The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
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 embryo rescue technique plays an essential role in developing new seedless grape varieties.To enhance the efficiency of seedless grape embryo rescue breeding,this study evaluated 22 hybrid combinations and systema...The embryo rescue technique plays an essential role in developing new seedless grape varieties.To enhance the efficiency of seedless grape embryo rescue breeding,this study evaluated 22 hybrid combinations and systematically investigated the effects of parental genotypes and plant hormones on embryo development and germination.Additionally,an in-depth analysis was conducted on the conversion of abnormal plantlets.Results indicate that‘Ruby Seedless’,‘Delight’,‘Huozhouheiyu’,‘Zitian Seedless’,and‘Zhengyan Seedless’are suitable as maternal parents,whereas‘Zitian Seedless’,‘Shennongxiangfeng’,‘Hongqitezao’,and‘Guibao’perform optimally as paternal parents.Among these,the crosses‘Ruby Seedless×Shennongxiangfeng’and‘Ruby Seedless×Zitian Seedless’exhibited the highest embryo rescue efficiency,with embryo development rates of 55.05 and 59.76%,yielding 1,348 and 2,235 viable plantlets,respectively.When 1.0 mg L^(–1) zeatin (ZT) was added to the MM3 medium supplemented with 0.2 mg L^(–1) indole-3-acetic acid (IAA),the embryo development rate of‘Ruby Seedless×Zitian Seedless’increased by 64.73%.In the WPM germination medium,supplementation with 0.2 mg L^(–1) ZT and 0.2 mg L^(–1) IAA resulted in the highest germination rate of 85.71%for the hybrid combination‘Huozhouheiyu×Shine Muscat’.Furthermore,3,365 abnormal plantlets were rescued via direct transformation and hypocotyl-induced adventitious bud regeneration,among which 1,234 were transformed into normal plantlets.Following hybridization,a total of 4,287 plants were successfully acclimatized and transplanted.This study provides theoretical insights to improve the efficiency of embryo rescue breeding in seedless grapes and offers valuable genetic resources for future breeding programs.展开更多
Dear Editor,Torpedo maculopathy(TM),first described by Roseman and Gass in 1992[1],is a rare congenital unilateral retinal pigment epithelium(RPE)abnormality.The term“torpedo maculopathy”was coined by Daily[2]in 199...Dear Editor,Torpedo maculopathy(TM),first described by Roseman and Gass in 1992[1],is a rare congenital unilateral retinal pigment epithelium(RPE)abnormality.The term“torpedo maculopathy”was coined by Daily[2]in 1993.TM typically spares the foveal center,is asymptomatic,and is often detected incidentally during routine ophthalmic examinations.Through literature search,we did not identify racial or regional differences in TM.It predominantly affects children,with an estimated prevalence of 2 per 100000 in individuals under 16 ages[3].While previous reports have focused on pediatric and adult populations,this study presents four cases of TM in preterm infants.展开更多
AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflamma...AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflammation rather than systemic autoimmunity or generalized orbital pressure.METHODS:Data included ophthalmic assessments and a panel of thyroid function and autoimmune biomarkers.Blinded radiological analysis of orbital computed tomography(CT)scans was performed to quantify sinus abnormalities and extraocular muscles(EOMs)involvement.Patients were categorized into two groups based on CT findings,those with no radiological evidence of sinus abnormalities(non-PSA control group)and those with identifiable PSA.Furthermore,ethmoid sinus mucosal biopsies from a subset of TAO patients and noninflammatory controls were subjected to histopathological analysis.RESULTS:Totally 121 TAO patients(mean age 42.4±12.8y,range 10-78y),male:female=42:79,were included.PSA was identified in 44.6%(n=54)of patients,with a distribution anatomically restricted to the maxillary(50.0%isolated)and ethmoid sinuses(18.5%isolated;29.6%combined).Compared to the non-PSA group(n=67),patients with PSA were significantly older(45.1±11.8 vs 40.3±13.2y;P=0.040)and were more likely to be male(55.6%vs 17.9%;P<0.001).They also had significantly higher proptosis(22.1±3.2 vs 20.7±2.9 mm;P<0.001).Medial/inferior rectus involvement was most frequent(88.4%vs 89.3%).Histopathological analysis of sinus mucosa from PSA patients provided direct evidence of pathology,revealing a dense,chronic lymphoplasmacytic