EAI Endorsed Transactions on Pervasive Health and Technology最新文献

{"title":"In vitro chronic wound healing using collagen and plant extract along with zinc nanoparticles","authors":"J. S. Bobby, S. Purnima, V. Mythily, B. Ghiri Rajan, S. Shubhankar, M. Sowmiya","doi":"10.4108/eetpht.10.5784","DOIUrl":"https://doi.org/10.4108/eetpht.10.5784","url":null,"abstract":"INTRODUCTION: While the use of zinc nanoparticles (ZnNPs) as an antibacterial agent in the biomedical industry has recently attracted significant attention, collagen has aroused significant interest as a biomaterial in medical and tissue engineering applications. \u0000OBJECTIVES: In order to create biofilm loaded with biosynthesized ZnNPs for use in chronic wound healing applications, type-I collagen was extracted from the study's subject. by the acid soluble collagen technique, collagen was isolated from the fish skin of the trevally and identified by SDS-PAGE. Aqueous extract from Cassia fistula leaves was also used to greenly manufacture stable ZnNPs, which were then characterized by UV-Vis, FTIR, and XRD measurements. \u0000METHODS: Collagen and ZnNPs were then added to polyvinyl alcohol (PVA), creating a thin biofilm that had a high biocompatibility due to the production method's absence of a chemical reducer and crosslinking agent. When tested against the harmful bacteria, both ZnNPs alone and PVA/Collagen/ZnNPs biofilms showed potent antibacterial activity. \u0000RESULTS: By using the MTT test, the cytotoxic effects of collagen and ZnNPs on the Vero cell line were evaluated. With 97.76% wound closure, the PVA/Collagen/ZnNPs biofilm demonstrated strong in vitro wound scratch healing efficacy. \u0000CONCLUSION: The findings show that the PVA/Collagen/ZnNPs film dramatically increased cell migration by 40.0% at 24 hours, 79.20% at 48 hours, and 97.76% at 74 hours.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Mental Fatigue and Burnout in the Workplace: A Survival Analysis Approach","authors":"R. Eswar Reddy, Santhi K","doi":"10.4108/eetpht.10.5719","DOIUrl":"https://doi.org/10.4108/eetpht.10.5719","url":null,"abstract":"INTRODUCTION: Employee burnout is a prevalent concern in contemporary workplaces, adversely impacting both individual well-being and organizational productivity. \u0000OBJECTIVES:  In this paper, we conducted a comprehensive analysis of a dataset focusing on mental fatigue and burnout among employees, employing various survival analysis techniques including Kaplan Meier, Nelson-Aalen, and Cox proportional-hazards models, as well as Frailty Models and Competing Risks Analysis. \u0000METHODS:  This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout. \u0000RESULTS: This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout. \u0000CONCLUSION: This study contributes valuable insights into understanding and addressing mental fatigue in the workplace, highlighting the importance of evidence-based interventions to support employee well-being and organizational resilience. The insights gained from this analysis inform evidence-based strategies for mitigating burnout-related risks and promoting a healthier work environment.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"2012 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Disease Diagnosis: Statistical Analysis of Haematological Parameters in Sickle Cell Patients, Integrating Predictive Analytics","authors":"Bhawna Dash, Soumyalatha Naveen, A. Um","doi":"10.4108/eetpht.10.5691","DOIUrl":"https://doi.org/10.4108/eetpht.10.5691","url":null,"abstract":"Sickle cell disease (SCD) affects 30 million people worldwide, causing a range of symptoms from mild to severe, including Vaso occlusive crises (VOC). SCD leads to damaging cycles of sickling and desickling of red blood cells due to HbS polymer formation, resulting in chronic haemolytic anaemia and tissue hypoxia. We propose using machine learning to categorize SCD patients based on haemoglobin, reticulocyte count, and LDH levels, crucial markers of hemolysis. Statistical analysis, particularly Linear Regression, demonstrates how haemoglobin depletion occurs using LDH and reticulocyte parameters. \u0000Bilirubin and haemoglobin, two integral biomarkers in clinical biochemistry and haematology, serve distinct yet interconnected roles in human physiology. Bilirubin, a product of heme degradation, is a critical indicator of liver function and various hepatic disorders, while haemoglobin, found in red blood cells, is responsible for oxygen transport throughout the body. Understanding the statistical relationship between these biomarkers has far-reaching clinical implications, enabling improved diagnosis, prognosis, and patient care. This research paper conducts a comprehensive statistical analysis of bilirubin and haemoglobin using various regression techniques to elucidate their intricate association. The primary objective of this study is to characterize the relationship between bilirubin and haemoglobin. Through meticulous data analysis, we explore whether these biomarkers exhibit positive, negative, or no correlation. Additionally, this research develops predictive models for estimating haemoglobin levels based on bilirubin data, offering valuable tools for healthcare professionals in clinical practice.