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Uncovering the Therapeutic Target and Molecular Mechanism of Upadacitinib on Sjogren's Syndrome. 揭示 Upadacitinib 对 Sjogren's 综合征的治疗靶点和分子机制。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-23 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241293519
Youguo Yang, Yuan Liu, Xiaofen Li, Yongping Zeng, Weiqian He, Juan Zhou

Objective: Upadacitinib, a selective Janus associated kinase 1 (JAK-1) inhibitor, can be prescribed particularly for the clinical treatment with Crohn's disease or rheumatoid arthritis. It is clinically observed that upadacitinib has been found with potential therapeutic effectiveness on Sjogren's syndrome (SS). However, the anti-SS targets and mechanisms involved in upadacitinib treatment remain uninvestigated.

Materials and methods: Thus, this study was designed to identify therapeutic targets and mechanisms of upadacitinib for treating SS through conducting network pharmacology and molecular docking analyses.

Results: In total, we identified 298 upadacitinib-related target genes, 1339 SS-related targets before collecting 56 overlapped target genes and 12 hub target genes. Upadacitinib largely exerted the critical biological processes including regulation of microenvironment homeostasis, inflammatory response, and cell apoptosis, and largely acted on pivotal molecular mechanisms including hypoxia-inducible factor 1 (HIF-1) signaling pathway, apoptosis pathway, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway, or Th17 cell differentiation pathway. Molecular docking data suggested that upadacitinib exhibited the high affinities with signal transducer and activator of transcription 3 (STAT3), HIF1A, poly(ADP-ribose) polymerase 1 (PARP1) target proteins, in which the structural interactions between upadacitinib and STAT3, HIF1A, PARP1 showed potential therapeutic activities against SS.

Conclusion: In conclusion, upadacitinib possesses the bright anti-inflammatory and anti-apoptotic activities on SS, and this study can provide a theoretical basis for clinical therapy of SS using upadacitinib.

目的:乌达帕替尼是一种选择性 Janus 相关激酶 1(JAK-1)抑制剂,特别适用于克罗恩病或类风湿性关节炎的临床治疗。临床观察发现,奥达帕替尼对 Sjogren's 综合征(SS)具有潜在疗效。然而,奥达替尼治疗SS的抗SS靶点和机制仍未得到研究:因此,本研究旨在通过开展网络药理学和分子对接分析,确定乌达替尼治疗SS的治疗靶点和机制:结果:我们共发现了 298 个达达替尼相关靶基因、1339 个 SS 相关靶基因,然后收集了 56 个重叠靶基因和 12 个枢纽靶基因。奥达替尼在很大程度上影响了微环境稳态调节、炎症反应和细胞凋亡等关键生物学过程,并在很大程度上作用于缺氧诱导因子1(HIF-1)信号通路、细胞凋亡通路、磷脂酰肌醇3-激酶/蛋白激酶B(PI3K/Akt)信号通路或Th17细胞分化通路等关键分子机制。分子对接数据表明,乌达替尼与信号转导和转录激活因子3(STAT3)、HIF1A、聚(ADP-核糖)聚合酶1(PARP1)靶蛋白具有高亲和力,其中乌达替尼与STAT3、HIF1A、PARP1之间的结构相互作用显示出对SS的潜在治疗活性:总之,奥达替尼对SS具有明显的抗炎和抗凋亡活性,该研究可为奥达替尼对SS的临床治疗提供理论依据。
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引用次数: 0
Cranial Defect Repair With 3D Designed Models. 利用 3D 设计模型修复颅骨缺损
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-14 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241291777
Sambardhan Dabadi, Raju Raj Dhungel

Cranioplasty is one of the most common neurosurgical procedure performed to repair cranial defect. Many materials and fabrication technique are used to prepare cranial implant in cases where autologous bone is not available. Polymethyl Methacrylate (PMMA) is one of the most common polymer used as bone substitute. PMMA fabricated using 3D printed models have shown better fit, symmetrical shape, and restore esthetic looks of patients. The use of 3D printed implants in medical procedures has several advantages over traditional manufacturing methods. 3D printing allows for greater precision, customization, and quicker implant time.

