range, maintaining excellent stability even at oblique incidence angles up to 50°. Leveraging the phase cancellation principle, various coding arrays are designed to precisely control the scattered beams, reducing the RCS by more than 10 dB. The comparison of simulation and experimental results further validates the wide application potential of this polarization converter in fields such as wireless communication, antenna engineering, and radar stealth.

Abstract Aims Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI‐powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. Methods and results We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co‐morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. Conclusions Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Abstract Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluate the performance of 7 analysis pipelines using seven simulated datasets, in terms of accuracy, identity, duplication rate, and computational resource consumption. We also compare the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Here, we show that Circle-Map and Circle_finder (bwa-mem-samblaster) outperform the other short-read pipelines. However, Circle_finder (bwa-mem-samblaster) exhibits notable redundancy in its outcomes. CReSIL is the most effective pipeline for eccDNA detection in long-read sequencing data at depths higher than 10X. Moreover, long-read sequencing-based Circle-Seq shows superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the suitable methods for eccDNA research.

BackgroundClinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code. ObjectiveThis study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. Currently, the process is complex, onerous, and time-consuming, whereas a real-time tool may be used to rapidly inform a guideline developer’s judgment about the applicability of evidence. MethodsThe National Institute for Health and Care Excellence—particularly the gout guideline development group—and the Scottish Intercollegiate Guidelines Network guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R Shiny (R Foundation for Statistical Computing) tool was designed and developed using electronic primary health care data linked with hospitalization and mortality data built upon an optimized data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality. The tool was deployed within a Trusted Research Environment, allowing only trusted preapproved researchers to conduct analysis. ResultsThe tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables 2 types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce an overview of statistical tables (eg, age, gender) of the index condition population and, within the overview groupings, produce details on, for example, electronic frailty index, comorbidities, and coprescriptions. The disclosure control mechanism is integral to the tool, limiting tabular counts to meet local governance needs. An exemplary result for gout as an index condition is presented to demonstrate the tool’s functionality. Guideline developers from the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network provided positive feedback on the tool. ConclusionsThe tool is a proof-of-concept, and the user feedback has demonstrated that this is a step toward computer-interpretable guideline development. Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time.

Environmental and economic factors have driven research into the recycling and applications of recycled carbon fibres (rCF). This paper presents a comparative study characterizing and comparing the mechanical and thermal performance of recycled short milled carbon fibre (rSMCF) on virgin and recycled polypropylene composites. The effects of rSMCF on VPP and RPP on mechanical performance were analysed and compared. At 5 wt% rSMCF, recycled polypropylene achieved 52.3% and 47.3% improvement on tensile and flexural modulus, while at the same rSMCF loading, virgin polypropylene only improved 37.7% and 17.5%, respectively. The un-notched impact strength of RPP-based composites reduced from 83.2 kJ/m2 to 60.1 kJ/m2 when rSMCF content increased from 1 wt% to 5 wt%, indicating future work should enhance the fibre/polymer interface performance. Crystal contents (χ0) of the PP/rSMCF composites were investigated by differential scanning calorimetry (DSC), and the results were analysed and mapped to the mechanical performance. The results of this study propose a novel scalable method for the production of high-performance VPP/RPP composite materials using rSMCF.

Abstract Early postoperative cerebral infarction (ePCI) is a serious complication of spontaneous intracerebral hemorrhage (SICH). Yet, no study has specifically focused on ePCI among SICH patients. Our study aims to investigate the characteristics, predictors, and outcomes of ePCI observed on computed tomography (CT) within 72 h after surgery in patients with supratentorial SICH. Data from a single-center SICH study conducted from May 2015 to September 2022 were retrospectively analyzed. We described the characteristics of ePCI. Predictors were identified through logistic regression analysis, and the impact of ePCI on six-month mortality was examined using a Cox regression model. Subgroup analyses and the “E-value” approach assessed the robustness of the association between ePCI and mortality. A retrospective analysis of 637 out of 3938 SICH patients found that 71 cases (11.1%) developed ePCI. The majority of ePCI cases occurred on the bleeding side (40/71, 56.3%) and affected the middle cerebral artery (MCA) territory (45/71, 63.4%). Multivariable analysis showed that the Glasgow Coma Scale (GCS) score (odds ratio (OR), 0.62; 95% CI, 0.48–0.8; p

Introduction Trace mineral micronutrients are essential for periodontal health, and unbalanced levels of trace minerals are related to chronic periodontitis, including copper. We attempted to explore the relationship between cuproptosis and periodontitis. Material and methods Bulk RNA-seq and single-cell RNA sequencing data were used to analyze expression of cuproptosis-related genes (CRGs) in periodontitis. A mouse periodontitis model was established to verify the expression of key genes. Results Cuproptosis-related genes were differentially expressed in periodontitis. RT-PCR results confirmed the alteration of five key genes in the mouse model. Conclusions Cuproptosis is an important player in the pathophysiological process of periodontitis.

