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Exploratory insights from the immuno-oncology hollow fiber assay: A pilot approach bridging In Vitro and In Vivo models 免疫肿瘤学中空纤维试验的探索性见解:一种连接体外和体内模型的试点方法。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100232
Tove Selvin , Malin Berglund , Anders Åkerström , Marco Zia , Jakob Rudfeldt , Malin Jarvius , Rolf Larsson , Claes R Andersson , Mårten Fryknäs
To facilitate the translation of immunotherapies from bench to bedside, predictive preclinical models are essential. We developed the in vivo immuno-oncology Hollow Fiber Assay (HFA) to bridge the gap between simpler cell-based in vitro assays and more complex mouse models for immuno-oncology drug evaluation. The assay involves co-culturing human cancer cell lines or primary patient-derived cancer cells with human immune cells inside semipermeable hollow fibers. Implanted intraperitoneally in mice, the fibers captured treatment-induced immune cell-mediated cancer cell killing following treatments with aCD3 and/or IL-2, demonstrating the feasibility of the assay. Traditional models require lengthy observation periods to monitor tumor growth and treatment response. The immuno-oncology HFA enables a rapid initial in vivo evaluation of immunological agents on cancer and immune cells of human origin, addressing two of the 3Rs — reduction and refinement — in animal research.
为了促进免疫疗法从实验室到床边的转化,预测性临床前模型是必不可少的。我们开发了体内免疫肿瘤中空纤维试验(HFA),以弥补基于细胞的简单体外试验和更复杂的免疫肿瘤药物评估小鼠模型之间的差距。该试验涉及将人类癌细胞系或原发患者来源的癌细胞与人类免疫细胞在半透性中空纤维内共培养。在小鼠腹腔内植入后,纤维捕获了aCD3和/或IL-2治疗后诱导的免疫细胞介导的癌细胞杀伤,证明了该试验的可行性。传统模型需要较长的观察期来监测肿瘤生长和治疗反应。免疫肿瘤学HFA使免疫制剂对癌症和人类起源的免疫细胞的快速初步体内评估成为可能,解决了动物研究中的两个3r -还原和细化。
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引用次数: 0
Automation of multiplex biochemical assays to enhance productivity and reduce cycle time using a modular robotic platform 自动化的多重生化分析,以提高生产力和减少使用模块化机器人平台的周期时间。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100233
Buyun Tang, Becky Lam, Stephanie Holley, Martha Torres, Theresa Kuntzweiler, Tatiana Gladysheva, Paul Lang
Pharmaceutical and biotechnology companies are increasingly being challenged to shorten the cycle time between design, make, test, and analyze (DMTA) compounds. Automation of multiplex assays to obtain multiparameter data on the same robotic run is instrumental in reducing cycle time. Consequently, an increasing need in automated systems to streamline laboratory workflows with the goal to expedite assay cycle time and enhance productivity has grown in industrial and academic institutions in the past decades. Herein, we present a customized robotic platform with operational modularity and flexibility, designed to automate entire assay workflows involving multistep reagent dispensing, mixing, lidding/de-lidding, incubation, centrifugation, and final readout steps by linking spinnaker robot with various peripheral instruments. Compared to manual workflows, the system can seamlessly execute processes with high efficiency, evaluated by standard assay validation protocols for robustness and reproducibility. Furthermore, the system can perform multiple, independent protocols in parallel, and has high-throughput capacity. In this publication, we demonstrate that the modular robotic platform can fully automate multiplex assay workflows through ‘one-click-and-run’ solution with tremendous benefits in liberating manual intervention, boosting productivity while producing high-quality data combined with reduced cycle time (>20 %), as well as expanding the capacity for higher throughput.
