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Computer-Aided Discovery of Abrus precatorius Compounds With Anti-Schistosomal Potential. 计算机辅助发现具有抗染色体潜能的 Abrus precatorius 化合物。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-10 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241294112
Ryman Shoko, Allen Mazadza

Schistosomiasis, which causes over 200 000 deaths annually, has since the 1970s been controlled by praziquintel. The reliance on a single drug to combat schistosomiasis, and reports of laboratory resistance to the drug, has created an urgent need in the scientific community to develop new chemotherapies to complement or supplement praziquantel. Medicinal plants are a potential reservoir of compounds with schistosomicidal activity. In the current study, we carried out computer-aided screening of Abrus precatorius compounds to discover compounds with potential to inhibit Schistosoma mansoni purine nucleoside phosphorylase (SmPNP). Thus, 99 compounds retrieved from Lotus Natural Compounds Database were docked into the active site of SmPNP. The top-ranked compounds were subjected to Lipinski's druglikeness and toxicity risk predictions. Three lead compounds, abrusogenin, cirsimaritin and hispidulin, were identified as having high binding affinities, favourable interactions with SmPNP active site residues and good toxicity risk prediction results. Molecular dynamics (MD) simulations were used to assess the stability of the interactions of these lead compounds with SmPNP. Collectively, analyses of the MD trajectories confirms that the lead compounds bound and interacted stably with active site residues of SmPNP. We conclude that abrusogenin, cirsimaritin and hispidulin could serve as hit compounds for the development of new antischistosomal drugs, based on plant-derived natural products. However, experimental studies are required to further evaluate the potentials of these compounds as possible therapeutics against schistosomiasis.

血吸虫病每年造成 20 多万人死亡,自 20 世纪 70 年代以来一直由吡喹酮控制。由于依赖单一药物防治血吸虫病,而且有报告称实验室发现了抗药性,因此科学界迫切需要开发新的化学疗法来补充吡喹酮。药用植物是具有杀血吸虫活性的化合物的潜在宝库。在目前的研究中,我们对 Abrus precatorius 的化合物进行了计算机辅助筛选,以发现具有抑制曼氏血吸虫嘌呤核苷磷酸酶(SmPNP)潜力的化合物。因此,将从莲花天然化合物数据库中检索到的 99 个化合物与 SmPNP 的活性位点对接。对排名靠前的化合物进行了利宾斯基药物相似性和毒性风险预测。确定了三个先导化合物:abrusogenin、cirsimaritin 和 hispidulin,它们具有较高的结合亲和力、与 SmPNP 活性位点残基的良好相互作用以及良好的毒性风险预测结果。分子动力学(MD)模拟用于评估这些先导化合物与 SmPNP 相互作用的稳定性。对分子动力学轨迹的综合分析证实,先导化合物与 SmPNP 的活性位点残基结合并发生了稳定的相互作用。我们的结论是,Abrusogenin、cirsimaritin 和 hispidulin 可作为基于植物天然产物开发新型抗血吸虫药物的热门化合物。不过,要进一步评估这些化合物作为血吸虫病治疗药物的潜力,还需要进行实验研究。
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引用次数: 0
Synthesis and Application of Sustainable Tricalcium Phosphate Based Biomaterials From Agro-Based Materials: A Review. 从农基材料中合成和应用可持续的磷酸三钙生物材料:综述。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241293525
Isiaka Oluwole Oladele, Samson Ademola Adekola, Newton Itua Agbeboh, Baraka Abiodun Isola-Makinde, Benjamin Omotayo Adewuyi

Trends in health care delivery systems have shifted as a result of the modern uses of biomaterials in medicine. Contrary to traditional medicine, modern healthcare are now useful in solving problems that were considered impossible some years back. One of the most significant factors to the most recent advancements in implant development has been the use of calcium based materials in the creation of necessary implants in the form of soft and hard tissues. With the advent of naturally sourced materials in the manufacturing of biomaterials, lots of attention are now focused on the different sources of agro-based resources that can be used for the product developments. These agro-based materials are now been considered for sustainable and ecological purposes in several areas of applications globally in the recent times. Hence, the review was carried out with focus on the sources, relevance, processing techniques and applications of tricalcium phosphate based biomaterials in modern day healthcare delivery. This review provides a historical and prospective picture of the crucial functions that materials based on tricalcium phosphate will play in fulfilling human requirements for medication.