With the rapid development of artificial intelligence, deep learning is considered a promising technique for intelligent fault diagnosis using large amounts of data in various industrial fields. Under such circumstances, imbalanced datasets in the real world will not only hinder the further development of classification models, but also degrade the performance of existing models. To overcome this limitation, this paper proposes a novel Mel-Frequency Cepstral Coefficent-based Generative Adversarial Network (MFCC-GAN) to augment the high-quality small class data. Specifically, the MFCC is first used to capture the time- and frequency-domain features of the real signals as a priori information, which is then fed into the generative model. The temporal structural features and energy features contained in a prior information can provide effective guidance for the process of the generative model to map the Gaussian distribution to the real-world distribution. Moreover, a contrastive loss is introduced to refine the discriminative features of the generated signals, aiming to improve the distinguishability among different health states. Experimental results show that the MFCC-GAN algorithm improves the quality and fidelity of the generated data compared to other state-of-the-art algorithms.

Outsourcing storage to the cloud can save storage costs and is commonly used in businesses. It should fulfill two major goals: storage efficiency and data confidentiality. Encrypted deduplication can achieve both goals via performing deduplication to eliminate the duplicate data within encrypted data. Traditional encrypted deduplication generates the encryption key on the client side, which poses a risk of offline brute-force cracking of the outsourced data. Server-aided encryption schemes have been proposed to strengthen the confidentiality of encrypted deduplication by distributing the encryption process to dedicated servers. Existing schemes rely on expensive cryptographic primitives to provide a decentralized setting on the dedicated servers for scalability. However, this incurs substantial performance slowdown and can not be applied in practical encrypted deduplication storage systems. In this paper, we propose a new decentralized server-aided encrypted deduplication approach for outsourced storage, called ECDedup, which leverages secret sharing to achieve secure and efficient key management. We are the first to use the coding matrix as the encryption key to couple the encryption and encoding processes in encrypted deduplication. We also propose a acceleration scheme to speed up the encryption process of our ECDedup. We prototype ECDedup in cloud environments, and our experimental results based on the real-world backup datasets show that ECDedup can improve the client throughput by up to 51.9% compared to the state-of-the-art encrypted deduplication schemes.

The ultrasonic vibration laser cladding method is a material-saving and green method to fabricate super abrasive structured grinding wheels. However, the mechanism of the ultrasonic vibration’s effect on the movement behavior of abrasive grains in the laser cladding process has not been clarified. To address this, the impacts of ultrasonic vibration on the abrasive grain distribution and movement behavior were experimentally studied, and the numerical simulation method was introduced to simulate the ultrasonic vibration laser cladding process. A two-dimensional Gaussian cross-section heat source model was developed, and its energy density conformed to a Gaussian distribution in both space and time. The simulations of the temperature and fluid fields of the melt pool were carried out. The CBN abrasive grains in the melt pool were subjected to gravity, the buoyancy force, the drag force of the metal fluid, and the sonophoretic radiation force of the ultrasonic vibration. Based on them, the effects of ultrasonic vibration on the movement behaviors and trajectories of the CBN abrasive grains were analyzed. The influence of the ultrasonic amplitude on the distribution of abrasive grains was studied. The simulation results revealed that the abrasive grains could be uniformly distributed on the surface of the cladding layer during the ultrasonic vibration laser cladding process.

OA08.02 X. Chen (1); T. Zhou (1); S. Schmidt (1); H. Duan (1); C. Cheng (1); G.-Y. Chuang (1); Y. Gu (1); M. Louder (1); B. Lin (1); C.-H. Shen [...], Background: The vaccine elicitation of broadly neutralizing antibodies (bnAbs) is a key HIV-research goal. The VRC01-class of bnAbs targets the CD4-binding site on the HIV-envelope trimer and requires extensive somatic hypermutation to neutralize effectively. Despite substantial progress in developing germline-engaging immunogens to activate VRC01-class bnAb precursors and using transgenic mouse models for eliciting VRC01-class antibodies, vaccine-induced VRC01-class antibodies starting from unmutated precursors have exhibited limited neutralization breadth, particularly against viruses bearing glycan at residue N276 (glycan276), which is present on most circulating strains. Methods: Here, using an immunoglobulin (Ig)-humanized mouse model in which the VRC01-class germline VH gene recombines with diverse mouse D and J.sub.H] gene segments and pairs with an unmutated rearranged light chain to create a diverse repertoire of unmutated VRC01-class precursors, we compared a "multi-strain, heterologous boost" sequential immunization strategy, which presents the conserved CD4bs in different gp120 cores or trimers for better immune focusing on the CD4bs, with previously tested strategies based on germline-binding 426c cores with different levels of glycan shielding of the CD4bs (Tian et al, 2016) or repeated eOD-GT8 60mer priming followed by diverse Env boosts. Results: With both the new strategy and the 426c-core strategy, we elicited cross-clade neutralizing serum titers against a sentinel pane of eight HIV-1 strains, including five bearing glycan276. We further identified multiple lineages of VRC01-class bnAbs from the immunized animals, including two neutralizing >50% of a 208-strain panel, and carried out mutagenesis and crystal structure analyses, revealing key sites of SHM and mechanisms for surmounting glycan276 to achieve neutralization breadth. Conclusions: Overall, our study provides proof-of-concept for the induction of VRC01-class bnAbs of greater than 50%-neutralization breadth by sequential immunization, succeeds in eliciting antibodies capable of recognizing glycan276-bearing strains, and uses longitudina analysis to pinpoint the development of specific SHM in response to specific immunogens.

