Medicine in Novel Technology and Devices最新文献

{"title":"Constant and cyclic shear stress regulate collagen fibers self-assembly","authors":"Chunyang Ma ,&nbsp;Shuaiyin Liu ,&nbsp;Jiangxue Wang ,&nbsp;Xiaolin Guo ,&nbsp;Yixing Ren ,&nbsp;Xufeng Niu","doi":"10.1016/j.medntd.2025.100384","DOIUrl":"10.1016/j.medntd.2025.100384","url":null,"abstract":"<div><div>Collagen fibers are a key structural component in the plaques of atherosclerosis (AS), playing a vital role in regulating plaque stability. Investigating the factors that affect collagen fiber self-assembly and their mechanisms not only aid in assessing disease risk but also provide a theoretical basis for developing new therapeutic strategies. In this study, the plaque development patterns of high-fat diet fed ApoE^−/− mice were observed, and an in vitro simulation system was constructed to conduct further research. Experimental findings indicate that the application of constant shear stress is detrimental to collagen self-assembly, and the hindrance effect increases with the magnitude of the shear stress. Periodic shear stress exhibits a dual effect on collagen fiber self-assembly. Although the applied shear stress partially impedes the assembly process, it also disrupts structural networks that inhibit collagen organization, thereby enabling continued assembly. Compared to constant shear stress, periodic shear stress is more conducive to collagen fiber growth. These results deepen the understanding of collagen self-assembly and provide new theoretical foundations for disease treatment.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100384"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595728","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":"Image analysis of diabetes pathology: classifying with precision via an upgraded resnet50 framework","authors":"Caisheng Liao ,&nbsp;Chenhao Pu ,&nbsp;Tianqi Chen ,&nbsp;Yuki Todo ,&nbsp;Kengo Furuichi ,&nbsp;Tomohisa Yabe ,&nbsp;DeLai Qiu","doi":"10.1016/j.medntd.2025.100385","DOIUrl":"10.1016/j.medntd.2025.100385","url":null,"abstract":"<div><div>Diabetes is a growing global health issue, and effective diagnostic tools are needed to support early detection. This study proposes an enhanced deep learning framework, SE-ResNet50, which integrates a squeeze-and-excitation (SE) block into the conventional ResNet50 architecture to improve the classification of diabetic kidney pathology from glomerular images. The SE block adaptively recalibrates feature responses, enabling the model to emphasize diagnostically relevant structures better. The proposed framework was trained and validated on a kidney tissue dataset from Kanazawa Medical University, achieving superior performance with an accuracy of 97.02 ​%, precision of 0.96, and an AUC of 0.9856. SE-ResNet50 exhibited superior robustness and generalizability compared to established CNN architectures such as EfficientNet B0, Inception V3, and ConvNeXt. Further visualization via Grad-CAM revealed that the model effectively localizes critical regions within glomerular images. These results highlight the potential of SE-ResNet50 as a reliable and interpretable tool for advancing diabetes-related CKD diagnosis in clinical settings.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100385"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587547","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":"Advanced artificial intelligence driven framework for lung cancer diagnosis leveraging SqueezeNet with machine learning algorithms using transfer learning","authors":"Vineet Mehan","doi":"10.1016/j.medntd.2025.100383","DOIUrl":"10.1016/j.medntd.2025.100383","url":null,"abstract":"<div><div>Lung cancer is a severe global public health problem, and early detection is important for improving patient wellbeing. This research presents an advanced Artificial Intelligence (AI) driven framework that integrates deep learning and machine learning techniques to enhance lung cancer classification in chest Computed Tomography (CT) scans. Leveraging transfer learning, SqueezeNet a lightweight Convolutional Neural Network (CNN) is employed for feature extraction, which is then processed by Machine Learning (ML) classifiers. A dataset comprising 950 chest scans from 110 test cases is used to classify tumors into benign, malignant, and normal categories. Among the tested models, SqueezeNet combined with Logistic Regression (LR) achieves the highest accuracy of 92.9 ​%. Performance evaluation is conducted using multiple classification metrics, including Confusion Matrix and Calibration plots, demonstrating the model's reliability in early lung cancer detection. The proposed AI-driven hybrid framework offers a promising approach to improving diagnostic accuracy, ultimately benefiting both patients and the healthcare system.