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classification of congenital cataracts based on multidimensional phenotypes and its association with visual outcomes

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 473-479页

作者： Yuan Tan Ying-Shi Zou Ying-Lin Yu Le-Yi Hu Ting Zhang Hui Chen Ling Jin Duo-Ru Lin Yi-Zhi Liu Hao-Tian Lin Zhen-Zhen Liu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060Guangdong ProvinceChina

●AIM:To establish a classification for congenital cataracts that can facilitate individualized treatment and help identify individuals with a high likelihood of different visual outcomes.●METHODS:Consecutive patients diagnosed with congenital cataracts and undergoing surgery between January 2005 and November 2021 were recruited.Data on visual outcomes and the phenotypic characteristics of ocular biometry and the anterior and posterior segments were extracted from the patients’medical records.A hierarchical cluster analysis was performed.The main outcome measure was the identification of distinct clusters of eyes with congenital cataracts.●RESULTS:A total of 164 children(299 eyes)were divided into two clusters based on their ocular features.Cluster 1(96 eyes)had a shorter axial length(mean±SD,19.44±1.68 mm),a low prevalence of macular abnormalities(1.04%),and no retinal abnormalities or posterior cataracts.Cluster 2(203 eyes)had a greater axial length(mean±SD,20.42±2.10 mm)and a higher prevalence of macular abnormalities(8.37%),retinal abnormalities(98.52%),and posterior cataracts(4.93%).Compared with the eyes in Cluster 2(57.14%),those in Cluster 1(71.88%)had a 2.2 times higher chance of good best-corrected visual acuity[classification may enable the personalization and prioritization of early interventions for patients who may gain the greatest benefit,thereby making strides toward precision medicine in the field of congenital cataracts.

关键词： classification congenital cataract phenotype visual acuity cluster analysis

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classification of anatomical morphology of cystic duct and its association with gallstone

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 307-317页

作者： Jia-Hai Zhu Song-Ling Zhao Qiang Kang Ya Zhu Li-Xin Liu Hao Zou Department of Hepatobiliary Pancreatic Surgery The Second Affiliated Hospital of Kunming Medical UniversityKunming 650106Yunnan ProvinceChina Department of Hepatobiliary Surgery The Second Affiliated HospitalKunming Medical UniversityKunming 650106Yunnan ProvinceChina

BACKGROUND Gallstones are common lesions that often require surgical intervention.Laparo-scopic cholecystectomy is the treatment of choice for symptomatic gallstones.Preoperatively,the anatomical morphology of the cystic duct(CD),needs to be accurately recognized,especially when anatomical variations occur in the CD,which is otherwise prone to bile duct injury.However,at present,there is no optimal classification system for CD morphology applicable in clinical practice,and the relationship between anatomical variations in CDs and gallstones remains to be explored.AIM To create a more comprehensive clinically applicable classification of the morphology of CD and to explore the correlations between anatomic variants of CD and gallstones.METHODS A total of 300 patients were retrospectively enrolled from October 2021 to January 2022.The patients were divided into two groups:The gallstone group and the nongallstone group.Relevant clinical data and anatomical data of the CD based on magnetic resonance cholangiopancreatography(MRCP)were collected and analyzed to propose a morphological classification system of the CD and to explore its relationship with gallstones.Multivariate analysis was performed using logistic regression analyses to identify the independent risk factors using variables that were significant in the univariate analysis.RESULTS Of the 300 patients enrolled in this study,200(66.7%)had gallstones.The mean age was 48.10±13.30 years,142(47.3%)were male,and 158(52.7%)were female.A total of 55.7%of the patients had a body mass index(BMI)≥24 kg/m2.Based on the MRCP,the CD anatomical typology is divided into four types:Type I:Linear,type II:n-shaped,type III:S-shaped,and type IV:W-shaped.Univariate analysis revealed differences between the gallstone and nongallstone groups in relation to sex,BMI,cholesterol,triglycerides,morphology of CD,site of CD insertion into the extrahepatic bile duct,length of CD,and angle between the common hepatic duct and CD.According to the multivariate analysis,female,BMI(≥24 kg/m2)

关键词： Cystic duct Gallstone classification Anatomy Magnetic resonance cholangiopancreatography Risk factor

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classification of Sailboat Tell Tail Based on Deep Learning

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Journal of Ocean University of China 2024年第3期23卷 710-720页

作者： CHANG Xiaofeng YU Jintao GAO Ying DING Hongchen LIU Yulong YU Huaming Department of Physical Education Ocean University of ChinaQingdao 266100China College of Big Data Qingdao University of Science and TechnologyQingdao 266061China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect.Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual fatigue.In this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease.This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction performance.Considering the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM classifier.All experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the classifier.The remaining one group was used as the test set for testing.The highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments.The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

关键词： tell tail sailboat classification deep learning

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Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition

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Cancer Biology & Medicine 2024年第5期21卷 363-381页

作者： Can Xu Pengyu Hou Xiang Li Menglin Xiao Ziqi Zhang Ziru Li Jianglong Xu Guoming Liu Yanli Tan Chuan Fang School of Clinical Medicine Hebei University Department of Neurosurgery Affiliated Hospital of Hebei University Hebei Key Laboratory of Precise Diagnosis and Treatment of Glioma School of Basic Medical Sciences Hebei University Department of Pathology Affiliated Hospital of Hebei University

Among central nervous system-associated malignancies, glioblastoma(GBM) is the most common and has the highest mortality rate. The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide. In precision medicine, research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity, as well as the refractory nature of GBM toward therapy. Deep understanding of the different molecular expression patterns of GBM subtypes is critical. Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes. The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors. These subtypes also exhibit high plasticity in their regulatory pathways, oncogene expression, tumor microenvironment alterations, and differential responses to standard therapy. Herein, we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype. Furthermore, we review the mesenchymal transition mechanisms of GBM under various regulators.

