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Abstract Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages.

Abstract Prevention of subsequent fracture is a major public health challenge in the field of osteoporosis prevention and treatment, and older women are at high risk for osteoporotic fractures. This study aimed to examine factors associated with subsequent fracture in older Chinese women with osteoporosis. We collected data on 9212 older female patients with osteoporotic fractures from 580 medical institutions in 31 provinces of China. Higher odds of subsequent fractures were associated with age of 70–79 years (OR 1.218, 95% CI 1.049–1.414), age ≥ 80 (OR 1.455, 95% CI 1.222–1.732), index fracture site was vertebrae (OR 1.472, 95% CI 1.194–1.815) and hip (OR 1.286, 95% CI 1.041–1.590), index fracture caused by fall (OR 1.822, 95% CI 1.281–2.591), strain (OR 1.587, 95% CI 1.178–2.139), no inducement (OR 1.541, 95% CI 1.043–2.277), and assessed as high risk of fracture (OR 1.865, 95% CI 1.439–2.416), BMD T-score ≤ −2.5 (OR 1.725, 95% CI 1.440–2.067), history of surgery (OR 3.941, 95% CI 3.475–4.471) and trauma (OR 8.075, 95% CI 6.941–9.395). Low risk of fall (OR 0.681, 95% CI 0.513–0.904), use of anti-osteoporosis medication (AOM, OR 0.801, 95% CI 0.693–0.926), and women who had received fall prevention health education (OR 0.583, 95% CI 0.465–0.730) associated with lower risk. The areas under the curve of the prediction model was 0.818. The sensitivity was 67.0% and the specificity was 82.0%. The prediction model showed a good ability to predict the risk of subsequent fracture in older women with osteoporotic fractures and are suitable for early self-measurement which may benefit post-fracture management.

Abstract The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.

Abstract There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate–poor performance status. A convenience sampling method was used. Demographic and clinical data and evaluation results for fatigue, pain, quality of life and Eastern Cooperative Oncology Group status were collected three days after the end of chemotherapy. Decision tree, random forest and logistic regression models were constructed. Ninety-four subjects in the case group had moderate to poor performance status, and 365 subjects in the control group had no or mild activity disorders. The random forest model was the most accurate model. Physical function, total protein, general quality of life within one week before chemotherapy, hemoglobin, pain symptoms and globulin were the main factors. Total protein and hemoglobin levels reflect nutritional status, and globulin levels are an index of liver function. Therefore, physical function, nutritional status, general quality of life and pain symptoms within one week before chemotherapy and liver function can be used to predict moderate–poor performance status. Nurses should pay more attention to patients with poor physical function, poor nutritional status, lower quality of life and pain symptoms after chemotherapy.

Abstract Topological photonic crystals, which offer topologically protected and back-scattering-immune transport channels, have recently gained significant attention for both scientific and practical reasons. Although most current studies focus on dielectric materials with weak dispersions, this study focuses on topological phases in dispersive materials and presents a numerical study of Chern insulators in gaseous-phase plasma cylinder cells. We develop a numerical framework to address the complex material dispersion arising from the plasma medium and external magnetic fields and identify Chern insulator phases that are experimentally achievable. Using this numerical tool, we also explain the flat bands commonly observed in periodic plasmonic structures, via local resonances, and how edge states change as the edge termination is periodically modified. This work opens up opportunities for exploring band topology in new materials with non-trivial dispersions and has potential radio frequency (RF) applications, ranging from plasma-based lighting to plasma propulsion engines.

Abstract Costly punishment of social norm transgressors by third-parties has been considered as a decisive stage in the evolution of human cooperation. An important facet of social relationship knowledge concerns the strength of the social ties between individuals, as measured by social distance. Yet, it is unclear how the enforcement of social norms is influenced by the social distance between a third-party and a norm violator at the behavioral and the brain system levels. Here, we investigated how social distance between punishers and norm-violators influences third-party punishment. Participants as third-party punished norm violators more severely as social distance between them increased. Using model-based fMRI, we disentangled key computations contributing to third-party punishment: inequity aversion, social distance between participant and norm violator and integration of the cost to punish with these signals. Inequity aversion increased activity in the anterior cingulate cortex and bilateral insula, and processing social distance engaged a bilateral fronto-parietal cortex brain network. These two brain signals and the cost to punish were integrated in a subjective value signal of sanctions that modulated activity in the ventromedial prefrontal cortex. Together, our results reveal the neurocomputational underpinnings of third-party punishment and how social distance modulates enforcement of social norms in humans.

Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications., (© 2024. The Author(s).)

