Medicine in Novel Technology and Devices最新文献

Development and validation of a multiplex RT-qPCR method for the simultaneous detection of influenza type A, B and SARS-COV-2 viruses

Medicine in Novel Technology and Devices Pub Date : 2025-01-22 DOI: 10.1016/j.medntd.2025.100350

Samira Karimkhani , Ehsan Lotfi , Fatemeh Karamali , Mahsa DarestaniFarahani , Reza Keikha , Mahmood Barati

{"title":"Development and validation of a multiplex RT-qPCR method for the simultaneous detection of influenza type A, B and SARS-COV-2 viruses","authors":"Samira Karimkhani , Ehsan Lotfi , Fatemeh Karamali , Mahsa DarestaniFarahani , Reza Keikha , Mahmood Barati","doi":"10.1016/j.medntd.2025.100350","DOIUrl":"10.1016/j.medntd.2025.100350","url":null,"abstract":"<div><div>The study aimed to evaluate the diagnosis between SARS-COV-2 and influenza viruses using the Multiplex qPCR molecular method, highlighting the importance of these methods in disease management.</div><div>In this study, primers and probes were designed for the hemagglutinin region (HA) of influenza A, the M region of influenza B virus, and the RdRp region of SARS-COV-2. Optimization was performed using qPCR methods, and the method's analytical sensitivity and specificity were assessed. Finally, the process was compared to the commercial kit, Generi-Biotech Company (GB SARS-CoV-2 Influenza A/B).</div><div>The best annealing temperature for this method was determined to be 58 °C. Analytical sensitivity showed detection limits of 500 copies of the virus genome for SARS-CoV-2, 250 copies for influenza A, and 500 copies for influenza B. Clinical evaluations confirmed that the designed kit exhibited 100 % sensitivity and specificity, identical to the GB commercial kit, establishing its comparable diagnostic performance.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159481","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}

引用次数: 0

GeneXAI: Influential gene identification for breast cancer stages using XAI-based multi-modal framework

Medicine in Novel Technology and Devices Pub Date : 2024-12-27 DOI: 10.1016/j.medntd.2024.100349

Sweta Manna, Sujoy Mistry, Debashis De

{"title":"GeneXAI: Influential gene identification for breast cancer stages using XAI-based multi-modal framework","authors":"Sweta Manna, Sujoy Mistry, Debashis De","doi":"10.1016/j.medntd.2024.100349","DOIUrl":"10.1016/j.medntd.2024.100349","url":null,"abstract":"<div><div>To provide improved treatment prediction and prognosis, analysis of the categorization of cancer stages and important genes in each stage is necessary. The study introduces a GeneXAI multi-modal approach, which classifies the cancer stages and identifies the influential genes by the explainable artificial intelligence models. In the first phase of the GeneXAI, a hybrid optimal feature selection method is applied to extract the imperative features using an early fusion technique. By using the imperative features, the stages of tumor, lymph nodes, and metastasis are identified, and finally, the accurate stage of cancer is classified. In the second phase, XAI such as SHAP and LIME has been utilized to identify the best genes for distinct cancer stages. Moreover, the genomic dataset's top genes were found using SHAP, while crucial genes were found by instance using LIME. Some influential genes such as (PLA2G10, MST1R F13B, and CAMK1) identified by the GeneXAI model, have also been recognized as equally important genes in the state-of-the-art biological models. The model illustrates the process of classifying the influential genes as prognostic or non-prognostic based on their clinical importance. The proposed framework achieves an average 5–7% higher accuracy than other state-of-the-art models by using the early fusion technique of a multi-modal approach.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159778","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}

引用次数: 0

CAR-T cell a potential platform for cancer therapy

Medicine in Novel Technology and Devices Pub Date : 2024-12-17 DOI: 10.1016/j.medntd.2024.100348

Azad Kumar Maurya , Shivani Saraf , Laxmikant Gautam , Rajeev Sharma

引用次数: 0

Blastocyst cavity expansion promotes DNA methylation during early development of mouse embryos

Medicine in Novel Technology and Devices Pub Date : 2024-12-17 DOI: 10.1016/j.medntd.2024.100347

Zheng Guo, Jing Du

引用次数: 0

Cover

Medicine in Novel Technology and Devices Pub Date : 2024-12-01 DOI: 10.1016/S2590-0935(24)00060-2