The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analysis on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation has unveiled that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. We have also identified similar differentiation trajectories of NK cells originating from proliferative NK cells across four stages. Notably, during early pregnancy, NK cells demonstrate the highest oxidative phosphorylation metabolism activity, and, in conjunction with macrophages and T cells, exhibit the strongest type II interferon response. With spatial transcriptome data, we have discerned that the most robust immune-non-immune interactions are associated with the promotion and inhibition of cell proliferation, differentiation and migration during four stages. Furthermore, we have compiled lists of stage-specific risk genes implicated in reproductive diseases, which hold promise as potential disease biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.

Abstract This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone.

Abstract Background Despite its prevalence, there is ongoing debate regarding the optimal management strategy for chronic subdural hematoma (CSDH), reflecting the variability in clinical presentation and treatment outcomes. This ambidirectional, nationwide, multicenter registry study aims to assess the efficacy and safety of multimodality treatment approaches for CSDH in the Chinese population. Methods/design A multicenter cohort of CSDH patients from 59 participating hospitals in mainland China was enrolled in this study. The treatment modalities encompassed a range of options and baseline demographics, clinical characteristics, radiographic findings, and surgical techniques were documented. Clinical outcomes, including hematoma resolution, recurrence rates, neurological status, and complications, were assessed at regular intervals during treatment, 3 months, 6 months, 1 year, and 2 years follow-up. Result Between March 2022 and August 2023, a comprehensive cohort comprising 2173 individuals who met the criterion was assembled across 59 participating clinical sites. Of those patients, 81.1% were male, exhibiting an average age of 70.12 ± 14.53 years. A historical record of trauma was documented in 48.0% of cases, while headache constituted the predominant clinical presentation in 58.1% of patients. The foremost surgical modality employed was the burr hole (61.3%), with conservative management accounting for 25.6% of cases. Notably, a favorable clinical prognosis was observed in 88.9% of CSDH patients at 3 months, and the recurrence rate was found to be 2.4%. Conclusion This registry study provides critical insights into the multimodality treatment of CSDH in China, offering a foundation for advancing clinical practices, optimizing patient management, and ultimately, improving the quality of life for individuals suffering from this challenging neurosurgical condition. Trial registration ChiCTR2200057179

To enhance the precision of wind power forecasting and the integration of renewable energy, a wind power prediction model, synthesising recursive Bayesian filtering state estimation with time-series data mining, was developed. Initially, the Autoregressive Integrated Moving Average Model (ARIMA)-Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model was utilised for mining historical wind power data and establishing a model. Subsequently, the double-parameter t-distribution was employed to fit the prior estimation error and observation error, which integrated observational information with prior estimates through a sophisticated recursive Bayesian filtering approach, culminating in the formulation of a robust predictive model. Validation of this model was conducted using a diverse dataset, encompassing wind farms with varying capacities and distinct time intervals. Simulation outcomes reveal that this model's forecasting accuracy markedly surpasses that of conventional methodologies. Notably, an enhanced predictive precision is observed in wind farms with larger capacities, particularly when shorter intervals of observational data are employed. This model demonstrates significant potential for advancing the accuracy and efficiency of wind power forecasting, a critical element in the optimization of renewable energy utilization.

