Medicine in Novel Technology and Devices最新文献_第6页

Corchorus olitorius (Jute) fiber enforced sustainable polymeric dressing material for antimicrobial activity 黄麻纤维增强抗菌活性的可持续高分子敷料

Medicine in Novel Technology and Devices Pub Date : 2025-02-01 DOI: 10.1016/j.medntd.2025.100352

Gupta Swati Sanjaykumar , Rishabha Malviya , Dhanalekshmi Unnikrishnan Meenakshi , Sathvik Belagodu Sridhar

{"title":"Corchorus olitorius (Jute) fiber enforced sustainable polymeric dressing material for antimicrobial activity","authors":"Gupta Swati Sanjaykumar , Rishabha Malviya , Dhanalekshmi Unnikrishnan Meenakshi , Sathvik Belagodu Sridhar","doi":"10.1016/j.medntd.2025.100352","DOIUrl":"10.1016/j.medntd.2025.100352","url":null,"abstract":"<div><div>Drug delivery to wounds has developed over time from the rudimentary administration of medication on the wound or the site of infection to more sophisticated drug delivery systems. For effectively and rapidly curing wounds, a diverse range of anti-bacterial drug delivery systems must be developed. The current study investigates the possibility of using an inexpensive and environment-friendly sustainable <em>Corchorus olitorius</em> fiber (COF) as a drug carrier for the antibacterial drug tetracycline HCl and its potential as a dressing material. The fibers were purified, grafted using acrylamide monomer, and tested for SEM, FT-IR, TGA, swelling index, swelling-de- swelling behavior, and chemical resistance. Drug loading was carried out, the highest drug loading percentage was found at 99.97 % and the formulation released medication according to the Baker-Lonsdale release kinetics pattern. The anti-microbial study showed effective inhibition of Staphylococcus aureus. The study demonstrates the successful synthesis and application of acrylamide-grafted COF loaded with tetracycline HCl, showcasing its potential as an effective wound dressing for wound care and management. Corchorus olitorius fiber is a cost-effective and eco-friendly material for preparing medicated polymeric dressings.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100352"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143297054","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}

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The history, current state and future possibilities of the non-invasive brain computer interfaces 非侵入性脑机接口的历史、现状和未来可能性

Medicine in Novel Technology and Devices Pub Date : 2025-02-01 DOI: 10.1016/j.medntd.2025.100353

Frederico Caiado, Arkadiy Ukolov

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Diabetes mellitus: The pathophysiology as a canvas for management elucidation and strategies 糖尿病：病理生理学作为管理阐明和策略的画布

Medicine in Novel Technology and Devices Pub Date : 2025-01-30 DOI: 10.1016/j.medntd.2025.100351

Franklyn Nonso Iheagwam , Olawumi Toyin Iheagwam

{"title":"Diabetes mellitus: The pathophysiology as a canvas for management elucidation and strategies","authors":"Franklyn Nonso Iheagwam , Olawumi Toyin Iheagwam","doi":"10.1016/j.medntd.2025.100351","DOIUrl":"10.1016/j.medntd.2025.100351","url":null,"abstract":"<div><div>Diabetes mellitus (DM) is a progressive metabolic disease characterised by high blood glucose due to autoimmune destruction of the β-islet of Langerhans or gradual development of insulin resistance and β-cell degeneration. Numerous risk factors, from genetic to environmental, are associated with this disease. Based on the global observed cases and etiopathogenesis, DM falls into three broad categories: type 1, 2, and gestational diabetes mellitus. A comprehensive search was used to identify relevant publications using targeted keywords associated with DM, pathophysiology, medication, characterised compounds, and others across prominent databases like PubMed, Scopus, and Web of Science. This review examines how DM pathophysiology influences the type of diagnosis, screening, treatment, and management regimen that is implemented. The link between DM and some mechanistic factors and activated glucose metabolic changes is discussed. Insights on the medications targeting various DM pathophysiology mechanisms, antidiabetic mechanisms of characterised compounds from natural products and computer-aided identification of antidiabetics from natural sources are reviewed. These findings could lay the groundwork for inventive therapeutic strategies and leads from natural products based on the proper elucidation of antidiabetic mechanisms, thereby improving management and the impact of DM.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"25 ","pages":"Article 100351"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143297053","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}

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Development and validation of a multiplex RT-qPCR method for the simultaneous detection of influenza type A, B and SARS-COV-2 viruses 同时检测甲型流感、乙型流感和SARS-COV-2病毒的多重RT-qPCR方法的建立和验证

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 GeneXAI：使用基于xai的多模态框架鉴定乳腺癌分期的影响基因

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}

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CAR-T cell a potential platform for cancer therapy CAR-T细胞是癌症治疗的潜在平台

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

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Blastocyst cavity expansion promotes DNA methylation during early development of mouse embryos 在小鼠胚胎早期发育过程中，囊胚腔扩张促进DNA甲基化

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

Zheng Guo, Jing Du

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Cover 封面

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

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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}

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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