由于生物材料在医学中的现代应用,医疗保健服务系统的趋势发生了转变。与传统医学相反,现代医疗保健现在可以解决几年前被认为不可能解决的问题。植入物发展的最新进展中,最重要的因素之一就是使用钙基材料制作软硬组织形式的必要植入物。随着天然材料在生物材料制造中的应用,人们开始关注可用于产品开发的各种农基资源。近来,这些农基材料在全球多个应用领域中被视为可持续的生态材料。因此,本综述的重点是磷酸三钙生物材料的来源、相关性、加工技术以及在现代医疗保健服务中的应用。这篇综述从历史和前瞻性的角度阐述了磷酸三钙材料在满足人类用药需求方面将发挥的关键作用。
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引用次数: 0
A Physical Framework to Study the Effect of Magnetic Fields on the Spike-Time Coding. 研究磁场对尖峰时间编码影响的物理框架
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-04 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241272380
Manuel Rivas, Marina Martinez-Garcia

A temporal neural code reliant on the pattern of spike times rather than spike rates offers a feasible mechanism for encoding information from weak periodic external stimuli, such as static or extremely low-frequency electromagnetic fields. Our model focuses on the influence of magnetic fields on neurotransmitter dynamics near the neuron membrane. Neurotransmitter binding to specific receptor sites on membrane proteins can regulate biochemical reactions. The duration a neurotransmitter spends in the bonded state serves as a metric for the magnetic field's capacity as a chemical regulator. By initiating a physical analysis of ligand-receptor binding, utilizing the alpha function for synaptic conductance, and employing a modified version of Bell's law, we quantified the impact of magnetic fields on the bond half-life time and, consequently, on postsynaptic spike timing.

依赖于尖峰时间模式而非尖峰率的时间神经编码为编码来自弱周期性外部刺激(如静态或极低频电磁场)的信息提供了一种可行的机制。我们的模型侧重于磁场对神经元膜附近神经递质动态的影响。神经递质与膜蛋白上的特定受体位点结合可调节生化反应。神经递质在结合状态下的持续时间是衡量磁场作为化学调节剂能力的指标。通过对配体-受体结合进行物理分析,利用突触传导的α函数,并采用修正版的贝尔定律,我们量化了磁场对结合半衰期时间的影响,以及由此对突触后尖峰计时的影响。
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引用次数: 0
Construction of Prognostic Prediction Models for Colorectal Cancer Based on Ferroptosis-Related Genes: A Multi-Dataset and Multi-Model Analysis. 基于铁突变相关基因构建结直肠癌预后预测模型:多数据集和多模型分析
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-02 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241293516
Tao Gan, Xiaomeng Wei, Yuanhao Xing, Zhili Hu

Background: Colorectal cancer (CRC) remains a significant health burden globally, necessitating a deeper understanding of its molecular landscape and prognostic markers. This study characterized ferroptosis-related genes (FRGs) to construct models for predicting overall survival (OS) across various CRC datasets.

Methods: In TCGA-COAD dataset, differentially expressed genes (DEGs) were identified between tumor and normal tissues using DESeq2 package. Prognostic genes were identified associated with OS, disease-specific survival, and progression-free interval using survival package. Additionally, FRGs were downloaded from FerrDb website, categorized into unclassified, marker, and driver genes. Finally, multiple models (Coxboost, Elastic Net, Gradient Boosting Machine, LASSO Regression, Partial Least Squares Regression for Cox Regression, Ridge Regression, Random Survival Forest [RSF], stepwise Cox Regression, Supervised Principal Components analysis, and Support Vector Machines) were employed to predict OS across multiple datasets (TCGA-COAD, GSE103479, GSE106584, GSE17536, GSE17537, GSE29621, GSE39084, GSE39582, and GSE72970) using intersection genes across DEGs, OS, disease-specific survival, and progression-free interval, and FRG categories.

Results: Six intersection genes (ASNS, TIMP1, H19, CDKN2A, HOTAIR, and ASMTL-AS1) were identified, upregulated in tumor tissues, and associated with poor survival outcomes. In the TCGA-COAD dataset, the RSF model demonstrated the highest concordance index. Kaplan-Meier analysis revealed significantly lower OS probabilities in high-risk groups identified by the RSF model. The RSF model exhibited high accuracy with AUC values of 0.978, 0.985, and 0.965 for 1-, 3-, and 5-year survival predictions, respectively. Calibration curves demonstrated excellent agreement between predicted and observed survival probabilities. Decision curve analysis confirmed the clinical utility of the RSF model. Additionally, the model's performances were validated in GSE29621 dataset.