The purpose of this study was to investigate the physiological and biochemical changes of the hepatopancreas and intestinal microbial structure of Penaeus vannamei under various levels of carbonate alkalinity stress. After Penaeus vannamei (body length 14.24 ± 2.13 cm, body weight 26.31 ± 3.26 g) was subjected to 96 h carbonate alkalinity stress, the alkalinity stress levels were E8 (8 mmol/L), E18 (18 mmol/L) and E28 (28 mmol/L), respectively. The activity of antioxidant enzymes was determined by enzyme markers, and then the intestinal microorganisms of Penaeus vannamei were analyzed by high-throughput sequencing technology. The results showed that, under the stress of high carbonate alkalinity, the mortality rate of Penaeus vannamei was as high as 75%, and hepatopancreas cells showed obvious deformation, abnormal nuclear shapes, and serious cell vacuolation. Under high carbonate alkalinity stress, superoxide dismutase activity, catalase activity and glutathione peroxidase activity in the Penaeus vannamei hepatopancreas were significantly lower than those in control group (p < 0.05), and malondialdehyde content was significantly lower than that in the control group (p < 0.05). Alkaline phosphatase activity in the experimental group was significantly different from that in the control group (p < 0.05). Moreover, the 16SrDNA high-throughput sequencing results showed that the intestinal abundance of Proteobacteria in Penaeus vannamei was significantly decreased (p < 0.05) under high carbonate alkalinity stress, and the abundance of Bacteroides was significantly increased (p < 0.05). At the genus level, the abundance of Chrysobacteria was significantly increased (p < 0.05). The functional prediction results of COG and KEGG showed that the functional abundance of RNA polymerase sigma-70 factor is direct bacterial or plastid core RNA polymerase and is specific to promoter elements that are situated 10 and 35 base-pairs upstream of transcription-initiation points—in the high carbonate alkalinity treatment group, this was higher than that in the control group. The functional abundance of signal transduction histidine kinase was lower than that of the control group. The results of this study not only indicated that Penaeus vannamei cell structure would change and mortality would increase under high carbonate alkalinity culture environment, but they also analyzed the changes of the intestinal microbial structure under carbonate alkalinity stress. This study could provide theoretical reference for Penaeus vannamei saline–alkali land culture.

Spheroids, as three-dimensional (3D) cell aggregates, can be prepared using various methods, including hanging drops, microwells, microfluidics, magnetic manipulation, and bioreactors. However, current spheroid manufacturing techniques face challenges such as complex workflows, the need for specialized personnel, and poor batch reproducibility. In this study, we designed a support-free, 3D-printed microwell chip and developed a compatible low-cell-adhesion process. Through simulation and experimental validation, we rapidly optimized microwell size and the coating process. We successfully formed three types of spheroids—human immortalized epidermal cells (HaCaTs), umbilical cord mesenchymal stem cells (UC-MSCs), and human osteosarcoma cells (MG63s)—on the chip. Fluorescent viability staining confirmed the biocompatibility and reliability of the chip. Finally, drug response experiments were conducted using the chip. Compared to traditional methods, our proposed strategy enables high-throughput production of size-controlled spheroids with excellent shape retention, while enhanced gas exchange during culture improves differentiation marker expression. This platform provides an efficient and cost-effective solution for biosensing applications, such as drug screening, disease modeling, and personalized therapy monitoring. Furthermore, the chip shows significant potential for real-time in vitro monitoring of cellular viability, reaction kinetics, and drug sensitivity, offering valuable advancements in biosensor technology for life sciences and medical applications.

Brain tumors are abnormal cell masses that can get originated in the brain spread from other organs. They can be categorized as either malignant (cancerous) or benign (noncancerous), and their growth rates and locations can impact the functioning of the nerve system. The timely detection of brain tumors is crucial for effective treatment and prognosis. In this study, a new approach has been proposed for diagnosing brain tumors using deep learning and a meta-heuristic algorithm. The method involves three main steps: (1) extracting features from brain MRI images using AlexNet, (2) reducing the complexity of AlexNet by employing an Extreme Learning Machine (ELM) network as a classification layer, and (3) fine-tuning the parameters of the ELM network using an Amended Grasshopper Optimization Algorithm (AGOA). The performance of the method has been evaluated on a publicly available dataset consisting of 20 patients with newly diagnosed glioblastoma that is compared with several state-of-the-art techniques. Experimental results demonstrate that the method achieves the highest accuracy, precision, specificity, F1-score, sensitivity, and MCC with values of 0.96, 0.94, 0.96, 0.96, 0.94, and 0.90, respectively. Furthermore, the robustness and stability of the method have been illustrated when subjected to different levels of noise and image resolutions. The proposed approach offers a rapid, accurate, and dependable diagnosis of brain tumors and holds potential for application in other medical image analysis tasks.