infiltrate and submucosal edema,validating the radiological findings as a true inflammatory process.No significant correlation was found with systemic autoimmune markers,including thyroid-stimulating hormone(TSH)receptor antibodies(TRAb,median 4.86 vs 2.71 IU/L,P=0.104).CONCLUSION:TAO is associated with a high prevalence of PSA in a pattern consistent with the orbital anatomy.The correlation with ipsilateral muscle thickening combined with the lack of association with proptosis laterality or systemic biomarkers lend strong support to a model of contiguous inflammation over systemic autoimmunity,a hypothesis that warrants further validation through longitudinal and mechanistic studies.展开更多
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nai...Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nail bed related changes encountered in UET recipients in the literature.The differential diagnosis of nail abnormalities in UET includes a wide range of systemic,local and iatrogenic conditions other than immune responses to the allograft.It requires interdisciplinary evaluation by primary transplant surgeons,pathologists,dermatologists and immunologists.The possible underlying mechanisms of nail pathology in UET and the management are discussed.It also underscores the importance of onychodystrophy and need for timely intervention and to improve outcomes in UET recipients.展开更多
Objective To analyze the diagnostic efficacy of lipid-related insulin resistance(IR)markers in patients with non-alcoholic fatty liver disease(NAFLD)and metabolic abnormalities(MA).Method Patients with NAFLD with MA,n...Objective To analyze the diagnostic efficacy of lipid-related insulin resistance(IR)markers in patients with non-alcoholic fatty liver disease(NAFLD)and metabolic abnormalities(MA).Method Patients with NAFLD with MA,non-NAFLD patients with MA,and patients with NAFLD without MA underwent liver biopsy.Homeostasis model assessment of insulin resistance(HOMA-IR),triglyceride/high-density lipoprotein cholesterol(TG/HDL-C),visceral obesity index(VAI),lipid accumulation product(LAP),and triglyceride glucose(TyG)index were analyzed.The diagnostic efficacy of these indicators of NAFLD was also evaluated.Results In the NAFLD-MA group,BMI,HOMA-IR,LAP,VAI,TyG index,and TG/HDL-C ratio were higher than those in the non-NAFLD-MA group(P<0.001).Logistic regression indicated that BMI and TyG index were independent risk factors for NAFLD.Receiver Operating Characteristic(ROC)curves analysis revealed that the Area Under the ROC Curve(AUC)for TyG-BMI was 0.819,and the optimal cutoff for NAFLD was TyG-BMI 39.77.For patients with NAFLD with or without MA,logistic regression analysis suggested that age,TG level,and TyG index were independent risk factors.The area under the ROC curve showed that AUC for the TyG index was 0.724.The optimal cutoff for NAFLD-non MA was a TyG index of 1.580.Conclusion TyG index has diagnostic value in both types of NAFLD;however,TyG-BMI is better in patients with NAFLD with MA and may be an effective screening indicator alone in patients with NAFLD without MA.展开更多
Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ...Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.展开更多
Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreou...Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.展开更多
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ...To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven si...As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.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
This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis d...This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis dysregu-lation,metabolic abnormalities,and neuroendocrine modulation.Research indicates that T2DM contributes to anxiety and depression through chronic low-grade inflammation,insulin resistance,gut microbiota imbalance,and hy-peractivation of the hypothalamic-pituitary-adrenal axis.Conversely,depression may increase the risk of T2DM via lifestyle disruption,immune activation,and neurotransmitter imbalance.Additionally,metabolic pathway disturbances-such as reduced adiponectin,impaired insulin signaling,and altered amino acid me-tabolism-may influence mood regulation and cognition.The article further examines emerging therapeutic strategies targeting these shared mechanisms,including anti-inflammatory treatments,gut microbiota modulation,hypothalamic-pituitary-adrenal axis interventions,metabolic therapies(e.g.,glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors),and multidisciplinary integrative management.Emphasizing the multisystem nature of diabetes-depression comorbidity,this work highlights the importance of incorporating mental health strategies into diabetes care to optimize outcomes and enhance patient quality of life.展开更多
