SLAS Technology最新文献

{"title":"Study on the quality of drug vial recognition and grasping and opening for pharmacy intravenous admixture robot based on nanophotonic sensing.","authors":"Li Guochun, Ye Jiandong, Zhao Huaibi, Xu Hong, Jin Haiyang, Huang Xinwu, Deng Gulin","doi":"10.1016/j.slast.2025.100244","DOIUrl":"10.1016/j.slast.2025.100244","url":null,"abstract":"<p><p>With the continuous progress of medical technology, traditional medicine bottle identification and management methods have problems such as low efficiency and large errors, and innovative solutions are urgently needed. Due to its high sensitivity and rapid response characteristics, this study aims to develop a robot system for intravenous infusion based on nanophotonics sensing to realize accurate identification, grasping and opening of medicine bottles in a dynamic environment, so as to improve the safety and efficiency of intravenous infusion. In this paper, an intelligent robot system with nanophotonics sensor is designed, which uses nanomaterials to produce high sensitivity sensor, so as to realize the information recognition of medicine bottle labels. The robot arm is set up to grasp and open the medicine bottle automatically through visual recognition system and feedback control algorithm. We conducted several rounds of experiments in a simulated environment to evaluate the recognition rate, grab success rate, and opening speed of the system. The experimental results show that the recognition system based on nano photonic sensor in bottle of accuracy and grab the success rate is higher, open time is greatly shortened, compared with traditional methods, the new system in recognition and operation were showed a significant advantage. The study demonstrates the potential of nanophotonics sensing technology in intravenous infusion robots. By optimizing the process of bottle recognition, grasping and opening, the system not only improves the safety and efficiency of medical operations, but is also expected to play a role in future medical automation. Future work will focus on further perfecting the system integration and optimization algorithm, so as to adapt to more complex clinical environment.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100244"},"PeriodicalIF":2.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical value of multi-slice spiral CT in evaluating preoperative TNN staging and postoperative recurrence and metastasis of colon carcinoma.","authors":"Huili Yang, Yun Chu","doi":"10.1016/j.slast.2025.100247","DOIUrl":"https://doi.org/10.1016/j.slast.2025.100247","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the clinical value of multi-slice spiral CT in preoperative TNN staging and postoperative recurrence and metastasis of colon carcinoma, and to provide evidence for the reliability of CT in the diagnosis of colon carcinoma METHODS: 89 patients with colon carcinoma diagnosed pathologically in our hospital from July 2020 to April 2023 were selected retrospectively. The preoperative TNN staging and postoperative recurrence and metastasis were monitored by 64 row 128 layer spiral CT. The diagnostic coincidence rate, TNM staging coincidence rate and postoperative recurrent TNM staging accuracy were evaluated according to the pathological diagnosis RESULTS: The diagnostic coincidence rate of multi-slice spiral CT was 97.8 % (87/89), and the detection rate of lymph nodes was 86.1 % (31/36). The coincidence rate of T staging was 93.3 % (83/89), N staging was 91.0 % (81/89), M staging was 100 % (Kappa=0.897,0.879, 1.000). The diagnosis of recurrent TNM stage was consistent (Kappa=0.893, 0.801, 1.000) CONCLUSION: Multi-slice spiral CT is of high diagnostic coincidence rate, high accuracy of TNM staging and rapid noninvasive examination. It can obtain reliable results in preoperative staging and postoperative recurrence and metastasis diagnosis, which is worth popularizing in clinic.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"100247"},"PeriodicalIF":2.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput hit identification with acoustic ejection mass spectrometry.","authors":"Xiujuan Wen, David G McLaren","doi":"10.1016/j.slast.2025.100245","DOIUrl":"10.1016/j.slast.2025.100245","url":null,"abstract":"<p><p>This mini-review provides an overview of recent developments in AEMS supporting hit identification in drug discovery, emphasizing its potential to enhance the quality and efficiency of label-free HTS. Future advancements that may further expand the role of AEMS in the drug discovery process will also be discussed.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100245"},"PeriodicalIF":2.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing NLP to investigate biomarker interactions and CVD risks in elderly chronic kidney disease patients.","authors":"Hongli Han","doi":"10.1016/j.slast.2025.100243","DOIUrl":"10.1016/j.slast.2025.100243","url":null,"abstract":"<p><p>Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among elderly patients. Understanding the interaction between several biomarkers and cardiovascular (CVD) risks is crucial for improving patient outcomes and tailoring personalized treatment strategies. There is much more to learn about the intricate relationship between biomarkers and CVD risks in elderly CKD patients. Research aims to harness natural language processing (NLP) strategies to investigate the interaction between biomarkers and CVD risks in elderly patients with CKD. This research examined how changes in baseline values of four novel and classic cardiac biomarkers relate to the danger of CVD, and all-cause death in a large cohort of patients with CKD. Initially, medical data were collected from EHR of elderly CKD patients. NLP technique, such as Named Entity Recognition (NER), is used to extract the relevant biomarkers and CVD risk factors from the data. Statistical techniques were applied to examine the associations between biomarkers and CVD risks. The predictive models, using a combination of structured and NLP-extracted features demonstrated improved accuracy in forecasting CVD outcomes compared to traditional methods. This investigation highlights the critical role of specific biomarkers like PTH and FGF-23 in predicting CVD outcomes, providing insights into the possibility of using EHR data for better patient management and enhancing predictive models for this high-risk population.