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Crosstalk between Wnt and bone morphogenetic protein signaling during osteogenic differentiati

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World Journal of Stem Cells 2024年第2期16卷 102-113页

作者： Pakkath Narayanan Arya iyyappan saranya Nagarajan Selvamurugan Department of Biotechnology School of BioengineeringSRM Institute of Science and TechnologyKattankulathur 603203India

Mesenchymal stem cells(MSCs)originate from many sources,including the bone marrow and adipose tissue,and differentiate into various cell types,such as osteoblasts and adipocytes.Recent studies on MSCs have revealed that many transcription factors and signaling pathways control osteogenic development.Osteogenesis is the process by which new bones are formed;it also aids in bone remodeling.Wnt/β-catenin and bone morphogenetic protein(BMP)signaling pathways are involved in many cellular processes and considered to be essential for life.Wnt/β-catenin and BMPs are important for bone formation in mammalian development and various regulatory activities in the body.Recent studies have indicated that these two signaling pathways contribute to osteogenic differen-tiation.Active Wnt signaling pathway promotes osteogenesis by activating the downstream targets of the BMP signaling pathway.Here,we briefly review the molecular processes underlying the crosstalk between these two pathways and explain their participation in osteogenic differentiation,emphasizing the canonical pathways.This review also discusses the crosstalk mechanisms of Wnt/BMP signaling with Notch-and extracellular-regulated kinases in osteogenic differentiation and bone development.

关键词： Bone Mesenchymal stem cells Osteogenic differentiation Wnt/β-catenin Bone morphogenetic proteins

Enhancing Security in QR Code Technology Using Ai: Exploration and Mitigation Strategies

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international Journal of intelligence Science 2024年第2期14卷 49-57页

作者： saranya Vaithilingam Santhosh Aradhya Mohan Shankar Department of information Technology University of the Cumberlands Williamsburg KY USA

The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (Ai). The abstract investigates the integration of Ai methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of Ai-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and Ai for the advancement of secure digital communication.

关键词： Artificial intelligence Cyber Security QR Codes Quishing Ai Framework Machine Learning Ai-Enhanced Security

Breast cancer awareness in reproductive women in the low-and middle-income countries:a scoping review

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Frontiers of Nursing 2024年第2期11卷 139-151页

作者： Made Satya Nugraha Gautama saranya Pimolkatekul Nguyen Ngoc Nhat Thanh Department of Nursing Faculty of MedicineUniversitas Pendidikan GaneshaBali 81116Indonesia School of Nursing College of NursingTaipei Medical UniversityTaipeiTaiwan 110China Global Health and Development Program College of Public HealthTaipei Medical UniversityTaipeiTaiwan 110China

Objective:This study aimed to assess breast cancer(BC)awareness among reproductive women in low-and middle-income countries(LMiCs),identify influencing factors,and propose feasible interventions or programs.Methods:We followed a 5-step process using a modified version of Arksey and O’Malley framework methodology.A comprehensive search was conducted on the Embase,PubMed,and CiNAHL electronic databases for literature published within 10 years(from 2012 to 2022).Results:Thirty-three papers published between 2012 and 2020,spanning 18 countries,were included.Of these,45.6%described a good level of knowledge,while 24.2%reported that women at reproductive ages had good awareness.Twelve influencing factors were identified in 3 categories:socio-demographic(family history,personal history,marital status,age,religion,income status,living place,and occupation),personal(self-efficacy,education,and perceived level),and external(advertisements promoting awareness).Educational programs were recommended in most(>72%)of the included studies.Conclusions:While most studies reported high levels of knowledge and awareness,some found low prevalence among certain groups.Factors affecting knowledge and awareness were classified into socio-demographic,personal,and external categories,with socio-demographic factors such as age,education,income,and marital status being the most frequently cited.The studies recommended implementing educational programs,health prevention strategies,and social interventions to increase BC knowledge and awareness.

关键词： awareness breast cancer low-and middle-income countries reproductive women scoping review

A comparison of the quality of life of parents of children using hearing aids and those using cochlear implants

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Journal of Otology 2022年第4期17卷 211-217页

作者： saranya Arya Mundayoor Prabuddha Bhatarai Prashanth Prabhu All india institute of Speech and Hearing MysoreIndia