Major depressive disorder, schizophrenia, and bipolar disorder are three major psychiatric disorders that significantly impact the well-being and overall health of patients. Some researches indicate that abnormalities in the gut microbiota can trigger certain psychiatric diseases. Microbiota-derived extracellular vesicles have the ability to transfer bioactive compounds into host cells, altering signaling and biological processes, ultimately influencing the mental health and illness of the host. This review aims to investigate the emerging roles of microbiota-derived extracellular vesicles in these three major psychiatric disorders and discusses their roles as diagnostic biomarkers and therapies for these psychiatric disorders.

Background: Birth asphyxia causes hypoxia or inadequate perfusion to the organs of newborns, leading to metabolism dysfunctions including blood glucose disorders. Methods: Neonates with and without birth asphyxia were retrospectively recruited from 53 hospitals in Hubei Province from January 1 to December 31, 2018. In summary, 875, 1139, and 180 cases in the control group, the mild asphyxia group, and the severe asphyxia group were recruited, respectively. Neonatal blood glucose values at postnatal 1, 2, 6, and 12 h (time error within 0.5 h was allowed) were gathered from the medical records. Results: The incidence rates of hyperglycemia in the control group, the mild asphyxia group and the severe asphyxia group were 2.97%, 7.90%, and 23.33%, respectively (p

Growth differentiation among farmed sea urchins (Strongylocentrotus intermedius) poses a significant challenge to aquaculture, with there being a limited understanding of the underlying molecular mechanisms. In this study, sea urchins with varying growth rates, reared under identical conditions, were analyzed for feeding behavior, gut microbiota, and transcriptomes. Large-sized sea urchins demonstrated significantly higher feeding ability and longer duration than smaller ones. The dominant phyla across all size groups were Campylobacterota, Proteobacteria, and Firmicutes, with Campylobacterota showing the highest abundance in small-sized sea urchins (82.6%). However, the families Lachnospiraceae and Pseudomonadaceae were significantly less prevalent in small-sized sea urchins. Transcriptome analysis identified 214, 544, and 732 differentially expressed genes (DEGs) in the large vs. medium, large vs. small, and medium vs. small comparisons, respectively. Gene Ontology and KEGG pathway analyses associated DEGs with key processes such as steroid biosynthesis, protein processing within the endoplasmic reticulum, and nucleotide sugar metabolism. Variations in phagosomes and signaling pathways indicated that size differences are linked to disparities in energy expenditure and stress responses. These findings provide a foundation for future investigations into the regulatory mechanisms underlying growth differences in S. intermedius and provide clues for the screening of molecular markers useful to improve sea urchin production.

Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model’s feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an mAP@0.5 of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the mAP@0.5 increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis.

Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the upward spraying mode and intelligent nozzle have gradually become the research hotspot of precision agriculture. This paper designs a leaf-bottom pest control robot with adaptive chassis and adjustable selective nozzle. Firstly, the adaptive chassis is designed based on the MacPherson suspension, which uses shock absorption to drive the track to swing within a 30° angle. Secondly, a new type of cone angle adjustable selective nozzle was developed, which achieves adaptive selective precision spraying under visual guidance. Then, based on a convolutional block attention module (CBAM), the multi-CBAM-YOLOv5s network model was improved to achieve a 70% recognition rate of leaf-bottom spotted bad point in video streams. Finally, functional tests of the adaptive chassis and the adjustable selective spraying system were conducted. The data indicate that the adaptive chassis can adapt to diverse single-ridge requirements of soybeans and corn while protecting the ridge slopes. The selective spraying system achieves 70% precision in pesticide application, greatly reducing the use of pesticides. The scheme explores a ridge-friendly leaf-bottom pest control plan, providing a technical reference for improving spraying effect, reducing pesticide usage, and mitigating environmental pollution.

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Abstract Background Bone or brain metastases may develop in 20–40% of individuals with late‐stage non‐small‐cell lung cancer (NSCLC), resulting in a median overall survival of only 4–6 months. However, the primary lung cancer tissue's distinctions between bone, brain and intrapulmonary metastases of NSCLC at the single‐cell level have not been underexplored. Methods We conducted a comprehensive analysis of 14 tissue biopsy samples obtained from treatment‐naïve advanced NSCLC patients with bone (n = 4), brain (n = 6) or intrapulmonary (n = 4) metastasis using single‐cell sequencing originating from the lungs. Following quality control and the removal of doublets, a total of 80 084 cells were successfully captured. Results The most significant inter‐group differences were observed in the fraction and function of fibroblasts. We identified three distinct cancer‐associated fibroblast (CAF) subpopulations: myofibroblastic CAF (myCAF), inflammatory CAF (iCAF) and antigen‐presenting CAF (apCAF). Notably, apCAF was prevalent in NSCLC with bone metastasis, while iCAF dominated in NSCLC with brain metastasis. Intercellular signalling network analysis revealed that apCAF may play a role in bone metastasis by activating signalling pathways associated with cancer stemness, such as SPP1‐CD44 and SPP1‐PTGER4. Conversely, iCAF was found to promote brain metastasis by activating invasion and metastasis‐related molecules, such as MET hepatocyte growth factor. Furthermore, the interaction between CAFs and tumour cells influenced T‐cell exhaustion and signalling pathways within the tumour microenvironment. Conclusions This study unveils the direct interplay between tumour cells and CAFs in NSCLC with bone or brain metastasis and identifies potential therapeutic targets for inhibiting metastasis by disrupting these critical cell–cell interactions.

