SLAS Technology最新文献

{"title":"AI-driven transcriptomic biomarker discovery for early identification of pediatric deterioration in Acute Care","authors":"Qing Wang ,&nbsp;Lina Sun ,&nbsp;Wei Meng ,&nbsp;Chen Chen","doi":"10.1016/j.slast.2025.100357","DOIUrl":"10.1016/j.slast.2025.100357","url":null,"abstract":"<div><div>Early detection of juvenile clinical deterioration in acute care settings remains a significant problem in modern healthcare. This paper presents an AI-powered predictive analytics platform that combines transcriptome biomarker signals with structured vital signs, laboratory data, and unstructured clinical notes to improve early warning capabilities. The system uses ClinicalBERT to extract insights from clinical narratives, XGBoost to analyze tabular clinical information, and long short-term memory (LSTM) networks to simulate temporal dynamics. A meta-classifier combines multimodal data to produce real-time risk ratings for clinical deterioration. The performance evaluation utilizing five-fold cross-validation showed great accuracy, with an AUROC of 0.91, AUPRC of 0.83, and an average early warning lead time of 5.6 hours. Predictive markers included higher lactate levels, heart rate patterns, SpO₂ variability, and transcriptome signals indicating systemic inflammatory activation. Ablation investigations proved the importance of multimodal data fusion in increasing prediction robustness. The suggested strategy provides a scalable, interpretable, and high-performing hospital integration system that enables biomarker-informed, precision-based pediatric intervention options.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100357"},"PeriodicalIF":3.7,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208410","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":"The RoboSeed facilitates automated extraction of cereal mature embryos","authors":"R. Berenstein ,&nbsp;V. Bloch ,&nbsp;A. Beery ,&nbsp;M.R. Prusty ,&nbsp;J. Awwad ,&nbsp;O. Amir-Segev ,&nbsp;S. Miterani ,&nbsp;M. Barak ,&nbsp;G. Lidor ,&nbsp;E. Fridman","doi":"10.1016/j.slast.2025.100355","DOIUrl":"10.1016/j.slast.2025.100355","url":null,"abstract":"<div><div>To overcome a critical bottleneck in plant biotechnology workflows, a semiautomated system RoboSeed was developed to extract mature embryos from cereal grains such as barley. In contrast to the commonly used manual extraction, the robot employs a precision-controlled pressing rod which applies mechanical force along an optimal trajectory and angle to detach intact embryos. A custom image-processing pipeline determines grain orientation and morphology, enabling precise rod alignment at the optimal force application point. Validation experiments using two barley cultivars (Noga and Golden Promise) and soaking duration of 10 and 20 h revealed optimal force application point relative location in the range 0.5–0.6, achieving maximum extraction success rates of 56.2 % (Noga) and 36 % (GP) after 20 h soaking. RoboSeed operated with a median cycle time of 20.9 s per extraction, translating to 37.2 s per successful embryo, compared to 27.9 s with expert manual extraction. While current throughput is lower than conventional methods, RoboSeed offers significant advantages in consistency, reduced reliance on operator skill, and potential for scaling. Future improvements include full automation of grain singulation, robotic arms for post-extraction handling, and expanded testing across additional genotypes. RoboSeed’s modular design provides a robust foundation for scalable, high-throughput embryo extraction, with potential to accelerate cereal transformation, gene mapping studies, and tissue culture-based research.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100355"},"PeriodicalIF":3.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139404","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":"Application of high-resolution magnetic resonance imaging (MRI) in the evaluation of acupuncture effects in traditional Chinese medicine","authors":"Yujiao Zhang ,&nbsp;Zijiao Yang ,&nbsp;Shuhua Huang ,&nbsp;Sujiao Sun ,&nbsp;Zhuxian Liang","doi":"10.1016/j.slast.2025.100349","DOIUrl":"10.1016/j.slast.2025.100349","url":null,"abstract":"<div><div>In the evaluation of acupuncture effects in traditional Chinese medicine (TCM), the unclear interpretation of microscopic mechanisms and the difficulty in verifying acupoint specificity due to insufficient resolution of MRI (Magnetic Resonance Imaging) are the main reasons for the difficulty. This paper adopts 7T ultra-high field MRI combined with dynamic ASL (arterial spin labeling) technology, taking advantage of its high