This paper investigates the fixed-time cluster formation tracking (CFT) problem for networked perturbed robotic systems (NPRSs) under directed graph information interaction, considering parametric uncertainties, external perturbations, and actuator input deadzone. To address this complex problem, a novel hierarchical fixed-time neural adaptive control algorithm is proposed based on a hierarchical fixed-time framework and a neural adaptive control strategy. The objective of this study is to achieve accurate CFT of NPRSs within a fixed time. Specifically, we design a distributed observer algorithm to estimate the states of the virtual leader within a fixed time accurately. By using these observers, a neural adaptive fixed-time controller is developed in the local control layer to ensure rapid and reliable system performance. Through the use of the Lyapunov argument, we derive sufficient conditions on the control parameters to guarantee the fixed-time stability of NPRSs. The theoretical results are eventually validated through numerical simulations, demonstrating the effectiveness and robustness of the proposed approach., (© 2024. The Author(s).)

Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex background in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone's C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving P macro and mAP50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, YOLOv8n, and SSD models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model, when applied to image data collected from projects, achieves a P macro of 80.5% and an R macro of 86.2%. This result demonstrates its excellent generalization capability and practicality. The model provides significant technical support for the intelligent detection of pavement distress., (© 2024. The Author(s).)

Triptolide (TP), a major active component of the herb Tripterygium wilfordii Hook F, has been shown excellent pharmacological effects on rheumatoid arthritis. However, TP is prone to causing severe organ toxicity, which limits its clinical application. In recent years, microneedle technology has provided a new option for the treatment of arthritis due to its advantages of efficient local transdermal drug delivery. In this study, we constructed a microneedle platform to deliver TP locally to the joints, thereby enhancing TP penetration and reducing systemic toxicity. Additionally, we investigated whether acupoint drug delivery can produce a synergistic effect of needles and drugs. First, TP was loaded into microneedles using polyvinylpyrrolidone and hyaluronic acid as matrix materials. Next, we established a rat adjuvant-induced arthritis (AIA) model to evaluate the therapeutic effect of TP-loaded microneedles. The experiments showed that TP-loaded microneedles alleviated the AIA rats' inflammatory response, joint swelling, and bone erosion. However, there was no significant difference in the therapeutic effect observed in the acupoint and non-acupoint administration groups. In conclusion, TP-loaded microneedles have the advantages of safety, convenience, and high efficacy over conventional administration routes, laying a foundation for the transdermal drug delivery system-based treatment of rheumatoid arthritis., (© 2024. The Author(s).)

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model's uncertain parameters., (© 2024. The Author(s).)

Research exploring the link between immune cell profiles and the development of endometritis remains scant. This gap necessitates further study to decode the complex interrelations influencing this condition. In this analysis, we leveraged two-sample Mendelian randomization to examine the causal ties between the phenotypes of immune cells and the incidence of endometritis. Our evaluation hinged on data from 3757 participants hailing from Sardinia, focusing on a diverse array of 731 immune phenotypes, and cross-referenced with endometritis data sourced from the UK Biobank. To ensure rigor, we performed sensitivity analyses, utilized MR-Egger and MR-Presso to check for pleiotropy, and applied Cochran's Q test for assessing the heterogeneity of our findings. Our investigation identified numerous immune characteristics associated with endometritis. For certain immune traits, a lower risk of endometritis was observed, including: Absolute Counts of CD39 + CD4 + T cells, CD25 + CD39 + CD4 regulatory T cells, and CD25 + + CD8 + T cells; Absolute Counts of Switched Memory B cells; CD19 expression on IgD + CD38dim and Switched Memory B cells; CD20 expression on IgD + CD38- Unswitched Memory B cells; percentage of Switched Memory B cells among lymphocytes; CD16-CD56 expression on HLA DR + Natural Killer cells; percentage of CD11c + CD62L- monocytes; CD86 expression on monocytes; CCR2 expression on CD14 + CD16 + monocytes; and CD14 expression on Monocytic Myeloid-Derived Suppressor Cells, with Odds Ratios (ORs) between 0.413 and 0.703. On the contrary, increased risks of endometritis were linked with: the percentage of Effector Memory CD4 + T cells within the CD4 + T cell population; percentages of HLA DR + T cells and HLA DR + CD8 + T cells among T cells; CD4 expression on CD28 + CD4 + T cells; CD20 expression on CD20- CD38- B cells; percentage of IgD + CD24 + B cells within the B cell population; CD62L expression on CD62L + myeloid Dendritic Cells; and Absolute Counts of Plasmacytoid Dendritic Cells, with ORs from 1.473 to 2.677, indicating these traits potentially elevate the risk of developing endometritis. Our research delineates distinct causal links between specific immune cell phenotypes and endometritis, offering new perspectives that could contribute to the pinpointing of new therapeutic avenues for this condition., (© 2024. The Author(s).)

