SLAS Technology最新文献

{"title":"Large language models in breast cancer reconstruction: A framework for patient-specific recovery and predictive insights","authors":"Chunrao Zheng,&nbsp;Qunfang Li,&nbsp;Geling Lu,&nbsp;Yuchang Mai,&nbsp;Yuan Hu","doi":"10.1016/j.slast.2025.100285","DOIUrl":"10.1016/j.slast.2025.100285","url":null,"abstract":"<div><div>Breast cancer reconstruction, a vital part of comprehensive cancer therapy, can be performed concurrently with cancer resection, improving both physical and psychological recovery for patients. However, the intricacy and variety of recovery demand a specialized strategy. Thus, a unique framework that uses Natural Language Processing (NLP) and Large Language Models (LLMs) is developed to improve patient-specific recovery and predictive insights during breast cancer reconstruction. Lemmatization/Stemming is used for pre-processing large volumes of data from medical records, clinical notes, and treatment histories and BioBERT, a model pretrained on biomedical texts to capture complex medical terminology used for feature extraction and aids in the transformation of text data into numerical vectors. The approach employs forecasting models like ChatGPT-4 and Gemini to offer insights into the likelihood of successful reconstruction and associated problems based on specific patient characteristics, treatment options, and recovery timelines. Using sophisticated LLMs, this framework provides clinicians with a powerful tool for personalizing care by anticipating postoperative complications, recovery durations, and psychosocial consequences. Furthermore, it allows for the development of targeted rehabilitation programs that are adapted to unique patient needs, enabling greater recovery and overall quality of life. This approach not only improves clinical decision-making but also empowers patients by offering personalized recovery strategies. As a result, the accuracy of ChatGPT-4 is 98.4 % and Gemini is 98.7 %; the score per response is 2.52 for ChatGPT-4 and 2.89 for Gemini. Readability of ChatGPT-4 is 93.0 % and Gemini is 94.5 %; a relevance score is 95.5 % and 94.0 % for ChatGPT-4 and Gemini, and time response is 2.5 s for ChatGPT-4 and 2.5 s for Gemini. Finally, this research indicates how NLP and LLMs can transform breast cancer reconstruction by offering predictive insights and promoting tailored, patient-centered therapy, bridging the gap between powerful computational technologies and life science research to better patient care.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100285"},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855238","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":"Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery","authors":"Bin Dai ,&nbsp;Xinyu Liang ,&nbsp;Yan Dai ,&nbsp;Xintian Ding","doi":"10.1016/j.slast.2025.100283","DOIUrl":"10.1016/j.slast.2025.100283","url":null,"abstract":"<div><div>The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characteristics, and the patients’ true risks cannot be fully revealed. The evaluation results based on imaging ignore the clinical symptoms and changes in quality of life of patients, limiting the understanding of the natural process of ASD and the comprehensive assessment of its risk factors, and hindering the development of effective prevention strategies. To improve the quality of postoperative management and effectively identify the characteristics of ASD, this paper studies the risk assessment of ASD after lumbar fusion surgery by combining the artificial intelligence (AI) medical image-aided diagnosis system. First, the collaborative attention mechanism is adopted to start with the extraction of single-modal features and fuse the multi-modal features of computed tomography (CT) and magnetic resonance imaging (MRI) images. Then, the similarity matrix is weighted to achieve the complementarity of multi-modal information, and the stability of feature extraction is improved through the residual network structure. Finally, the fully connected network (FCN) is combined with the multi-task learning framework to provide a more comprehensive assessment of the risk of ASD. The experimental analysis results show that compared with three advanced models, three dimensional-convolutional neural networks (3D-CNN), U-Net++, and deep residual networks (DRN), the accuracy of the model in this paper is 3.82 %, 6.17 %, and 6.68 % higher respectively; the precision is 0.56 %, 1.09 %, and 4.01 % higher respectively; the recall is 3.41 %, 4.85 %, and 5.79 % higher respectively. The conclusion shows that the AI medical image-aided diagnosis system can help to accurately identify the characteristics of ASD and effectively assess the risks after lumbar fusion surgery.