SLAS Technology最新文献

英文中文

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

SLAS Technology

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

SLAS Technology

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

SLAS Technology

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

Continuous glucose data construction and risk assessment application of diabetic retinopathy complications for patients with type 2 diabetes mellitus 2 型糖尿病患者连续血糖数据构建及糖尿病视网膜病变并发症风险评估应用。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-11-13 DOI: 10.1016/j.slast.2024.100221

Yaguang Zhang , Liansheng Liu , Hong Qiao

Managing diabetes mellitus (DM) includes achieving acceptable blood glucose levels and minimizing the risk of complications from DM. The appropriate glucose sensing method is continuous glucose monitoring (CGM). Effective evaluation metrics that reflect glucose fluctuations can be realized. However, compared with self-monitoring of blood glucose (SMBG), CGM data are not easy to obtain. Therefore, this article studies a fusion model to achieve this objective, including Gaussian process regression (GPR) and long short-term memory (LSTM). Compared with the three commonly used LSTM, GPR, and support vector machine, the proposed model can construct accurate results. By using the constructed CGM data, the conventional metrics, such as the mean amplitude of glycemic excursion (MAGE), mean blood glucose (MBG), standard deviation (SD), and time in range (TIR), are calculated. These metrics and other variables are input into statistical methods to realize diabetic retinopathy risk assessment. In this way, the relationship between the glycemic variability of the constructed CGM data by the mathematical model and DR could be achieved. The utilized statistical methods include single-factor analysis and binary multivariate logistic regression analysis. Results show that fasting blood glucose, disease course, history of hypertension, MAGE and TIR are independent risk factors for DR.

糖尿病（DM）的管理包括达到可接受的血糖水平，并将糖尿病并发症的风险降至最低。适当的血糖检测方法是连续血糖监测（CGM）。可以实现反映血糖波动的有效评估指标。然而，与自我血糖监测（SMBG）相比，CGM 数据不易获得。因此，本文研究了一种融合模型来实现这一目标，包括高斯过程回归（GPR）和长短期记忆（LSTM）。与三种常用的 LSTM、GPR 和支持向量机相比，本文提出的模型可以构建精确的结果。利用构建的 CGM 数据，可以计算出血糖偏移的平均幅度（MAGE）、平均血糖（MBG）、标准偏差（SD）和范围内时间（TIR）等常规指标。这些指标和其他变量被输入统计方法，以实现糖尿病视网膜病变风险评估。这样，数学模型构建的 CGM 数据的血糖变异性与 DR 之间的关系就可以实现。使用的统计方法包括单因素分析和二元多元逻辑回归分析。结果显示，空腹血糖、病程、高血压病史、MAGE 和 TIR 是导致 DR 的独立风险因素。

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引用次数: 0

Melanoma-on-a-chip model for anticancer drug injecting delivery method 用于抗癌药物注射给药方法的芯片上黑色素瘤模型。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-11-12 DOI: 10.1016/j.slast.2024.100219

Pedro Herreros , Ana López-Hernández , Miguel Holgado , María Fe Laguna Heras

The pharmaceutical and cosmetic industries are encountering a challenge in adopting new study models for product development. there has been a growing interest in organ-on-a-chip systems, and particularly for generating skin models. While numerous alternatives replicating high-fidelity skin models exist, there is a notable absence of melanoma study's methodology specifically on these microfluidic chips. This work introduces a novel skin-on-a-chip device featuring two microfluidic chambers, facilitating a 3D cell co-culture involving fibroblasts, keratinocytes, and melanoma cells. The design of this organ-on-a-chip has enabled the administration of the anticancer treatment Gemcitabine using an injection system within the chip. The results of this work have shown a significant impact on the co-culture distribution of cells, decreasing the population of cancerous cells after the administration of Gemcitabine. The work presented in this article demonstrates the effectiveness of the chip and the administration method for testing anti-melanoma therapies and position this technology as an enhanced fidelity model for studying melanoma while providing an alternative for real-time monitoring of drug testing.

制药和化妆品行业在采用新的研究模型进行产品开发时遇到了挑战。人们对片上器官系统，尤其是生成皮肤模型的兴趣与日俱增。虽然有许多复制高保真皮肤模型的替代品，但专门在这些微流控芯片上研究黑色素瘤的方法却明显缺乏。这项工作介绍了一种新颖的皮肤芯片装置，它具有两个微流室，便于成纤维细胞、角质细胞和黑色素瘤细胞的三维细胞共培养。这种芯片上器官的设计使人们能够利用芯片内的注射系统注射抗癌治疗药物吉西他滨。这项工作的结果表明，在注射吉西他滨后，细胞的共培养分布产生了显著影响，减少了癌细胞的数量。本文介绍的工作证明了芯片和给药方法在测试抗黑色素瘤疗法方面的有效性，并将这项技术定位为研究黑色素瘤的增强保真度模型，同时为药物测试的实时监测提供了一种替代方法。

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引用次数: 0

Emerging trends in application of magnetic beads in biopharma industry 生物制药行业应用磁珠的新趋势。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-11-10 DOI: 10.1016/j.slast.2024.100224

Jianli Zhao

引用次数: 0

A novel multi-omics approach for identifying key genes in intervertebral disc degeneration 识别椎间盘退变关键基因的新型多指标方法

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-11-09 DOI: 10.1016/j.slast.2024.100223

