SLAS Technology最新文献_第9页

CircTNFRSF19 facilitates triple negative breast cancer cell growth by regulating N6-methyladenosine modification of B3GNT5: Medical biological image simulation CircTNFRSF19通过调节B3GNT5的n6 -甲基腺苷修饰促进三阴性乳腺癌细胞生长：医学生物图像模拟

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-06-01 DOI: 10.1016/j.slast.2025.100308

Lei Xue , Yuhui Zhou , Feng Liu , Jie Dang , Yu Yan

The role of cell surface receptors and glycosylation modification in cancer development has become a focus of research. In particular, the role of tumor necrosis factor receptor superfamily member 19 (TNFRSF19) and β-1, 3-N-acetylglucosamine transferase 5 (B3GNT5) in tumor cell growth has attracted extensive attention. The aim of this study was to investigate the role of CircTNFRSF19 in regulating the N6-methyladenosine (m6A) modification of B3GNT5 and how this modification promotes the growth of TNBC cells. The expression levels of CircTNFRSF19 and B3GNT5 in TNBC cell lines were detected by real-time quantitative PCR (qPCR). Then the m6A modification pattern of B3GNT5 was analyzed by m6A methylation sequencing technology, and the interaction between CircTNFRSF19 and B3GNT5 was verified by RNA immunoprecipitation (RIP) experiment. Through medical thermal image simulation technology, we conducted real-time monitoring and analysis of temperature changes during cell growth to assess the effects of CircTNFRSF19 and B3GNT5m6A modifications on cell metabolism and growth rate. The RIP experiment further confirmed the direct interaction between CircTNFRSF19 and B3GNT5. CRISPR/Cas9 gene editing experiments showed that after CircTNFRSF19 was knocked out, the m6A modification level of B3GNT5 was significantly decreased, and the growth rate of TNBC cells was also significantly slowed down. The application of medical thermal image simulation technology revealed that the metabolic activity of the cells in the CircTNFRSF19 knockout group was reduced, and the temperature change of the cell growth area was significantly different from that in the control group.

细胞表面受体和糖基化修饰在肿瘤发生中的作用已成为研究热点。特别是肿瘤坏死因子受体超家族成员19 （TNFRSF19）和β- 1,3 - n -乙酰氨基葡萄糖转移酶5 （B3GNT5）在肿瘤细胞生长中的作用引起了广泛关注。本研究的目的是探讨CircTNFRSF19在调节B3GNT5的n6 -甲基腺苷（m6A）修饰中的作用，以及这种修饰如何促进TNBC细胞的生长。采用实时定量PCR （real-time quantitative PCR, qPCR）检测CircTNFRSF19和B3GNT5在TNBC细胞系中的表达水平。然后通过m6A甲基化测序技术分析B3GNT5的m6A修饰模式，并通过RNA免疫沉淀（RIP）实验验证CircTNFRSF19与B3GNT5的相互作用。我们通过医学热图像模拟技术，实时监测和分析细胞生长过程中的温度变化，评估CircTNFRSF19和B3GNT5m6A修饰对细胞代谢和生长速率的影响。RIP实验进一步证实了CircTNFRSF19与B3GNT5之间的直接相互作用。CRISPR/Cas9基因编辑实验表明，敲除CircTNFRSF19后，B3GNT5的m6A修饰水平显著降低，TNBC细胞的生长速度也明显减慢。应用医学热图像模拟技术发现，CircTNFRSF19基因敲除组细胞代谢活性降低，细胞生长区域温度变化与对照组有明显差异。

{"title":"CircTNFRSF19 facilitates triple negative breast cancer cell growth by regulating N6-methyladenosine modification of B3GNT5: Medical biological image simulation","authors":"Lei Xue , Yuhui Zhou , Feng Liu , Jie Dang , Yu Yan","doi":"10.1016/j.slast.2025.100308","DOIUrl":"10.1016/j.slast.2025.100308","url":null,"abstract":"<div><div>The role of cell surface receptors and glycosylation modification in cancer development has become a focus of research. In particular, the role of tumor necrosis factor receptor superfamily member 19 (TNFRSF19) and β-1, 3-N-acetylglucosamine transferase 5 (B3GNT5) in tumor cell growth has attracted extensive attention. The aim of this study was to investigate the role of CircTNFRSF19 in regulating the N6-methyladenosine (m6A) modification of B3GNT5 and how this modification promotes the growth of TNBC cells. The expression levels of CircTNFRSF19 and B3GNT5 in TNBC cell lines were detected by real-time quantitative PCR (qPCR). Then the m6A modification pattern of B3GNT5 was analyzed by m6A methylation sequencing technology, and the interaction between CircTNFRSF19 and B3GNT5 was verified by RNA immunoprecipitation (RIP) experiment. Through medical thermal image simulation technology, we conducted real-time monitoring and analysis of temperature changes during cell growth to assess the effects of CircTNFRSF19 and B3GNT5m6A modifications on cell metabolism and growth rate. The RIP experiment further confirmed the direct interaction between CircTNFRSF19 and B3GNT5. CRISPR/Cas9 gene editing experiments showed that after CircTNFRSF19 was knocked out, the m6A modification level of B3GNT5 was significantly decreased, and the growth rate of TNBC cells was also significantly slowed down. The application of medical thermal image simulation technology revealed that the metabolic activity of the cells in the CircTNFRSF19 knockout group was reduced, and the temperature change of the cell growth area was significantly different from that in the control group.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100308"},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial for the special issue: “Natural language processing and large language models in life sciences” 特刊社论：“生命科学中的自然语言处理和大语言模型”。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-06-01 DOI: 10.1016/j.slast.2025.100296