To assess the relative performance of simple screening methods for sarcopenia in Chinese community-dwelling older adults. Data of older adults aged ≥ 60 were collected through a cross-sectional investigation. Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. The accuracy of screening methods was evaluated using sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the ROC curves (AUC). The AUC value greater than 0.8 represented the good screening ability. A total of 918 older adults (44.3% men, mean age 70.4 ± 6.5 years) were included in this study. The overall prevalence rates of possible sarcopenia, confirmed sarcopenia, and severe sarcopenia were 59.5%, 12.8%, and 5.9%, respectively. In men, the SARC-F-EBM and Ishii tests indicated good screening capabilities for confirmed sarcopenia, with an AUC of 0.81 (95% CI: 0.77-0.85) and 0.80 (95% CI: 0.76-0.84), respectively. In women, the highest AUC was also achieved using the SARC-F-EBM at 0.79 (95% CI: 0.75-0.82), followed by the Ishii test at 0.77 (95% CI: 0.74-0.81), showing the moderate efficacy. A ranking diagram showed that SARC-F-EBM was most likely to be considered the best method for diagnosing sarcopenia in terms of AUC and sensitivity, regardless of sex. We recommend the SARC-F-EBM for sarcopenia screening in community-dwelling Chinese older adults when respondents are able to answer the questionnaire accurately; otherwise, the Ishii test consisting entirely of objective measures could be used., (© 2024. The Author(s).)

The Chinese mitten crab (Eriocheir sinensis) holds significant importance as a popular aquaculture food source; however, there are concerns about its potential contamination with polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs) from both food and aquatic environment. To assess the associated health risks and identify potential sources of contamination in crabs, a comprehensive investigation was conducted, including a total of 70 samples from the crab food web. The results demonstrated that crabs predominantly exhibited elevated concentrations of PCBs and dl-PCBs, with mean concentrations of 12 207 ± 11 962 pg g -1 and 554 ± 203 pg g -1 , respectively, while PCDD/Fs concentrations were comparatively lower at 20 ± 17 pg g -1 . The accumulation of PCBs in crabs significantly surpassed that of PCDD/Fs. The material balance of PCDD/Fs and PCBs in the crab food web was estimated, indicating that sediments and feeds likely constitute the two primary sources of PCDD/Fs and PCBs in crabs. The monthly intake of PCDD/Fs and PCBs through crab consumption accounted for 30% of the dietary intake, which was well below the provisional tolerable monthly intake (PTMI) limit. The weekly intake of PCDD/Fs and PCBs for adults consuming one crab (100 g) does not pose health risks and the recommended weekly intake of white crabmeat and brown crabmeat is 443 g and 21 g, respectively., (© 2024. The Author(s).)

The health-related quality of life (HRQOL) of college students is not promising and needs to be improved through effective interventions. This study aimed to investigate the associations of nutrition literacy (NL) with HRQOL and anxiety among college students, and to identify the role of anxiety in the relationship between NL and HRQOL. The cross-sectional survey was conducted via the "Wenjuanxing" platform from September to November 2023. The Food and Nutrition Literacy Questionnaire (FNLQ), the Generalized Anxiety Disorder (GAD-7) diagnostic tool and the SF-12 scale were used to assess NL, anxiety, and HRQOL, respectively. Logistic regression models, mediation analysis, additive and multiplicative interaction analyses were used. 2066 college students participated in the analysis. After adjusting for potential confounders, we found that college students with higher NL had higher HRQOL (OR = 2.52, 95% CI: 2.09-3.03, p < 0.001) and a lower risk of anxiety (OR = 0.53, 95% CI: 0.39-0.73, p < 0.001). Additionally, anxious college students had lower HRQOL (OR = 0.16, 95% CI: 0.11-0.24, p < 0.001). Furthermore, mediation analysis confirmed that anxiety was a partial mediator of the relationship between NL and HRQOL (β = 0.600, 95% CI: 0.406-0.779/0.430-0.818). Significant additive interactions were found between NL and Anxiety (RERI = 6.96, 95% CI: 2.74-11.17; AP = 0.51, 95% CI: 0.37-0.64; SI = 2.21, 95% CI: 1.58-3.07). Higher levels of NL are associated with better HRQOL and lower anxiety among college students. Additionally, anxiety partially mediated the relationship between NL and HRQOL. Furthermore, there is a synergy between NL and anxiety., (© 2024. The Author(s).)

This study employed Environmental DNA (eDNA) barcoding technology to delve into the influence of the tributaries and mainstem on fish diversity and spatiotemporal distribution in a hotspot fish conservation area in the upper Yangtze River. A total of 123 fish species were detected, belonging to 7 orders, 19 families, and 77 genera. The composition of fish species in tributaries is similar to that in mainstem, with higher fish community diversity in tributaries during the spring and summer. Exploration of fish ecotypes revealed significant differences between mainstem and tributaries. The fish community is mainly influenced by key environmental factors such as water temperature, dissolved oxygen, electrical conductivity, and ammonia nitrogen, with a higher impact of these factors on tributaries than on mainstem. In conclusion, while tributaries and mainstem in the Jiangjin section exhibit similarities in fish community composition, there are notable differences in community structure and diversity. Therefore, the protection of not only mainstem but also tributaries and their associated fish habitats is crucial for promoting the overall health and sustainability., (© 2024. The Author(s).)