Myocardial ischemia(MI)is a pathophysiological condition in which the myocardium is unable to maintain normal cardiac function due to insufficient coronary artery blood and oxygen supply,as well as abnormal myocardial...Myocardial ischemia(MI)is a pathophysiological condition in which the myocardium is unable to maintain normal cardiac function due to insufficient coronary artery blood and oxygen supply,as well as abnormal myocardial energy metabolism[1].Ginsenoside Rbi(Rbi),one of the most abundant natural ingredients in ginseng and Panax notoginseng,has been proven to protect the heart from MI/reperfusion injury(RI)[2].展开更多
Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play p...Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.展开更多
Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate p...Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
1临床资料患儿,女,3岁,主因“发现右耳听力差3年”就诊。家长3年前发现患儿右耳听力差,站于右侧呼唤患儿反应迟钝,说话发声及面部表情正常。体格检查:双侧耳廓及外耳道未见明显异常。声导抗:双耳A型。声反射:左侧90 dB SPL,右侧未引出...1临床资料患儿,女,3岁,主因“发现右耳听力差3年”就诊。家长3年前发现患儿右耳听力差,站于右侧呼唤患儿反应迟钝,说话发声及面部表情正常。体格检查:双侧耳廓及外耳道未见明显异常。声导抗:双耳A型。声反射:左侧90 dB SPL,右侧未引出。行为测听:左侧听力正常,右侧极重度感音神经性听力下降。耳声发射(OAE):左侧正常,右侧全频未引出。展开更多
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
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.
基金supported by the China Agriculture Research System of MOF and MARA (CARS-29-yc-3)。
文摘The embryo rescue technique plays an essential role in developing new seedless grape varieties.To enhance the efficiency of seedless grape embryo rescue breeding,this study evaluated 22 hybrid combinations and systematically investigated the effects of parental genotypes and plant hormones on embryo development and germination.Additionally,an in-depth analysis was conducted on the conversion of abnormal plantlets.Results indicate that‘Ruby Seedless’,‘Delight’,‘Huozhouheiyu’,‘Zitian Seedless’,and‘Zhengyan Seedless’are suitable as maternal parents,whereas‘Zitian Seedless’,‘Shennongxiangfeng’,‘Hongqitezao’,and‘Guibao’perform optimally as paternal parents.Among these,the crosses‘Ruby Seedless×Shennongxiangfeng’and‘Ruby Seedless×Zitian Seedless’exhibited the highest embryo rescue efficiency,with embryo development rates of 55.05 and 59.76%,yielding 1,348 and 2,235 viable plantlets,respectively.When 1.0 mg L^(–1) zeatin (ZT) was added to the MM3 medium supplemented with 0.2 mg L^(–1) indole-3-acetic acid (IAA),the embryo development rate of‘Ruby Seedless×Zitian Seedless’increased by 64.73%.In the WPM germination medium,supplementation with 0.2 mg L^(–1) ZT and 0.2 mg L^(–1) IAA resulted in the highest germination rate of 85.71%for the hybrid combination‘Huozhouheiyu×Shine Muscat’.Furthermore,3,365 abnormal plantlets were rescued via direct transformation and hypocotyl-induced adventitious bud regeneration,among which 1,234 were transformed into normal plantlets.Following hybridization,a total of 4,287 plants were successfully acclimatized and transplanted.This study provides theoretical insights to improve the efficiency of embryo rescue breeding in seedless grapes and offers valuable genetic resources for future breeding programs.
基金Supported by the National Natural Science Foundation of China(No.82070991).
文摘Dear Editor,Torpedo maculopathy(TM),first described by Roseman and Gass in 1992[1],is a rare congenital unilateral retinal pigment epithelium(RPE)abnormality.The term“torpedo maculopathy”was coined by Daily[2]in 1993.TM typically spares the foveal center,is asymptomatic,and is often detected incidentally during routine ophthalmic examinations.Through literature search,we did not identify racial or regional differences in TM.It predominantly affects children,with an estimated prevalence of 2 per 100000 in individuals under 16 ages[3].While previous reports have focused on pediatric and adult populations,this study presents four cases of TM in preterm infants.
基金Supported by The National Natural Science Foundation of China(No.82101180)the Fund for Beijing Science&Technology Development of TCM(No.BJZYYB-2023-17)the Beijing Municipal Natural Science Foundation grant(No.7252093).