Akshi Kumar , MPS Bhatia

引用次数: 0

SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders SNet：一种新的卷积神经网络架构，用于胃肠疾病的高级内镜图像分类。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-30 DOI: 10.1016/j.slast.2025.100304

Samra Siddiqui , Junaid A. Khan , Tallha Akram , Meshal Alharbi , Jaehyuk Cha , Dina A. AlHammadi

With the intent of assisting gastroenterologists from all over the world, the proposed work aims to eliminate the effort required to achieve accurate diagnoses. Statistically, gastrointestinal diseases often result in fatal disorders, contributing to a significant number of fatalities. The upper gastrointestinal tract (GIT) includes the stomach, esophagus, and duodenum, while the lower one comprises a section of the small intestine, namely the ileum, as well as the large intestine, including the colon. The challenges associated with GIT tract issues are apparently complex. Therefore, multiple challenges exist regarding CAD (Computer-aided diagnosis) and endoscopy, including a lack of annotated images, a dark background, poor contrast, and an irregular pattern. The objective of this research is to develop a robust deep network, called SNet, that offers a solution to complex classification problems. Firstly, the endoscopic images undergo preprocessing before being subjected to feature extraction. This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. This facilitates cross-dataset system evaluation, resulting in an efficient system for an unseen image diagnosis. To avoid the problem of “curse of dimensionality”, the most discriminant feature information is selected based on proposed minimum redundancy maximum relevance (MRMR) algorithm. The proposed architecture has been evaluated using a range of performance metrics, such as accuracy, sensitivity, specificity, and Area under curve (AUC). With classification accuracy as the main metric, the achieved accuracy is 98.45% on the Kvasir v1, and 97.83% on the Kvasir v2 datasets.

为了帮助来自世界各地的胃肠病学家，建议的工作旨在消除实现准确诊断所需的努力。据统计，胃肠道疾病往往导致致命的疾病，造成大量死亡。上消化道包括胃、食道和十二指肠，下消化道包括小肠的一段，即回肠，以及大肠，包括结肠。与GIT通道问题相关的挑战显然是复杂的。因此，CAD（计算机辅助诊断）和内窥镜检查存在多重挑战，包括缺乏注释图像、暗背景、对比度差和不规则图案。这项研究的目的是开发一个强大的深度网络，称为SNet，为复杂的分类问题提供解决方案。首先对内窥镜图像进行预处理，然后进行特征提取。这一步包括图像大小调整和增强步骤。所提出的卷积神经网络（CNN）模型由放置在不同层的六个块组成。为了能够跨不同数据集对所提出的框架进行详尽的评估，该模型在非常复杂的HyperKvasir数据集上进行了训练，随后在Kvasir v1和v2数据集上进行了测试。这有助于跨数据集系统评估，从而形成一个有效的系统，用于未见过的图像诊断。为了避免“维数诅咒”的问题，基于所提出的最小冗余最大相关（MRMR）算法选择最具判别性的特征信息。所提出的体系结构已经使用一系列性能指标进行了评估，例如准确性、灵敏度、特异性和曲线下面积（AUC）。以分类准确率为主要指标，在Kvasir v1数据集上实现的准确率为98.45%，在Kvasir v2数据集上实现的准确率为97.83%。