The emergence of Staphylococcus epidermidis as a significant nosocomial pathogen necessitates advancements in more efficient antimicrobial resistance profiling. However, existing culture-based and PCR-based antimicrobial susceptibility testing methods are far too slow or costly. This study combines machine learning with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to develop predictive models for various antibiotics using a comprehensive dataset containing thousands of S. epidermidis isolates. Optimized machine learning models utilized feature selection and achieved high AUROC scores ranging from 0.80 to 0.95 while maintaining AUPRC scores up to 0.97. Shapley Additive exPlanations were employed to analyze relevant features and assess the significance of corresponding protein biomarkers while also verifying that predictive power was derived from the detection of proteins rather than noise. Antimicrobial resistance models were validated externally to evaluate model performance outside the original data collection site. The approaches and findings in this study demonstrate a significant advancement in rapid, cost-effective antimicrobial resistance profiling, offering a promising solution for improving treatments for nosocomial infections and being potentially applicable to other microbial pathogens in the future., (© 2024. The Author(s).)

Youth soccer specialty schools are the primary project to strengthen the foundation of soccer talents, improve the level of soccer development, and achieve the dream of Chinese soccer. Taking 32,775 soccer specialty schools in China as the research sample, ArcGIS spatial analysis technology is used to explore the spatial distribution characteristics of soccer specialty schools and their influencing factors. The analysis shows that the overall distribution of the country forms five quantity concentration area and eight high-density core areas, with a high degree of spatial concentration and significant differences between the East and West. The factors influencing soccer specialty schools include nine factors in four dimensions: historical development, geographical environment, socio-economic, and educational resources. It was found that geographic environment and socio-economy provide the foundation and background for developing educational resources. However, the direct role of educational resources is more significant in a specific educational program, such as soccer specialty schools, followed by socio-economy and geographic environment. This study comprehensively analyzes and interprets soccer specialty schools' spatial distribution characteristics and influencing factors. These results enrich the research on the spatial layout of sports, providing necessary theoretical support and practical references for the layout and high-quality development of related industries in China and other countries or regions., (© 2024. The Author(s).)

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risks under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It's based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: (1) suicide attempt; (2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ~ 0.77 (95% CI 0.75-0.78) for suicide attempt and AUPR ~ 0.31 (95% CI 0.28-0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race differed across phenotypes. Scalable phenotyping models, like most healthcare AI, require algorithmovigilance and debiasing prior to implementation., (© 2024. The Author(s).)

In addressing the finite element model and actual structural error of the sprayer boom truss, this study aims to achieve high-precision dynamic characteristics, enhance simulation credibility, make informed optimization decisions, and reduce testing costs. The research investigates the dynamic behavior of the sprayer boom truss through modal experiments and finite element simulations. Initially, modal parameters of the sprayer boom are obtained through experimental testing, validating their reasonableness and reliability. Subsequently, Ansys Workbench18.0 simulation software was employed to analyze the finite element model of the sprayer boom, revealing a maximum relative error of 11.93% compared to experimental results. To improve accuracy, a kriging-based response surface model was constructed, and multi-objective parameter adjustments using the MOGA algorithm reduce the maximum relative error to 4.6%. Sensitivity analysis further refines the model by optimizing target parameters, resulting in a maximum relative error of 4.96%. These findings demonstrate the effective enhancement of the corrected finite element model's precision, with the response surface method outperforming sensitivity analysis the maximum relative error between the updated finite element model and experimental results was within the engineering allowable range, confirming the effectiveness of the updated model., (© 2024. The Author(s).)

A novel, parameter-independent multiscale correlational constitutive model has been devised to predict thermomechanical properties of Si-diamond-SiC and Si-diamond composites, including the effective elastic modulus, effective bulk modulus, effective shear modulus, effective Poisson's ratio, average coefficients of thermal expansion as well as thermal conductivity. Based on this model, the effective thermomechanical response of two kinds of composites was simulated, and the underlying mechanisms of thermomechanical coupling between constituents were also critically evaluated. The findings were shown that the effective elastic properties of composites, including effective elastic modulus, effective bulk modulus, effective shear modulus, increased with diamond and SiC, and that the introduction of dispersed diamond with highly thermal conductivity and lowly thermal expansion significantly enhanced thermophysical properties of Si-diamond-SiC composites. The thermomechanical coupling of these composites was influenced by the effective elastic properties of composites and the disparity in the intrinsic properties of constituents., (© 2024. The Author(s).)

To evaluate the effect of irradiation on the preservation of potatoes, fresh potatoes were selected as the irradiation objects, and irradiated with 60 Co-γ radiation source for 0, 100, 200, 500 and 1000 Gy, respectively. During the irradiation, the well-packaged Y 1.79 Bi 0.01 Eu 0.2 MgTiO 6 novel thermoluminescence dosimeter material was placed together with the potatoes at the same position. Then, the potatoes were stored in the same temperature and humidity environment, and the quality changes of the potatoes were observed. The Y 1.79 Bi 0.01 Eu 0.2 MgTiO 6 material had good performance indicators, and was used to measure the irradiation dose of the potatoes. The experiment showed that irradiation could appropriately extend the storage time of potatoes, and gamma irradiation of about 1000 Gy could achieve the best preservation effect. The main pathogenic fungi that cause dry rot of potatoes were Fusarium solani and Fusarium oxysporum, and the appropriate dose of 60 Co-γ irradiation could effectively inhibit the spread and growth of these fungi., (© 2024. The Author(s).)