颅骨成形术是修复颅骨缺损最常见的神经外科手术之一。在没有自体骨的情况下,许多材料和制造技术被用来制作颅骨植入物。聚甲基丙烯酸甲酯(PMMA)是最常用的骨替代聚合物之一。使用三维打印模型制作的 PMMA 具有更好的贴合性、对称性,并能恢复患者的美观。与传统制造方法相比,在医疗程序中使用三维打印植入物具有多项优势。三维打印可以实现更高的精度、定制化和更快的植入时间。
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引用次数: 0
Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs. 基于深度学习的全景 X 光片牙齿撞击检测。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-05 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241288319
He Zhicheng, Wang Yipeng, Li Xiao

Objective: The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.

Study design: Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.

Results: With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.

Conclusion: This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.

研究目的研究设计:研究设计:撞击牙是一种可引起并发症的牙科问题,可通过 X 光片进行诊断。我们利用 1016 张 X 光图像修改了用于单个牙齿分割的 SAM 模型。数据集分为训练集、验证集和测试集,比例为 16:3:1。我们对 SAM 模型进行了改进,通过聚焦牙齿中心来自动检测撞击牙齿,从而获得更准确的结果:在 200 个历元、批量大小等于 1 和学习率为 0.001 的条件下,随机图像对模型进行了训练。测试集的结果显示,SAM 相关模型的准确率高达 86.73%,F1 分数为 0.5350,IoU 为 0.3652:本研究对 MedSAM 进行了微调,用于 X 射线图像中的撞击牙分割,为牙科诊断提供了帮助。要提高牙科医生的诊断能力,进一步提高模型的准确性和选择至关重要。
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引用次数: 0
Advancements in Tissue Engineering: A Review of Bioprinting Techniques, Scaffolds, and Bioinks. 组织工程学的进步:生物打印技术、支架和生物材料综述》。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241288099
Shervin Zoghi

Tissue engineering is a multidisciplinary field that uses biomaterials to restore tissue function and assist with drug development. Over the last decade, the fabrication of three-dimensional (3D) multifunctional scaffolds has become commonplace in tissue engineering and regenerative medicine. Thanks to the development of 3D bioprinting technologies, these scaffolds more accurately recapitulate in vivo conditions and provide the support structure necessary for microenvironments conducive to cell growth and function. The purpose of this review is to provide a background on the leading 3D bioprinting methods and bioink selections for tissue engineering applications, with a specific focus on the growing field of developing multifunctional bioinks and possible future applications.

组织工程是一个多学科领域,它利用生物材料恢复组织功能并协助药物开发。在过去的十年中,三维(3D)多功能支架的制造在组织工程和再生医学中已变得司空见惯。由于三维生物打印技术的发展,这些支架能更准确地再现体内条件,并提供有利于细胞生长和功能的微环境所需的支撑结构。本综述旨在介绍组织工程应用中的主要三维生物打印方法和生物墨水选择的背景,特别关注不断发展的多功能生物墨水开发领域和未来可能的应用。
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引用次数: 0
Conceptualizing Patient as an Organization With the Adoption of Digital Health. 随着数字医疗的采用,将患者概念化为一个组织。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241277292
Atantra Das Gupta

The concept of viewing a patient as an organization within the context of digital healthcare is an innovative and evolving concept. Traditionally, the patient-doctor relationship has been centered around the individual patient and their interactions with healthcare providers. However, with the advent of technology and digital healthcare solutions, the dynamics of this relationship are changing. Digital healthcare platforms and technologies enable patients to have more control and active participation in managing their health and healthcare processes. This shift empowers patients to take on a more proactive role, similar to how an organization functions with various stakeholders, goals, and strategies. The prevalence of mobile phones and wearables is regarded as an important factor in the acceptance of digital health.

Objective: This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well.

Results: PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (b > 0.1, t > 1.96, P < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness.

Conclusions: Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.

