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Unlocking hypoglycemia–associated brain microvascular dysfunction:critical insights from proteomic analysis

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Neural Regeneration Research 2025年第6期20卷 1707-1708页

作者： siva.s.v.P.sakamuri Anil sakamuri Department of Chemistry Auburn University at MontgomeryMontgomeryALUSA vascular Biology Center and Department of Medicine Medical College of Georgia at Augusta UniversityAugustaGAUSA

Hypoglycemia-a critical complication linked to worsened brain function in diabetic subjects:Hypoglycemia is characterized by a decline in circulatory glucose levels below sta nda rd physiological *** hypoglycemia,classified as level 1 hypoglycemia,is defined by blood glucose levels below 70 mg/dL and can be effectively addressed through carbohydrate *** hypoglycemia,denoted by blood glucose levels less than 54 mg/dL,poses a life-threatening risk if left *** with type 1 and type 2 diabetes undergoing insulin treatment are particularly susceptible to hypoglycemia due to impaired counterregulatory mechanisms.

关键词： impaired intake critical

Grain boundary effect on structural, optical, and electrical properties of sol-gel synthesized Fe-doped snO_(2) nanoparticles

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Chinese Physics B 2021年第4期30卷 555-564页

作者： Archana v lakshmi Mohan Kathirvel P saravanakumar s Department of sciences Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Research and Development Center Bharathiar UniversityCoimbatore-641046TamilnaduIndia GRD Center for Materials Research Department of PhysicsP S G College of TechnologyCoimbatore-641004TamilnaduIndia Department of Physics N S S CollegePandalam-689501KeralaIndia

Tin oxide(snO_(2)) and iron-doped tin oxide(sn_(1-x)Fe_(x)O_(2),x=0.05 wt%,0.10 wt%) nanoparticles are synthesized by the simple sol-gel *** structural characterization using x-ray diffraction(XRD) confirms tetragonal rutile phases of the *** variations in lattice parameters and relative intensity with Fe-doping concentration validate the incorporation of iron into the *** compressive strain present in the lattice estimated by using peak profile analysis through using Williamson-Hall plot also exhibits the influence of grain boundary formation in the *** radiative recombination and quenching observed in optical characterization by using photoluminescence spectrum(PL) and the shift in the band gap estimated from Uv-visible diffused reflectance spectrum corroborate the grain boundary *** spectrum and the morphological analysis by using a field emission scanning electron microscope(FEsEM) also indicate the formation of grain *** compositional analysis by using energy dispersive x-ray spectrum(EDAX) confirms Fe in the snO_(2) *** conductivity studies exhibit that the impendence increases with doping concentration increasing and the loss factor decreases at high frequencies with doping concentration increasing,which makes the sn_(1-x)Fe_(x)O_(2) a potential candidate for device applications.

关键词： sol-gel compressive strain grain boundary AC conductivity

NDN Content Poisoning Mitigation Using Bird swarm Optimization and Trust value

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Intelligent Automation & soft Computing 2023年第4期36卷 833-847页

作者： s.v.vijaya Karthik J.Arputha vijaya selvi Department of Electronics and Communication Engineering Kings College of EngineeringPudukkottai DistrictTamilnaduIndia

Information-Centric Networking(ICN)is considered a viable strategy for regulating Internet consumption using the Internet’s underlying *** Named Data Networking(NDN)and its reference-based implementa-tion,the NDN Forwarding Daemon(NFD),are the most established ICN solu-tions,their vulnerability to the Content Poisoning Attack(CPA)is regarded as a.s.vere threat that might dramatically impact this *** Poisoning can signiﬁcantly minimize the impact of NDN’s universal data *** veriﬁcation signatures to protect against content poisoning attacks may be imprac-tical due to the associated costs and the volume of messages sent across the net-work,resulting in high computational ***,in this research,we designed a method in NDN called Bird swarm Optimization Algorithm-Based Content Poisoning Mitigation(BsO-Content Poisoning Mitigation scheme).By aggregating the security information of entire routers along the full path,this sys-tem introduces the BsO to explore the secure transmission path and alter the con-tent retrieval ***,based on the determined trustworthy value of each node,the BsO-Content Poisoning Mitigation scheme can bypass malicious routers,preventing them from disseminating illicit content in the ***-ally,the suggested technique can minimize content poisoning utilizing removing erroneous Data packets from the cache-store during the pathﬁnding *** proposed method has been subjected to extensive analysis compared with the ROM scheme and improved performance justiﬁed in several ***-Con-tent Poisoning Mitigation scheme is more efﬁcient and faster than the ROM tech-nique in obtaining valid Data packets and resulting in a higher good cache hit ratio in a comparatively less amount of time.

关键词： Named data network content poisoning bird swarm optimization content validation fake content

A Hybrid Artificial Intelligence Model for skin Cancer Diagnosis

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Computer systems science & Engineering 2021年第5期37卷 233-245页

作者： v.vidya lakshmi J.s.Leena Jasmine Research scholar Anna UniversityChennaiTamil Nadu600025India velammal Engineering college ChennaiTamil Nadu600066India

Melanoma or skin cancer is the most dangerous and deadliest *** the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin *** this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer *** uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the *** the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical *** the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and shearlet(sheT)transforms as feature extraction *** MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging ***,the optimization process does not converge,and the model may not be *** overcome these drawbacks,EWHMLP is *** show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.