Aiming at the problem that an Interior Permanent Magnet Synchronous Motor (IPMSM) cannot be smoothly started in zero-low speed range and smoothly transitioned to medium-high speed range by a single observer. This paper proposes a full-speed range control algorithm based on the fusion of pulsating high-frequency injection and back electromotive force (EMF) position error information. In the low-speed range or at start-up, a square-wave high-frequency signal is injected, and the obtained high-frequency current signal is processed to obtain the rotor position error information. The phase shift due to the introduction of a filter is reduced, which improves the control bandwidth and reduces the noise. To ensure smooth switching of the observer, the observer uses a dual second-order generalized integrator module to output the angular frequency in the low-speed range. A higher-order sliding mode observer based on an inverse EMF model obtains rotor position error information at high speeds. During switching, the rotor position information is processed by a fusion strategy, and the obtained hybrid information is fed into the system to improve the stability of the motor operation. A 0.2 kW IPMSM position sensorless vector control system verifies the algorithm's accuracy., (© 2024. The Author(s).)

This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence., (© 2024. The Author(s).)

Spiral phase contrast imaging alleviates the information load by extracting the geometric features of objects and is one of the most representative branches of instant imaging processing. The self-healing capacity of edge detectors can enhance their robustness to obstacles in practical applications. Here, a self-healing spiral phase contrast imaging scheme is proposed and experimentally demonstrated by a liquid crystal edge detector combining a spiral phase, an axicon phase, and a lens phase. The spiral phase is encoded into a liquid crystal by photopatterning. Self-healing contrast imaging is characterized by a series of edge images of both high-contrast amplitude-type and low-contrast phase-type objects. This work extends the self-healing capacity of these detectors to instant imaging processing and paves the way for optical applications with self-healing features., (© 2024. The Author(s).)

Over recent years, as digitalization and intelligence in oil wellbore have increased, so have the stricter requirements for wireless communication technology in terms of distance, accuracy, and portability. As a result, it's necessary to rely on more advanced and efficient wireless communication technologies to meet the industry's needs. However, traditional communication technologies such as cables and optical fibers have inherent shortcomings in construction, data interpretation, and cost. ELF electromagnetic waves are an ideal solution for communication in complex wellbore conditions due to long-distance communication and strong penetration capabilities, making it a highly effective option. Based on the theory of network splitting, this paper establishes a polygonal multiple-delays uncertainty coupled complex network model of ELF electromagnetic waves propagating through the casing in layered media and designs a controller, including expressions for the intensity of the magnetic and electric fields in different directions, and the propagation and distribution characteristics in different media. We determined that the optimal transmitting frequency of ELF electromagnetic waves under general conditions is 12.7 Hz. Based on field experiments, we verified that ELF electromagnetic waves can enable wireless wellbore communication within 1500 m without relays. We also analyzed the impact of casing thread deformation on ELF electromagnetic wave propagation due to high-temperature and high-pressure environments. We used simulation experiments to solve the distribution relationship between the electric and magnetic fields of the solenoids through casing and strata, as well as the coupling coefficients between the transmitting and receiving solenoids, and explore how different transmitting frequencies affect the efficiency of signal propagation. Both theories and experiments have verified the correctness of the model, and have also demonstrated the reliability and continuity of using ELF electromagnetic waves to achieve wireless wellbore communication, which provides a theoretical basis and feasibility for subsequent engineering applications., (© 2024. The Author(s).)

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making., (© 2024. The Author(s).)

Algin oligosaccharides have been applied in diverse industries and could be innovative synthesized by alginate-degrading bacteria. For enhance the alginate degradation efficiency to produce more algin oligosaccharides, a mutant strain (Cobetia sp. cqz5-12-M1) was obtained through the complex mutagenesis using UV and the alkylating agent 1-methyl-3-nitro-1-nitrosoguanidine. The enzyme activity of the fermentation supernatant of mutant exhibited a significant 38.09% (53.98 ± 0.69 U/mL) increase, and its optimal growth conditions were determined as: 5 g/L sodium alginate, 5 g/L yeast powder, 30 g/L NaCl, 2 g/L K 2 HPO 4 , 2 g/L KH 2 PO 4 , 1 g/L MgSO 4 •7H 2 O, 0.01 g/L FeSO 4 •7H 2 O, pH 6.5, and 34 ℃. Moreover, its optimal degradation conditions were identified as: 5 g/L sodium alginate, 5 g/L yeast powder, 30 g/L NaCl, 2 g/L K 2 HPO 4 , 2 g/L KH 2 PO 4 , 1 g/L MgSO 4 •7H 2 O, 0.01 g/L FeSO 4 •7H 2 O, pH 6.5, 31 ℃ and 72 h, yielding an enzyme activity of 120.98 ± 1.40 U/mL in the fermentation supernatant. Conclusive experiments on reagent tolerance revealed the growth of the mutant strain was significantly inhibited by 3% hydrogen peroxide, 5% carbolic acid, and 10 mg/mL gatifloxacin. Additionally, the alginate degradation capacity of mutant strain was highly significantly inhibited by 75% ethanol and all tested antibiotics., (© 2024. The Author(s).)