Xuan Zhao , Qijun Wang , Shuaikang Wang , Wei Wang , Xiaolong Chen , Shibao Lu

Many different cell types and complex molecular pathways are involved in intervertebral disc degeneration (IDD). We used a multi-omics approach combining single-cell RNA sequencing (scRNA-seq), differential gene expression analysis, and Mendelian randomization (MR) to clarify the underlying genetic architecture of IDD. We identified 1,164 differentially expressed genes (DEGs) across four important cell types associated with IDD using publicly available single-cell datasets. A thorough gene network analysis identified 122 genes that may be connected to programmed cell death (PCD), a crucial route in the etiology of IDD. SLC40A1, PTGS2, and GABARAPL1 have been identified as noteworthy regulatory genes that may impede the advancement of IDD. Furthermore, distinct cellular subpopulations and dynamic gene expression patterns were revealed by functional enrichment analysis and pseudo-temporal ordering of chondrocytes. Our results highlight the therapeutic potential of GABARAPL1, PTGS2, and SLC40A1 targeting in the treatment of IDD.

椎间盘变性（IDD）涉及许多不同的细胞类型和复杂的分子通路。我们采用了一种结合单细胞 RNA 测序、差异基因表达分析和孟德尔随机化的多组学方法来阐明 IDD 的潜在遗传结构。我们利用公开的单细胞数据集，在与 IDD 相关的四种重要细胞类型中发现了 1,164 个差异表达基因 (DEG)。通过全面的基因网络分析，我们发现了 122 个可能与程序性细胞死亡有关的基因，而程序性细胞死亡是 IDD 病因学中的一个重要途径。SLC40A1、PTGS2 和 GABARAPL1 被确定为可能阻碍 IDD 进展的值得注意的调控基因。此外，通过对软骨细胞进行功能富集分析和伪时序排序，还发现了不同的细胞亚群和动态基因表达模式。我们的研究结果凸显了 GABARAPL1、PTGS2 和 SLC40A1 靶向治疗 IDD 的治疗潜力。

引用次数: 0

Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study 用于早期预测胆囊腺癌生存率的机器学习模型：对比研究

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-11-09 DOI: 10.1016/j.slast.2024.100220

Weijia Wang , Xin Li , Haiyuan Yu , Fangxuan Li , Guohua Chen

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70 %) and validation (30 %) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

胆囊腺癌（GBAC）是一种高度恶性的癌症，其预后并不乐观。为了促进个体化风险分层并改善临床决策，本研究着手创建并验证一种能够准确预测胆囊腺癌患者早期生存结果的机器学习模型。我们使用 SEER 数据库（2010-2020 年）中的数据对五种模型--RSF、Cox 回归、GBM、XGBoost 和 Deepsurv 进行了比较。数据集被分为训练集（70%）和验证集（30%），并使用C指数、ROC曲线、校准曲线和决策曲线分析（DCA）来评估模型的性能。在 1 年、2 年和 3 年的生存间隔中，RSF 模型在校准、辨别和临床净效益方面的表现优于其他模型。根据 SHAP 分析，AJCC 分期是预测生存率的最重要指标。根据 RSF 导出的风险评分，将患者分为高、中、低风险组，结果显示生存率存在显著差异。这些结果表明，RSF 模型具有作为 GBAC 患者早期生存预测工具的潜力，可提高个体化治疗和决策水平。

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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

SLAS Technology

Pub Date : 2024-11-09 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

Management of experimental workflows in robotic cultivation platforms 机器人培养平台的实验工作流程管理。

IF 2.5 4区医学 Q3 BIOCHEMICAL RESEARCH METHODS

SLAS Technology

Pub Date : 2024-10-30 DOI: 10.1016/j.slast.2024.100214

Lucas Kaspersetz , Britta Englert , Fabian Krah , Ernesto C. Martinez , Peter Neubauer , M. Nicolas Cruz Bournazou

In the last decades, robotic cultivation facilities combined with automated execution of workflows have drastically increased the speed of research in biotechnology. In this work, we present the design and deployment of a digital infrastructure for robotic cultivation platforms. We implement a Workflow Management System, using Directed Acyclic Graphs, based on the open-source platform Apache Airflow to increase traceability and the automated execution of experiments. We demonstrate the integration and automation of experimental workflows in a laboratory environment with a heterogeneous device landscape including liquid handling stations, parallel cultivation systems, and mobile robots. The feasibility of our approach is assessed in parallel E. coli fed-batch cultivations with glucose oscillations in which different elastin-like proteins are produced. We show that the use of workflow management systems in robotic cultivation platforms increases automation, robustness and traceability of experimental data.

在过去几十年里，机器人培养设施与自动执行工作流程相结合，大大提高了生物技术研究的速度。在这项工作中，我们介绍了机器人培养平台数字基础设施的设计和部署。我们在开源平台 Apache Airflow 的基础上，使用有向无环图实施了一个工作流管理系统，以提高实验的可追溯性和自动化执行。我们在实验室环境中演示了实验工作流的集成和自动化，实验室环境中的异构设备包括液体处理站、并行培养系统和移动机器人。在生产不同弹性蛋白的葡萄糖振荡并行大肠杆菌分批进行培养的过程中，对我们方法的可行性进行了评估。我们的研究表明，在机器人培养平台中使用工作流管理系统可以提高实验数据的自动化、稳健性和可追溯性。

引用次数: 0

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