关键词： skin cancer multi-directional systems curvelet contourlet shearlet multi-layer perceptron

Convergence of COvID-19 and recurrent stroke:In-hospital mortality risks explored

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World Journal of virology 2025年第1期14卷 5-8页

作者： Basavraj s Nagoba shree v Dhotre Ajay M Gavkare sachin s Mumbre Pradnya s Dhotre Department of Microbiology Maharashtra Institute of Medical Sciences and Research(Medical College)Latur 413531India Department of Microbiology Ashwini Rural Medical CollegeSolapur 413001India Department of Community Medicine Ashwini Rural Medical CollegeSolapur 413001India Department of Biochemistry Ashwini Rural Medical CollegeSolapur 413001India

This editorial comments on the article by Desai et al,which investigates the impact of coronavirus disease 2019(COvID-19)on in-hospital mortality among patients with recurrent stroke using data from the 2020 National Inpatient *** findings reveal significantly higher mortality rates in COvID-19-positive patients compared to non-COvID-19 patients,particularly among middle-aged individuals,males,and ethnic *** editorial explores the underlying mechanisms contributing to these outcomes and discusses the clinical implications for targeted management strategies in high-risk *** results emphasize the need for comprehensive approaches to mitigate the heightened risks faced by recurrent stroke patients during the COvID-19 pandemic.

关键词： Recurrent stroke COvID-19 In-hospital mortality Nationwide analysis stroke admissions Infectious diseases Chronic health conditions Hypercoagulability

LeafspotNet:A deep learning framework for detecting leaf spot disease in jasmine plants

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Artificial Intelligence in Agriculture 2024年第2期12卷 1-18页

作者： shwetha v Arnav Bhagwat vijaya Laxmi Department of Electrical and Electronics Engineering Manipal Institute of TechnologyManipalManipal Academy of Higher EducationManipal 576104KarnatakaIndia

Leaf blight spot disease,caused by bacteria and fungi,poses a threat to plant health,leading to leaf discoloration and diminished agricultural *** response,we present a MobileNetv3 based classifier designed for the Jasmine plant,leveraging lightweight Convolutional Neural Networks(CNNs)to accurately identify disease *** model integrates depth wise convolution layers and max pool layers for enhanced feature extraction,focusing on crucial low level features indicative of the *** preprocessing techniques,including data augmentation with Conditional GAN and Particle swarm Optimization for feature selection,the classifier achieves robust *** on curated datasets demonstrates an outstanding 97%training accuracy,highlighting its *** world testing with diverse conditions,such as extreme camera angles and varied lighting,attests to the model's resilience,yielding test accuracies between 94%and 96%.The dataset's tailored design for CNN based classification ensures result ***,the model's lightweight classification,marked by fast computation time and reduced size,positions it as an efficient solution for real time *** comprehensive approach underscores the proposed classifier's significance in addressing leaf blight spot dis-ease challenges in commercial crops.

关键词： CNN Detection Leaf CGAN Augmentation Classification swarm optimization Disease detection Tomato leaf Casava leaf

Effect of confined one-gluon-exchange potential and instanton-induced interaction on nucleon–nucleon interaction

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Communications in Theoretical Physics 2020年第6期72卷 83-88页

作者： s Raghavendra C vanamali shastry v K Nilakanthan K B vijaya Kumar Department of Physics Mangalore UniversityMangalagangotri 574199India Department of Post-Graduate studies in Physics SDM CollegeUjire 574240India Department of Education in science and Mathematics Regional Institute of Education MysoreMysuru 570006India Department of Post Graduate studies in Physics St.Aloysius College(Autonomous)Mangalore 575003India

The effect of confined one-gluon-exchange potential and instanton-induced interaction potential in the singlet(1s0)and triplet(3s1)channels for nucleon–nucleon interaction has been investigated in the framework of the relativistic harmonic model using the resonating group method in the adiabatic limit with the Born–Oppenheimer *** contributions of the different components of the interaction potentials have been analyzed.

关键词： nucleon–nucleon interaction relativistic harmonic model instanton-induced interaction resonating group method confined one-gluon-exchange

semantic segmentation of Lumbar vertebrae Using Meijering U-Net (MU-Net) on spine Magnetic Resonance Images

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CMEs-COMPUTER MODELING IN ENGINEERING & sCIENCEs 2024年第1期142卷 733-757页

作者： lakshmi, s. v. v. Darmanayagam, shiloah Elizabeth Cyril, sunil Retmin Raj Anna Univ Coll Engn Guindy Dept Comp Sci & Engn Chennai 600025 India Anna Univ Madras Inst Technol Dept Informat Technol Chennai 600044 India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues, including muscles, ligaments, and intervertebral discs. U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy. This work proposes a modified U-Net architecture namely MU-Net, consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra s1. Pseudo-colour mask images were generated and used as ground truth for training the model. The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar spine MRI Dataset publicly available from Mendeley Data. The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79% of pixel accuracy (PA), 98.66% of dice similarity coefficient (DsC), 97.36% of Jaccard coefficient, and 92.55% mean Intersection over Union (mean IoU) metrics using the mentioned dataset.

关键词： Computer aided diagnosis (CAD) magnetic resonance imaging (MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

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Intelligent Automation & soft Computing 2023年第6期36卷 3675-3690页

作者： v.Gowri v.vijaya Chamundeeswari Department of CsE SRM Institute of Science and TechnologyRamapuram CampusChennaiTamilnaduIndia Department of CsE Saveetha Engineering CollegeChennaiTamilnaduIndia

Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical ***,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space *** this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher *** work integrates a significance-based attribute selection(sAs)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac *** pro-posed sAs algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing *** selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets *** effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,Heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,***,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or *** the experimental results,we can conclude that the integration of sAs and BFPA outperforms other classifiers recently reported in the literature.

关键词： Data classification decision forest feature selection healthcare data heart disease prediction penalizing attributes