在数字医疗的背景下,将患者视为一个组织,是一个创新且不断发展的概念。传统意义上的医患关系一直以患者个人及其与医疗服务提供者的互动为中心。然而,随着技术和数字医疗解决方案的出现,这种关系的动态正在发生变化。数字医疗保健平台和技术使患者能够更多地控制和积极参与管理自己的健康和医疗保健过程。这种转变使患者有能力扮演更加积极主动的角色,就像一个组织如何与不同的利益相关者、目标和战略一起运作一样。手机和可穿戴设备的普及被认为是影响人们接受数字医疗的一个重要因素:本研究旨在利用 TAM(技术接受模型)、HB(健康信念模型)和 UTAUT(技术接受和使用统一理论)确定影响采用意向的因素。其论点是,采用该技术能使患者创造资源(即数据),使患者从单纯的消费者转变为生产者:PLS分析表明,健康信念和感知易用性对感知数字医疗有用性有积极影响,系统能力对感知易用性有积极影响。感知的有用性、客户的改变意愿和参照群体的影响是影响普通人群采用意愿的决定性变量,而隐私保护和数据安全则是不确定的变量。在线资源和用户指南并不支持采用意愿。
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引用次数: 0
Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation. 基于深度学习的暴露于脂多糖的牙周韧带干细胞外泌体微RNA分析揭示了凋亡和炎症体衍生途径的激活。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-09-06 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241277639
Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Sivasankari Thilagar, Deepavalli Arumuganainar, Deepti Shrivastava, Artak Heboyan

Background: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.

Methods: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.

Results: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R 2 = .952, RMSE = 0.332), Linear Regression (R 2 = .949, RMSE = 0.343), and SVM (R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.

Conclusion: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.

背景:LPS(尤其是来自牙龈脓疱疮杆菌的 LPS)会增加牙周炎症因子的产生,加剧对牙周组织的损害。来自牙周韧带干细胞的外泌体改变了细菌 LPS 带来的再生和修复。外泌体携带的 MiRNA 会影响受体细胞的表观遗传功能。因此,本研究旨在利用深度学习算法发现细菌LPS暴露的PDLSC干细胞中的新型微RNA生物标记物,以了解其激活途径:方法:利用NCBI GEO DATA SET GSE163489,发现健康和LPS诱导的PDLSC细胞中差异表达最多的微RNA。深度学习分析采用了随机森林(Random Forest)、人工神经网络(Artificial Neural Network c)、支持向量机(SVM)和线性回归(Linear Regression)模型,并在橙色数据挖掘工具包中实施,从而确定了新型 microRNA 生物标记。橙色数据挖掘工具包用于对 microRNA 表达数据进行深度学习分析,为分类、回归和聚类等机器学习任务提供了用户友好型环境:随机森林是最优秀的模型,与神经网络(R 2 = .952,RMSE = 0.332)、线性回归(R 2 = .949,RMSE = 0.343)和 SVM(R 2 = .931,RMSE = 0.398)相比,随机森林的 R 2 得分最高(0.985),RMSE 最低(0.189)。这表明随机森林具有捕捉 microRNA 表达数据中潜在模式的卓越能力。鉴于其强大的性能,随机森林有望识别新型生物标记物、开发更准确的诊断工具,并有可能指导对牙周病患者进行分层,以采取有针对性的治疗干预措施:当前的研究利用对 microRNA 表达数据的深度学习分析,确定了与炎症小体激活和抗凋亡通路相关的新型生物标志物。这些发现有望指导牙周病新型治疗策略的开发。然而,未来的研究还需要使用独立的数据集和实验方法来验证这些生物标志物。
{"title":"Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.","authors":"Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Sivasankari Thilagar, Deepavalli Arumuganainar, Deepti Shrivastava, Artak Heboyan","doi":"10.1177/11795972241277639","DOIUrl":"10.1177/11795972241277639","url":null,"abstract":"<p><strong>Background: </strong>The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.</p><p><strong>Methods: </strong>Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.</p><p><strong>Results: </strong>Random Forest emerged as the superior model, achieving the highest <i>R</i> <sup>2</sup> score (.985) and the lowest RMSE (0.189) compared to Neural Networks (<i>R</i> <sup>2</sup> = .952, RMSE = 0.332), Linear Regression (<i>R</i> <sup>2</sup> = .949, RMSE = 0.343), and SVM (<i>R</i> <sup>2</sup> = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.</p><p><strong>Conclusion: </strong>The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277639"},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor. 基于深度学习的脑肿瘤自动检测智能决策支持系统。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241277322
Zahid Ullah, Mona Jamjoom, Manikandan Thirumalaisamy, Samah H Alajmani, Farrukh Saleem, Akbar Sheikh-Akbari, Usman Ali Khan

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.