{"title":"SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders","authors":"Samra Siddiqui , Junaid A. Khan , Tallha Akram , Meshal Alharbi , Jaehyuk Cha , Dina A. AlHammadi","doi":"10.1016/j.slast.2025.100304","DOIUrl":"10.1016/j.slast.2025.100304","url":null,"abstract":"<div><div>With the intent of assisting gastroenterologists from all over the world, the proposed work aims to eliminate the effort required to achieve accurate diagnoses. Statistically, gastrointestinal diseases often result in fatal disorders, contributing to a significant number of fatalities. The upper gastrointestinal tract (GIT) includes the stomach, esophagus, and duodenum, while the lower one comprises a section of the small intestine, namely the ileum, as well as the large intestine, including the colon. The challenges associated with GIT tract issues are apparently complex. Therefore, multiple challenges exist regarding CAD (Computer-aided diagnosis) and endoscopy, including a lack of annotated images, a dark background, poor contrast, and an irregular pattern. The objective of this research is to develop a robust deep network, called SNet, that offers a solution to complex classification problems. Firstly, the endoscopic images undergo preprocessing before being subjected to feature extraction. This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. This facilitates cross-dataset system evaluation, resulting in an efficient system for an unseen image diagnosis. To avoid the problem of “<em>curse of dimensionality</em>”, the most discriminant feature information is selected based on proposed minimum redundancy maximum relevance (MRMR) algorithm. The proposed architecture has been evaluated using a range of performance metrics, such as accuracy, sensitivity, specificity, and Area under curve (AUC). With classification accuracy as the main metric, the achieved accuracy is 98.45% on the Kvasir v1, and 97.83% on the Kvasir v2 datasets.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100304"},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Therapeutic potential of PDA@MT in mitigating oxidative stress in obstructive sleep apnea based on biomedical images 基于生物医学图像的PDA@MT缓解阻塞性睡眠呼吸暂停氧化应激的治疗潜力。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-29 DOI: 10.1016/j.slast.2025.100309

Zeming Zhang , Wenhui Wang , Li Wei , Li Han , Zaiyan Wang , Hao Chen

Obstructive sleep apnea (OSA) is a common sleep disorder that affects breathing and is accompanied by increased oxidative stress, leading to multiple health problems. This study evaluated the therapeutic efficacy of PDA@MT on oxidative stress and OSA model, providing new ideas for the treatment of OSA. Firstly, PDA@MT nanoparticles were synthesized and their embedding efficiency and drug loading capacity were evaluated. The physicochemical properties of the particles were analyzed by means of particle size and ζ potential test, transmission electron microscope (TEM) imaging and sample stability test. Subsequently, cell viability assay, cell uptake assay and antioxidant assay were performed to evaluate the therapeutic effect of nanoparticles in vitro. OSA rat models were established, and histological analysis, immunofluorescence detection and reactive oxygen species (ROS) detection were performed to evaluate the efficacy of PDA@MT in vivo, and finally statistical analysis was performed. PDA@MT nanoparticles showed good cytocompatibility and significant antioxidant capacity, and could effectively reduce ROS levels in vitro. Multiple validated evaluations have shown that PDA@MT significantly improves respiratory status in model rats in OSA models, showing promising therapeutic potential. Biosafety evaluation results showed that PDA@MT is safe for use in vivo. Medical thermal images play a key role in evaluating the therapeutic effect of the nanoparticles and provide an important basis for further research and development.

阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，会影响呼吸，并伴有氧化应激增加，导致多种健康问题。本研究评价PDA@MT对氧化应激及OSA模型的治疗效果，为OSA的治疗提供新的思路。首先，合成PDA@MT纳米颗粒，并对其包埋效率和载药能力进行评价。采用粒径、ζ电位测试、透射电镜（TEM）成像和样品稳定性测试等方法对颗粒的理化性质进行了分析。随后，通过细胞活力测定、细胞摄取测定和抗氧化测定来评价纳米颗粒的体外治疗效果。建立OSA大鼠模型，进行组织学分析、免疫荧光检测和活性氧（ROS）检测，评价PDA@MT在体内的疗效，最后进行统计学分析。PDA@MT纳米颗粒具有良好的细胞相容性和显著的抗氧化能力，并能有效降低体外ROS水平。多次经过验证的评估表明PDA@MT可显著改善OSA模型大鼠的呼吸状态，具有良好的治疗潜力。生物安全性评价结果表明PDA@MT在体内使用是安全的。医学热成像在评价纳米颗粒的治疗效果方面发挥着关键作用，为进一步的研究和开发提供了重要依据。

{"title":"Therapeutic potential of PDA@MT in mitigating oxidative stress in obstructive sleep apnea based on biomedical images","authors":"Zeming Zhang , Wenhui Wang , Li Wei , Li Han , Zaiyan Wang , Hao Chen","doi":"10.1016/j.slast.2025.100309","DOIUrl":"10.1016/j.slast.2025.100309","url":null,"abstract":"<div><div>Obstructive sleep apnea (OSA) is a common sleep disorder that affects breathing and is accompanied by increased oxidative stress, leading to multiple health problems. This study evaluated the therapeutic efficacy of PDA@MT on oxidative stress and OSA model, providing new ideas for the treatment of OSA. Firstly, PDA@MT nanoparticles were synthesized and their embedding efficiency and drug loading capacity were evaluated. The physicochemical properties of the particles were analyzed by means of particle size and ζ potential test, transmission electron microscope (TEM) imaging and sample stability test. Subsequently, cell viability assay, cell uptake assay and antioxidant assay were performed to evaluate the therapeutic effect of nanoparticles <em>in vitro</em>. OSA rat models were established, and histological analysis, immunofluorescence detection and reactive oxygen species (ROS) detection were performed to evaluate the efficacy of PDA@MT <em>in vivo</em>, and finally statistical analysis was performed. PDA@MT nanoparticles showed good cytocompatibility and significant antioxidant capacity, and could effectively reduce ROS levels <em>in vitro</em>. Multiple validated evaluations have shown that PDA@MT significantly improves respiratory status in model rats in OSA models, showing promising therapeutic potential. Biosafety evaluation results showed that PDA@MT is safe for use <em>in vivo</em>. Medical thermal images play a key role in evaluating the therapeutic effect of the nanoparticles and provide an important basis for further research and development.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100309"},"PeriodicalIF":2.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced high-throughput embryonic photomotor response assays in zebrafish using a multi-camera array microscope 利用多相机阵列显微镜进行斑马鱼胚胎高通量光度反应分析。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-28 DOI: 10.1016/j.slast.2025.100310