制药和生物技术公司在缩短DMTA化合物的设计、制造、测试和分析周期方面面临着越来越大的挑战。在同一机器人运行中获得多参数数据的多重分析自动化有助于缩短周期时间。因此,在过去的几十年里,工业和学术机构越来越需要自动化系统来简化实验室工作流程,以加快分析周期时间并提高生产力。在此,我们提出了一个具有操作模块化和灵活性的定制机器人平台,旨在通过将spinnaker机器人与各种外围仪器连接起来,自动化整个分析工作流程,包括多步骤试剂分配、混合、盖上/去盖、培养、离心和最终读数步骤。与手动工作流程相比,该系统可以无缝地执行流程,效率高,并通过标准分析验证方案进行鲁棒性和可重复性评估。此外,该系统可以并行执行多个独立的协议,具有较高的吞吐能力。在本出版物中,我们证明了模块化机器人平台可以通过“一点击即运行”的解决方案完全自动化多路分析工作流程,在解放人工干预方面具有巨大的优势,提高了生产率,同时产生了高质量的数据,减少了周期时间(>20%),并扩大了更高吞吐量的容量。
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引用次数: 0
Prosthesis repair of oral implants based on artificial intelligenc`e finite element analysis 基于人工智能有限元分析的口腔种植体修复。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100226
Yi Sun
Dentists often suggest dental implants to replace missing teeth; nevertheless, mechanical issues can develop with these implants, which could lead to prosthesis replacement or repairs. When investigating implant systems' mechanical characteristics and stress distribution, finite element analysis (FEA) is a popular computational tool. In biomechanical investigations, this strategy is widely used. However, traditional FEA methods can be tedious and require expert expertise for accurate simulation and translation of results. To automate and simplify the process of mending oral implant prostheses, the article suggests a new framework called AI-FEA. The three primary parts that make up the suggested AI-FEA framework are 1. An AI-powered model creation module that utilizes data from medical imaging to autonomously construct 3D finite element designs that are unique to each patient. Utilizing deep learning approaches, this module segments and reconstructs three-dimensional geometries from computed tomography (CT) or cone-beam CT data using material properties and boundary conditions. 2. A FEA solver that runs simulations to test the way the implant system handles different loads. This component uses state-of-the-art numerical methods to model the implant and bone interface and determine stress distributions. 3. An AI-based decision support system that takes all that data and recommends the best way to fix the prosthesis. Combining FEA findings with patient-specific variables, this decision support system uses machine learning algorithms educated on an extensive dataset of implant failure instances and repair results to provide the optimal repair strategy. For patients experiencing issues with oral implants, the suggested AI-FEA framework might mean huge time and skill savings in prosthesis repair planning, leading to better, more individualized care.
牙医经常建议种植牙齿来代替缺失的牙齿;然而,这些植入物可能会产生机械问题,这可能导致假体更换或修复。在研究种植体系统的力学特性和应力分布时,有限元分析(FEA)是一种流行的计算工具。在生物力学研究中,这种策略被广泛使用。然而,传统的有限元分析方法可能是繁琐的,需要专家的专业知识来准确模拟和翻译结果。为了实现口腔种植体修复过程的自动化和简化,本文提出了一种名为AI-FEA的新框架。组成建议的AI-FEA框架的三个主要部分是1;基于人工智能的模型创建模块,利用医学成像数据自主构建每个患者独特的3D有限元设计。该模块利用深度学习方法,利用材料属性和边界条件,从计算机断层扫描(CT)或锥束CT数据中分割和重建三维几何形状。2. 运行模拟以测试植入系统处理不同负载的方式的FEA求解器。该组件使用最先进的数值方法来模拟种植体和骨界面并确定应力分布。3. 这是一个基于人工智能的决策支持系统,它可以收集所有数据,并推荐修复假肢的最佳方法。将FEA结果与患者特定变量相结合,该决策支持系统使用机器学习算法,该算法基于广泛的植入物故障实例和修复结果数据集,以提供最佳修复策略。对于遇到口腔种植体问题的患者,建议的AI-FEA框架可能意味着在修复计划中节省大量时间和技能,从而实现更好,更个性化的护理。
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引用次数: 0
Notes on AEMS methods development for high throughput experimentation in drug discovery 药物发现中高通量实验的AEMS方法发展综述。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100234
Meghav Verma , Nate Hoxie , John Janiszewski , Charles Bonney , Matthew D. Hall , Sam Michael , Tom Covey , Jonathan H. Shrimp
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引用次数: 0
Integrating data mining and network pharmacology for traditional Chinese medicine for drug discovery of diabetic peripheral neuropathy 结合数据挖掘与中药网络药理学研究糖尿病周围神经病变药物发现。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100228