脑肿瘤(BT)是一种可怕的疾病,也是导致人类死亡的首要原因之一。脑肿瘤的发展主要分为两个阶段,体积、形态和结构各不相同,可以通过化疗、放疗和外科手术等特殊临床程序治愈。过去几年,随着放射组学和医学影像研究的革命性进展,计算机辅助诊断系统(CAD),尤其是深度学习,在各种疾病的自动检测和诊断中发挥了关键作用,极大地为医疗临床医生提供了准确的决策支持系统。因此,卷积神经网络(CNN)是从医学图像中检测各种疾病的常用方法,因为它能够从所调查的图像中提取独特的特征。本研究利用深度学习方法从大脑图像中提取不同的特征,以检测 BT。因此,本研究开发了从头开始的 CNN 和迁移学习模型(VGG-16、VGG-19 和 LeNet-5),并在脑图像上进行了测试,以建立检测 BT 的智能决策支持系统。由于深度学习模型需要大量数据,因此使用了数据扩增技术来合成现有数据集,以便利用最合适的检测模型。通过超参数调整,为训练模型设置了最佳参数。结果显示,VGG 模型的准确率为 99.24%,平均精确度为 99%,平均召回率为 99%,平均特异性为 99%,平均 f1 分数为 99%,均优于其他模型。与文献中其他最先进的模型相比,提出的模型在准确率、灵敏度、特异性和 f1 分数方面都有更好的表现。此外,对比分析表明,所提出的模型是可靠的,它们可以用于检测 BT,也可以帮助医疗从业人员诊断 BT。
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引用次数: 0
Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles. 深度学习预测牙龈脓肿释放的外膜囊泡中的炎症诱导蛋白编码 mRNA。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-08-30 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241277081
Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Muthupandian Saravanan, Hadush Negash Meles, Artak Heboyan

Aim: The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences.

Material and methods: The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation.

Results: Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship.

Conclusion: In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.

目的:Insilco 研究使用深度学习算法预测编码蛋白质的 pg m RNA 序列:NCBI GEO DATA SET GSE218606的GEO R工具发现了P.G外膜囊泡中差异表达最大的mRNA。Genemania 分析了差异表达基因网络。转录组学数据被收集起来,并标注在牙龈炎蛋白编码 mRNA 序列和假基因、lincRNA 和双向启动子 lincRNA 上。机器学习工具 Orange 对预处理后的数据进行分析和预测。奈夫贝叶斯、神经网络和梯度下降法将数据分为训练集和测试集,从而得出准确的结果。在模型验证后,对交叉验证、模型准确性和 ROC 曲线进行了评估:使用曲线下面积(AUC)、分类准确率(CA)、F1 分数、精确度、召回率和特异性等指标对神经网络、奈夫贝叶斯和梯度提升这三种模型进行了评估。与神经网络(AUC:0.721,CA:0.391,F1:0.314)和 Naive Bayes(AUC:0.701,CA:0.172,F1:0.114)相比,梯度提升法取得了均衡的性能(AUC:0.72,CA:0.41,F1:0.32)。虽然统计测试显示模型之间没有明显差异,但梯度提升模型的精确度与召回率之间的关系更为平衡:结论:利用机器学习技术进行的硅学分析成功地预测了牙龈卟啉单胞菌 OMVs 中的蛋白编码 mRNA 序列。梯度提升法在AUC、分类准确率和精确度-召回率等指标上表现均衡,优于其他模型(神经网络、Naive Bayes),这表明它有潜力成为预测牙龈卟啉菌OMVs中蛋白编码mRNA的可靠工具。
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引用次数: 0
Reclassify High-Grade Serous Ovarian Cancer Patients Into Different Molecular Subtypes With Discrepancy Prognoses and Therapeutic Responses Based on Cancer-Associated Fibroblast-Enriched Prognostic Genes. 基于癌症相关成纤维细胞富集的预后基因,将高分化浆液性卵巢癌患者重新划分为预后和治疗反应不一致的不同分子亚型
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-08-30 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241274024
Xiangxiang Liu, Guoqiang Ping, Dongze Ji, Zhifa Wen, Yajun Chen