Julia Jamison , Thomas Jedidiah Jenks Doman , Zoe Antenucci , John Efromson , Connor Johnson , Michael T. Simonich , Mark Harfouche , Lisa Truong , Robyn L. Tanguay

Developing automated, high-throughput screening platforms for early-stage drug development and toxicology assessment requires robust model systems that can predict human responses. Zebrafish embryos have emerged as an ideal vertebrate model for this purpose due to their rapid development, genetic homology to humans, and amenability to high-throughput screening. However, existing commercial imaging platforms face significant technical limitations in capturing early developmental behaviors. We present the validation of the Kestrel™, a novel high-throughput imaging platform featuring a 24-camera array that enables simultaneous acquisition of high-resolution video data across 96-well plates. This system overcomes key technical limitations through its unique optical design and automated image processing pipeline. Unlike current commercial systems, which require specialized setup and can only image subsets of wells, the Kestrel provides comprehensive plate imaging at 9.6 µm resolution with 10+ Hz video capture across an 8 × 12 cm field of view. We validated the system using zebrafish embryonic photomotor response (EPR) assays, demonstrating its ability to track behavioral responses in chorionated and dechorionated embryos without workflow modifications. The system successfully detected concentration-dependent responses to ethanol, methanol, and bisphenol A across different plate formats and well volumes. Notably, the Kestrel enabled equivalent detection of behavioral responses in chorionated and dechorionated embryos, eliminating the need for the dechorionation process while maintaining assay sensitivity. This technological advancement provides a robust platform for high-throughput chemical screening in drug discovery and toxicology applications, offering significant improvements in throughput, sensitivity, and reproducibility with a highly relevant vertebrate model.

开发用于早期药物开发和毒理学评估的自动化、高通量筛选平台需要能够预测人类反应的强大模型系统。斑马鱼胚胎由于其快速发育、与人类基因同源性以及易于高通量筛选而成为理想的脊椎动物模型。然而，现有的商业成像平台在捕捉早期发育行为方面面临着重大的技术限制。我们展示了Kestrel™的验证，这是一种新型的高通量成像平台，具有24个摄像头阵列，可以同时采集96孔板的高分辨率视频数据。该系统通过其独特的光学设计和自动图像处理流水线，克服了关键的技术限制。目前的商业系统需要专门的设置，只能对井的子集进行成像，与此不同，Kestrel提供9.6µm分辨率的全面板成像，在8×12 cm的视场范围内进行10+ Hz的视频捕获。我们使用斑马鱼胚胎光运动反应（EPR）实验验证了该系统，证明了它能够在不修改工作流程的情况下跟踪绒毛膜剥离和去绒毛膜剥离胚胎的行为反应。该系统成功检测了不同板型和孔体积对乙醇、甲醇和双酚A的浓度依赖性反应。值得注意的是，红隼能够在脱去绒毛膜和脱去绒毛膜的胚胎中进行等效的行为反应检测，在保持检测灵敏度的同时消除了脱去绒毛膜过程的需要。这一技术进步为药物发现和毒理学应用中的高通量化学筛选提供了一个强大的平台，在高度相关的脊椎动物模型中提供了通量，灵敏度和可重复性的显着改进。

