Pub Date : 2024-12-01DOI: 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.
{"title":"Exploratory insights from the immuno-oncology hollow fiber assay: A pilot approach bridging In Vitro and In Vivo models","authors":"Tove Selvin , Malin Berglund , Anders Åkerström , Marco Zia , Jakob Rudfeldt , Malin Jarvius , Rolf Larsson , Claes R Andersson , Mårten Fryknäs","doi":"10.1016/j.slast.2024.100232","DOIUrl":"10.1016/j.slast.2024.100232","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100232"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787295","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}
Pub Date : 2024-12-01DOI: 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.
{"title":"Automation of multiplex biochemical assays to enhance productivity and reduce cycle time using a modular robotic platform","authors":"Buyun Tang, Becky Lam, Stephanie Holley, Martha Torres, Theresa Kuntzweiler, Tatiana Gladysheva, Paul Lang","doi":"10.1016/j.slast.2024.100233","DOIUrl":"10.1016/j.slast.2024.100233","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100233"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786946","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}
Pub Date : 2024-12-01DOI: 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.
{"title":"Prosthesis repair of oral implants based on artificial intelligenc`e finite element analysis","authors":"Yi Sun","doi":"10.1016/j.slast.2024.100226","DOIUrl":"10.1016/j.slast.2024.100226","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100226"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787505","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}
Pub Date : 2024-12-01DOI: 10.1016/j.slast.2024.100234
Meghav Verma , Nate Hoxie , John Janiszewski , Charles Bonney , Matthew D. Hall , Sam Michael , Tom Covey , Jonathan H. Shrimp
{"title":"Notes on AEMS methods development for high throughput experimentation in drug discovery","authors":"Meghav Verma , Nate Hoxie , John Janiszewski , Charles Bonney , Matthew D. Hall , Sam Michael , Tom Covey , Jonathan H. Shrimp","doi":"10.1016/j.slast.2024.100234","DOIUrl":"10.1016/j.slast.2024.100234","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100234"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787248","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}
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.
{"title":"Integrating data mining and network pharmacology for traditional Chinese medicine for drug discovery of diabetic peripheral neuropathy","authors":"Jing Ping , Hong-Zheng Hao , Zhen-Qi Wu , Yong-Ju Yang , He-Shan Yu","doi":"10.1016/j.slast.2024.100228","DOIUrl":"10.1016/j.slast.2024.100228","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100228"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787296","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}
Pub Date : 2024-12-01DOI: 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.
{"title":"Prediction of postoperative mechanical complications in ASD patients based on total sequence and proportional score of spinal sagittal plane","authors":"Wenbin Jiang, Huagang Shi, Tao Gu, Zonglin Cai, Qinglong Li","doi":"10.1016/j.slast.2024.100222","DOIUrl":"10.1016/j.slast.2024.100222","url":null,"abstract":"<div><div>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 (<em>P</em> < 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.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100222"},"PeriodicalIF":2.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632548","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}
Pub Date : 2024-11-23DOI: 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。
{"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}
Pub Date : 2024-11-22DOI: 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.
{"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}
Pub Date : 2024-11-21DOI: 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.
{"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}
Pub Date : 2024-11-17DOI: 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.
{"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}
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