关键词： Glioblastoma characteristic classification mesenchymal transition molecular phenotype

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classification and antioxidant assays of polyphenols:a review

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未来食品学报(英文) 2024年第3期4卷 193-204页

作者： Yuxi Lang Ningxuan Gao Zhihuan Zang Xianjun Meng Yang Lin Shufang Yang Yiyun Yang Zhufeng Jin Bin Li College of Food Science Shenyang Agricultural UniversityShenyang 110866China College of Animal Science and Veterinary Medicine Shenyang Agricultural UniversityShenyang 110866China Key Laboratory of Marine Fishery Resources Exploitment&Utilization of Zhejiang Province Hangzhou 310014China Zhejiang Lanmei Technology Co. LtdZhuji 311800China

Polyphenols are widely recognized as the effective antioxidants,which are divided into flavonoids,phenolic acids,stilbenes,lignans,tannins and so on.They could regulate internal functions and protect the body from diseases related to oxidative damage.Due to the fact that their antioxidant capacity is influenced by the structure,stability and bioavailability,the detection of their bioactivity should be considered comprehensively.Currently,the methods for measuring the antioxidant capacity of phenolic compounds are divided into chemical,cell-based and in vivo assays.The chemical assays include 2,2-diphenyl-l-picrylhydrazyl(DPPHI),2,2"-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS),ferric reducing/antioxidant power(FRAP),oxygen radical absorbance capacity(ORAC),peroxyI radical scavenging capacity(PSC),which are rapid identification method,but their reaction mechanism has a great gap with the internal body response.The cell-based assays are more consistent with biological reaction,but still do not take the bioavailability into consideration.The in vivo assays,which commonly utilized Caenorhabditis elegans or rats as models,are more representative,but these methods are more complex and spend longer.This review summarizes the antioxidant evaluation methods of phenolic compounds and discusses their advantages and limitations comparatively,which could help discriminate and select the appropriate assay in the actual operation,and facilitate the development of comprehensive approaches as well.

关键词： Polyphenols classification Antioxidant assays Chemical assays Cell-based assays In vivo assays

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Diffraction deep neural network-based classification for vector vortex beams

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Chinese Physics B 2024年第3期33卷 387-392页

作者：彭怡翔陈兵王乐赵生妹 Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications (NJUPT)Nanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

关键词： vector vortex beam diffractive deep neural network classification atmospheric turbulence

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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(Information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of Electrical and Information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of Electronics and Information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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classification and reconstructive algorithm for nasal alar defect in Asians

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Chinese Journal of Plastic and Reconstructive Surgery 2024年第1期6卷 22-27页

作者： Renpeng Zhou Dongze Lyu Chen Wang Danru Wang Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine

Background: The nasal alar defect in Asians remains a challenging issue, as do clear classification and algorithm guidance, despite numerous previously described surgical techniques. The aim of this study is to propose a surgical algorithm that addresses the appropriate surgical procedures for different types of nasal alar defects in Asian patients.Methods: A retrospective case note review was conducted on 32 patients with nasal alar defect who underwent reconstruction between 2008 and 2022. Based on careful analysis and our clinical experience, we proposed a classification system for nasal alar defects and presented a reconstructive algorithm. Patient data, including age,sex, diagnosis, surgical options, and complications, were assessed. The extent of surgical scar formation was evaluated using standard photography based on a 4-grade scar scale.Results: Among the 32 patients, there were 20 males and 12 females with nasal alar defects. The predominant cause of trauma in China was industrial factors. The majority of alar defects were classified as type I C(n=8,25%), comprising 18 cases(56.2%); there were 5 cases(15.6%) of type II defect, 7(21.9%) of type Ⅲ defect, and2(6.3%) of type IV defect. The most common surgical option was auricular composite graft(n=8, 25%), followed by bilobed flap(n=6, 18.8%), free auricular composite flap(n=4, 12.5%), and primary closure(n=3, 9.4%).Satisfactory improvements were observed postoperatively.Conclusion: Factors contributing to classifications were analyzed and defined, providing a framework for the proposed classification system. The reconstructive algorithm offers surgeons appropriate procedures for treating nasal alar defect in Asians.

关键词： Nasal alar Defect classification Algorithm Surgical methods

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Curve classification Based onMean-Variance Feature Weighting and Its Application

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Computers, Materials & Continua 2024年第5期79卷 2465-2480页

作者： Zewen Zhang Sheng Zhou Chunzheng Cao School of Mathematics and Statistics Nanjing University of Information Science and TechnologyNanjing210044China Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and TechnologyNanjing210044China

The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.

关键词： Functional data analysis classification feature weighting B-splines

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Deep Learning-Based ECG classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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