spatial resolution and quantitative blood perfusion imaging, to achieve dynamic visualization of microcirculation at acupuncture points in patients with ulcerative colitis. Ulcerative colitis is an ideal site to verify the feasibility of this method because its lesion site is clear and easy to correspond with the body surface acupoint. This paper establishes a high-resolution imaging protocol based on 7T magnetic resonance imaging, adopts 0.5mm spatial resolution, and optimizes scanning parameters to adapt to the microstructural imaging requirements of the acupoint area. This paper introduces pCASL (pseudo-continuous arterial spin labeling) technology, sets the labeling duration and perfusion delay time, captures the changes in perfusion volume before and after acupuncture over time, and obtains a dynamic perfusion sequence. This paper adopts umbilical moxibustion therapy, selects specific meridian acupoints, sets a standard acupuncture stimulation scheme (needle insertion depth, frequency, and needle retention time), and simultaneously performs MRI scanning to achieve real-time acupuncture imaging acquisition. The acquired multi-time point images can be rigidly registered and mapped with standard templates, the blood flow intensity change curve of the acupuncture-related area can be extracted, and the time-perfusion function can be constructed to analyze the local response pattern. The experimental results show that the ΔCBF (Delta Cerebral Blood Flow) of 7T-ASL at Shenque, Tianshu and Zhongwan are 0.15, 0.12 and 0.18 respectively, and it has high sensitivity in capturing tiny blood flow changes under sub-millimeter resolution. The SNR (Signal-to-Noise Ratio) at Shenque, Tianshu and Zhongwan are 22, 25 and 24 respectively, and the CNR (Contrast-to-Noise Ratio) is 6.2, 6.5 and 6.7 respectively, which has significant advantages in the spatial identification of sensitive areas of neural regulation and the identification of perfusion response. The average rising rate, peak time and recovery time in all acupoints were 2.44%/s, 7.2s and 11.5s respectively, and the acupuncture effect took effect faster in local areas.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100349"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114945","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":"Cognitive evaluation model and high-resolution medical images in sports injury rehabilitation under bone density changes","authors":"Wenping Li ,&nbsp;Zhiming Gu","doi":"10.1016/j.slast.2025.100350","DOIUrl":"10.1016/j.slast.2025.100350","url":null,"abstract":"<div><div>In the study of bone density changes and sports injury rehabilitation, traditional image processing technology lacks accuracy in analysis, rehabilitation assessment methods lack quantitative and systematic analysis, and interdisciplinary comprehensive evaluation is lacking. This paper constructs an innovative cognitive assessment model that combines bone density changes, sports injury rehabilitation, and high-resolution medical image analysis. It uses high-resolution CT (Computed Tomography) images and X-ray images to extract bone density data. It uses image processing technology to remove noise, enhance, and standardize, providing accurate bone density values for subsequent input. GCN (Graph Convolutional Network) can be used to automatically identify and classify images of sports injury sites, extract features of the injured area, record and analyze the patient's physical activities during the rehabilitation stage, and evaluate the recovery process of sports injuries in real time. Combining bone density data with sports injury imaging features, XGBoost (Extreme Gradient Boosting) is used to build a cognitive evaluation model, which conducts a comprehensive analysis of multi-dimensional data and provides personalized rehabilitation evaluation. It can integrate technologies from fields such as medicine, engineering, and computer science to establish an interdisciplinary comprehensive evaluation system, achieve multi-angle and multi-dimensional analysis, and ensure the comprehensiveness and accuracy of the model. The experimental results show that the MAE (Mean Absolute Error) of the GCN in this paper is 0.131 in 10 different injury sites, and the average MSE (Mean Squared Error) is about 0.032, which has higher image analysis accuracy. The average accuracy and R² of XGBoost in six different samples are about 0.87 and 0.91, respectively, and the prediction effect of the cognitive evaluation model is apparent.