Abstract The persistent dysregulation and accumulation of poisonous proteins from destructive neural tissues and cells activate pathological mechanisms after traumatic brain injury (TBI). The lymphatic drainage system of the brain, composed of the glymphatic system and meningeal lymphatic vessels (MLVs), plays an essential role in the clearance of toxic waste after brain injury. The neuroprotective effect of interleukin 33 (IL-33) in TBI mice has been demonstrated; however, its impact on brain lymphatic drainage is unclear. Here, we established a fluid percussion injury model to examine the IL-33 administration effects on neurological function and lymphatic drainage in the acute brain of TBI mice. We verified that exogenous IL-33 could improve the motor and memory skills of TBI mice and demonstrated that in the acute phase, it increased the exchange of cerebrospinal and interstitial fluid, reversed the dysregulation and depolarization of aquaporin-4 in the cortex and hippocampus, improved the drainage of MLVs to deep cervical lymph nodes, and reduced tau accumulation and glial activation. We speculate that the protective effect of exogenous IL-33 on TBI mice’s motor and cognitive functions is related to the enhancement of brain lymphatic drainage and toxic metabolite clearance from the cortex and hippocampus in the acute stage. These data further support the notion that IL-33 therapy may be an effective treatment strategy for alleviating acute brain injury after TBI.

A new method with an improved deep residual shrinkage network is proposed to address the problems of subtle differences in spot characteristics among different rice diseases and low recognition rate under noise interference. First, to reduce the number of network parameters as well as arithmetic cost and increase the nonlinearity of the model, the InceptionA module is embedded in the original network, and the convolutional kernels in the original residual structure are replaced by multiple small-sized convolutional kernels. Second, in order to strengthen the spot features, Convolutional Block Attention Module (CBAM) lightweight attention mechanism is introduced to achieve more effective information extraction. Exponential Linear Units (ELU) and Focal loss function are introduced to jointly guide the model training during the network training process, and 10-fold cross-validation method is used. The proposed InceptionA and CBAM-based DRSN (ICDRSN) obtains 98.89% mean average precision, 98.65% accuracy and 98.68% recall for three rice leaf disease data. Among them, the recognition accuracy is improved by 2.6%, 3.34%, 1.86%, and 2.23% compared with the Densenet, Shufflenet, Mobilenet, and Resnet models, respectively. These results verify that the ICDRSN model is stable, reliable, accurate, fast, and has satisfactory generalization ability.

Skin lesions not only disrupt appearance and barrier functionality but also lead to severe microbial infections and immune-inflammatory responses, seriously affect physical and mental health. In situ printing involves the direct deposition of bio-ink to create or repair damaged tissues or organs within a clinical setting. In this study, we designed and fabricated a novel portable in situ printer. This handheld instrument exhibits excellent printing performance, allowing hydrogels to be patterned and molded on surfaces according to specific requirements. By utilizing a dual-component hydrogels co-printing approach with high and low viscosities, we achieved in situ cell-laden printing using low-viscosity hydrogel. This demonstrates the advantages of the device in maintaining cell viability and achieving hydrogel structuring. This approach opens up the possibilities for the efficient encapsulation of active components such as drugs, proteins, and cells, enabling controlled macro- and micro-structuring of hydrogels. This breakthrough finding highlights the potential of our technical approach in dermatological treatment and wound repair, by dynamically adapting and regulating microenvironments in conjunction with hydrogel scaffolds and cell reparative impetus.

The effects of different processing methods on the nutritional components of sea cucumber (Apostichopus japonicus) are of concern to consumers who select sea cucumber products. This study employed liquid chromatography tandem mass spectrometry to examine the metabolites in fresh, unsoaked salted, soaked salted, and instant sea cucumber body wall samples sourced from Dalian, China. Metabolites were evaluated utilizing partial least squares discriminant analysis (PLS-DA) and subsequently subjected to KEGG metabolic pathway analysis for further investigation. PLS-DA effectively discriminated the body wall metabolites of sea cucumbers obtained via various processing techniques. The differential metabolites identified predominantly encompassed amino acids, lipids, and carbohydrates. Subsequent KEGG metabolic pathway analysis demonstrated a significant association between lipid, carbohydrate, and amino acid metabolism and the specific processing methods employed. The assessment of nutritional differences corresponding to the various A. japonicus processing methods was conducted. The findings of this study can assist in the choice of sea cucumber products and the selection of suitable processing methods.