{"title":"Enhanced high-throughput embryonic photomotor response assays in zebrafish using a multi-camera array microscope","authors":"Julia Jamison , Thomas Jedidiah Jenks Doman , Zoe Antenucci , John Efromson , Connor Johnson , Michael T. Simonich , Mark Harfouche , Lisa Truong , Robyn L. Tanguay","doi":"10.1016/j.slast.2025.100310","DOIUrl":"10.1016/j.slast.2025.100310","url":null,"abstract":"<div><div>Developing automated, high-throughput screening platforms for early-stage drug development and toxicology assessment requires robust model systems that can predict human responses. Zebrafish embryos have emerged as an ideal vertebrate model for this purpose due to their rapid development, genetic homology to humans, and amenability to high-throughput screening. However, existing commercial imaging platforms face significant technical limitations in capturing early developmental behaviors. We present the validation of the Kestrel™, a novel high-throughput imaging platform featuring a 24-camera array that enables simultaneous acquisition of high-resolution video data across 96-well plates. This system overcomes key technical limitations through its unique optical design and automated image processing pipeline. Unlike current commercial systems, which require specialized setup and can only image subsets of wells, the Kestrel provides comprehensive plate imaging at 9.6 µm resolution with 10+ Hz video capture across an 8 × 12 cm field of view. We validated the system using zebrafish embryonic photomotor response (EPR) assays, demonstrating its ability to track behavioral responses in chorionated and dechorionated embryos without workflow modifications. The system successfully detected concentration-dependent responses to ethanol, methanol, and bisphenol A across different plate formats and well volumes. Notably, the Kestrel enabled equivalent detection of behavioral responses in chorionated and dechorionated embryos, eliminating the need for the dechorionation process while maintaining assay sensitivity. This technological advancement provides a robust platform for high-throughput chemical screening in drug discovery and toxicology applications, offering significant improvements in throughput, sensitivity, and reproducibility with a highly relevant vertebrate model.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100310"},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach 机器学习驱动的阿尔茨海默病预测：一种深度集成建模方法

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-17 DOI: 10.1016/j.slast.2025.100298

Mustafa Lateef Fadhil Jumaili , Emrullah Sonuç

Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework that combines five deep learning architectures (VGG16, VGG19, ResNet50, InceptionV3, and EfficientNetB7) to improve the accuracy of AD diagnosis. We use a comprehensive dataset of 3,714 MRI brain scans collected from specialized clinics in Iraq, categorized into three classes: NonDemented (834 images), MildDemented (1,824 images), and VeryDemented (1,056 images). The proposed voting ensemble model achieves a diagnostic accuracy of 99.32% on our dataset. The effectiveness of the model is further validated on two external datasets: OASIS (achieving 86.6% accuracy) and ADNI (achieving 99.5% accuracy), demonstrating competitive performance compared to existing approaches. Moreover, the proposed model exhibits high precision and recall across all stages of dementia, providing a reliable and robust tool for early AD detection. This study highlights the effectiveness of ensemble learning in AD diagnosis and shows promise for clinical applications.

阿尔茨海默病（AD）是一种进行性神经系统疾病，其特征是由于脑细胞死亡导致认知能力下降，通常在晚年表现出来。早期和准确的检测对于有效的疾病管理和治疗至关重要。本研究提出了一个集成学习框架，该框架结合了五个深度学习架构（VGG16, VGG19, ResNet50， InceptionV3和effentnetb7），以提高AD诊断的准确性。我们使用了从伊拉克专门诊所收集的3714个MRI脑扫描的综合数据集，分为三类：非痴呆（834张图像），轻度痴呆（1824张图像）和重度痴呆（1056张图像）。所提出的投票集成模型在我们的数据集上达到了99.32%的诊断准确率。该模型的有效性在两个外部数据集上进一步验证：OASIS（达到86.6%的准确率）和ADNI（达到99.5%的准确率），与现有方法相比，展示了具有竞争力的性能。此外，该模型在痴呆的所有阶段都具有较高的准确性和召回率，为早期AD检测提供了可靠和强大的工具。本研究强调了集成学习在AD诊断中的有效性，并显示了临床应用的前景。

{"title":"ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach","authors":"Mustafa Lateef Fadhil Jumaili , Emrullah Sonuç","doi":"10.1016/j.slast.2025.100298","DOIUrl":"10.1016/j.slast.2025.100298","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework that combines five deep learning architectures (VGG16, VGG19, ResNet50, InceptionV3, and EfficientNetB7) to improve the accuracy of AD diagnosis. We use a comprehensive dataset of 3,714 MRI brain scans collected from specialized clinics in Iraq, categorized into three classes: NonDemented (834 images), MildDemented (1,824 images), and VeryDemented (1,056 images). The proposed voting ensemble model achieves a diagnostic accuracy of 99.32% on our dataset. The effectiveness of the model is further validated on two external datasets: OASIS (achieving 86.6% accuracy) and ADNI (achieving 99.5% accuracy), demonstrating competitive performance compared to existing approaches. Moreover, the proposed model exhibits high precision and recall across all stages of dementia, providing a reliable and robust tool for early AD detection. This study highlights the effectiveness of ensemble learning in AD diagnosis and shows promise for clinical applications.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100298"},"PeriodicalIF":2.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury 基于人工智能的脑卒中神经损伤患者术后自我护理能力研究

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-12 DOI: 10.1016/j.slast.2025.100299