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100350"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114994","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":"Construction of a nomogram prediction model for screening of serum markers for lower extremity vasculopathy secondary to type 2 diabetes mellitus","authors":"Jingjing Yang ,&nbsp;Jinyan Chen ,&nbsp;Lanying Shen","doi":"10.1016/j.slast.2025.100352","DOIUrl":"10.1016/j.slast.2025.100352","url":null,"abstract":"<div><h3>Objective</h3><div>To screen serum markers for secondary lower extremity angiopathy (LEAD) in patients with type 2 diabetes mellitus (T2DM) and construct a nomogram prediction model accordingly.</div></div><div><h3>Methods</h3><div>The clinical data of 200 T2DM patients admitted to the hospital from December 2022 to October 2024 were retrospectively collected. It was also divided into modeling group (<em>n</em> = 160) and internal validation group (<em>n</em> = 40) in a 4:1 ratio by using the leave-out method. As the external validation group, clinical data from 100 T2DM patients who were admitted to other hospitals within the same time period were also gathered. Combined with previous reports of collecting serum marker data related to LEAD secondary to T2DM, key serum markers were screened using LASSO regression. Moreover, multifactorial analysis helped to clarify independent risk factors, and a nomogram prediction model was built and tested for accuracy.</div></div><div><h3>Results</h3><div>The incidence of LEAD in 200 T2DM patients in the hospital was 21.00 % (42/200). A total of 14 variables were screened by LASSO regression analysis. After multifactorial analysis, it was found that disease duration, history of alcohol consumption, mean platelet volume, fasting blood glucose, fibrinogen, high-sensitivity C-reactive protein, insulin-like growth factor 1, nucleotide binding oligomerization domain like receptor protein 3 were independent risk factors for LEAD secondary to T2DM.The results of model validation showed AUCs of 0.971, 0.900, and 0.959 for the modeling cohort, internal validation cohort, and external validation cohort, respectively. The Hosmer-Lemeshow test was <em>χ²</em>=6.607, 7.962, and 6.585 (<em>p</em> &gt; 0.05). Positive net benefits were obtained by intervening with patients using a nomogram model within the high-risk threshold of 0 to 0.9.</div></div><div><h3>Conclusion</h3><div>In this study, eight risk factors associated with LEAD secondary to T2DM are screened by LASSO regression and multifactorial analysis, and a nomogram prediction model is constructed.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100352"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115029","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":"Low code, high impact: Application of low-code platforms to enable and democratize the development of laboratory digitalization and automation applications","authors":"Jonas Austerjost ,&nbsp;Elias Knöchelmann ,&nbsp;Thomas Kruse ,&nbsp;Janina Kilian ,&nbsp;Bastian Quaas ,&nbsp;Michael W. Olszowy","doi":"10.1016/j.slast.2025.100353","DOIUrl":"10.1016/j.slast.2025.100353","url":null,"abstract":"<div><div>Conventionally, the initialization of new prototypes and concepts in laboratory automation and life science software applications has required close collaboration between hardware and software experts, as well as lab personnel such as biologists, chemists, biotechnologists, or process engineers. This setup - still common today - often means that the ideas and requests of lab personnel must be translated into software applications by software developers, which frequently results in long development times. Low-Code Development Platforms (LCDPs) seek to address this challenge by providing a way to accelerate application development by reducing dependence on traditional software development methods, empowering lab personnel to build applications without writing extensive amount of code. By offering a visual, drag-and-drop interface, lab personnel can actively participate in the software development process. This helps democratize application creation and can lead to the quick setup of software solutions tailored to laboratory needs.</div><div>This study demonstrates the implementation of four different use cases in a bioprocessing laboratory environment using an open-source LCDP and commercially available upstream and downstream equipment. The LCDP facilitated the integration and control of different device types with varying communication protocols also enabling dashboarding, monitoring and data processing capabilities. This methodology highlights the suitability of LCDPs for rapidly prototyping and evaluating laboratory and bioprocess automation pipelines, potentially expediting the development of biotechnological production processes and products. All developed components are made available through a publicly accessible repository, facilitating reuse and further development by the scientific community.