Summary: High-grade serous ovarian cancer (HGSOC) is a hormone-related cancer with high mortality and poor prognosis. Based on the transcriptome of 57,444 cells in ascites from 10 patients with HGSOC (including 5 pre-menopausal and 5 post-menopausal patients), we identified 14 cell clusters which were further classified into 6 cell types, including T cells, B cells, NK cells, myeloid cells, epithelial cells, and stromal cells. We discovered an increased proportion of epithelial cells and a decreased proportion of T cells in pre-menopausal ascites compared with post-menopausal ascites. GO analysis revealed the pre-menopausal tumor microenvironments (TME) are closely associated with viral infection, while the post-menopausal TME are mostly related to the IL-17 immune pathway. SPP1/CD44-mediated crosstalk between myeloid cells and B cells, NK cells, and stromal cells mainly present in the pre-menopausal group, while SPP1/PTGER4 -mediated crosstalk between myeloid cells and epithelial cells mostly present in the post-menopausal group.

Summary: Primary Sjögren’s syndrome (pSS) is a complex autoimmune disease characterized by lymphocytic infiltration and exocrine dysfunction, particularly affecting the salivary gland (SG). We employed single-cell RNA sequencing to investigate cellular heterogeneity in 11 patients with pSS and 5 non-SS controls. Notably, patients with pSS exhibited downregulated SOX9 in myoepithelial cells, potentially associated with impaired epithelial regeneration. An expanded ACKR1+ endothelial subpopulation in patients with pSS suggested a role in facilitating lymphocyte transendothelial migration. Our analysis of immune cells revealed expanded IGHD+ naive B cells in peripheral blood from patients with pSS. Pseudotime trajectory analysis outlined a bifurcated differentiation pathway for peripheral B cells, enriching three subtypes (VPREB3+ B, BANK1+ B, CD83+ B cells) within SGs in patients with pSS. Fibroblasts emerged as pivotal components in a stromal-immune interaction network, potentially driving extracellular matrix disruption, epithelial regeneration impairment, and inflammation. Our study illuminates immune and stromal cell heterogeneity in patients with pSS, offering insights into therapeutic strategies.

Super-precision bearing lubrication condition is essential for equipment’s overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status.

The dermal–epidermal junction (DEJ), located between the dermal–epidermal layers in human skin tissue, plays a significant role in its function. However, the limitations of biomaterial properties and microstructure fabrication methods mean that most current tissue engineered skin models do not consider the existence of DEJ. In this study, a nanofiber membrane that simulates the fluctuating structure of skin DEJ was prepared by the composite molding process. Electrospinning is a technique for the production of nanofibers, which can customize the physical and biological properties of biomaterials. At present, electrospinning technology is widely used in the simulation of customized natural skin DEJ. In this study, four different concentration ratios of poly (lactic-co-glycolic acid) (PLGA) and polycaprolactone (PCL) nanofiber membranes were prepared based on electrospinning technology. We selected a 15%PLGA + 5%PCL nanofiber membrane with mechanical properties, dimensional stability, hydrophilicity, and biocompatibility after physical properties and biological characterization. Then, the array-based microstructure model was prepared by three-dimensional (3D) printing. Subsequently, the microstructure was created on a 15%PLGA + 5%PCL membrane by the micro-imprinting process. Finally, the cell proliferation and live/dead tests of keratinocytes (HaCaTs) and fibroblasts (HSFs) were measured on the microstructural membrane and flat membrane. The results showed that 15%PLGA + 5%PCL microstructure membrane was more beneficial to promote the adhesion and proliferation of HaCaTs and HSFs than a flat membrane.

