SLAS Technology最新文献_第7页

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

SLAS Technology 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

{"title":"Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples","authors":"Heguang Ji , Xuejiao Yin , Wan Ee Ang , Abdullah Bin Rawshan , Susan Gay , Jing Ma , Chiu Cheong Aw , Chang Liu","doi":"10.1016/j.slast.2024.100227","DOIUrl":"10.1016/j.slast.2024.100227","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100227"},"PeriodicalIF":2.5,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

SLAS Technology Pub Date : 2024-11-22 DOI: 10.1016/j.slast.2024.100225

Wanhui Wang , Xiaodan Liu , Xuedong Li , Bo Geng , Enyang Zhao

{"title":"Application of MRI imaging technology based on magnetic nanoparticles in diagnosis and prognosis evaluation of prostate cancer","authors":"Wanhui Wang , Xiaodan Liu , Xuedong Li , Bo Geng , Enyang Zhao","doi":"10.1016/j.slast.2024.100225","DOIUrl":"10.1016/j.slast.2024.100225","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100225"},"PeriodicalIF":2.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

SLAS Technology Pub Date : 2024-11-21 DOI: 10.1016/j.slast.2024.100217

Shenglin Jiang, Di Zhu, Xiumin Li, Lijie Li

{"title":"Genetic diagnosis of peripheral blood interleukin-1 in premature infants based on bioinformatics and optical imaging","authors":"Shenglin Jiang, Di Zhu, Xiumin Li, Lijie Li","doi":"10.1016/j.slast.2024.100217","DOIUrl":"10.1016/j.slast.2024.100217","url":null,"abstract":"<div><div>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<em>.</em></div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100217"},"PeriodicalIF":2.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

SLAS Technology Pub Date : 2024-11-17 DOI: 10.1016/j.slast.2024.100218

Jian Guo, Yu Xue

{"title":"Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening","authors":"Jian Guo, Yu Xue","doi":"10.1016/j.slast.2024.100218","DOIUrl":"10.1016/j.slast.2024.100218","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100218"},"PeriodicalIF":2.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Yaguang Zhang , Liansheng Liu , Hong Qiao

{"title":"Continuous glucose data construction and risk assessment application of diabetic retinopathy complications for patients with type 2 diabetes mellitus","authors":"Yaguang Zhang , Liansheng Liu , Hong Qiao","doi":"10.1016/j.slast.2024.100221","DOIUrl":"10.1016/j.slast.2024.100221","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100221"},"PeriodicalIF":2.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Melanoma-on-a-chip model for anticancer drug injecting delivery method","authors":"Pedro Herreros , Ana López-Hernández , Miguel Holgado , María Fe Laguna Heras","doi":"10.1016/j.slast.2024.100219","DOIUrl":"10.1016/j.slast.2024.100219","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100219"},"PeriodicalIF":2.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

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

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

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

{"title":"Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study","authors":"Weijia Wang , Xin Li , Haiyuan Yu , Fangxuan Li , Guohua Chen","doi":"10.1016/j.slast.2024.100220","DOIUrl":"10.1016/j.slast.2024.100220","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100220"},"PeriodicalIF":2.5,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

引用次数: 0