Jing Ping , Hong-Zheng Hao , Zhen-Qi Wu , Yong-Ju Yang , He-Shan Yu
The purpose of this study was to examine the therapeutic potential of core traditional Chinese medicine (CTCM) in the treatment of diabetic peripheral neuropathy (DPN) through the use of a data-driven approach that combined network pharmacology and data mining. Important components of traditional Chinese medicine (TCM) and the targets that correspond with them were found through the examination of numerous databases and clinical prescriptions. The possible therapeutic pathways were investigated, with an emphasis on the AGE-RAGE pathway that was discovered via network pharmacology analysis. By evaluating histopathological alterations, inflammatory and apoptotic markers, microcirculation, and blood hypercoagulability in a rat model of DPN, the effectiveness of CTCM was confirmed.Through experimental validation in DPN rats, it was shown that CTCM improved histopathology, decreased inflammation and apoptosis, improved microcirculation, and corrected coagulation abnormalities in addition to alleviating neuropathic pain. These studies show the value of data-driven approaches in advancing traditional medicine research for drug development and offer a mechanistic basis for CTCM's therapeutic potential in DPN.
本研究的目的是通过使用结合网络药理学和数据挖掘的数据驱动方法,研究核心中药(CTCM)治疗糖尿病周围神经病变(DPN)的治疗潜力。通过对大量数据库和临床处方的检查,发现了中药的重要成分及其对应的靶点。研究了可能的治疗途径,重点研究了通过网络药理学分析发现的AGE-RAGE途径。通过评估大鼠DPN模型的组织病理学改变、炎症和凋亡标志物、微循环和血液高凝性,证实了CTCM的有效性。通过DPN大鼠的实验验证,发现CTCM改善组织病理学,减少炎症和细胞凋亡,改善微循环,纠正凝血异常,减轻神经性疼痛。这些研究显示了数据驱动方法在推进传统医学研究以促进药物开发方面的价值,并为CTCM在DPN中的治疗潜力提供了机制基础。
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引用次数: 0
Prediction of postoperative mechanical complications in ASD patients based on total sequence and proportional score of spinal sagittal plane 基于脊柱矢状面总序列和比例评分预测 ASD 患者术后机械并发症
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-12-01 DOI: 10.1016/j.slast.2024.100222
Wenbin Jiang, Huagang Shi, Tao Gu, Zonglin Cai, Qinglong Li
This article aimed to predict the occurrence of postoperative mechanical complications in adult spinal deformity (ASD) patients through the total sequence and proportional score of the spinal sagittal plane, to improve the quality of life of patients after surgery. The study adopted a comprehensive evaluation and data analysis method, including data collection and preprocessing, feature selection, model construction and training, and constructed a prediction model based on the Random Forest (RF) algorithm. The experimental results showed that the model significantly reduced the risk of complications in randomized controlled trials. The incidence of mechanical complications in the experimental group was 10 %, while that in the control group was 25 %, with statistical significance (P < 0.05). In addition, in retrospective data analysis, the accuracy of the article's model on five datasets ranged from 89 % to 93 %, outperforming logistic regression and support vector machine models, and performing well on other performance data. In prospective studies, the model's predictions showed good consistency with the actual occurrence of complications. Sensitivity analysis shows that the model has low sensitivity to changes in key parameters and exhibits stability, indicating that the model proposed in this article is suitable for uncertain medical environments. The expert rating further confirmed the effectiveness and practicality of the model in predicting postoperative mechanical complications in ASD patients, with the highest score reaching 4.9. These data demonstrate the high accuracy and clinical potential of the model in predicting postoperative complications of ASD.