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100353"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114986","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":"Single-step purification and formulation of antibody-drug conjugates using a miniaturized tangential flow filtration system","authors":"Muhammad Sajed ,&nbsp;Zahoor Khan ,&nbsp;Muhammad Usman Ashraf ,&nbsp;Hafsa Iftikhar ,&nbsp;Talha Bin Rahat ,&nbsp;Samia Falak ,&nbsp;Salman Fozail ,&nbsp;Quiterie Gue ,&nbsp;Raul Pardo ,&nbsp;Lance Ramsey ,&nbsp;Muhammad Saqib Shahzad","doi":"10.1016/j.slast.2025.100351","DOIUrl":"10.1016/j.slast.2025.100351","url":null,"abstract":"<div><div>Antibody-drug conjugates (ADCs) are a promising therapeutic modality that enables the delivery of cytotoxic drugs to the target cells that express the corresponding antigen. However, the purification of ADCs while ensuring product safety, homogeneity, and stability is a challenging task due to their complex and fragile structure. Size exclusion chromatography (SEC), the conventional method for ADC purification, is time-consuming as it requires multiple column washes and equilibration steps. Moreover, subsequent formulation of ADCs, typically using dead-end filtration (DEF), further complicates the production workflow. We compared SEC+DEF with the µPulse®, a miniaturized and automated tangential flow filtration system, for purification and formulation of ADCs. Quality analysis revealed that both approaches were equally gentle as comparable drug-to-antibody ratios (DARs) and monomer purities were observed in the purified samples. Most importantly, both methods exhibited equivalent cleanup efficiency with a 99.8% reduction in free linker-drug concentration. The endotoxin loads comprised 0.11 EU mg^-1 for the µPulse and 0.07 EU mg^-1 for SEC+DEF, ensuring validation of the safe application of purified ADCs in living systems. However, the µPulse performed purification and formulation of ADCs simultaneously as compared to SEC+DEF, which required multiple manual interventions. Our results indicate that the µPulse is a gentle, single-step, and walk-away approach for the purification of ADCs.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100351"},"PeriodicalIF":3.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098086","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":"IoT-based approach for diabetes patient monitoring using machine learning","authors":"Sarra Ayouni ,&nbsp;Muhammad Hamza Khan ,&nbsp;Muhammad Ibrahim ,&nbsp;Mohamed Maddeh ,&nbsp;Nadeem Sarwar ,&nbsp;Nazik Alturki","doi":"10.1016/j.slast.2025.100348","DOIUrl":"10.1016/j.slast.2025.100348","url":null,"abstract":"<div><div>This study presents an IoT-based framework for real-time diabetes monitoring and management, addressing key limitations identified in previous studies by integrating four datasets: BVH Dataset, PIMA Diabetes Dataset, Simulated Dataset, and an Integrated Dataset. The proposed approach ensures diverse demographic representation and a wide range of features including real-time vital signs (e.g., oxygen saturation, pulse rate, temperature) and subjective variables (e.g., skin color, moisture, consciousness level). Advanced preprocessing techniques, including Kalman Filtering for noise reduction, KNN imputation for addressing missing data, and SMOTE-ENN for improving data quality and class balance, were employed. These methods resulted in a 25 % improvement in Recall and a 20 % increase in the F1-score, demonstrating the model's effectiveness and robustness.</div><div>By applying PCA and SHAP for feature engineering, high-impact features were identified, enabling the tuning of models such as Random Forest, SVM, and Logistic Regression, which achieved an accuracy of 97 % and an F1-score of 0.98. A novel triage system, integrated with edge and cloud computing, classifies health status in real-time (Green, Yellow, Red, Black), reducing latency by 35 %. The proposed system sets a new benchmark for scalable, individualized diabetes care in IoT-based healthcare solutions, demonstrating significant improvements in accuracy, response time, and feature incorporation compared to prior works.