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100283"},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828765","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":"AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes","authors":"Ang Li ,&nbsp;Yunxin Wang ,&nbsp;Hongxu Chen","doi":"10.1016/j.slast.2025.100286","DOIUrl":"10.1016/j.slast.2025.100286","url":null,"abstract":"<div><div>The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. The complexity of analysing diverse biomedical datasets, such as electrocardiograms (ECG), clinical records, genetic screening reports, and imaging results, poses challenges in obtaining precise early diagnoses. To address these issues, we introduce a hybrid machine learning (ML) framework that integrates the Wolf Pack Search Algorithm Dynamic Random Forest (WPSA-DRF) with a RoBERTa-based LLM to enhance the accuracy of cardiovascular disease predictions. Using advanced NLP techniques, including biomedical text mining, entity recognition, and feature extraction, the system processes structured and unstructured clinical data to detect abnormalities associated with sudden cardiac arrest (SCA), arrhythmias, and genetic cardiomyopathies. The proposed system achieves a diagnostic accuracy of 92.5 %, precision of 92.7 %, recall of 99.23 %, and F1-score of 95.6 %, outperforming traditional diagnostic methodologies. Furthermore, the research underscores the role of LLMs in personalized medicine, identifying patient-specific risk factors and optimizing treatment pathways for cardiac patients. This work highlights how NLP-driven AI solutions are transforming biomedical research, accelerating early disease detection, and improving clinical decision-making for both athletes and the general population.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100286"},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855237","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":"A hybrid PKI and spiking neural network approach for enhancing security and energy efficiency in IoMT-based healthcare 5.0","authors":"Dipalee D․Rane Chaudhari ,&nbsp;Manisha S. Bhende ,&nbsp;Aadam Quraishi ,&nbsp;Azzah AlGhamdi ,&nbsp;Ismail Keshta ,&nbsp;Mukesh Soni ,&nbsp;Brajesh Kumar Singh ,&nbsp;Haewon Byeon ,&nbsp;Mohammad Shabaz","doi":"10.1016/j.slast.2025.100284","DOIUrl":"10.1016/j.slast.2025.100284","url":null,"abstract":"<div><div>In the rapidly evolving field of healthcare 5.0, the Internet of Medical Things (IoMT) is expected to be an enabler that allows smart medical devices to collaborate and communicate with healthcare networks to speed up procedures, enhance care, and improve disease management. However, one of the critical issues for these networks still remains the secure and energy-efficient transmission of sensitive patient data. Thus, a novel security framework is proposed in this work, in which a Public Key Infrastructure- Energy-Efficient Routing Protocol (PKI-EERP) with a Zebra Optimization Algorithm (ZOA) is incorporated in spiking neural networks. The method combines data security robustness of the spiking neural networks to detect anomalies and check for access control purposes, with the PKI encryption to provide safe encryption and key management. The ZOA optimizes energy consumption in WSNs, and as a result transmission energy is significantly reduced up to 35 % compared to other implementations, and the network lifetime is increased by about 30 % through effective load balancing. It enhances both the privacy and energy efficiency that are essential for the safe and reliable operation of IoMT systems in contemporary healthcare environments, thus improving patient outcomes as well as standards of operations.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100284"},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851489","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":"WITHDRAWN: An ultra-performance liquid chromatography-tandem mass spectrometry method for the determination of deoxycholic acid and application to a pharmacokinetic study in human plasma.","authors":"Wanlin Xi, Diyi Fu, Ni Wu, Fei Liu, Cuixia Zhang, Ruijie Wan, Zhen Wu, Rui Chen, Qian Zhao","doi":"10.1016/j.slast.2025.100289","DOIUrl":"10.1016/j.slast.2025.100289","url":null,"abstract":"<p><p>This article has been withdrawn at the request of the author(s) and/or editor due to an error in the publishing process. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies-and-standards/article-withdrawal.</p>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":" ","pages":"100289"},"PeriodicalIF":3.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057659","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":"Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients","authors":"Jiantu Lin ,&nbsp;Bo Wang ,&nbsp;Shaoguang Chen ,&nbsp;Fengling Cao ,&nbsp;Jingbin Zhang ,&nbsp;Zirong Lu","doi":"10.1016/j.slast.2025.100281","DOIUrl":"10.1016/j.slast.2025.100281","url":null,"abstract":"<div><h3>Background</h3><div>Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal disease-specific brain structural and functional changes and their potential genetic basis by analyzing the characteristics of brain magnetic resonance imaging (MRI) in SCH patients and related gene expression patterns. Methods: Differentially expressed genes (DEGs) between SCH and healthy control (NC) groups in the GSE48072 dataset were identified and functionally analyzed, and a protein-protein interaction (PPI) network was fabricated to screen for core genes (CGs). Meanwhile, MRI data from the COBRE, the Human Connectome Project (HCP), the 1000 Functional Connectomes Project (FCP), and the Consortium for Reliability and Reproducibility (CoRR) were utilized to explore differences in brain activity patterns between SCH patients and NC group using a 3D deep aggregation network (3D DANet) machine learning approach. A correlation analysis was performed between the identified CGs and MRI imaging characteristics. Results: 82 DEGs were collected from the GSE48072 dataset, primarily involved in cytotoxic granules, growth factor binding, and graft-versus-host disease pathways. The construction of the PPI network revealed <em>KLRD1, KLRF1, CD244, GZMH, GZMA, GZMB, PRF1</em>, and <em>SLAMF6</em> as CGs. SCH patients exhibited relatively enhanced activity patterns in the frontoparietal attention network (FAN) and default mode network (DMN) across four datasets, while showing a trend of weakening in most other networks. The 3D DANet demonstrated higher accuracy, specificity, and sensitivity in brain image classification. The correlation between enhancement of the DMN and genetic abnormalities was the strongest, followed by the enhancement of the frontal and parietal attention networks. In contrast, the correlation between the weakening of the sensory-motor network and occipital network and genetic abnormalities was relatively weak. The strongest correlation was observed between MRI characteristics and the <em>KLRD1</em> and <em>CD244</em> genes. Conclusion: The granzyme-mediated programmed cell death signaling pathway is related to pathogenesis of SCH, and <em>CD244</em> may serve as potential biological markers for diagnosing SCH. The correlation between enhancement of the DMN and genetic abnormalities was the strongest, followed by the enhancement of the frontal and parietal attention networks. In contrast, the correlation between weakening of the sensory-motor network and occipital network and genetic abnormalities was relatively weak. Additionally, the strongest correlation was observed between MRI features and the <em>KLRD1</em> and <em>CD244</em> genes. The use of the 3D DANet method has improved the detection precision of brain structural and functional changes in SCH patients, providing a new perspective for understanding the biological basis of the disease.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100281"},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755687","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}

{"title":"Precision through electric-field assisted automatable high throughput sample preparation of dried blood spots for neonatal abstinence syndrome detection","authors":"Ramisa Fariha,&nbsp;John Murphy,&nbsp;Nondi Walters,&nbsp;Emma Rothkopf,&nbsp;Oluwanifemi D. Okoh,&nbsp;Nabil M. Lawandy,&nbsp;Anubhav Tripathi","doi":"10.1016/j.slast.2025.100282","DOIUrl":"10.1016/j.slast.2025.100282","url":null,"abstract":"<div><div>In the United States, approximately 20 % of pregnant women disclose opioid misuse, contributing significantly to the widespread occurrence of Neonatal Abstinence Syndrome (NAS) in neonates exposed to opioids during gestation. Current NAS diagnosis heavily relies on clinical observation of symptoms, with the Finnegan Neonatal Abstinence Scoring System (FNASS) serving as the gold standard due to challenges associated with obtaining biological specimens from newborns. This methodological constraint poses difficulties in achieving accurate quantitative assessments and implementing timely therapeutic interventions. This study introduces a pioneering approach employing a cylindrical electrode-equipped device designed for the extraction of opioids from minute Dried Blood Spot (DBS) samples, thus optimizing the diagnostic pathway for NAS. The methodology integrates Liquid Chromatography tandem Mass Spectrometry (LC-MS/MS) for precise quantification of five distinct opioids. By demonstrating the efficacy of DBS microsamples as a robust quantitative diagnostic medium, this research highlights its potential to expedite NAS detection in infants. The innovative methodology promises superior diagnostic precision and accelerated processing times compared to current protocols, thereby addressing existing NAS diagnostic limitations and advancing maternal and infant healthcare practices.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100282"},"PeriodicalIF":2.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744480","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":"An out-of-hours cell culture autopilot proof of concept based on a modular architecture using the SiLA2 open standard","authors":"Patrick Courtney,&nbsp;Raphael Lieberherr,&nbsp;William Speed,&nbsp;Ricardo Gaviria,&nbsp;Yordan Alipiev,&nbsp;Miguel van der Heijden,&nbsp;Oliver Peter","doi":"10.1016/j.slast.2025.100279","DOIUrl":"10.1016/j.slast.2025.100279","url":null,"abstract":"<div><div>There has been much talk and substantial progress in automated and flexible smart lab concepts in biopharma R&amp;D. This is acknowledged to be important in enabling the acceleration of innovation and digitization of R&amp;D operations. However, many proposals stop short of full end-to-end automation – limiting out-of-hours operation, which is particularly important in tasks such as cell culture - or are locked to a particular vendor's offering in a dedicated system - which can limit the flexibility and shared use access so important in R&amp;D.</div><div>In this contribution we describe a proof-of-concept of a fully integrated automated adherent cell culture system based on a modular architecture that allows integration of the most recent developments on the market (cell imaging, collaborative cloud robotics, mobile robots) as well as reuse of existing legacy devices (incubators and refrigerators). This creates a “cell culture autopilot” for small-scale cell culture, with repetitive media exchange, confluency checking, and splitting steps which are typically labor-intensive and must take place at times outside the working day.