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100383"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587546","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":"Interface-driven human brain injury mechanisms in blast exposure: A fluid–structure interaction model","authors":"Joseph Amponsah ,&nbsp;Archibong Archibong-Eso ,&nbsp;Y.A.K. Fiagbe ,&nbsp;Richard Bruce ,&nbsp;Tabbi Wilberforce Awotwe ,&nbsp;Samuel Adjei","doi":"10.1016/j.medntd.2025.100381","DOIUrl":"10.1016/j.medntd.2025.100381","url":null,"abstract":"<div><div>Blast-induced traumatic brain injury (bTBI) presents a significant challenge for military personnel and civilians exposed to explosions. Beyond combat, bTBI can arise from civilian incidents like industrial accidents (chemical-plant or mining blasts), accidental demolition blasts, fireworks factory explosions, and residential gas-leak detonations. The precise mechanisms by which blast waves damage the brain are still developing. Studies suggest that bTBI is primarily an interface-driven injury, where mechanical forces concentrate at anatomical boundaries including gray–white matter junctions, cortical sulci, cerebrospinal fluid (CSF) spaces, and perivascular structures. Recent research has shown that fluid structure interaction (FSI) simulations are instrumental in capturing shock wave transmission through the skull, CSF, and brain tissue, directly informing the design of protective gear. Here, we developed a high-resolution FSI model of the human head with approximately five million elements and detailed anatomical features (sulci, gyri, CSF compartments, vascular structures) to examine these biomechanical interactions. We employed Friedlander waveform to simulate the blast wave, with adjustments for attenuation through the skull and pressure transmission into the CSF and brain, with peak overpressures ranging from 100 to 1000 kPa and durations up to 6 ms. Our findings indicate that local CSF pressures dropping below its vapor pressure (around –90 kPa) can initiate cavitation, particularly within sulcal and ventricular spaces. This cavitation is accompanied by elevated shear stresses at adjacent gray–white matter interfaces, with strain rates exceeding 250 s⁻¹, co-localizing with diffuse axonal injury (DAI) thresholds. Higher overpressures (500 kPa) also induced intraventricular cavitation and elevated periventricular strain rates. Blast orientation significantly influenced injury distribution, lateral blasts resulted in more diffuse stress fields, while frontal blasts localized damage to anterior cortical regions.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604693","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":"MBTC-Net: Multimodal brain tumor classification from CT and MRI scans using deep neural network with multi-head attention mechanism","authors":"Satrajit Kar,&nbsp;Pawan Kumar Singh","doi":"10.1016/j.medntd.2025.100382","DOIUrl":"10.1016/j.medntd.2025.100382","url":null,"abstract":"<div><div>Brain tumors pose a singularly formidable threat in contemporary healthcare due to their diverse histological profiles and unpredictable clinical behavior. Their spectrum ranges from slow-growing benign tumors to highly aggressive malignancies in sensitive anatomical locations. This necessitates an intensified focus on their pathophysiology and demands precise characterization for patient-specific therapeutic solutions. Techniques to correctly identify brain tumors using artificial intelligence are often employed for addressing segmentation and detection tasks; however, the lack of generalizable results hinders medical practitioners from incorporating them into the diagnostic process. Predominantly reliant on Magnetic Resonance Imaging, research on other imaging methods like Positron Emission Tomography &amp; Computed Tomography, is scarce due to a dearth of open-access datasets. Our study proposes a robust MBTC-Net framework by leveraging EfficientNetV2B0 for extracting high-dimensional feature maps, followed by reshaping into sequences and applying multi-head attention to capture contextual dependencies. After reintroducing the attention output into a spatial structure, we perform average pooling before transitioning to dense layers, enhanced with batch normalization and dropout. The model is fine-tuned with the Adamax optimizer to classify various kinds of brain tumors using softmax from T1-weighted, T1 Contrast-Enhanced, &amp; T2-weighted MRI sequences and CT scans. To reduce the risk of overfitting, measures such as stratified 5-fold cross-validation have been extensively implemented across 3 open-access Kaggle datasets, obtaining 97.54 ​% (15-class), 97.97 ​% (6-class), and 99.34 ​% (2-class) accuracies, respectively. We have also applied Grad-CAM to decipher and visually analyze the predictions made by this framework. This research underscores the need for multimodal training of CT scans and MRI sequences for deploying a sturdy framework in real-time environments and advancing the well-being of patients.