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Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

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International Journal of Ophthalmology(English edition) 2024年第1期17卷 1-6页

作者： Yu-Ke Ji Rong-Rong Hua Sha Liu Cui-Juan Xie Shao-Chong Zhang Wei-Hua Yang Eye Hospital Nanjing Medical UniversityNanjing 210000Jiangsu ProvinceChina college of Electronic Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210000Jiangsu ProvinceChina Shenzhen Eye Institute Shenzhen Eye HospitalJinan UniversityShenzhen 518000Guangdong ProvinceChina

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

关键词： deep learning artificial intelligence Swin Transformer diagnostic model retinal vein occlusion color fundus photographs

Seropositivity and Other Determinants Associated with Toxoplasmosis in Local Buffalo in Iraq

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Open Journal of Veterinary Medicine 2024年第1期14卷 1-9页

作者： Rafid Jawad Kazem Imad Kazem Ali Department of Animal Production College of Agriculture Sumer University Al-Rifai Iraq

A cross-sectional study was conducted to determine the Seropositivity of Toxoplasma gondii in water buffalo in three Iraqi governorates (Baghdad, Dhi Qar, and Maysan) and to estimate the risk aspects related to infestation throughout the period from January to December 2019. A total of 430 serum samples were inspected with a commercial ELISA (Enzyme linked immunosorbent assay) kit. Indirect multi-species kit. The overall Seropositivity of T. gondii in the examined local buffalo was 7.4%, and the highest rate (9.3%) was in Baghdad Governorate. A multivariate regression analysis revealed that adult buffalo (OR = 7.10;95% CI: 0.87-57.68;P = 0.067) and young herds (OR = 8.42;95% CI: 1.07-66.02;P = 0.043) were more subject to infestation from young buffalo and large herds. Furthermore, the hazard of toxoplasmosis was increased in winter especially among animals in contact with cats. It is therefore requisite to determine risk aspects to evaluate which mitigation, control, prevention and procedures should be carried out to diminish, control and prevent infestation with T. gondii and its propagation.

关键词： Toxoplasmosis Buffalo Local Seropositivity Iraq

Human-artificial intelligence interaction in gastrointestinal endoscopy

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World Journal of Gastrointestinal Endoscopy 2024年第3期16卷 126-135页

作者： John R Campion Donal B O'Connor Conor Lahiff Department of Gastroenterology Mater Misericordiae University HospitalDublin D07 AX57Ireland School of Medicine University College DublinDublin D04 C7X2Ireland Department of Surgery Trinity College DublinDublin D02 R590Ireland

The number and variety of applications of artificial intelligence(AI)in gastr-ointestinal(GI)endoscopy is growing rapidly.New technologies based on machine learning(ML)and convolutional neural networks(CNNs)are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures,in detection,diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators.Platforms based on ML and CNNs require regulatory approval as medical devices.Interactions between humans and the technologies we use are complex and are influenced by design,behavioural and psychological elements.Due to the substantial differences between AI and prior technologies,important differences may be expected in how we interact with advice from AI technologies.Human-AI interaction(HAII)may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability.Human factors influencing HAII may include automation bias,alarm fatigue,algorithm aversion,learning effect and deskilling.Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

关键词： Artificial intelligence Machine learning Human factors Computer-aided detection Colonoscopy Adenoma detection rate

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Photonics Research 2024年第4期12卷 804-812页

作者： XIAOYU NIE HAOTIAN SONG WENHAN REN ZHEDONG ZHANG TAO PENG MARLAN O.SCULLY Institute for Quantum Science and Engineering Texas A&M UniversityCollege StationTexas 77843USA School of Physics Xi’an Jiaotong UniversityXi’an 710049China Department of Physics City University of Hong KongHong KongChina Baylor University WacoTexas 76706USA

The generation of speckle patterns via random matrices, statistical definitions, or apertures may not always result in optimal outcomes. Issues such as correlation fluctuations in low ensemble numbers and diffraction in long-distance propagation can arise. Instead of improving results of specific applications, our solution is catching deep correlations of patterns with the framework, Speckle-Net, which is fundamental and universally applicable to various systems. We demonstrate this in computational ghost imaging(CGI) and structured illumination microscopy(SIM). In CGI with extremely low ensemble number, it customizes correlation width and minimizes correlation fluctuations in illuminating patterns to achieve higher-quality images. It also creates non-Rayleigh nondiffracting speckle patterns only through a phase mask modulation, which overcomes the power loss in the traditional ring-aperture method. Our approach provides new insights into the nontrivial speckle patterns and has great potential for a variety of applications including dynamic SIM, X-ray and photo-acoustic imaging, and disorder physics.