文摘AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflammation rather than systemic autoimmunity or generalized orbital pressure.METHODS:Data included ophthalmic assessments and a panel of thyroid function and autoimmune biomarkers.Blinded radiological analysis of orbital computed tomography(CT)scans was performed to quantify sinus abnormalities and extraocular muscles(EOMs)involvement.Patients were categorized into two groups based on CT findings,those with no radiological evidence of sinus abnormalities(non-PSA control group)and those with identifiable PSA.Furthermore,ethmoid sinus mucosal biopsies from a subset of TAO patients and noninflammatory controls were subjected to histopathological analysis.RESULTS:Totally 121 TAO patients(mean age 42.4±12.8y,range 10-78y),male:female=42:79,were included.PSA was identified in 44.6%(n=54)of patients,with a distribution anatomically restricted to the maxillary(50.0%isolated)and ethmoid sinuses(18.5%isolated;29.6%combined).Compared to the non-PSA group(n=67),patients with PSA were significantly older(45.1±11.8 vs 40.3±13.2y;P=0.040)and were more likely to be male(55.6%vs 17.9%;P<0.001).They also had significantly higher proptosis(22.1±3.2 vs 20.7±2.9 mm;P<0.001).Medial/inferior rectus involvement was most frequent(88.4%vs 89.3%).Histopathological analysis of sinus mucosa from PSA patients provided direct evidence of pathology,revealing a dense,chronic lymphoplasmacytic infiltrate and submucosal edema,validating the radiological findings as a true inflammatory process.No significant correlation was found with systemic autoimmune markers,including thyroid-stimulating hormone(TSH)receptor antibodies(TRAb,median 4.86 vs 2.71 IU/L,P=0.104).CONCLUSION:TAO is associated with a high prevalence of PSA in a pattern consistent with the orbital anatomy.The correlation with ipsilateral muscle thickening combined with the lack of association with proptosis laterality or systemic biomarkers lend strong support to a model of contiguous inflammation over systemic autoimmunity,a hypothesis that warrants further validation through longitudinal and mechanistic studies.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
文摘Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nail bed related changes encountered in UET recipients in the literature.The differential diagnosis of nail abnormalities in UET includes a wide range of systemic,local and iatrogenic conditions other than immune responses to the allograft.It requires interdisciplinary evaluation by primary transplant surgeons,pathologists,dermatologists and immunologists.The possible underlying mechanisms of nail pathology in UET and the management are discussed.It also underscores the importance of onychodystrophy and need for timely intervention and to improve outcomes in UET recipients.
基金Beijing Research Ward Excellence Program(BRWEP2024W102170101)The National Key Research and Development Program(2022YFC2603500,2022YFC2603505)+5 种基金Beijing Municipal Health Commission high-level public health technical personnel construction project(discipline leader-03-26,discipline backbone-02-28)Capital’s Funds for Health Improvement and Research(2022-1-2172)Beijing Hospitals Authority Clinical medicine Development of special funding support(ZLRK202301)Beijing Hospitals Authority"peak"talent training program(DFL20241803)National Key Research and Development Program of China(2023YFC2306900)National Key Research and Development Program of Ministry of Science and Technology(2023YFC2308105).
文摘Objective To analyze the diagnostic efficacy of lipid-related insulin resistance(IR)markers in patients with non-alcoholic fatty liver disease(NAFLD)and metabolic abnormalities(MA).Method Patients with NAFLD with MA,non-NAFLD patients with MA,and patients with NAFLD without MA underwent liver biopsy.Homeostasis model assessment of insulin resistance(HOMA-IR),triglyceride/high-density lipoprotein cholesterol(TG/HDL-C),visceral obesity index(VAI),lipid accumulation product(LAP),and triglyceride glucose(TyG)index were analyzed.The diagnostic efficacy of these indicators of NAFLD was also evaluated.Results In the NAFLD-MA group,BMI,HOMA-IR,LAP,VAI,TyG index,and TG/HDL-C ratio were higher than those in the non-NAFLD-MA group(P<0.001).Logistic regression indicated that BMI and TyG index were independent risk factors for NAFLD.Receiver Operating Characteristic(ROC)curves analysis revealed that the Area Under the ROC Curve(AUC)for TyG-BMI was 0.819,and the optimal cutoff for NAFLD was TyG-BMI 39.77.For patients with NAFLD with or without MA,logistic regression analysis suggested that age,TG level,and TyG index were independent risk factors.The area under the ROC curve showed that AUC for the TyG index was 0.724.The optimal cutoff for NAFLD-non MA was a TyG index of 1.580.Conclusion TyG index has diagnostic value in both types of NAFLD;however,TyG-BMI is better in patients with NAFLD with MA and may be an effective screening indicator alone in patients with NAFLD without MA.