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100382"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534121","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":"Corneal material nonlinearity and its effects on the comparative study of corneal biomechanics","authors":"Yingmei Fan ,&nbsp;Yudi Han ,&nbsp;Xuefei Li ,&nbsp;Shenglong Luo ,&nbsp;Xin Zhang ,&nbsp;Lvfu He ,&nbsp;Yuxuan Wang ,&nbsp;Fangjun Bao ,&nbsp;Ahmed Elsheikh ,&nbsp;Junjie Wang","doi":"10.1016/j.medntd.2025.100380","DOIUrl":"10.1016/j.medntd.2025.100380","url":null,"abstract":"<div><div>Corneal biomechanical evaluation has become a research hotspot with wide clinical relevance in fields such as keratoconus, corneal cross-linking, refractive surgery, and glaucoma. Despite advances in in-vivo corneal biomechanical imaging, ex-vivo experiments remain essential for comparing biomechanical effects of diseases and treatment protocols. However, due to the nonlinear behaviour of corneal material, comparisons of biomechanical metrics obtained under different loading conditions can be difficult and misleading. Furthermore, biomechanical estimations conducted under unrealistic loads that exceed normal living conditions may have limited clinical relevance. The current study aims to evaluate this issue by assessing the biomechanical properties of the cornea under physiologic and extreme conditions. Stress-strain data were obtained for porcine corneal specimens subjected to widely different strain rates (0.8 ​%, 25 ​%, 83 ​% and 420 ​%/min). In all cases, the behaviour closely matched a two-stage pattern consisting of an initial nonlinear low-stress region followed by a linear region under higher stresses. Tangent moduli (Et) were calculated at physiologic stress levels (0.01, 0.015, and 0.02 ​MPa) and extreme ones (0.15, 1.0, and 2.0 ​MPa). While comparisons at physiologic stress levels showed similar increasing trends in Et with faster strain rates, at high/non-physiologic stress levels, Et values remained relatively unchanged across strain rates from 25 ​% to 420 ​%/min. These findings underscore the importance of testing corneal behaviour under physiologic loading levels and signal cautions when interpreting results under high loads that do not represent normal tissue conditions.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100380"},"PeriodicalIF":0.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480391","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":"Additive manufacturing in bone science: A cutting-edge review of its potential and progress","authors":"Md Shakil Chowdhury ,&nbsp;Md Shah Oliullah ,&nbsp;Md Zisat Hossen ,&nbsp;Mehedi Hasan Manik ,&nbsp;MD Abu Hurayra ,&nbsp;Md. Zobair Al Mahmud ,&nbsp;Nayem Hossain ,&nbsp;Md Hosne Mobarak ,&nbsp;Md Didarul Islam","doi":"10.1016/j.medntd.2025.100379","DOIUrl":"10.1016/j.medntd.2025.100379","url":null,"abstract":"<div><div>In the medical industry, additive manufacturing, or AM, sometimes referred to as 3D printing, has completely changed bone regeneration and healing. With the use of this technology, complex scaffolds and implants that nearly resemble natural bone structures may be created. Orthopedic patient-specific solutions may be created by utilizing a variety of AM processes, including as fused deposition modeling, stereolithography, and selective laser sintering. Compared to conventional bone grafting techniques, AM lowers risks while promoting cellular development, differentiation, and osseointegration. When bioactive chemicals are used with biocompatible materials like metals, ceramics, and polymers, bone tissue engineering becomes even more effective. The potential of AM in bone regeneration is reviewed in this research along with an analysis of its uses, benefits, and new materials. Issues including material constraints, expenses, and regulatory issues are discussed, and suggestions for more study are included. AM has the potential to significantly improve clinical procedures and patient outcomes by revolutionizing the fields of bone tissue engineering and orthopedic implants.