During the COVID-19 pandemic, delirium became a major complication that worsened patient outcomes. However, the factors influencing the severity of delirium in patients with COVID-19 have not been determined. We conducted this study to detect influencing factors associated with subtypes of delirium in patients with COVID-19. We included 1774 adult inpatients with COVID-19 from January to February 2023 at 7 sites in China. And used the 3 min Confusion Assessment Method and the Richmond Agitation-Sedation Scale for site assessment to identify and classify subtypes of delirium. Laboratory data were obtained from the Hospital Information System. After multivariate analysis, hypoactive delirium was significantly associated with age, the serum albumin concentration, frailty and sarcopenia, and health and nutritional status. Mixed delirium was significantly associated with age, D-dimer level, sarcopenia, health status and nutritional status. Additionally, hyperactive delirium was significantly associated with age, procalcitonin levels, frailty status and health status. Our findings suggest that poor nutritional status and low serum albumin concentration can help detect patients at high risk of developing hypoactive and mixed delirium. Additionally, clinical staff should pay more attention to patients with inflammatory conditions to assess and detect delirium because many influencing factors are involved in the common pathological mechanism of inflammation., (© 2024. The Author(s).)

The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body. Among the 8 nodes, node number 1 is located in the center of the grid (the center of the human body). The number of rounds is 9000 and the nodes are adopted with the initial energy of the nodes of 0.5 J and the radio range of 10 m. MATLAB software was used to simulate the WBAN network, which consists of IoT sensors embedded in the human body. The eight-item health assessment tool takes the following into account: pulse rate, blood pressure (mm Hg), serum cholesterol (mg/dl), temperature (°C), exercise-induced angina, and exercise-induced ST-wave depression, major blood vessels are counted using a medical procedure called endoscopy that involves examining the alveoli, which are small air sacs in the lungs where gas exchange occurs. We compared the number of major vessels at rest with the maximal heart rate during activity. The sensors were responsible for sending this data to the health center (base station). The data collected from the installation of these 8 sensors on 303 patients were collected and evaluated by machine learning method using MLP neural network method. Finally, it can be claimed that the present study has provided an automated method of determining the health of people using the IoT in a way that provides a state of health with an accuracy of over 99% and can be used in medical centers., (© 2024. The Author(s).)

To explore the advantages and disadvantages of 3D Slicer reconstruction and 3D printing localization combined with transcranial neuroendoscope in the surgical treatment of deep cerebral micro cavernous hemangiomas. Method The clinical data of patients with deep cerebral micro cavernous hemangiomas treated by our hospital from June 2022 to February 2023 using 3D Slicer reconstruction and 3D printing localization technology combined with transcranial endoscopic surgery were retrospectively analyzed. A total of 5 cases with complete data were collected, including 2 males and 3 females, aged 9-59 years. All 5 patients had deep supratentorial cavernous hemangiomas with a diameter of less than 1.5 cm, and had clinical symptoms such as headache or epilepsy, and had been diagnosed by CT or MRI. Repeated bleeding from small cavernous hemangiomas in the deep brain can lead to clinical symptoms such as recurrent headache and epilepsy, and is required surgical treatment. However, cavernous hemangiomas often have smaller lesions and are difficult to locate in the deep part. Without neuronavigation, surgery can become extremely difficult. Our team's newly developed 3D Slicer reconstruction and 3D printing localization technology which could provide new options for surgical treatment of small cavernous hemangiomas or other small lesions in the deep brain, but its accuracy and safety still need to be verified by further clinical research., (© 2024. The Author(s).)

We retrospectively investigated the correlation between the spinal cord compression angle and increased signal intensity (ISI) in 118 patients with ossification of the posterior longitudinal ligament (OPLL). Patients were analyzed based on the presence and shape of ISI on magnetic resonance imaging. Various indicators, including the spinal cord compression angle, were measured through imaging examinations. Spearman's correlation and logistic regression were used for analyses. Significant positive correlations were observed between the ISI grade and the spinal cord compression angle, maximum spinal canal occupying rate, cervical range of motion, and segmental range of motion. The spinal cord compression ratio and Japanese Orthopaedic Association (JOA) score were negatively correlated with the ISI grade. Regression analysis revealed that the spinal cord compression angle and JOA scores were independent factors that significantly influenced ISI grade. The odds ratio of ISI was 3.858 (95% confidence interval: 0.974-15.278) when comparing the highest and lowest quartiles of the spinal cord compression angle. Patients with a spinal cord compression angle > 35° had more severe imaging manifestations. Thus, a spinal cord compression angle > 35° could serve as a significant indicator of OPLL severity, and greater attention should be focused on treating patients with larger spinal cord compression angles., (© 2024. The Author(s).)

