BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifyi...BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
In this paper,we obtain a normality criterion for families of meromorphic functions concerning‘wandering’shared functions,which generalizes and improves Montel’s criterion and the related results due to Schwick,Xu-...In this paper,we obtain a normality criterion for families of meromorphic functions concerning‘wandering’shared functions,which generalizes and improves Montel’s criterion and the related results due to Schwick,Xu-Fang,Xu-Qiu,and Grahl-Nevo.Also,a normality relationship between two families is given.展开更多
On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to f...On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.展开更多
Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic ...Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.展开更多
This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firs...This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firstly, to simplify the analysis and design of predefined-time controller, a Predefined-time Stability Criterion is proposed in the form of Composite Lyapunov Function (CLF-PSC). Besides simplicity, the CLF-PSC also has the advantage of less conservativeness due to utilization of initial state information. Secondly, a concept of Lp-Norm-Normalized Sign Function (LPNNSF) is proposed based on the CLF-PSC. Different from traditional norm-normalized sign function, the Lp-norm of LPNNSF can be selected arbitrarily according to practical control task requirements, which means that the proposed LPNNSF is more generalized and more convenient for calculation. Thirdly, a predefined-time disturbance observer and predefined-time controller are designed based on the LPNNSF. The observer has the property of predefined-time convergence to achieve quicker and more accurate estimation of the lumped disturbance. The controller has less control input and chattering phenomenon than traditional predefined-time controller. In addition, by introducing the observer into the controller, the closed-loop system enjoys high precision and strong robustness. Finally, the effectiveness of the proposed controller is verified by numerical simulations. By employing the controller, the MSRS can carry assembly modules to the desired pre-assembly configuration accurately within predefined time.展开更多
In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of ...In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in s...Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.展开更多
Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine...Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.展开更多
BACKGROUND Patients with chronic hepatitis B(CHB)require long-term antiviral therapy.The effects of different antiviral drugs on kidney function are unclear.There is a lack of effective markers for monitoring early re...BACKGROUND Patients with chronic hepatitis B(CHB)require long-term antiviral therapy.The effects of different antiviral drugs on kidney function are unclear.There is a lack of effective markers for monitoring early renal impairment.AIM To investigate the rate of abnormal renal function index and related potential hazards in patients with CHB.METHODS Clinical data of patients with CHB with urinaryβ2-microglobulin(β2-M)detec-tion,including demographic characteristics,hepatitis B virus(HBV)DNA,serum liver function(alanine aminotransferase,aspartate aminotransferase,total bilirubin,direct bilirubin),serum renal function(urea nitrogen,creatinine),blood lipid index(high density lipoprotein,low density lipoprotein,cholesterol,trigly-ceride),liver imaging,and other routine tests were retrospectively collected.The normal level of urinaryβ2-M and estimated glomerular filtration rate(eGFR)is defined as<0.173 mg/L and≥90 mL/min/1.73 m^(2),retrospectively.The pro-portion of patients with abnormal renal function index and related risk factors were analyzed.RESULTS A total of 500 patients with CHB were enrolled;these patients were aged 44.7±10.8 years,67.2%(336/500)were male,57.2%(286/500)were treated with anti-viral drugs,and 52.2%(261/500)had an HBV-related family history.In total,28.8%(144/500)of patients had fatty liver,35.0%(175/500)had liver fibrosis,and 13.2%(66/500)had cirrhosis.The proportion of patients with eGFR<90 mL/min/1.73 m^(2) was 43.2%(216/500),and the abnormal rate of urinaryβ2-M was 56.2%(281/500).There was no significant difference in the abnormal rate of urinaryβ2-M between the untreated group and the antiviral treated group(54.2%vs 57.7%;P=0.25).The abnormal rate ofβ2-M after long-term entecavir treatment(more than 1 year)was 54.6%(89/163).In the treatment group,56.4%(92/163)of patients with eGFR≥90 mL/min/1.73 m^(2) had abnormal urinaryβ2-M.CONCLUSION In patients with CHB,a higher proportion had greater urinaryβ2-M levels than eGFR for renal injury.Male patients should pay more attention to renal function and use antiviral regimens with a renal safety profile.展开更多
Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to s...Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.展开更多
BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major ...BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.展开更多
BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly...BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly impact the quality of life of patients.AIM To explore the correlations between ocular surface function,sleep quality,and anxiety/depression in patients with DED.METHODS This was a cross-sectional investigative study that included 358 patients with DED between January 2022 and January 2025.Ocular surface was assessed using the ocular surface disease index(OSDI),tear film break-up time,fluorescein staining score,and Schirmer I test.The Pittsburgh Sleep Quality Index(PSQI),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were used to evaluate sleep quality and anxiety/depression levels.Correlation and linear regression analyses were used to explore the relationships.RESULTS The mean PSQI score of the patients was 9.94±2.18;the mean SAS score was 47.30±4.90,and the mean SDS score was 50.08±5.52.These suggested a prevalence of sleep and psychological abnormalities.There was a significant correlation between the indicators of ocular surface function(OSDI,tear film break-up time,fluorescein staining,and Schirmer I test)and PSQI,SAS,and SDS scores(P<0.05).Moreover,multiple regression revealed that age≥50 years(β=1.55,P=0.029),PSQI scores(β=0.58,P<0.001),SAS scores(β=0.17,P=0.017),and SDS scores(β=0.15,P=0.019)were independent predictors of the OSDI scores.CONCLUSION Ocular surface function in patients with DED is closely related to sleep quality and anxiety/depression,emphasizing the need for holistic clinical management.展开更多
Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.B...Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.Based on the Joule effect,the solid carbon sources can be rapidly heated to ultra-high temperatures(>3000 K)through instantaneous high-energy current pulses during FJH,thus driving the rapid rearrangement and graphitization of carbon atoms.This technology demonstrates numerous advantages,such as solvent-and catalyst-free features,high energy conversion efficiency,and a short process cycle.In this review,we have systematically summarized the technology principle and equipment design for FJH,as well as its raw materials selection and pretreatment strategies.The research progress in the FJH synthesis of flash graphene,carbon nanotubes,graphene fibers,and anode hard carbon,as well as its by-products,is also presented.FJH can precisely optimize the microstructures of carbon materials(e.g.,interlayer spacing of turbostratic graphene,defect concentration,and heteroatom doping)by regulating its operation parameters like flash voltage and flash time,thereby enhancing their performances in various applications,such as composite reinforcement,metal-ion battery electrodes,supercapacitors,and electrocatalysts.However,this technology is still challenged by low process yield,macroscopic material uniformity,and green power supply system construction.More research efforts are also required to promote the transition of FJH from laboratory to industrial-scale applications,thus providing innovative solutions for advanced carbon materials manufacturing and waste management toward carbon neutrality.展开更多
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
BACKGROUND The therapeutic role of neurodynamic mobilization in improving lower limb function in patients with mild post-traumatic knee osteoarthritis remains poorly understood.AIM To further elucidate the role of neu...BACKGROUND The therapeutic role of neurodynamic mobilization in improving lower limb function in patients with mild post-traumatic knee osteoarthritis remains poorly understood.AIM To further elucidate the role of neurodynamic mobilization in facilitating knee joint functional recovery.METHODS Thirty-two patients with post-traumatic knee osteoarthritis treated at Chonghua Hospital of Traditional Chinese Medicine(Guilin)from March 2024 to August 2025 were randomly assigned to a control group(n=16)or an intervention group(n=16).Both groups received eight weeks of conventional treatment;and the intervention group additionally underwent neurodynamic mobilization.Outcomes including pain assessed by the visual analogue scale,active range of motion,Lysholm score,stork stand test,single hop test,and Y-balance test were assessed before and after the intervention.RESULTS There were no significant differences between the two groups in baseline characteristics,including gender,age,body mass index,or surgical side(P>0.05).Two-way repeated-measures analysis of variance demonstrated significant time×group interaction effects for the visual analogue scale score(F=13.364,P<0.05),Lysholm knee score(F=20.385,P<0.05),stork stand test(F=103.756,P<0.05),and Y-balance test score(F=8.089,P<0.05).CONCLUSION Neurodynamic mobilization effectively reduces pain,improves knee function,and enhances lower limb balance in patients with mild post-traumatic knee osteoarthritis.展开更多
Carbon-based air cathodes offer low cost,high electrical conductivity,and structural tunability.However,they suffer from limited catalytic activity and inefficient gas transport,and they typically rely on noble metal ...Carbon-based air cathodes offer low cost,high electrical conductivity,and structural tunability.However,they suffer from limited catalytic activity and inefficient gas transport,and they typically rely on noble metal additives or complex multilayer configurations.To tackle these issues,this study devised a self-activated integrated carbon-based air cathode.By integrating in situ catalytic site construction with structural optimization,the strategy not only induces the formation of oxygen functional groups(─C─OH,─C═O,─COOH),hierarchical pores,and uniformly distributed active sites,but also establishes a favorable electronic and mass-transport environment.Furthermore,the roll-pressing-based integrated design streamlines electrode construction,reinforces interfacial bonding,and significantly enhances mechanical stability.Density functional theory(DFT)calculations show that oxygen functional groups initiate hydrogen bonding interaction and promote charge enrichment,which improves the activity of the cathode and facilitates intermediate adsorption/desorption in oxygen reduction and evolution reactions processes.As a result,the integrated air cathode-based rechargeable zinc-air batteries(RZABs)achieve a high specific capacity of 811 mAh g^(-1).It also performs well in quasi-solid-state RZABs and silicon-air batteries systems across a wide temperature range,demonstrating strong adaptability and application potential.This study provides a scalable and cost-effective design strategy for high-performance carbon-based air cathodes,offering new insights into advancing durable and practical metal-air energy systems.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
文摘BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金Supported by the National Natural Science Foundation of China(Grant No.11471163)。
文摘In this paper,we obtain a normality criterion for families of meromorphic functions concerning‘wandering’shared functions,which generalizes and improves Montel’s criterion and the related results due to Schwick,Xu-Fang,Xu-Qiu,and Grahl-Nevo.Also,a normality relationship between two families is given.