Hui Zhao , Na Li , Jianmei Zhang

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the rate of hospital visits and hospitalizations. In this study, patients in a local hospital of third-grade class-A hospital were used as cases. Artificial intelligence was used to conduct continuous nursing intervention for the patients who were discharged from the stroke by using the WeChat platform, regular follow-up and home care. Afterwards, the collected data were given a post-processing, independent-samples t-test for two groups. After 3 months of extended care, the BI (Barthel Index) score of the intervention group has increased by 23.87 points, and the depression self-rating scale score has decreased by 9.12 points. Compared with the control group, the patients' self-care ability, depression state, compliance with health guidance and laboratory indicators were also better than those in the control group, and the differences were statistically significant (P < 0.05). Compared with the control group, the trend of increasing the scores of each index was more significant in the intervention group.

据相关资料统计，中风是一种比较常见的脑血管疾病，其发病率高达185/10万~ 219/10万。持续护理可以改善脑卒中患者的生活质量，降低住院率。本研究以某地方三级甲等医院的患者为病例。采用人工智能对脑卒中出院患者进行持续护理干预，采用微信平台，定期随访，居家护理。之后，对收集到的数据进行后处理，对两组进行独立样本t检验。延长护理3个月后，干预组BI （Barthel指数）得分提高23.87分，抑郁自评量表得分下降9.12分。与对照组比较，患者的生活自理能力、抑郁状态、健康指导依从性、实验室指标均优于对照组，差异均有统计学意义(P <；0.05)。与对照组相比，干预组各指标得分的上升趋势更为显著。

{"title":"Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury","authors":"Hui Zhao , Na Li , Jianmei Zhang","doi":"10.1016/j.slast.2025.100299","DOIUrl":"10.1016/j.slast.2025.100299","url":null,"abstract":"<div><div>According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the rate of hospital visits and hospitalizations. In this study, patients in a local hospital of third-grade class-A hospital were used as cases. Artificial intelligence was used to conduct continuous nursing intervention for the patients who were discharged from the stroke by using the WeChat platform, regular follow-up and home care. Afterwards, the collected data were given a post-processing, independent-samples <em>t</em>-test for two groups. After 3 months of extended care, the BI (Barthel Index) score of the intervention group has increased by 23.87 points, and the depression self-rating scale score has decreased by 9.12 points. Compared with the control group, the patients' self-care ability, depression state, compliance with health guidance and laboratory indicators were also better than those in the control group, and the differences were statistically significant (<em>P</em> < 0.05). Compared with the control group, the trend of increasing the scores of each index was more significant in the intervention group.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100299"},"PeriodicalIF":2.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scalable deep attention mechanism of instance segmentation for the investigation of chromosome 染色体研究实例分割的可扩展深度注意机制。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-11 DOI: 10.1016/j.slast.2025.100306

Neelam Umbreen , Sara Ali , Hasan Sajid , Yasar Ayaz , Shrooq Alsenan , Yunyoung Nam , So Yeon Kim , Muhammad Baber Sial

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imaging.

由于染色体形状固有的复杂性、可变性和高质量注释数据集的稀缺性，染色体中期图像分割在细胞遗传学和基因组学中是一项关键但具有挑战性的任务。本研究提出了一种鲁棒的实例分割框架，该框架将自动注释管道与增强的深度学习架构集成在一起，以应对这些挑战。介绍了一个新的数据集，包括中期图像和相应的核图，并以COCO格式标注了24个染色体类别的精确实例分割信息。为了克服人工标注过程的劳动强度，采用基于特征的图像配准技术，利用SIFT和同源性，实现了染色体从核图到中期图像的精确映射，显著提高了标注质量和分割性能。该框架包括一个自定义Mask R-CNN模型，增强了基于注意力的特征金字塔网络（AttFPN）、空间注意力机制和LastLevelMaxPool块，用于卓越的多尺度特征提取，并将注意力集中在图像的关键区域。实验评估证明了该模型的有效性，在IoU = 0.50:0.95时平均精度（mAP）为0.579，mAP和AP50分别比基线Mask R-CNN和Mask R-CNN与AttFPN分别提高了3.94%和5.97%。值得注意的是，所提出的架构在分割小型和中型染色体方面表现出色，解决了现有方法的关键限制。本研究不仅引入了最先进的分割框架，而且提供了一个基准数据集，为生物医学成像中染色体实例分割设定了新的标准。自动化数据集创建与先进模型设计的集成提供了可扩展和可转移的解决方案，为解决生物医学和细胞遗传学成像其他领域的类似挑战铺平了道路。

{"title":"A scalable deep attention mechanism of instance segmentation for the investigation of chromosome","authors":"Neelam Umbreen , Sara Ali , Hasan Sajid , Yasar Ayaz , Shrooq Alsenan , Yunyoung Nam , So Yeon Kim , Muhammad Baber Sial","doi":"10.1016/j.slast.2025.100306","DOIUrl":"10.1016/j.slast.2025.100306","url":null,"abstract":"<div><div>Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imaging.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100306"},"PeriodicalIF":2.5,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and optimization of a fluid flow splitting device for low-flow applications