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100348"},"PeriodicalIF":3.7,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071195","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 cytokine detection platform for evaluation of chemical induced microglial activation","authors":"Shu Yang ,&nbsp;Kelly E. Carstens ,&nbsp;Ibukunoluwa Ipaye ,&nbsp;Xing Chen ,&nbsp;Helena T. Hogberg ,&nbsp;Nicole Kleinstreuer ,&nbsp;Thomas B. Knudsen ,&nbsp;Menghang Xia","doi":"10.1016/j.slast.2025.100347","DOIUrl":"10.1016/j.slast.2025.100347","url":null,"abstract":"<div><div>Environmental chemical exposure, such as pesticides and heavy metals, may contribute to neurodegenerative disorders through neuroinflammation. This study aims to identify suitable <em>in vitro</em> microglial models for assessing cytokine responses to potential neurotoxicants, particularly focusing on human induced pluripotent stem cell-derived microglia (hiMG). In this study, we evaluated the cytokine secretion profiles of four microglial cell types—hiMG, HMC3, IM-HM, and BV2—upon stimulation with lipopolysaccharides (LPS) using cytokine arrays. Our findings showed cytokine response patterns in hiMG cells that most closely resemble <em>in vivo</em> conditions, with significant increases in interleukin 6 (IL-6) and tumor necrosis factor-alpha (TNF-α) levels, the latter being uniquely expressed after LPS treatment. Consequently, we developed a homogeneous time-resolved fluorescence (HTRF) assay platform in a 1536-well plate format for high-throughput screening of environmental chemicals using hiMG cells. After LPS treatment, the assay window for secretion of IL-6 and TNF-α increased 3.71-fold and 2.62-fold over the vehicle control group, respectively, with respective EC₅₀ values of approximately 50 ng/mL and 90 ng/mL for IL-6 and TNF-α. We also assessed the response activity of hiMG to other stimuli, including interferon gamma and various catecholamine compounds, and nine environmental chemicals with evidence of cytokine-inducing potential in other <em>in vitro</em> assays. While all nine tested agents stimulated IL-6 and TNF-α production, three compounds (<em>e.g.</em>, picoxystrobin) showed significant stimulation of both cytokines. ​This study establishes a reliable high-throughput platform for detecting inflammatory effects of environmental toxicants in a microglial cell assay, contributing valuable insights into their neuroinflammatory potential and possible implications for neurodegenerative disorders.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100347"},"PeriodicalIF":3.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056195","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":"Staining Triad: A fully automated and zero-waste flow cytometry staining system fostering the 3R to 4R transition","authors":"Santosh Dhule ,&nbsp;Eric Corriveau ,&nbsp;Christopher Lepsy ,&nbsp;Sophie Tourdot","doi":"10.1016/j.slast.2025.100345","DOIUrl":"10.1016/j.slast.2025.100345","url":null,"abstract":"<div><div>Despite significant advances in instrumentation and robotics, the automation of cell staining in flow cytometry remains largely unaddressed. While sample acquisition in flow cytometry has been fully automated, sample staining continues to be a predominantly manual process—requiring substantial time, labor, and cost. Additionally, the repetitive nature of manual staining introduces monotony and increases the likelihood of human error. The Staining Triad presented here achieves full automation of the staining process, requiring only the input of samples and reagents, with no manual intervention. Staining performed using the Staining Triad showed a comparable biomarker profile to that of conventional manual staining. The modular and adaptable system design enables the flexibility to tailor throughput and accommodate assay-specific requirements, thereby extending its applicability to plate-based ligand binding assays. Moreover, the system eliminates the need for pipette tips, exemplifying a sophisticated and sustainable solution that enhances laboratory efficiency while reducing human error and the primary source of plastic waste in flow cytometry staining. Although 3R initiatives (Reduce, Reuse, Recycle) have helped decrease laboratory plastic waste volumes, substantial amounts are still incinerated or end up in landfills, where they persist in the environment for decades. This limitation underscores the need to incorporate a fourth R—\"Remove/Replace\"—into sustainability strategies. As flow cytometry becomes increasingly integral across various biotechnology disciplines, it is imperative to streamline associated workflows to accelerate drug discovery while preserving the environment that sustains life.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100345"},"PeriodicalIF":3.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978504","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}