</div><div>The system is built around the open lab communication standard SiLA2 and various other open-source resources to create three ways in which the SiLA2 standard can be leveraged. This choice of connectivity options provides freedom to integrate the most appropriate device while minimizing undesired vendor-lock in This paper provides sufficient details for the reader to access the resources to build on such a system for cell culture and other applications. We believe this to be the first report of a true vendor-agnostic system operating in a commercial environment.</div><div>This paper corresponds to the special issue on Robotics in Laboratory Automation as it describes robotics for labware transportation within a shared environment, and an automation framework supporting physical and logical interoperability.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100279"},"PeriodicalIF":2.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744474","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":"Building new model of intelligent mutual support for the elderly based on the decision support platform of the health management data center","authors":"Zewei Hu ,&nbsp;Jinxian Wang ,&nbsp;Haiwen Nie ,&nbsp;Yicheng Liu ,&nbsp;Xing Li","doi":"10.1016/j.slast.2025.100274","DOIUrl":"10.1016/j.slast.2025.100274","url":null,"abstract":"<div><div>At this stage, China is in the stage of population aging. With the continuous growth of the elderly population, the pension problem is also increasingly serious. The existing pension model has such problems as small service scope, low service level, and few types of services, which is difficult to provide good pension services for the elderly. Therefore, it is necessary to actively explore some new pension models to break this situation. Therefore, based on the analysis of the current situation of the elderly who lost their independence in the western rural areas, this paper put forward a mutual pension model. In order to optimize the model, this paper also constructed a health management data decision support platform based on Internet technology. In this paper, the platform was applied to the intelligent mutual-aid pension model to realize intelligent management of the mutual-aid pension model. At the same time, this paper also carried out further experimental research on the recommendation performance of elderly care services by combining the discriminant analysis method. From the experimental results, in terms of service recommendation time, the average test result of this method was 18.51 s, while the average test result of the traditional method was 25.18 s; In terms of recommended coverage, the average test result of this method was 88.63 %, and the average test result of the traditional method was 84.28 %; In terms of recommendation accuracy, the average test result of this method was 92.60 %, and the average test result of the traditional method was 88.56 %. To sum up, this method can effectively improve the performance of pension service recommendation, so as to provide more accurate pension services for the elderly.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100274"},"PeriodicalIF":2.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733005","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}

{"title":"Review on biphasic blood drying method for rapid pathogen detection in bloodstream infections","authors":"Jongwon Lim ,&nbsp;Katherine Koprowski ,&nbsp;Matthew Wester ,&nbsp;Enrique Valera ,&nbsp;Rashid Bashir","doi":"10.1016/j.slast.2025.100276","DOIUrl":"10.1016/j.slast.2025.100276","url":null,"abstract":"<div><div>Rapid and accurate detection of pathogenic microorganisms in blood is critical for diagnosing life-threatening conditions such as bloodstream infections (BSIs). Current methods for the detection and identification of bacteria from large volumes of blood (5 mL) involve culture steps followed by DNA extraction/purification/concentration and Polymerase Chain Reaction (PCR)-based nucleic acid amplification. DNA extraction and amplification directly from blood samples is hampered by the complexity of the blood matrix, resulting in time-consuming and labor-intensive processes. This review delves into recent advancements in molecular diagnostics based on blood drying, coined as ‘biphasic reaction’, and highlights this new technique that attempts to overcome the limitations of traditional sample preparation and amplification processes. The biphasic blood drying method, in combination with isothermal amplification methods such as loop-mediated isothermal amplification (LAMP) or recombinase polymerase amplification (RPA), has recently been shown to improve the sensitivity of detection of bacterial, viral, and fungal pathogens from ∼1 mL of whole blood, while minimizing DNA loss and avoiding the use of extraction/purification/concentration kits. Furthermore, the biphasic approach in combination with LAMP has been shown to be a culture-free method capable of detecting bacteria in clinical samples with a sensitivity of ∼1 CFU/mL in ∼2.5 h. This represents a significant reduction in detection and identification time compared to current clinical procedures based on bacterial culture prior to PCR amplification. This review paper aims to be a guide to identify new opportunities for future advancements and applications of the biphasic technology.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100276"},"PeriodicalIF":2.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702236","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}