Conclusions: The study underscores the prognostic relevance of 6 intersection genes in CRC, providing insights into potential therapeutic targets and biomarkers for patient stratification. The RSF model demonstrates robust predictive performance, suggesting its utility in clinical risk assessment and personalized treatment strategies.

背景:结直肠癌(CRC)仍然是全球重大的健康负担,因此有必要深入了解其分子结构和预后标志物。本研究对铁蛋白沉积相关基因(FRGs)进行了特征描述,以构建预测各种 CRC 数据集的总生存率(OS)的模型:在TCGA-COAD数据集中,使用DESeq2软件包鉴定了肿瘤组织和正常组织之间的差异表达基因(DEGs)。使用生存软件包鉴定与OS、疾病特异性生存和无进展间期相关的预后基因。此外,还从 FerrDb 网站下载了 FRGs,并将其分为未分类基因、标记基因和驱动基因。最后,使用多种模型(Coxboost、Elastic Net、Gradient Boosting Machine、LASSO 回归、Partial Least Squares Regression for Cox Regression、Ridge Regression、Random Survival Forest [RSF]、stepwise Cox Regression、Supervised Principal Components analysis、和支持向量机)来预测多个数据集(TCGA-COAD、GSE103479、GSE106584、GSE17536、GSE17537、GSE29621、GSE39084、GSE39582 和 GSE72970)的 OS,预测时使用了 DEGs、OS、疾病特异性生存期、无进展间隔和 FRG 类别的交叉基因。结果发现了六个交叉基因(ASNS、TIMP1、H19、CDKN2A、HOTAIR 和 ASMTL-AS1),它们在肿瘤组织中上调,并与不良生存结果相关。在 TCGA-COAD 数据集中,RSF 模型的一致性指数最高。Kaplan-Meier分析显示,在RSF模型确定的高风险组中,OS概率明显较低。RSF 模型的准确度很高,1 年、3 年和 5 年生存预测的 AUC 值分别为 0.978、0.985 和 0.965。校准曲线显示,预测的生存概率与观察到的生存概率非常吻合。决策曲线分析证实了 RSF 模型的临床实用性。此外,该模型的性能在 GSE29621 数据集中也得到了验证:该研究强调了 6 个交叉基因在 CRC 预后中的相关性,为潜在的治疗靶点和患者分层的生物标志物提供了见解。RSF模型显示出强大的预测性能,表明其在临床风险评估和个性化治疗策略中的实用性。
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引用次数: 0
On Mechanical Behavior and Characterization of Soft Tissues. 论软组织的力学行为和特征。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-02 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241294115
Radhika Chavan, Nitin Kamble, Chetan Kuthe, Sandeep Sarnobat

The growth and advancements done in solid mechanics and metallurgy have come up with various characterization techniques that help in prediction of elastic properties of different types of materials-isotropic, anisotropic, transverse isotropic, etc. Soft tissues which refer to fibrous tissues, fat, blood vessels, muscles and other tissues that support the body were found to have some control over its mechanical properties. This mechanical behavior of soft tissues has recently shifted the attention of many researchers to develop methods to characterize and describe the mechanical response of soft tissues. The paper discusses the biomechanical nature of soft tissues and the work done to characterize their elastic properties. The paper gives a review of the behavior and characteristics of soft tissues extracted from various experimental tests employed in their characterization. Soft tissues exhibit complex behavior and various complexities are involved in their experimental testing due to their small size and fragile nature. The paper focuses on the conventionally used tensile and compression tests and the difficulties encountered in soft tissue characterization. It also describes the utility of ultrasound technique which is a non-destructive method to characterize soft tissues. Tensile and compression test used to characterize materials are destructive in nature. Ultrasound technique can provide a better way to characterize material in a non-destructive manner.