基金supported by the National Research Foundation of Korea(NRF)grant for RLRC funded by the Korea government(MSIT)(No.2022R1A5A8026986,RLRC)supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-01304,Development of Self-Learnable Mobile Recursive Neural Network Processor Technology)+3 种基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Grand Information Technology Research Center support program(IITP-2024-2020-0-01462,Grand-ICT)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)supported by the Korea Technology and Information Promotion Agency for SMEs(TIPA)supported by the Korean government(Ministry of SMEs and Startups)’s Smart Manufacturing Innovation R&D(RS-2024-00434259).
文摘On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.
文摘Renormalization group analysis has been proposed to eliminate secular terms in perturbation solutions of differential equations and thus expand the domain of their validity.Here we extend the method to treat periodic orbits or limit cycles.Interesting normal forms could be derived through a generalization of the concept'resonance',which offers nontrivial analytic approximations.Compared with traditional techniques such as multi-scale methods,the current scheme proceeds in a very straightforward and simple way,delivering not only the period and the amplitude but also the transient path to limit cycles.The method is demonstrated with several examples including the Duffing oscillator,van der Pol equation and Lorenz equation.The obtained solutions match well with numerical results and with those derived by traditional analytic methods.
基金co-supported by the National Natural Science Foundation of China(Nos.12372048,12102343)the Key Program of the National Natural Science Foundation of China(No.U2013206)+1 种基金the China Postdoctoral Science Foundation(No.2023M742835)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023A1515011421).
文摘This paper presents a predefined-time controller for Multiple Space transportation Robots System (MSRS), which can be applied in on-orbit assembly tasks to transport modules to pre-assembly configuration quickly. Firstly, to simplify the analysis and design of predefined-time controller, a Predefined-time Stability Criterion is proposed in the form of Composite Lyapunov Function (CLF-PSC). Besides simplicity, the CLF-PSC also has the advantage of less conservativeness due to utilization of initial state information. Secondly, a concept of Lp-Norm-Normalized Sign Function (LPNNSF) is proposed based on the CLF-PSC. Different from traditional norm-normalized sign function, the Lp-norm of LPNNSF can be selected arbitrarily according to practical control task requirements, which means that the proposed LPNNSF is more generalized and more convenient for calculation. Thirdly, a predefined-time disturbance observer and predefined-time controller are designed based on the LPNNSF. The observer has the property of predefined-time convergence to achieve quicker and more accurate estimation of the lumped disturbance. The controller has less control input and chattering phenomenon than traditional predefined-time controller. In addition, by introducing the observer into the controller, the closed-loop system enjoys high precision and strong robustness. Finally, the effectiveness of the proposed controller is verified by numerical simulations. By employing the controller, the MSRS can carry assembly modules to the desired pre-assembly configuration accurately within predefined time.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2022R1A2C2012243).
文摘In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
基金supported by the National Natural Science Foundation of China,Nos.82072165 and 82272256(both to XM)the Key Project of Xiangyang Central Hospital,No.2023YZ03(to RM)。
文摘Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury.
基金supported by the Budgeted Fund of Shanghai University of Traditional Chinese Medicine(Natural Science)(No.2021LK037)the Open Project of Qinghai Province Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation(No.2021-ZY-03).
文摘Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.