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100379"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480387","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":"Artificial intelligence in pharmaceutical sciences: A comprehensive review","authors":"Priyanka Kandhare,&nbsp;Mrunal Kurlekar,&nbsp;Tanvi Deshpande,&nbsp;Atmaram Pawar","doi":"10.1016/j.medntd.2025.100375","DOIUrl":"10.1016/j.medntd.2025.100375","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical sciences has catalyzed transformative advancements across drug discovery, clinical development, manufacturing, and post-market surveillance. This review comprehensively examines AI's role in modern pharmacotherapy, beginning with its historical evolution in life sciences and progressing to cutting-edge applications such as AlphaFold-driven protein modeling, natural language processing (NLP) for biomedical literature mining, and AI-augmented pharmacovigilance. Methodologically, we synthesize interdisciplinary insights from peer-reviewed literature (2013–2023), highlighting innovations in cheminformatics (e.g., QSAR, RDKit), predictive toxicology, and personalized medicine. Case studies illustrate AI's capacity to compress drug development timelines, as seen in COVID-19 repurposing efforts and <em>de novo</em> kinase inhibitor design. However, challenges persist, including algorithmic bias, regulatory ambiguities, and the “black-box” nature of deep learning models. By critically evaluating successes and limitations, this review underscores AI's potential to redefine pharmaceutical innovation while advocating for robust frameworks to ensure ethical, transparent, and clinically translatable AI deployment.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100375"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307139","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":"Micro-CT and histological evaluation of alveolar ridge preservation with Bio-Oss® and THE Graft®: A randomized controlled trial","authors":"Lurong Jia ,&nbsp;Tong Wang ,&nbsp;Kai Dong ,&nbsp;Quanlin Chen ,&nbsp;Chao Wang ,&nbsp;Wenjuan Zhou ,&nbsp;Zhonghao Liu","doi":"10.1016/j.medntd.2025.100377","DOIUrl":"10.1016/j.medntd.2025.100377","url":null,"abstract":"<div><div>This randomized clinical trial aimed to compare volumetric changes and histological characteristics of new bone in extraction sockets grafted with Bio-Oss® or THE Graft®. Furthermore, it sought to further assess the quality and quantity of bone regeneration at 3- and 6-month intervals. Thirty-two patients requiring single-tooth extraction (excluding third molars) and ridge preservation in preparation for subsequent implant placement were randomized to receive either graft material (Bio-Oss® or THE Graft®) covered by a collagen membrane. At 3 months, BV/TV was higher in the DBBM groups than in the DPBM groups, while mineralized bone proportions were similar between groups. By 6 months, BV/TV remained significantly higher in DPBM groups than in the DBBM groups, and a higher proportion of mineralized bone was observed in the DPBM groups. Both groups showed increasing BV/TV and mineralized bone over time. In conclusion, both Bio-Oss® and THE Graft® effectively promoted socket bone regeneration, with healing time influencing bone formation. Long-term results suggest THE Graft® may enhance mineralized bone percentage more than Bio-Oss®.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100377"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471606","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":"Artificial intelligence and biosensors: Transforming cancer diagnostics","authors":"Maryam Althobiti ,&nbsp;Trinh Thi Trang Nhung ,&nbsp;Swati Verma ,&nbsp;Raef R. Albugami ,&nbsp;Rajender Kumar","doi":"10.1016/j.medntd.2025.100378","DOIUrl":"10.1016/j.medntd.2025.100378","url":null,"abstract":"<div><div>Cancer is one of the leading causes of death worldwide. Early detection of cancer can play a decisive role in cancer treatment and improving survival rates. Conventional cancer detection methods, such as biopsy, imaging and blood tests are generally invasive and time-consuming, and their results have accuracy issues. Biosensors with artificial intelligence integration play a significant and evolving role in cancer diagnostics, offering non-invasive, rapid, and highly sensitive methods for early detection, monitoring, and treatment of cancer. Biosensors detect specific biomarkers associated with cancerous cells or tumours, such as nucleic acid (DNA, RNA), small molecules, peptides, proteins and metabolites. In recent years, many predictive artificial intelligence models and bioinformatics tools have been developed to integrate biosensors, emerging as powerful tools for cancer diagnostics. This review explores the role of biosensors in cancer detection, the development and application of predictive AI models and bioinformatics tools in cancer detection through biosensor technologies, and the challenges associated with their clinical adoption.</div></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"27 ","pages":"Article 100378"},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313049","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}