The treatment of multidrug-resistant tracheobronchial tuberculosis poses challenges, and research investigating the efficacy of bedaquiline or delamanid as treatment for this condition is limited. This retrospective cohort study was conducted from 2017 to 2021. The study extracted data of patients with multidrug-resistant tracheobronchial tuberculosis from medical records and followed up on prognoses. Participants were divided into three groups: the bedaquiline, delamanid, and control group. Clinical outcomes and the risk factors associated with early culture conversion were analyzed. This study included 101 patients, with 32, 25, and 44 patients in the bedaquiline, delamanid, and control groups respectively. The differences in the treatment success rates among the three groups did not show statistical significance. Both the bedaquiline and delamanid groups had significantly higher culture conversion rates compared to the control after 2 or 6 months of treatment, with significantly shorter median times to culture conversion (bedaquiline group: 2 weeks, delamanid group: 2 weeks, control group: 12 weeks, P < 0.001). Treatment with bedaquiline or delamanid were identified as independent predictors of culture conversion at 2 months (bedaquiline group: aOR = 13.417, 95% CI 4.067-44.260, delamanid group: aOR = 9.333, 95% CI 2.498-34.878) or 6 months (bedaquiline group: aOR = 13.333, 95% CI 3.379-52.610, delamanid group: aOR = 5.000, 95% CI 1.357-18.426) of treatment through multivariable logistic regression analyses. The delamanid group showed better improvement in lumen stenosis compared to bedaquiline. Regimens containing bedaquiline or delamanid may accelerate the culture conversion during the early treatment phase in multidrug-resistant tracheobronchial tuberculosis, and delamanid appears to have the potential to effectively improve airway stenosis., (© 2024. The Author(s).)

Head and neck squamous carcinoma (HNSC) is a prevalent malignant disease, with the majority of patients being diagnosed at an advanced stage. Endoplasmic reticulum stress (ERS) is considered to be a process that promotes tumorigenesis and impacts the tumor microenvironment (TME) in various cancers. The study aims to investigate the predictive value of ERS in HNSC and explore the correlation between ERS-related genes and TME. A series of bioinformatics analyses were carried out based on mRNA and scRNA-seq data from the TCGA and GEO databases. We conducted RT-qPCR and western blot to validate the signature, and performed cell functional experiments to investigate the in vitro biological functions of the gene. We identified 63 ERS-related genes that were associated with outcome and stage in HNSC. A three-gene signature (ATF6, TRIB3, and UBXN6) was developed, which presents predictive value in the prognosis and immunotherapy response of HNSC patients. The high-risk group exhibited a worse prognosis but may benefit from immunotherapy. Furthermore, there was a significant correlation between the signature and immune infiltration. In the high-risk group, fibroblasts were more active in intercellular communication, and more T cells were observed at the end of the sequential phase. The genes in the ERS-related signature were overexpressed in HNSC cells, and the knockdown of TRIB3 significantly inhibited cell proliferation and migration. This study established a novel ERS-related signature that has potential implications for HNSC therapy and the understanding of TME., (© 2024. The Author(s).)

Central nervous system Infections (CNSIs) is a disease characterized by complex pathogens, rapid disease progression, high mortality rate and high disability rate. Here, we evaluated the clinical value of metagenomic next generation sequencing (mNGS) in the diagnosis of central nervous system infections and explored the factors affecting the results of mNGS. We conducted a retrospective study to compare mNGS with conventional methods including culture, smear and etc. 111 suspected CNS infectious patients were enrolled in this study, and clinical data were recorded. Chi-square test were used to evaluate independent binomial variables, taking p < 0.05 as statistically significant threshold. Of the 111 enrolled cases, 57.7% (64/111) were diagnosed with central nervous system infections. From these cases, mNGS identified 39.6% (44/111) true-positive cases, 7.2% (8/111) false-positive case, 35.1% (39/111) true-negative cases, and 18.0% (20/111) false-negative cases. The sensitivity and specificity of mNGS were 68.7% (44/64) and 82.9% (39/47), respectively. Compared with culture, mNGS provided a higher pathogen detection rate in CNSIs patients (68.7% (44/64) vs. 26.5% (17/64), p < 0.0001). Compared to conventional methods, positive percent agreement and negative percent agreement was 84.60% (44/52) and 66.1% (39/59) separately. At a species-specific read number (SSRN) ≥ 2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] 0.758, 95% confidence interval [CI] 0.663-0.854). In bacterial CNSIs patients with significant CSF abnormalities (CSF WBC > 300*10 6 /L), the positive rate of CSF mNGS is higher. To sum up, conventional microbiologic testing is insufficient to detect all neuroinvasive pathogens, and mNGS exhibited satisfactory diagnostic performance in CNSIs and with an overall detection rate higher than culture (p < 0.0001)., (© 2024. The Author(s).)