文摘BACKGROUND Patients with chronic hepatitis B(CHB)require long-term antiviral therapy.The effects of different antiviral drugs on kidney function are unclear.There is a lack of effective markers for monitoring early renal impairment.AIM To investigate the rate of abnormal renal function index and related potential hazards in patients with CHB.METHODS Clinical data of patients with CHB with urinaryβ2-microglobulin(β2-M)detec-tion,including demographic characteristics,hepatitis B virus(HBV)DNA,serum liver function(alanine aminotransferase,aspartate aminotransferase,total bilirubin,direct bilirubin),serum renal function(urea nitrogen,creatinine),blood lipid index(high density lipoprotein,low density lipoprotein,cholesterol,trigly-ceride),liver imaging,and other routine tests were retrospectively collected.The normal level of urinaryβ2-M and estimated glomerular filtration rate(eGFR)is defined as<0.173 mg/L and≥90 mL/min/1.73 m^(2),retrospectively.The pro-portion of patients with abnormal renal function index and related risk factors were analyzed.RESULTS A total of 500 patients with CHB were enrolled;these patients were aged 44.7±10.8 years,67.2%(336/500)were male,57.2%(286/500)were treated with anti-viral drugs,and 52.2%(261/500)had an HBV-related family history.In total,28.8%(144/500)of patients had fatty liver,35.0%(175/500)had liver fibrosis,and 13.2%(66/500)had cirrhosis.The proportion of patients with eGFR<90 mL/min/1.73 m^(2) was 43.2%(216/500),and the abnormal rate of urinaryβ2-M was 56.2%(281/500).There was no significant difference in the abnormal rate of urinaryβ2-M between the untreated group and the antiviral treated group(54.2%vs 57.7%;P=0.25).The abnormal rate ofβ2-M after long-term entecavir treatment(more than 1 year)was 54.6%(89/163).In the treatment group,56.4%(92/163)of patients with eGFR≥90 mL/min/1.73 m^(2) had abnormal urinaryβ2-M.CONCLUSION In patients with CHB,a higher proportion had greater urinaryβ2-M levels than eGFR for renal injury.Male patients should pay more attention to renal function and use antiviral regimens with a renal safety profile.
基金Supported by The Natural Science Foundation of China,No.82374292the Plans for Major Provincial Science and Technology Projects of Anhui Province,No.202303a07020003the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine,No.ZYYCXTD-C-202401.
文摘Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
基金Supported by Suzhou Clinical Medical Center for Mood Disorders,No.Szlcyxzx202109Suzhou Key Laboratory,No.SZS2024016Multicenter Clinical Research on Major Diseases in Suzhou,No.DZXYJ202413.
文摘BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.
文摘BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly impact the quality of life of patients.AIM To explore the correlations between ocular surface function,sleep quality,and anxiety/depression in patients with DED.METHODS This was a cross-sectional investigative study that included 358 patients with DED between January 2022 and January 2025.Ocular surface was assessed using the ocular surface disease index(OSDI),tear film break-up time,fluorescein staining score,and Schirmer I test.The Pittsburgh Sleep Quality Index(PSQI),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were used to evaluate sleep quality and anxiety/depression levels.Correlation and linear regression analyses were used to explore the relationships.RESULTS The mean PSQI score of the patients was 9.94±2.18;the mean SAS score was 47.30±4.90,and the mean SDS score was 50.08±5.52.These suggested a prevalence of sleep and psychological abnormalities.There was a significant correlation between the indicators of ocular surface function(OSDI,tear film break-up time,fluorescein staining,and Schirmer I test)and PSQI,SAS,and SDS scores(P<0.05).Moreover,multiple regression revealed that age≥50 years(β=1.55,P=0.029),PSQI scores(β=0.58,P<0.001),SAS scores(β=0.17,P=0.017),and SDS scores(β=0.15,P=0.019)were independent predictors of the OSDI scores.CONCLUSION Ocular surface function in patients with DED is closely related to sleep quality and anxiety/depression,emphasizing the need for holistic clinical management.
基金supported by the National Natural Science Foundation of China(52276196)the Foundation of State Key Laboratory of Coal Combustion(FSKLCCA2508)the High-level Talent Foundation of Anhui Agricultural University(rc412307).