关键词： CGI Deep ghost

Phytochemicals of Aloe barbadensis miller as Potential Inhibitors of Uropathogenic Escherichia coli for Urinary Tract Infection Therapy: An in Silico Approach

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Open Journal of Biophysics 2024年第2期14卷 99-120页

作者： Mikidadi Salehe Gurisha Pulapa Venkata Kanaka Rao Laxmikanth Cherupally Department of Physics College of Natural and Mathematical Sciences University of Dodoma Dodoma Tanzania

Urinary tract infections (UTIs) are common infections caused by normal skin or rectum bacteria that get into the urethra and infect the urinary tract. Although the infection can affect various parts of the tract, bladder infections are the most prevalent kind. Uropathogenic Escherichia Coli (UPEC) is the most common pathogen associated with UTI development. Therefore, inhibiting the UPEC protein target (PDB ID: 8BVD) appears to be a promising therapeutic strategy. Therefore, in this study, molecular docking and dynamics were conducted to examine the antibacterial activity of Aloe barbadensis miller against UPEC bacteria. The Aloe barbadensis miller natural compounds licochalcone A, palmidin B and palmidin C were downloaded from PubChem with amoxicillin, which was used as a control drug and studied against the target molecule. The potential parameters examined were the docking scores, absorption, distribution, metabolism, excretion, toxicity (ADMET), bioavailability, root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding, radius of gyration, and potential energy of the system. Docking scores showed that all ligands demonstrated an admirable candidature as an inhibitor to 8BVD molecule, and the score hierarchy is licochalcone A (-6.4 kcal/mol), palmidin C (-6.1 kcal/mol), palmidin B (-6.0 kcal/mol), and amoxicillin (-5.9 kcal/mol). All ligands appeared to have good drug-like properties and oral bioavailability. Molecular dynamic studies showed that all ligands exhibited an excellent nominee as inhibitors in their vicinity at 20 ns. However, there is a relatively high fluctuation of palmidin B compared with other compounds, which seems to be more stable. This work suggests that the selected phytoconstituents could be used as inhibitors of the 8BVD protein in the fight against UTIs. However, further investigation on the clinical and experimental validation of UTI treatment’s specific mechanisms and effects is still welcomed.

关键词： Uropathogenic Escherichia Coli Phytochemicals Molecular Docking Ligand Hydrogen Bond

Effects of Exogenous H_(2)O_(2) on ROS Metabolism in Nitraria tangutorum Bobr. Leaves Under Salt Stress

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Journal of Northeast Agricultural University(English Edition) 2024年第1期31卷 20-27页

作者： Zhang Nan Cahaeraduqin Sunaer Du Jinxue Pan Yiliang Wang Rui Liu Wei college of Horticulture and Landscape Architecture Northeast Agricultural UniversityHarbin 150030China

Salt stress is a major abiotic stress limiting plant growth and yield. In the present study, the effects of exogenous H_(2)O_(2) on the reactive oxygen species(ROS) metabolism and the antioxidant system in leaves of Nitralia tangutorum Bobr. under salt stress were studied. N. tangutorum seedlings were subjected to 200 mmol·L^(-1) NaCl treatment with or without the exogenous application of H_(2)O_(2) for 7 days. The results showed that NaCl stress significantly increased the relative conductivity, the contents of thiobarbituric acid reactive substances(TBARS) and ROS(H_(2)O_(2) and O_(2)^(·-)), as well as promoted the activities of antioxidant enzymes including superoxide dismutase(SOD), peroxidase(POD), catalase(CAT), and ascorbate peroxidase(APX) in N. tangutorum leaves. In addition, exogenous H_(2)O_(2) decreased the relative conductivity, the contents of TBARS, H_(2)O_(2) and O_(2)^(·-), while further enhanced the activities of antioxidant enzymes. These results indicated that H_(2)O_(2) effectively alleviated the adverse effects of NaCl stress on N. tangutorum through the regulation of ROS metabolism.

关键词： H_(2)O_(2) NaCl stress reactive oxygen species antioxidant enzymes Nitraria tangutorum Bobr

A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

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Journal of Computer and Communications 2024年第4期12卷 153-170页

作者： Nada Al Zahrani Ramdane Hedjar Mohamed Mekhtiche Mohamed Bencherif Taha Al Fakih Fattoh Al-Qershi Muna Alrazghan Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Information System College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

关键词： Pneumonia Thoracic Pathology COVID-19 Deep Learning Multi-Task Learning

Research on Biometric Identification Method of Nuclear Cold Source Disaster Based on Deep Learning

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Journal of Computer and Communications 2024年第1期12卷 162-176页

作者： Kai Liu Yadong Wu Dan Luo Jinzhao Zhang Weihan Zhang college of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.

关键词： Image Recognition Image Recognition Gamma Adjust Transposed Convolution

Orchestrating seed storage protein and starch accumulation toward overcoming yield–quality trade-off in cereal crops

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Journal of Integrative Plant Biology 2024年第3期66卷 468-483页

作者： Shuanghe Cao Bingyan Liu Daowen Wang Awais Rasheed Lina Xie Xianchun Xia Zhonghu He State Key Laboratory of Crop Gene Resources and Breeding/National Wheat Improvement Center Institute of Crop SciencesBeijing 100081China college of Agronomy Henan Agricultural UniversityZhengzhou 450002China International Maize and Wheat Improvement Center(CIMMYT)China Office Chinese Academy of Agricultural SciencesBeijing 100081China

Achieving high yield and good quality in crops is essential for human food security and health.However,there is usually disharmony between yield and quality.Seed storage protein(SSP)and starch,the predominant components in cereal grains,determine yield and quality,and their coupled synthesis causes a yield–quality trade-off.Therefore,dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and quality.Here,we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops,including maize,rice and wheat.We further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand insights.We also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular breeding.Finally,future perspectives on major challenges are proposed.

关键词： cereal crops grain yield and quality seed storage protein starch synergistic regulation