Biomedical Engineering and Computational Biology最新文献

{"title":"Correspondence to \"Conceptualizing Patient as an Organization with the Adoption of Digital Health\".","authors":"Hinpetch Daungsupawong, Viroj Wiwanitkit","doi":"10.1177/11795972241293514","DOIUrl":"10.1177/11795972241293514","url":null,"abstract":"","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293514"},"PeriodicalIF":2.3,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.","authors":"G Siva Shankar, Edeh Michael Onyema, Balasubramanian Prabhu Kavin, Venkataramaiah Gude, Bvv Siva Prasad","doi":"10.1177/11795972241278907","DOIUrl":"10.1177/11795972241278907","url":null,"abstract":"<p><p>One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241278907"},"PeriodicalIF":2.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the Therapeutic Target and Molecular Mechanism of Upadacitinib on Sjogren's Syndrome.","authors":"Youguo Yang, Yuan Liu, Xiaofen Li, Yongping Zeng, Weiqian He, Juan Zhou","doi":"10.1177/11795972241293519","DOIUrl":"10.1177/11795972241293519","url":null,"abstract":"<p><strong>Objective: </strong>Upadacitinib, a selective Janus associated kinase 1 (JAK-1) inhibitor, can be prescribed particularly for the clinical treatment with Crohn's disease or rheumatoid arthritis. It is clinically observed that upadacitinib has been found with potential therapeutic effectiveness on Sjogren's syndrome (SS). However, the anti-SS targets and mechanisms involved in upadacitinib treatment remain uninvestigated.</p><p><strong>Materials and methods: </strong>Thus, this study was designed to identify therapeutic targets and mechanisms of upadacitinib for treating SS through conducting network pharmacology and molecular docking analyses.</p><p><strong>Results: </strong>In total, we identified 298 upadacitinib-related target genes, 1339 SS-related targets before collecting 56 overlapped target genes and 12 hub target genes. Upadacitinib largely exerted the critical biological processes including regulation of microenvironment homeostasis, inflammatory response, and cell apoptosis, and largely acted on pivotal molecular mechanisms including hypoxia-inducible factor 1 (HIF-1) signaling pathway, apoptosis pathway, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway, or Th17 cell differentiation pathway. Molecular docking data suggested that upadacitinib exhibited the high affinities with signal transducer and activator of transcription 3 (STAT3), HIF1A, poly(ADP-ribose) polymerase 1 (PARP1) target proteins, in which the structural interactions between upadacitinib and STAT3, HIF1A, PARP1 showed potential therapeutic activities against SS.</p><p><strong>Conclusion: </strong>In conclusion, upadacitinib possesses the bright anti-inflammatory and anti-apoptotic activities on SS, and this study can provide a theoretical basis for clinical therapy of SS using upadacitinib.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241293519"},"PeriodicalIF":2.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cranial Defect Repair With 3D Designed Models.","authors":"Sambardhan Dabadi, Raju Raj Dhungel","doi":"10.1177/11795972241291777","DOIUrl":"https://doi.org/10.1177/11795972241291777","url":null,"abstract":"<p><p>Cranioplasty is one of the most common neurosurgical procedure performed to repair cranial defect. Many materials and fabrication technique are used to prepare cranial implant in cases where autologous bone is not available. Polymethyl Methacrylate (PMMA) is one of the most common polymer used as bone substitute. PMMA fabricated using 3D printed models have shown better fit, symmetrical shape, and restore esthetic looks of patients. The use of 3D printed implants in medical procedures has several advantages over traditional manufacturing methods. 3D printing allows for greater precision, customization, and quicker implant time.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241291777"},"PeriodicalIF":2.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs.","authors":"He Zhicheng, Wang Yipeng, Li Xiao","doi":"10.1177/11795972241288319","DOIUrl":"10.1177/11795972241288319","url":null,"abstract":"<p><strong>Objective: </strong>The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.</p><p><strong>Study design: </strong>Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.</p><p><strong>Results: </strong>With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.</p><p><strong>Conclusion: </strong>This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241288319"},"PeriodicalIF":2.3,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Tissue Engineering: A Review of Bioprinting Techniques, Scaffolds, and Bioinks.","authors":"Shervin Zoghi","doi":"10.1177/11795972241288099","DOIUrl":"10.1177/11795972241288099","url":null,"abstract":"<p><p>Tissue engineering is a multidisciplinary field that uses biomaterials to restore tissue function and assist with drug development. Over the last decade, the fabrication of three-dimensional (3D) multifunctional scaffolds has become commonplace in tissue engineering and regenerative medicine. Thanks to the development of 3D bioprinting technologies, these scaffolds more accurately recapitulate in vivo conditions and provide the support structure necessary for microenvironments conducive to cell growth and function. The purpose of this review is to provide a background on the leading 3D bioprinting methods and bioink selections for tissue engineering applications, with a specific focus on the growing field of developing multifunctional bioinks and possible future applications.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241288099"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conceptualizing Patient as an Organization With the Adoption of Digital Health.","authors":"Atantra Das Gupta","doi":"10.1177/11795972241277292","DOIUrl":"https://doi.org/10.1177/11795972241277292","url":null,"abstract":"<p><p>The concept of viewing a patient as an organization within the context of digital healthcare is an innovative and evolving concept. Traditionally, the patient-doctor relationship has been centered around the individual patient and their interactions with healthcare providers. However, with the advent of technology and digital healthcare solutions, the dynamics of this relationship are changing. Digital healthcare platforms and technologies enable patients to have more control and active participation in managing their health and healthcare processes. This shift empowers patients to take on a more proactive role, similar to how an organization functions with various stakeholders, goals, and strategies. The prevalence of mobile phones and wearables is regarded as an important factor in the acceptance of digital health.</p><p><strong>Objective: </strong>This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well.</p><p><strong>Results: </strong>PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (<i>b</i> > 0.1, <i>t</i> > 1.96, <i>P</i> < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness.</p><p><strong>Conclusions: </strong>Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277292"},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.","authors":"Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Sivasankari Thilagar, Deepavalli Arumuganainar, Deepti Shrivastava, Artak Heboyan","doi":"10.1177/11795972241277639","DOIUrl":"10.1177/11795972241277639","url":null,"abstract":"<p><strong>Background: </strong>The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.</p><p><strong>Methods: </strong>Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.</p><p><strong>Results: </strong>Random Forest emerged as the superior model, achieving the highest <i>R</i> ² score (.985) and the lowest RMSE (0.189) compared to Neural Networks (<i>R</i> ² = .952, RMSE = 0.332), Linear Regression (<i>R</i> ² = .949, RMSE = 0.343), and SVM (<i>R</i> ² = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.</p><p><strong>Conclusion: </strong>The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277639"},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.","authors":"Zahid Ullah, Mona Jamjoom, Manikandan Thirumalaisamy, Samah H Alajmani, Farrukh Saleem, Akbar Sheikh-Akbari, Usman Ali Khan","doi":"10.1177/11795972241277322","DOIUrl":"10.1177/11795972241277322","url":null,"abstract":"<p><p>Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average <i>f</i>1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and <i>f</i>1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277322"},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.","authors":"Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Muthupandian Saravanan, Hadush Negash Meles, Artak Heboyan","doi":"10.1177/11795972241277081","DOIUrl":"10.1177/11795972241277081","url":null,"abstract":"<p><strong>Aim: </strong>The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences.</p><p><strong>Material and methods: </strong>The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation.</p><p><strong>Results: </strong>Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), <i>F</i>1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, <i>F</i>1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship.</p><p><strong>Conclusion: </strong>In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277081"},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}