基金supported by y the Applied Research Advancement Project in Engineering University of PAP(WYY202304)Research and Innovation Team Project in Engineering University of PAP(KYTD202306)Funding for postgraduate education and teaching.
文摘Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.
基金supported by the National Natural Science Foundation of China(62271337,62371326,and 62371328)the National Key Research and Development Program of China(2019FYC1710204)+1 种基金the National Clinical Key Specialty Construction Project(10000015Z155080000004)the Natural Science Foundation of Jiangsu Province(BK20231310).
文摘Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.
基金support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
基金Key Research and Development Plan of Shaanxi Province(2023-YBGY-493)。
文摘As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.
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.
基金Supported by the Quzhou Science and Technology Plan Project funded by the Quzhou Municipal Science and Technology Bureau,No.2022K67,No.2022K69,and No.2024K076.
文摘This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis dysregu-lation,metabolic abnormalities,and neuroendocrine modulation.Research indicates that T2DM contributes to anxiety and depression through chronic low-grade inflammation,insulin resistance,gut microbiota imbalance,and hy-peractivation of the hypothalamic-pituitary-adrenal axis.Conversely,depression may increase the risk of T2DM via lifestyle disruption,immune activation,and neurotransmitter imbalance.Additionally,metabolic pathway disturbances-such as reduced adiponectin,impaired insulin signaling,and altered amino acid me-tabolism-may influence mood regulation and cognition.The article further examines emerging therapeutic strategies targeting these shared mechanisms,including anti-inflammatory treatments,gut microbiota modulation,hypothalamic-pituitary-adrenal axis interventions,metabolic therapies(e.g.,glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors),and multidisciplinary integrative management.Emphasizing the multisystem nature of diabetes-depression comorbidity,this work highlights the importance of incorporating mental health strategies into diabetes care to optimize outcomes and enhance patient quality of life.
文摘Myocardial ischemia(MI)is a pathophysiological condition in which the myocardium is unable to maintain normal cardiac function due to insufficient coronary artery blood and oxygen supply,as well as abnormal myocardial energy metabolism[1].Ginsenoside Rbi(Rbi),one of the most abundant natural ingredients in ginseng and Panax notoginseng,has been proven to protect the heart from MI/reperfusion injury(RI)[2].
基金supported by the National Natural Science Foundation of China(No.82171599 and No.32270901)the National Key Research and Developmental Program of China(2022YFC2702601 and 2022YFA0806303)the Global Select Project(DJKLX-2022010)of the Institute of Health and Medicine,Hefei Comprehensive National Science Center.
文摘Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.
基金supported in part by the Chunhui Project of the Ministry of Education of China(HZKY20220429)the Department of Science&Technology of Liaoning Province(2022-MS-300)the Educational Department of Liaoning Province(LJKMZ20220561)
文摘Dear Editor,As an important energy storage device,lithium-ion battery plays a vital role in electric aircrafts,which are new and promising equipment of transportation in the future with low carbon emissions.Accurate prediction of the state of charge(SOC)of lithium-ion batteries is of great importance in reducing the probability of abnormal accidents and ensuring flight safety.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘1临床资料患儿,女,3岁,主因“发现右耳听力差3年”就诊。家长3年前发现患儿右耳听力差,站于右侧呼唤患儿反应迟钝,说话发声及面部表情正常。体格检查:双侧耳廓及外耳道未见明显异常。声导抗:双耳A型。声反射:左侧90 dB SPL,右侧未引出。行为测听:左侧听力正常,右侧极重度感音神经性听力下降。耳声发射(OAE):左侧正常,右侧全频未引出。