Cancer-associated fibroblasts (CAFs) play critical roles in the metastasis and therapeutic response of high-grade serous ovarian cancer (HGSC). Our study intended to select HGSC patients with unfavorable prognoses and therapeutic responses based on CAF-enriched prognostic genes. The bulk RNA and single-cell RNA sequencing (scRNA-seq) data of tumor tissues were collected from the TCGA and GEO databases. The infiltrated levels of immune and stromal cells were estimated by multiple immune deconvolution algorithms and verified through immunohistochemical analysis. The univariate Cox regression analyses were used to identify prognostic genes. Gene Set Enrichment Analysis (GSEA) was conducted to annotate enriched gene sets. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore potential alternative drugs. We found the infiltered levels of CAFs were remarkedly elevated in advanced and metastatic HGSC tissues and identified hundreds of genes specifically enriched in CAFs. Then we selected 6 CAF-enriched prognostic genes based on which HGSC patients were reclassified into 2 subclusters with discrepancy prognoses. Further analysis revealed that the HGSC patients in cluster-2 tended to undergo poor responses to traditional chemotherapy and immunotherapy. Subsequently, we selected 24 novel potential therapeutic drugs for cluster-2 HGSC patients. Moreover, we discovered a positive correlation of infiltrated levels between CAFs and monocytes/macrophages in HGSC tissues. Collectively, our study successfully reclassified HGSC patients into 2 different subgroups that have discrepancy prognoses and responses to current therapeutic methods.

癌症相关成纤维细胞(CAFs)在高级别浆液性卵巢癌(HGSC)的转移和治疗反应中起着关键作用。我们的研究旨在根据CAF富集的预后基因筛选出预后和治疗反应不良的HGSC患者。我们从TCGA和GEO数据库中收集了肿瘤组织的大量RNA和单细胞RNA测序(scRNA-seq)数据。免疫细胞和基质细胞的浸润水平由多种免疫解旋算法估算,并通过免疫组化分析进行验证。单变量 Cox 回归分析用于确定预后基因。基因组富集分析(Gene Set Enrichment Analysis,GSEA)用于注释富集基因组。癌症药物敏感性基因组学(GDSC)数据库用于探索潜在的替代药物。我们发现,在晚期和转移性 HGSC 组织中,CAFs 的潜入水平显著升高,并确定了数百个特异性富集于 CAFs 的基因。然后,我们筛选出了6个富含CAF的预后基因,并据此将HGSC患者重新划分为2个预后不同的亚群。进一步分析发现,亚群-2 中的 HGSC 患者对传统化疗和免疫疗法的反应往往较差。随后,我们为群组-2 的 HGSC 患者筛选出了 24 种新型潜在治疗药物。此外,我们还发现HGSC组织中CAFs和单核细胞/巨噬细胞的浸润水平呈正相关。总之,我们的研究成功地将 HGSC 患者重新分为两个不同的亚组,这两个亚组在预后和对现有治疗方法的反应上存在差异。
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引用次数: 0
Validity of the Moshkov Test Regarding a Spine Asymmetry in Young Patients. 关于年轻患者脊柱不对称的莫什科夫测试的有效性
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-08-22 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241272381
Ihor Zanevskyy, Olena Bodnarchuk, Lyudmyla Zanevska

An aim of the research is to improve validity of the Moshkov test in relation to the body dimensions of young patients. This short report presents a new research that adds to previous studies about validity of the Moshkov test regarding a spine asymmetry in young patients. Because children body's dimensions are smaller than adults' ones, results indices of the Moshkov test are less as well. These results have been corrected proportionally to a half sum of rhombus sides' lengths. Mechanical and mathematical modeling using Wolfram Mathematica computer package has been done during Moshkov rhombus modification. The modified rhombus model made it possible to improve validity of the test regarding smaller dimension of young patients' bodies. The results are presented in a graph nomogram that is comprehensive for practical specialists which are not familiar with using of mathematical methods.

这项研究的目的之一是提高莫什科夫测试对年轻患者身体尺寸的有效性。这篇简短的报告介绍了一项新的研究,该研究补充了之前关于莫什科夫测试在年轻患者脊柱不对称方面的有效性的研究。由于儿童的身体尺寸小于成人,因此莫什科夫测试的结果指数也较小。这些结果已根据菱形边长的一半之和按比例进行了修正。在对莫什科夫菱形进行修改时,使用 Wolfram Mathematica 计算机软件包进行了机械和数学建模。修改后的菱形模型可以提高测试的有效性,使年轻患者的身体尺寸更小。测试结果以图表形式呈现,对于不熟悉使用数学方法的实用专家来说非常全面。
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Biomedical Engineering and Computational Biology
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