The evaluation of natural ventilation potential for effective sustainable options and innovative green building design strategies is of great interest to architects, researchers and governments. From a retrospective review, we found that the potential evaluation of natural ventilation (NV) cooling effectiveness in the same category based on similar meteorological uncertainty, research objectives and objects showed significant differences. Uncertainties added and uncertainty propagation (both model form uncertainties and parameter uncertainties) could result in large discrepancies between simulation outcomes and real scenarios, especially in the design performance modeling (DPM) phase. In this conceptual design stage, a few parameters are available and therefore decisive. It is necessary to review and identify the key performance indicators and explore the extent to which deviations are caused by inconsistencies or biases in model information. As a basis for more concrete research, we propose statistical tests based on quantitative evaluations to explore the rule of natural ventilation potential volatility and identify whether there is a significant potential improvement resulting from the critical parameter enhancement with the optimal relationship. The showcase is applied in China, where there has been a significant amount of criticism regarding the current building climate zoning due to the perceived coarseness of the system and where there has been an active exploration into the possibility of redefining building climate zoning with a view toward improving its accuracy and effectiveness., (© 2024. The Author(s).)

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages., (© 2024. The Author(s).)

Rapid and safe hemostasis is crucial for the survival of bleeding patients in prehospital care. It is urgent to develop high performance hemostatic material to control the massive hemorrhage in the military field and accidental trauma. In this work, an efficient protein hemostat of thrombin was immobilized onto commercial gauze, which was mediated by self-polymerization and anchoring of tannic acid (TA). Through TA treatment, the efficient immobilization of thrombin was achieved, preserving both the biological activity of thrombin and the physical properties of the dressing, including absorbency, breathability, and mechanical performance. Moreover, in the presence of TA coating and thrombin, Gau@TA/Thr could obviously shortened clotting time and enriched blood components such as plasma proteins, platelets, and red blood cells, thereby exhibiting an enhanced in vitro coagulation effect. In SD rat liver volume defect and artery transection hemorrhage models, Gau@TA/Thr still had outstanding hemostatic performance. Besides, the Gau@TA/Thr gauze had inherent antibacterial property and demonstrated excellent biocompatibility. All results suggested that Gau@TA/Thr would be a potential candidate for treating uncontrollable hemorrhage in prehospital care., (© 2024. The Author(s).)

Mitigating pre-harvest sprouting (PHS) and post-harvest food loss (PHFL) is essential for enhancing food securrity. To reduce food loss, the use of plant derived specialized metabolites can represent a good approach to develop a more eco-friendly agriculture. Here, we have discovered that soybean seeds hidden underground during winter by Tscherskia triton and Apodemus agrarius during winter possess a higher concentration of volatile organic compounds (VOCs) compared to those remaining exposed in fields. This selection by rodents suggests that among the identified volatiles, 3-FurAldehyde (Fur) and (E)-2-Heptenal (eHep) effectively inhibit the growth of plant pathogens such as Aspergillus flavus, Alternaria alternata, Fusarium solani and Pseudomonas syringae. Additionally, compounds such as Camphene (Cam), 3-FurAldehyde, and (E)-2-Heptenal, suppress the germination of seeds in crops including soybean, rice, maize, and wheat. Importantly, some of these VOCs also prevent rice seeds from pre-harvest sprouting. Consequently, our findings offer straightforward and practical approaches to seed protection and the reduction of PHS and PHFL, indicating potential new pathways for breeding, and reducing both PHS and pesticide usage in agriculture., (© 2024. The Author(s).)

Gamma knife radiosurgery (GKRS) is recommended as the first-line treatment for brain metastases of lung adenocarcinoma (LUAD) in many guidelines, but its specific mechanism is unclear. We aimed to study the changes in the proteome of brain metastases of LUAD in response to the hyperacute phase of GKRS and further explore the mechanism of differentially expressed proteins (DEPs). Cancer tissues were collected from a clinical trial for neoadjuvant stereotactic radiosurgery before surgical resection of large brain metastases (ChiCTR2000038995). Five brain metastasis tissues of LUAD were collected within 24 h after GKRS. Five brain metastasis tissues without radiotherapy were collected as control samples. Proteomics analysis showed that 163 proteins were upregulated and 25 proteins were downregulated. GO and KEGG enrichment analyses showed that the DEPs were closely related to ribosomes. Fifty-three of 70 ribosomal proteins were significantly overexpressed, while none of them were underexpressed. The risk score constructed from 7 upregulated ribosomal proteins (RPL4, RPS19, RPS16, RPLP0, RPS2, RPS26 and RPS25) was an independent risk factor for the survival time of LUAD patients. Overexpression of ribosomal proteins may represent a desperate response to lethal radiotherapy. We propose that targeted inhibition of these ribosomal proteins may enhance the efficacy of GKRS., (© 2024. The Author(s).)