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-10 DOI: 10.1016/j.slast.2025.100305

Alexis K. Yates , Heather N. Murray , Ethan S. Lippmann

Microfluidic devices are defined by the application of fluid flow to micron-scale features. Inherent to most experiments involving microfluidic devices is the need to precisely and reproducibly control fluid flow at the microliter scale, often through multiple inlet ports on a single device. While the number of fluid channels per device varies, perfusing multiple inputs requires either the use of multiple flow controllers (often syringe or peristaltic pumps) or the ability to evenly divide fluid across outlets. Towards the latter approach, while a handful of commercial systems exist for splitting fluid flow, these set-ups require significant financial investment, multiple flow control and sensing components, and restrict the user to a predetermined perfusion control system. Simple in-line splitting devices, such a manifolds or T junctions, fail to achieve flow splitting at low flow rates often used in microfluidic systems. To increase capabilities for flow-controlled experiments, we performed experimental analyses of the physical considerations governing even flow splitting under low flow, leading to the design of a microdevice (µ-Split) that can be directly inserted into existing microfluidic set-ups. The µ-Split allows for reproducible, even flow splitting from 10 uL/min to > 2.5 mL/min. By testing multiple device geometries in combination with multiphysics simulations, we identified the design and fabrication features underlying the splitting precision achieved by the µ-Split. Overall, this work provides a useful tool to simplify microfluidic experiments that require evenly divided flow streams, while minimizing the overall device footprint.

微流控装置是通过应用流体流动来定义微米尺度的特征。涉及微流体装置的大多数实验固有的是需要精确和可重复地控制流体在微升尺度上的流动，通常通过单个装置上的多个入口端口。虽然每个设备的流体通道数量各不相同，但灌注多个输入需要使用多个流量控制器（通常是注射器或蠕动泵）或在各个出口均匀分配流体的能力。对于后一种方法，虽然存在少数用于分离流体流动的商业系统，但这些设置需要大量的财务投资，多个流量控制和传感组件，并且限制用户使用预定的灌注控制系统。简单的在线分裂装置，如流形或T结，不能实现通常用于微流体系统的低流速下的流动分裂。为了提高流动控制实验的能力，我们对低流量下控制均匀流动分裂的物理因素进行了实验分析，从而设计了一种可以直接插入现有微流体装置的微装置（µ-Split）。µ-Split允许可重复的，均匀的流动分裂从10 uL/min到>；2.5毫升/分钟。通过结合多物理场模拟测试多种器件几何形状，我们确定了μ -Split实现分裂精度的设计和制造特征。总的来说，这项工作提供了一个有用的工具，以简化需要均匀划分流的微流体实验，同时最大限度地减少整体设备占地面积。

{"title":"Design and optimization of a fluid flow splitting device for low-flow applications","authors":"Alexis K. Yates , Heather N. Murray , Ethan S. Lippmann","doi":"10.1016/j.slast.2025.100305","DOIUrl":"10.1016/j.slast.2025.100305","url":null,"abstract":"<div><div>Microfluidic devices are defined by the application of fluid flow to micron-scale features. Inherent to most experiments involving microfluidic devices is the need to precisely and reproducibly control fluid flow at the microliter scale, often through multiple inlet ports on a single device. While the number of fluid channels per device varies, perfusing multiple inputs requires either the use of multiple flow controllers (often syringe or peristaltic pumps) or the ability to evenly divide fluid across outlets. Towards the latter approach, while a handful of commercial systems exist for splitting fluid flow, these set-ups require significant financial investment, multiple flow control and sensing components, and restrict the user to a predetermined perfusion control system. Simple in-line splitting devices, such a manifolds or T junctions, fail to achieve flow splitting at low flow rates often used in microfluidic systems. To increase capabilities for flow-controlled experiments, we performed experimental analyses of the physical considerations governing even flow splitting under low flow, leading to the design of a microdevice (µ-Split) that can be directly inserted into existing microfluidic set-ups. The µ-Split allows for reproducible, even flow splitting from 10 uL/min to > 2.5 mL/min. By testing multiple device geometries in combination with multiphysics simulations, we identified the design and fabrication features underlying the splitting precision achieved by the µ-Split. Overall, this work provides a useful tool to simplify microfluidic experiments that require evenly divided flow streams, while minimizing the overall device footprint.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100305"},"PeriodicalIF":2.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CirnetamorNet: An ultrasonic temperature measurement network for microwave hyperthermia based on deep learning 基于深度学习的微波热疗超声测温网络

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2025-05-09 DOI: 10.1016/j.slast.2025.100297

Fanbing Cui , Yongxing Du , Ling Qin , Baoshan Li , Chenlu Li , Xianwei Meng

Objective

Microwave thermotherapy is a promising approach for cancer treatment, but accurate noninvasive temperature monitoring remains challenging. This study aims to achieve accurate temperature prediction during microwave thermotherapy by efficiently integrating multi-feature data, thereby improving the accuracy and reliability of noninvasive thermometry techniques.