引用次数: 0

A systematic review on recent methods on deep learning for automatic detection of Alzheimer's disease 深度学习用于阿尔茨海默病自动检测的最新方法系统综述

Medicine in Novel Technology and Devices Pub Date : 2024-11-19 DOI: 10.1016/j.medntd.2024.100343

Radhakrishna Chamakuri, Hyma Janapana

{"title":"A systematic review on recent methods on deep learning for automatic detection of Alzheimer's disease","authors":"Radhakrishna Chamakuri, Hyma Janapana","doi":"10.1016/j.medntd.2024.100343","DOIUrl":"10.1016/j.medntd.2024.100343","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is the most frequent cause of dementia, however, and it is caused by a number of different disorders. With regard to the elderly population all over the world, Alzheimer's disease is the seventh largest cause of mortality, disability, and reliance. Depression, social isolation, inactivity, alcohol, smoking, obesity, diabetes, high blood pressure, and age are all variables that can increase the likelihood of getting dementia. Other risk factors include social isolation, depression, and smoking. A diagnosis of Alzheimer's disease at an earlier stage may improve the odds of receiving care and therapy. Medical professionals often diagnose AD based on a limited number of symptoms. On the other hand, it is now possible to identify and categorize Alzheimer's disease (AD) because of technological advancements such as artificial intelligence (AI). However, to identify the current AI-enabled approaches, we must conduct an investigation into the state of the art. This breakthrough in diagnosis methodologies will enable the development of the Clinical Decision Support System (CDSS), capable of automatically diagnosing Alzheimer's disease (AD) without human expertise. In this publication, we conduct a systematic review of sixty research articles previously reviewed by other researchers. The systematic review sheds light on the synthesis of new knowledge and ideas. This study discusses the current approaches for machine learning, deep learning methods, ensemble models, transfer learning, and methods used for early Alzheimer's disease diagnosis. This paper provides answers to a large number of research issues and synthesizes fresh information that is helpful to the reader on many elements of AI-enabled approaches for Alzheimer's disease diagnosis. In addition, it has the potential to stimulate additional research into more effective methods of computer-based intelligent identification of Alzheimer's disease.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703128","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}

引用次数: 0

Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction 肌电信号和机器学习计算在预测平地摔倒的步态模式识别中的应用

Medicine in Novel Technology and Devices Pub Date : 2024-11-16 DOI: 10.1016/j.medntd.2024.100341

Shuo Zhang , Biao Chen , Chaoyang Chen , Maximillian Hovorka , Jin Qi , Jie Hu , Gui Yin , Marie Acosta , Ruby Bautista , Hussein F. Darwiche , Bryan E. Little , Carlos Palacio , John Hovorka

{"title":"Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction","authors":"Shuo Zhang , Biao Chen , Chaoyang Chen , Maximillian Hovorka , Jin Qi , Jie Hu , Gui Yin , Marie Acosta , Ruby Bautista , Hussein F. Darwiche , Bryan E. Little , Carlos Palacio , John Hovorka","doi":"10.1016/j.medntd.2024.100341","DOIUrl":"10.1016/j.medntd.2024.100341","url":null,"abstract":"<div><div>Abnormal gaits including pelvic obliquity gait and knee hyperextension gait are common clinical symptoms related to flat-ground fall among elder adults. This study aimed to determine the feasibility of using lower limb myoelectrical signals (electromyographic signals, EMG) for gait pattern recognition and to identify the optimal machine learning (ML) algorithms for EMG signal processing. Seven healthy subjects were recruited with their EMG signals collected from eight muscles of the lower limbs during walking with normal and abnormal gaits. Four basic ML algorithms including support vector machine (SVM), K-nearest neighbor (kNN), decision tree (DT), and naive Bayes (NB), and five deep learning models including convolutional neural network (CNN), long-short term memory (LSTM), bidirectional long short-term memory (BiLSTM), and CNN-BiLSTM were used to process the EMG signals recorded under different gaits. Statistical analysis was performed to compare the accuracy of individual ML algorithms in discriminating gait patterns. The overall accuracy was 95.78 % for SVM, 95.09 % for CNN-LSTM, and 96.28 % for CNN-BiLSTM, respectively. The overall accuracy was 90.25 % for DT, 92.62 % for kNN, 91.27 % for NB, and 90.34 % for CNN, respectively. The accuracy was 67.39 % for LSTM and 74.75 % for BiLSTM, respectively. Most ML algorithms in this study had an accuracy greater than 90 % in EMG-based abnormal gait pattern recognition except for LSTM and BiLSTM. This study provides novel technology for evaluation of gait pattern recognition related to flat ground fall.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703127","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}