本文旨在通过脊柱矢状面总序列和比例评分预测成人脊柱畸形(ASD)患者术后机械并发症的发生率,提高患者术后的生活质量。该研究采用综合评价和数据分析方法,包括数据采集和预处理、特征选择、模型构建和训练,构建了基于随机森林(RF)算法的预测模型。实验结果表明,该模型能显著降低随机对照试验中的并发症风险。实验组的机械并发症发生率为 10%,而对照组为 25%,差异有统计学意义(P
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引用次数: 0
Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples 声发射质谱仪中的自动清洁:增强系统鲁棒性,实现复杂样品的大规模高通量分析
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-11-23 DOI: 10.1016/j.slast.2024.100227
Heguang Ji , Xuejiao Yin , Wan Ee Ang , Abdullah Bin Rawshan , Susan Gay , Jing Ma , Chiu Cheong Aw , Chang Liu
The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.
高通量质谱(HT-MS)技术的飞速发展使 MS 成为各学科中举足轻重的分析工具。在高通量药物发现和开发领域,质谱仪在各个阶段都发挥着至关重要的作用,其意义尤为突出。声发射质谱(AEMS)是最近加入 HT-MS 领域的一种新技术,它在高分析通量和高数据质量之间实现了平衡。尤其是 AEMS 的在线稀释功能可直接分析大规模的复杂反应溶液,而无需进行样品清理,因此成为大规模高通量筛选的热门选择。然而,大量的复杂基质会引起对系统稳健性的担忧,特别是样品传输线的潜在堵塞。本研究通过在 AEMS 系统中引入集成自动清洗功能来应对这一挑战。这一改进大大提高了系统的稳健性,而不会对检测执行时间造成额外要求。清洗方法延长了电极的使用寿命,证明它能有效地长时间保持系统性能,展示了它在连续大样本高通量分析应用中的潜力。
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引用次数: 0
Application of MRI imaging technology based on magnetic nanoparticles in diagnosis and prognosis evaluation of prostate cancer 基于磁性纳米粒子的磁共振成像技术在前列腺癌诊断和预后评估中的应用。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-11-22 DOI: 10.1016/j.slast.2024.100225
Wanhui Wang , Xiaodan Liu , Xuedong Li , Bo Geng , Enyang Zhao
Objective: Objective: Prostate cancer is one of the most common malignant tumors in men. Early diagnosis and prognosis evaluation are of great significance for the treatment and prevention of prostate cancer. The purpose of this study was to explore the application of magnetic nanoparticle-based MRI imaging technology in the diagnosis and prognosis assessment of prostate cancer. A total of 81 patients in our hospital from September 2018 to January 2021 were selected as the study objects, all suspected prostate cancer patients, and prostate detection was performed under the guidance of MRI and rectal ultrasound.According to the pathological results, the patients were divided into prostate cancer cluster group and benign prostatic hyperplasia group. Imaging of prostate cancer is achieved by the response of magnetic nanoparticles to magnetic fields. MRI images of patients were collected and analyzed using professional software. It can provide high-resolution images that enable accurate detection and localization of tumors, and the technology can also assess the severity of prostate cancer and predict a patient's prognosis.