Methods

We proposed an enhanced recurrent neural network architecture, namely CirnetamorNet. The experimental data acquisition system is developed by using the material that simulates the characteristics of human tissue to construct the body model. Ultrasonic image data at different temperatures were collected, and 5 parameters with high temperature correlation were extracted from gray scale covariance matrix and Homodyned-K distribution. Using multi-feature data as input and temperature prediction as output, the CirnetamorNet model is constructed by multi-head attention mechanism. Model performance was evaluated by analyzing training losses, predicting mean square error and accuracy, and ablation experiments were performed to evaluate the contribution of each module.

Results

Compared with common models, the CirnetamorNet model performs well, with training losses as low as 1.4589 and mean square error of only 0.1856. Its temperature prediction accuracy of 0.3 °C exceeds that of many advanced models. Ablation experiments show that the removal of any key module of the model will lead to performance degradation, which proves that the collaboration of all modules is significant for improving the performance of the model.

Conclusion

The proposed CirnetamorNet model exhibits exceptional performance in noninvasive thermometry for microwave thermotherapy. It offers a novel approach to multi-feature data fusion in the medical field and holds significant practical application value.

目的微波热疗是一种很有前景的癌症治疗方法，但准确的无创温度监测仍然具有挑战性。本研究旨在通过对多特征数据的有效整合，实现微波热疗过程中温度的准确预测，从而提高无创测温技术的准确性和可靠性。方法提出一种改进型递归神经网络结构，即CirnetamorNet。利用模拟人体组织特性的材料构建人体模型，研制了实验数据采集系统。采集不同温度下的超声图像数据，从灰度协方差矩阵和Homodyned-K分布中提取温度相关性较高的5个参数。以多特征数据为输入，温度预测为输出，采用多头注意机制构建CirnetamorNet模型。通过分析训练损失，预测均方误差和精度来评估模型的性能，并进行烧蚀实验来评估每个模块的贡献。结果与常用模型相比，CirnetamorNet模型表现良好，训练损失低至1.4589，均方误差仅为0.1856。其温度预测精度为0.3°C，超过了许多先进的模型。烧蚀实验表明，去掉模型的任何一个关键模块都会导致性能下降，这证明了各个模块的协作对于提高模型的性能是非常重要的。结论所提出的CirnetamorNet模型在微波热疗的无创测温中表现出优异的性能。它为医学领域的多特征数据融合提供了一种新的方法，具有重要的实际应用价值。

{"title":"CirnetamorNet: An ultrasonic temperature measurement network for microwave hyperthermia based on deep learning","authors":"Fanbing Cui , Yongxing Du , Ling Qin , Baoshan Li , Chenlu Li , Xianwei Meng","doi":"10.1016/j.slast.2025.100297","DOIUrl":"10.1016/j.slast.2025.100297","url":null,"abstract":"<div><h3>Objective</h3><div>Microwave thermotherapy is a promising approach for cancer treatment, but accurate noninvasive temperature monitoring remains challenging. This study aims to achieve accurate temperature prediction during microwave thermotherapy by efficiently integrating multi-feature data, thereby improving the accuracy and reliability of noninvasive thermometry techniques.</div></div><div><h3>Methods</h3><div>We proposed an enhanced recurrent neural network architecture, namely CirnetamorNet. The experimental data acquisition system is developed by using the material that simulates the characteristics of human tissue to construct the body model. Ultrasonic image data at different temperatures were collected, and 5 parameters with high temperature correlation were extracted from gray scale covariance matrix and Homodyned-K distribution. Using multi-feature data as input and temperature prediction as output, the CirnetamorNet model is constructed by multi-head attention mechanism. Model performance was evaluated by analyzing training losses, predicting mean square error and accuracy, and ablation experiments were performed to evaluate the contribution of each module.</div></div><div><h3>Results</h3><div>Compared with common models, the CirnetamorNet model performs well, with training losses as low as 1.4589 and mean square error of only 0.1856. Its temperature prediction accuracy of 0.3 °C exceeds that of many advanced models. Ablation experiments show that the removal of any key module of the model will lead to performance degradation, which proves that the collaboration of all modules is significant for improving the performance of the model.</div></div><div><h3>Conclusion</h3><div>The proposed CirnetamorNet model exhibits exceptional performance in noninvasive thermometry for microwave thermotherapy. It offers a novel approach to multi-feature data fusion in the medical field and holds significant practical application value.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100297"},"PeriodicalIF":2.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0