引用次数: 0

Hotspots and emerging trend analysis of extracorporeal membrane oxygenation complication research: A qualitative systematic review of literature 体外膜肺氧合并发症研究的热点和新趋势分析：文献定性系统回顾

Medicine in Novel Technology and Devices Pub Date : 2024-11-13 DOI: 10.1016/j.medntd.2024.100342

Xiaowei Dong , Yuan Li , Zejian Jin , Sheng Liu , Zengsheng Chen

{"title":"Hotspots and emerging trend analysis of extracorporeal membrane oxygenation complication research: A qualitative systematic review of literature","authors":"Xiaowei Dong , Yuan Li , Zejian Jin , Sheng Liu , Zengsheng Chen","doi":"10.1016/j.medntd.2024.100342","DOIUrl":"10.1016/j.medntd.2024.100342","url":null,"abstract":"<div><div>As an effective extracorporeal life support technology, ECMO is mainly used in patients with acute cardiopulmonary failure, but it can still cause related complications such as thrombosis, bleeding, infection, and cannulation problems, seriously endangering patients' lives and health. This review used bibliometric methods to retrieve a total of 2789 relevant documents from 2003 to March 2023 from the core database of Web of Science, used CiteSpace to draw a knowledge map, and conducted publication trend analysis, subject journal analysis, keyword co-occurrence analysis, cluster analysis, timeline and time zone diagram analysis, emergent keyword analysis. Quantitatively and qualitatively analyze the research hotspots of ECMO and its complications, and then predict the development trend in this field. It aims to provide clinicians, scientists, and related practitioners with an overview of the latest research related to the optimal design of ECMO systems, avoidance and mitigation of complications, and provide directions for subsequent research.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"24 ","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697929","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}

引用次数: 0

Efficient quantum of mechanical simulation of diffusion-weighted MRI 扩散加权磁共振成像的高效量子力学模拟

Medicine in Novel Technology and Devices Pub Date : 2024-11-08 DOI: 10.1016/j.medntd.2024.100339

Ahmed J. Allami , Hany Akeel Al-Hussaniy , Amjad Ibraim Oraibi , Zuhair Abdulkareem Dawah

{"title":"Efficient quantum of mechanical simulation of diffusion-weighted MRI","authors":"Ahmed J. Allami , Hany Akeel Al-Hussaniy , Amjad Ibraim Oraibi , Zuhair Abdulkareem Dawah","doi":"10.1016/j.medntd.2024.100339","DOIUrl":"10.1016/j.medntd.2024.100339","url":null,"abstract":"<div><div>Modern magnetic resonance imaging (MRI) experiments require simultaneous spin and spatial dynamics treatment. This paper aims to demonstrate the possibility of simulating a large spin system for diffusion-weighted MRI experiments. The numerical simulation of diffusion MRI depends on Bloch-Torrey equations. The latter describe the behavior of spin systems under the influence of magnetic fields and diffusion processes. They are particularly relevant in magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) studies. The equations deal with uncoupled (−1/2) spins associated with three-dimensional spatial dynamics represented by diffusion and flow. The proposed method recommends using an unopened Kronecker product for evolution generators to minimize the simulation time and reduce the occupied computer memory. Two and three-dimensional diffusion-weighted magnetic resonance imaging associated with four pairs of coupled spin systems were utilized to achieve the study's goals. Utilizing four pairs of coupled spin systems in MRI simulation enhances accuracy and realism in modeling interactions between spins, leading to improved tissue characterization and diagnostic capabilities. The obtained results were impossible using previous simulation packages. The results of the study demonstrate that the Spinach library can simulate complex spin systems with significant spatial dynamics.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"24 ","pages":"Article 100339"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697930","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}

引用次数: 0

Examining mental workload based on multiple physiological signals: Review of the multi-attribute task battery (MATB) technique 根据多种生理信号检查心理工作量：多属性任务电池（MATB）技术回顾

Medicine in Novel Technology and Devices Pub Date : 2024-11-05 DOI: 10.1016/j.medntd.2024.100340

Jiapu Chai, Yan Li

引用次数: 0