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"20 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140721787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm","authors":"Binbin Han, Fuliang Zhang, Zhenyun Chang, Fang Feng","doi":"10.4108/eetpht.10.5147","DOIUrl":"https://doi.org/10.4108/eetpht.10.5147","url":null,"abstract":"INTRODUCTION: Cancer has become one of the most prevalent diseases with the highest mortality rate in the world, and timely detection and early acceptance of medical therapeutic interventions are effective means of controlling the progression of cancer patients and improving their post-intervention outcomes.OBJECTIVES: To make the defects of incomplete features, low accuracy and low real-time performance of current tumour diagnosis methods.METHODS: This paper proposes a tumour diagnosis method based on the improved MRFO algorithm to improve the optimization process of DBN network parameters. Firstly, the diagnostic features are extracted by analysing the tumour diagnosis identification problem; then, the manta ray foraging optimization algorithm is improved by combining the good point set initialization strategy, the adaptive control parameter strategy and the distribution estimation strategy, and the tumour diagnostic model based on the improved manta ray foraging optimization algorithm to optimize the parameters of the depth confidence network is constructed; finally, the high accuracy and real-time performance of the proposed method are verified by the analysis of simulation experiments.RESULTS: The results show that the proposed method improves the accuracy of the diagnostic model.CONLUSION: Addresses the problem of poor accuracy and real-time availability of tumour diagnostic methods.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"36 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation","authors":"Pokkuluri Kiran Sree, S. Devi N","doi":"10.4108/eetpht.10.5685","DOIUrl":"https://doi.org/10.4108/eetpht.10.5685","url":null,"abstract":"INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system. \u0000OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures. \u0000METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures. \u0000RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE)  and area under the receiver operating characteristic curve (AUC-ROC). \u0000CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"223 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140730534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks","authors":"Shikha Verma, Aditi Sharan","doi":"10.4108/eetpht.10.5649","DOIUrl":"https://doi.org/10.4108/eetpht.10.5649","url":null,"abstract":"INTRODUCTION: Finding biomarkers that are closely associated with cancer-related traits is critical to the advancement of cancer research, especially when it comes to personalised treatment. The objective of this research is to explore multiple biomarker categories, including genetics, proteins, and chemicals, in order to better understand the complex terrain of cancer. \u0000OBJECTIVES: Few of the objectives include examining a variety of biomarker types, such as chemical, protein, and genetic markers and determining which important biomarker signatures correspond to each cancer hallmark. \u0000Also the study aims to perform a comparative analysis to show how the SVM model's features incorporating identified biomarkers improves classification performance. \u0000METHODS: The study includes NLP and ML techniques for the identification and classification of biomarkers for the hallmark of cancer dataset. \u0000RESULTS: The discovery of important biomarker signatures connected to every cancer hallmark is one of the study's primary findings. In addition, our new SVM-based classification model performed well in the multilabel text classification of PubMed abstracts, showing a significant improvement in performance when the biomarkers were used as features. \u0000CONCLUSION: To sum up, this study makes a substantial contribution to the area of cancer research by identifying important biomarker signatures connected to many cancer hallmarks.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"84 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glaucoma Detection Using Explainable AI and Deep Learning","authors":"Najeeba Afreen, Rajanikanth Aluvalu","doi":"10.4108/eetpht.10.5658","DOIUrl":"https://doi.org/10.4108/eetpht.10.5658","url":null,"abstract":"INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. \u0000OBJECTIVES:  conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection. \u0000 RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). \u0000CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools","authors":"Tarandeep Kaur Bhatia, Samagya Rathi, Thipendra P Singh, Biswayan Naha","doi":"10.4108/eetpht.10.5639","DOIUrl":"https://doi.org/10.4108/eetpht.10.5639","url":null,"abstract":"INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children. \u0000OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users. \u0000METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go.  All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated. \u0000RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment. \u0000CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"39 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans","authors":"Aniket Jhariya, Dhvani Parekh, Joshua Lobo, A. Bongale, Ruchi Jayaswal, Prachi Kadam, Shruti Patil, Tanupriya Choudhury","doi":"10.4108/eetpht.10.5632","DOIUrl":"https://doi.org/10.4108/eetpht.10.5632","url":null,"abstract":"INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.CONCLUSION: PCA is crucial for improving the interpretability of MRI data.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"26 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda","authors":"Aditya Tandon, Vivek Kumar, Tanupriya Choudhury","doi":"10.4108/eetpht.10.5640","DOIUrl":"https://doi.org/10.4108/eetpht.10.5640","url":null,"abstract":"INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process. \u0000OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts. \u0000METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method. \u0000RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality. \u0000CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.","PeriodicalId":506478,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140742379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}