Objective:The goal of this study was to evaluate the quality of life of parents of children who use hearing aids(HA)with those who use cochlear implants(Ci)in the indian context and document any differences found.Methods:The Kannada version of the AQoL-4D was administered in a modified fashion to 131 parents(87 HA and 44 Ci).Sociodemographic details were collected for supplemental information on the intervention strategy used.Results:A total of 49 parents(29 HA and 20 Ci)responded to the questionnaire sent.The mean total scores for both the groups were similar(HA group=17.9(SD=5.5),Ci group=17.2(SD=3.4)),as was the score for the first subscale(HA group=8.6(SD=2.9);Ci group=8.5(SD=2.6))of the AQoL-4D.No significant differences were found between the two groups on either scores[Total Score:U(N_(HA)=29,NCi=20)=280.5,z=0.194,p>0.05;Subscale 1 Score:U(N_(HA)=29,NCi=20)=281.5,z=-0.176,p>0.05].The degree of hearing loss in the hearing aid group was equivalent to that of the cochlear implant group but this did not appear to influence parental quality of life.Conclusion:Parents of children with hearing aids and cochlear implants appear to be similar on several psychosocial factors in the realms of functional,social,and psychological well-being.in terms of parental quality of life,hearing aids and cochlear implants appear to be equally effective intervention techniques.

关键词： Quality of life Parents Cochlear implants Hearing aids Children Hearing impaired

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

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Computer Systems Science & Engineering 2023年第4期45卷 545-562页

作者： A.K.S.saranya T.Jaya Department of Electronics and Communication Engineering Ponjesly College of EngineeringNagercoilIndia Department of Electronics and Communication Engineering CSI Institute of TechnologyNagercoilIndia

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.in contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.in the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CiF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

关键词： Deep neural network krill herd optimization stack auto-encoder adaptive filter enthalpy based empirical mode decomposition robotic process automation

A Machine Learning-Based Technique with intelligent WordNet Lemmatize for Twitter Sentiment Analysis

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intelligent Automation & Soft Computing 2023年第4期36卷 339-352页

作者： S.saranya G.Usha Computing Technology SRMISTChennaiTamil NaduIndia

Laterally with the birth of the internet,the fast growth of mobile stra-tegies has democratised content production owing to the widespread usage of social media,resulting in a detonation of short informal writings.Twitter is micro-blogging short text and social networking services,with posted millions of quick messages.Twitter analysis addresses the topic of interpreting users’tweets in terms of ideas,interests,and views in a range of settings andﬁelds.This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event.Although sentiment examination of these texts is useful for a variety of reasons,it is typically seen as a difﬁcult undertaking due to the fact that these messages are frequently short,informal,loud,and rich in linguistic ambiguities such as polysemy.Furthermore,most contemporary sentiment analysis algorithms are based on clean data.in this paper,we offers a machine-learning-based sentiment analysis method that extracts features from Term Frequency and inverse Document Frequency(TF-iDF)and needs to apply deep intelligent wordnet lemmatize to improve the excellence of tweets by removing noise.We also utilise the Random Forest network to detect the emotion of a tweet.To authenticate the proposed approach performance,we conduct extensive tests on publically accessible datasets,and theﬁndings reveal that the suggested technique signiﬁcantly outperforms sentiment classiﬁcation in multi-class emotion text data.

关键词： Random Forest sentiment analysis social media term frequency and inverse document frequency twitter wordnet lemmatize

identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

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intelligent Automation & Soft Computing 2023年第4期36卷 1137-1149页

作者： P.saranya P.Asha Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

in today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classiﬁcation(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classiﬁer produces higher classiﬁcation accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

关键词： Cancer detection extensive data analysis candidate feature selection deep neural classiﬁcation clustering disease inﬂuence rate

ioT information Status Using Data Fusion and Feature Extraction Method

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Computers, Materials & Continua 2022年第1期70卷 1857-1874页

作者： S.S.saranya N.Sabiyath Fatima Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and TechnologyVandalurChennaiTamil NaduIndia

The internet of Things(ioT)role is instrumental in the technological advancement of the healthcare industry.Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0.Healthcare ioT systems are the emergence of this foresight.The communication systems between the sensing nodes and the processors;and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies.At present,many new technologies supplement these empowering technologies.So,in this research work,a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data.in the initial stage,ioT devices are gathered and pre-processed for fusion processing.Dynamic Bayesian Network is considered an improved balance for tractability,a tool for CDF operations.improved Principal Component Analysis is deployed for feature extraction along with dimension reduction.Lastly,this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination.in this research,Deep Belief Neural Network and Support VectorMachine are hybridized for healthcare data prediction.Thus,the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.

关键词： ioT data fusion improved principal component analysis hybrid learning model

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

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intelligent Automation & Soft Computing 2023年第2期35卷 2029-2042页

作者： K.saranya K.Premalatha Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.in this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.in addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log veriﬁcation that protects the personalized sensitive information.Further,linear integrity veriﬁcation provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

关键词： Privacy-preserving crypto policy medical data mining integrity and veriﬁcation personalized records cryptography