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100243"},"PeriodicalIF":2.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do-it-yourself instrument integration into an existing mammalian cell line development automation platform.","authors":"Jie Ding, Kee Wee Tan, Xiaoyue Chen","doi":"10.1016/j.slast.2025.100246","DOIUrl":"https://doi.org/10.1016/j.slast.2025.100246","url":null,"abstract":"<p><p>Laboratory automation in the biopharmaceutical industry as a rule requires contracted service from highly professional automation solution provider, at times involving the purchase and development of specialized or customized hardware and software, which can be proprietary and expensive. Alternatively, with the availability of open-source software customized for automation, it is possible to automate existing laboratory instruments in a do-it-yourself (DIY), low-cost, and flexible fashion. In this work, we used an open-source scripting language, AutoIt, to integrate an existing microplate imager into an existing automation platform that is already equipped with a 4-axis robotic arm and an automated incubator, to achieve automation of the imaging procedure in our cell line development workflow. Furthermore, optimizations were performed using AutoIt to improve the overall automated imaging process, namely i) incorporating an automated scan profile selection step, ii) setting up automated handling of system errors, and iii) setting up remote handling of system errors. In summary, the use of AutoIt for DIY instrument integration proves to be cost-saving, versatile, and able to enhance the efficiency of automation workflows in the laboratory.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"100246"},"PeriodicalIF":2.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Artificial intelligence-driven predictive framework for early detection of still birth\" [SLAS Technology Volume 29, Issue 6, 100203, December 2024].","authors":"Sarah A Alzakari, Asma Aldrees, Muhammad Umer, Lucia Cascone, Nisreen Innab, Imran Ashraf","doi":"10.1016/j.slast.2024.100236","DOIUrl":"https://doi.org/10.1016/j.slast.2024.100236","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100236"},"PeriodicalIF":2.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automation and miniaturization of high-throughput qPCR for gene expression profiling.","authors":"Santhi Raveendran, Asma Saeed, Mahesh Kumar Reddy Kalikiri, Harshitha Shobha Manjunath, Alia Al Massih, Muna Al Hashmi, Iman Al Azwani, Basirudeen Syed Ahamed Kabeer, Rebecca Mathew, Sara Tomei","doi":"10.1016/j.slast.2024.100241","DOIUrl":"10.1016/j.slast.2024.100241","url":null,"abstract":"<p><p>Quantitative PCR (qPCR) is a technique commonly employed in laboratories and core facilities. In our previous study, we had shown the possibility to automate steps in a panel-specific gene expression workflow by pairing Mosquito HV with BioMark HD. Here we aimed to automate the full workflow and explore miniaturization capabilities. Each step of the gene expression workflow was scripted on Mosquito HV genomics software. We performed three different automated runs: i. Replicates of a Reference RNA sample (obtained by pooling RNA isolated from 10 healthy individuals) were run on an immunology gene expression panel. We tested the full reaction (FR) and three miniaturization conditions, namely: 1.5x, 2.5x and 5x; the data obtained from the automated FR replicates was compared to the data obtained from the manual processing; ii. Biological RNA samples (isolated from n = 45 individuals) were run as FR and 1.5x on the immunology gene expression panel; iii. Biological RNA samples (isolated from n = 45 individuals) were run as FR and 1.5x on a pregnancy gene expression panel. The expression of each gene was calculated using the 2^{(-delta Ct)} method. Successful amplification was observed for the reference samples when using FR and 1.5x conditions. The 2.5x condition exhibited suboptimal amplification with a lower success rate while the 5x condition retrieved no amplification. The 2.5x and 5x miniaturization conditions were excluded from further runs. A strong significant positive correlation was observed between the manual and automated workflows for the reference RNA sample, underscoring the robustness of the gene expression assay. The automation of the immunology and pregnancy gene expression panels on the 45 individual samples retrieved a success rate >70 % for both the FR and the 1.5x miniaturization conditions. A significant positive correlation was also observed between the FR and 1.5x miniaturization conditions for both panels. Our results show that the adoption and the 1.5x miniaturization capabilities of Mosquito HV system for automating the gene expression workflow did not interfere with data quality and reproducibility.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100241"},"PeriodicalIF":2.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation.","authors":"Nagalakshmi R, Surbhi Bhatia Khan, Ananthoju Vijay Kumar, Mahesh T R, Mohammad Alojail, Saurabh Raj Sangwan, Mo Saraee","doi":"10.1016/j.slast.2024.100238","DOIUrl":"https://doi.org/10.1016/j.slast.2024.100238","url":null,"abstract":"<p><p>This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97%, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100238"},"PeriodicalIF":2.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Life Sciences Discovery and Technology Highlights.","authors":"Tal Murthy, Jamien Lim","doi":"10.1016/j.slast.2024.100235","DOIUrl":"https://doi.org/10.1016/j.slast.2024.100235","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100235"},"PeriodicalIF":2.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"<p><p>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.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}