随着固体力学和冶金学的发展和进步,各种表征技术应运而生,有助于预测各向同性、各向异性、横向各向同性等不同类型材料的弹性特性。软组织指的是纤维组织、脂肪、血管、肌肉和其他支撑身体的组织,人们发现这些组织的机械特性具有一定的可控性。近来,软组织的这种机械行为引起了许多研究人员的关注,他们开始开发表征和描述软组织机械响应的方法。本文讨论了软组织的生物力学性质以及表征其弹性特性的工作。本文综述了从表征软组织的各种实验测试中提取的软组织行为和特征。软组织表现出复杂的行为,由于其体积小和易碎的特性,其实验测试涉及各种复杂问题。本文重点介绍了传统的拉伸和压缩试验,以及在软组织表征中遇到的困难。本文还介绍了超声波技术的实用性,它是表征软组织的一种非破坏性方法。用于表征材料特性的拉伸和压缩试验具有破坏性。超声波技术可以提供一种以非破坏性方式表征材料特性的更好方法。
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引用次数: 0
Commentary on "Large-Scale Pancreatic Cancer Detection via Non-Contrast CT and Deep Learning". 关于 "通过非对比 CT 和深度学习大规模检测胰腺癌 "的评论。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-31 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241293521
Ibrahem Alshybani

Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges. Notably, its training predominantly on East Asian datasets raises concerns about generalizability across diverse populations. Additionally, PANDA's ability to detect rare lesions, such as pancreatic neuroendocrine tumors (PNETs), could be improved by integrating other imaging modalities. High specificity is a strength, but it also poses risks of false positives, which may lead to unnecessary procedures and increased healthcare costs. Implementing a tiered diagnostic approach and expanding training data to include a wider demographic are essential steps for enhancing PANDA's clinical utility and ensuring its successful global implementation, ultimately shifting the focus from late diagnosis to proactive early detection.

Cao 等人介绍了 PANDA,这是一种利用非对比 CT 扫描早期检测胰腺导管腺癌(PDAC)的人工智能模型。虽然该模型前景广阔,但也面临着一些挑战。值得注意的是,它主要在东亚数据集上进行训练,这引起了人们对其在不同人群中通用性的担忧。此外,PANDA 检测胰腺神经内分泌肿瘤(PNET)等罕见病变的能力还可以通过整合其他成像模式来提高。高特异性是其优势,但也存在假阳性的风险,可能导致不必要的手术和医疗成本的增加。实施分级诊断方法和扩大培训数据以纳入更广泛的人群是提高 PANDA 临床实用性和确保其在全球成功实施的必要步骤,最终将重点从晚期诊断转移到主动早期检测。
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引用次数: 0
Correspondence to "Conceptualizing Patient as an Organization with the Adoption of Digital Health". 对应 "采用数字医疗将患者视为一个组织的概念化"。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-29 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241293514
Hinpetch Daungsupawong, Viroj Wiwanitkit
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引用次数: 0
Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks. 利用虚拟化和基于深度前馈网络的极限学习算法诊断乳腺癌。
IF 2.3 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-28 eCollection Date: 2024-01-01 DOI: 10.1177/11795972241278907
G Siva Shankar, Edeh Michael Onyema, Balasubramanian Prabhu Kavin, Venkataramaiah Gude, Bvv Siva Prasad

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.

乳腺癌是导致全球妇女死亡的主要原因之一。早期发现和及时治疗可以降低与乳腺癌相关的死亡风险。云计算和机器学习对于当今的疾病诊断至关重要,但对于那些生活在遥远地方、医疗条件差的人来说尤为重要。基于机器学习的诊断工具可以作为初级阅读器,帮助放射科医生正确诊断疾病,而基于云计算的技术也可以帮助远程诊断和远程医疗服务。基于人工神经网络(ANN)的疾病诊断技术的前景吸引了一些研究人员的关注。拟议研究的 4 种方法包括预处理、特征提取和分类。预处理最初采用的是智能窗口删除(SWVD)技术。它包括萨维茨基-戈莱(S-G)平滑、更新的两级滤波和自适应时间窗口划分。该技术通过自适应预分析每个信道的特异性,将其分为多个时间段。然后,在每个窗口上使用改变的 2 级滤波过程来检索一些肿瘤信息。在应用 S-G 平滑处理并整合破碎的时间序列后,整个过程就完成了。为了提供有效的特征提取,使用了基于深度残差的多类架构(DRMFA)。在组织学照片中,识别微小和大尺寸斑块中细胞和组织层面的特征。最后,一种全新的定制策略结合了更好的乌鸦饲养--ELM。深度学习和极限学习机(ELM)是已经开发出来的概念(ACF-ELM)。在诊断疾病方面,基于云的 ELM 的表现与某些尖端技术类似。根据 DDSM 和 INbreast 数据集的结果,基于云的 ELM 方法击败了其他解决方案。重要的实验结果显示,数据输入的准确度为0.9845,精确度为0.96,召回率为0.94,F1得分为0.95。
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引用次数: 0
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
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