Background: Idiopathic pulmonary fibrosis (IPF) is a fatal and chronic interstitial lung disease. Intricate pathogenesis of pulmonary fibrosis and only two approved medications with side effects and high cost bring us the challenge of fully understanding this lethal disease and urgency to find more safe and low-cost therapeutic alternatives. Purpose: Demethyleneberberine (DMB) has been demonstrated to have various anti-inflammatory, antioxidant, antifibrosis and anti-cancer bioactivities. The objective of this study was to evaluate the effect of DMB on pulmonary fibrosis and investigate the mechanism. Methods: Bleomycin (BLM)-induced pulmonary fibrosis was established in mice to evaluate the antifibrotic effect of DMB in vivo. A549 and MRC5 cells were used to evaluate the effect of DMB on epithelial–mesenchymal transition (EMT) and fibroblast–myofibroblast transition (FMT) in vitro. High throughput sequencing, biotin–avidin system and site-directed mutagenesis were applied to explore the mechanism of DMB in alleviating pulmonary fibrosis. Results: DMB alleviated BLM-induced pulmonary fibrosis in vivo by improving the survival state of mice, significantly reducing pulmonary collagen deposition and oxidative stress and improving lung tissue morphology. Meanwhile, DMB was demonstrated to inhibit epithelial–mesenchymal transition (EMT) and fibroblast–myofibroblast transition (FMT) in vitro. High throughput sequencing analysis indicated that GREM1, a highly upregulated profibrotic mediator in IPF and BLM-induced pulmonary fibrosis, was significantly downregulated by DMB. Furthermore, USP11 was revealed to be involved in the deubiquitination of GREM1 in this study and DMB promoted the ubiquitination and degradation of GREM1 by inhibiting USP11. Remarkably, DMB was demonstrated to selectively bind to the Met776 residue of USP11, leading to disruption of USP11 deubiquitinating GREM1. In addition, DMB presented an equivalent antifibrotic effect at a lower dose compared with pirfenidone and showed no obvious toxicity or side effects. Conclusions: This study revealed that USP11/GREM1 could be a potential target for IPF management and identified that DMB could promote GREM1 degradation by inhibiting USP11, thereby alleviating pulmonary fibrosis.

Black point disease caused by Bipolaris sorokiniana is a problem in wheat production worldwide. We aimed to identify major quantitative trait loci (QTL) for resistance to black point and develop molecular markers for marker-assisted selection (MAS). A recombinant inbred line (RIL) population derived from a cross between Wanyuanbai 1 (susceptible) and SN4143 (resistant) was evaluated for black point response at three locations during two years under artificial inoculation with B. sorokiniana, providing data for six environments. Thirty resistant and 30 susceptible RILs were selected to form resistant and susceptible bulks, respectively, that were genotyped by the wheat 660K SNP array; 685 single-nucleotide polymorphisms (SNPs) were identified, among which 385 (56.2%) and 115 (16.8%) were located on chromosomes 4A and 2B, respectively. Bulked segregant RNA-Seq analysis identified candidate regions on chromosomes 4A (4.60–40.28 Mb) and 5A (1.22–48.47 Mb). Genetic linkage maps were constructed for chromosomes 2B, 4A, and 5A using 59 polymorphic dCAPS and SSR markers. Finally, two QTL, designated QBB.hau-4A and QBB.hau-5A, were detected on chromosomes 4A and 5A, respectively. The resistance allele of QBB.hau-4A was derived from SN4143, and that of QBB.hau-5A came from Wanyuanbai 1. QBB.hau-4A with a large and consistent effect (15.1%) is likely to be a new locus for black point resistance. The markers linked to QBB.hau-4A and QBB.hau-5A have potential application in MAS-based breeding.

Abstract Background Natural killer (NK) cells are innate lymphoid cells that mediate antitumour and antiviral responses. However, very little is known about how ageing influences human NK cells, especially at the single-cell level. Methods We applied single-cell sequencing (scRNA-seq) to human lymphocytes and NK cells from 4 young and 4 elderly individuals and then analysed the transcriptome data using Seurat. We detected the proportion and phenotype of NK cell subsets in peripheral blood samples from a total of 62 young and 52 elderly healthy donors by flow cytometry. We also used flow cytometry to examine the effector functions of NK cell subsets upon IFN-α/IL-12+IL-15/K562/IL-2 stimulation in vitro in peripheral blood samples from a total of 64 young and 63 elderly healthy donors. We finally studied and integrated single-cell transcriptomes of NK cells from 15 young and 41 elderly COVID-19 patients with those from 12 young and 6 elderly healthy control individuals to investigate the impacts of ageing on NK cell subsets in COVID-19 disease. Results We discovered a memory-like NK subpopulation (NK2) exhibiting the largest distribution change between elderly and young individuals among lymphocytes. Notably, we discovered a unique NK subset that was predominantly CD52+ NK2 cells (NK2.1). These memory-like NK2.1 cells accumulated with age, exhibited proinflammatory characteristics, and displayed a type I interferon response state. Integrative analyses of a large-cohort COVID-19 dataset and our datasets revealed that NK2.1 cells from elderly COVID-19 patients are enriched for type I interferon signalling, which is positively correlated with disease severity in COVID-19. Conclusions We identified a unique memory-like NK cell subset that accumulates with ageing and correlates with disease severity in COVID-19. Our results identify memory-like NK2.1 cells as a potential target for developing immunotherapies for infectious diseases and for addressing age-related dysfunctions of the immune system.