目标目标: 前列腺癌是男性最常见的恶性肿瘤之一:前列腺癌是男性最常见的恶性肿瘤之一。早期诊断和预后评估对前列腺癌的治疗和预防具有重要意义。本研究旨在探讨基于磁纳米粒子的磁共振成像技术在前列腺癌诊断和预后评估中的应用。选取我院2018年9月-2021年1月共81例患者作为研究对象,均为疑似前列腺癌患者,在磁共振成像和直肠超声的引导下进行前列腺检测,根据病理结果将患者分为前列腺癌群组和良性前列腺增生组。前列腺癌的成像是通过磁性纳米粒子对磁场的反应来实现的。患者的磁共振成像图像由专业软件采集和分析。该技术可提供高分辨率图像,准确检测和定位肿瘤,还可评估前列腺癌的严重程度,预测患者的预后。
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引用次数: 0
Genetic diagnosis of peripheral blood interleukin-1 in premature infants based on bioinformatics and optical imaging 基于生物信息学和光学成像的早产儿外周血白细胞介素-1 基因诊断。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-11-21 DOI: 10.1016/j.slast.2024.100217
Shenglin Jiang, Di Zhu, Xiumin Li, Lijie Li
Preterm labor is a severe health concern among expectant mothers, affecting approximately 5 % to 7 % of all pregnancies worldwide, and is associated with various factors, including genes, peripheral blood, and immunological functions. In our study, we examined the role of familial genetics in preterm labor to address knowledge gaps and provide more evidence on the concept. We searched the GEO database for applicable genes and found that the GSE26315 and GSE73685 series were relevant. We then performed an analysis using the GEO2R, GEPIA2, STRING, and KEGG enrichment pathways. Our findings are consistent with the literature regarding the association between preterm birth and familial genetics, peripheral blood, and interleukin-1. Interleukin-1 exploits immunological functions by inducing uterine inflammation, creating an unfavorable environment for fetal survival. Similarly, peripheral blood induces premature labor, with higher levels in the amniotic fluid indicating a higher rate of preterm birth. Inheritance of the familial genes responsible for preterm birth passes down the trait.
早产是一个严重影响准妈妈健康的问题,约占全球妊娠总数的 5% 到 7%,与基因、外周血和免疫功能等多种因素有关。在我们的研究中,我们探讨了家族遗传在早产中的作用,以填补知识空白并为这一概念提供更多证据。我们在 GEO 数据库中搜索了适用的基因,发现 GSE26315 和 GSE73685 系列与之相关。然后,我们使用 GEO2R、GEPIA2、STRING 和 KEGG 富集途径进行了分析。我们的研究结果与有关早产与家族遗传、外周血和白细胞介素-1之间关系的文献一致。白细胞介素-1通过诱导子宫炎症发挥免疫功能,为胎儿的生存创造不利环境。同样,外周血会诱发早产,羊水中的白细胞介素-1 含量越高,早产率越高。导致早产的家族基因会遗传给后代。
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引用次数: 0
Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening 磁共振成像和人工智能算法在癌症筛查中的应用。
IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-11-17 DOI: 10.1016/j.slast.2024.100218
Jian Guo, Yu Xue
In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct in-depth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.
在这个癌症高发的社会,癌症筛查已成为降低癌症发病率和死亡率的重要方法。CT 等传统的癌症筛查方法存在一定的局限性,难以适应大规模、周期性的癌症筛查场景。磁共振成像技术是 CT 方法的有效辅助方法,它能以较低的剂量和较低的成本实现较高的图像分辨率。因此,磁共振成像已成为临床上最常用的成像方法,也是医学影像领域的重点研究方向。因此,本文拟对基于磁共振成像和人工智能算法的图像特征提取在癌症筛查中的应用进行深入研究。本文将粒子群优化算法引入人工智能模型的学习中,并对其进行了进一步改进。并对比了混沌粒子群优化、遗传粒子群优化、灰狼算法等多种算法,以验证本文提出的算法的有效性和可行性。在此基础上,进一步改进和验证了智能优化算法。实验结果表明,本文提出的新方法具有较强的容错能力,从前端、后端、外部、数据库、基础设施五个方面对癌症筛查管理系统的各个功能模块进行了优化设计。
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引用次数: 0
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SLAS Technology
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