文摘Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.Based on the Joule effect,the solid carbon sources can be rapidly heated to ultra-high temperatures(>3000 K)through instantaneous high-energy current pulses during FJH,thus driving the rapid rearrangement and graphitization of carbon atoms.This technology demonstrates numerous advantages,such as solvent-and catalyst-free features,high energy conversion efficiency,and a short process cycle.In this review,we have systematically summarized the technology principle and equipment design for FJH,as well as its raw materials selection and pretreatment strategies.The research progress in the FJH synthesis of flash graphene,carbon nanotubes,graphene fibers,and anode hard carbon,as well as its by-products,is also presented.FJH can precisely optimize the microstructures of carbon materials(e.g.,interlayer spacing of turbostratic graphene,defect concentration,and heteroatom doping)by regulating its operation parameters like flash voltage and flash time,thereby enhancing their performances in various applications,such as composite reinforcement,metal-ion battery electrodes,supercapacitors,and electrocatalysts.However,this technology is still challenged by low process yield,macroscopic material uniformity,and green power supply system construction.More research efforts are also required to promote the transition of FJH from laboratory to industrial-scale applications,thus providing innovative solutions for advanced carbon materials manufacturing and waste management toward carbon neutrality.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金Supported by the Central Guided Local Science and Technology Development Fund Project for Science and Technology Innovation Base Construction,No.Guike ZY24212046National Natural Science Foundation of China,No.U22A2092+3 种基金Guangxi Education Science“the 14th Five-Year Plan”2024 Special Project“Research on Steam Education Practice in Rehabilitation Engineering”,No.2024ZJY304the Research Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi,No.2025KY2255the Innovation Project of GUET Graduate Education,No.2025YCXB010Natural Science Research Project of Guilin Life and Health Career Technical College,No.2025GKKY04.
文摘BACKGROUND The therapeutic role of neurodynamic mobilization in improving lower limb function in patients with mild post-traumatic knee osteoarthritis remains poorly understood.AIM To further elucidate the role of neurodynamic mobilization in facilitating knee joint functional recovery.METHODS Thirty-two patients with post-traumatic knee osteoarthritis treated at Chonghua Hospital of Traditional Chinese Medicine(Guilin)from March 2024 to August 2025 were randomly assigned to a control group(n=16)or an intervention group(n=16).Both groups received eight weeks of conventional treatment;and the intervention group additionally underwent neurodynamic mobilization.Outcomes including pain assessed by the visual analogue scale,active range of motion,Lysholm score,stork stand test,single hop test,and Y-balance test were assessed before and after the intervention.RESULTS There were no significant differences between the two groups in baseline characteristics,including gender,age,body mass index,or surgical side(P>0.05).Two-way repeated-measures analysis of variance demonstrated significant time×group interaction effects for the visual analogue scale score(F=13.364,P<0.05),Lysholm knee score(F=20.385,P<0.05),stork stand test(F=103.756,P<0.05),and Y-balance test score(F=8.089,P<0.05).CONCLUSION Neurodynamic mobilization effectively reduces pain,improves knee function,and enhances lower limb balance in patients with mild post-traumatic knee osteoarthritis.
基金funded by the National Nature Science Foundation of China(62264006,62574102)“Thousand Talents Program”of Yunnan Province for Young Talents,Innovative Research Teams(in Science and Technology)in the University of Yunnan Province(IRTSTYN),XingDian Talent Support Program for Young Talents,and Frontier Research Team of Kunming University 2023,The Basic Research Project of Yunnan Province(Nos.202201AU070022)+2 种基金Kunming University Talent Introduction Fund(Nos.YJL20024)Yunnan Province Education Department Scientific Research Fund Project(Nos.2024Y759)Undergraduate Innovation and Entrepreneurship Training Program Project of Yunnan Provincial(202411393005)。
文摘Carbon-based air cathodes offer low cost,high electrical conductivity,and structural tunability.However,they suffer from limited catalytic activity and inefficient gas transport,and they typically rely on noble metal additives or complex multilayer configurations.To tackle these issues,this study devised a self-activated integrated carbon-based air cathode.By integrating in situ catalytic site construction with structural optimization,the strategy not only induces the formation of oxygen functional groups(─C─OH,─C═O,─COOH),hierarchical pores,and uniformly distributed active sites,but also establishes a favorable electronic and mass-transport environment.Furthermore,the roll-pressing-based integrated design streamlines electrode construction,reinforces interfacial bonding,and significantly enhances mechanical stability.Density functional theory(DFT)calculations show that oxygen functional groups initiate hydrogen bonding interaction and promote charge enrichment,which improves the activity of the cathode and facilitates intermediate adsorption/desorption in oxygen reduction and evolution reactions processes.As a result,the integrated air cathode-based rechargeable zinc-air batteries(RZABs)achieve a high specific capacity of 811 mAh g^(-1).It also performs well in quasi-solid-state RZABs and silicon-air batteries systems across a wide temperature range,demonstrating strong adaptability and application potential.This study provides a scalable and cost-effective design strategy for high-performance carbon-based air cathodes,offering new insights into advancing durable and practical metal-air energy systems.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
