SLAS Technology最新文献

{"title":"Metabolite fingerprinting by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry","authors":"Alena N. Joignant,&nbsp;Fan Pu,&nbsp;Shaun M. McLoughlin,&nbsp;James W. Sawicki,&nbsp;Andrew J. Radosevich,&nbsp;Renze Ma,&nbsp;Jon D. Williams,&nbsp;Sujatha M. Gopalakrishnan,&nbsp;Nathaniel L. Elsen","doi":"10.1016/j.slast.2025.100272","DOIUrl":"10.1016/j.slast.2025.100272","url":null,"abstract":"<div><div>The adoption of mass spectrometry for high-throughput screening in drug discovery has become increasingly prevalent and has enabled label-free screening against diverse targets. Cellular assays for phenotypic screening, however, are primarily conducted by microscopy as there remain many challenges associated with conducting phenotypic screens via ultra-high throughput mass spectrometry.</div><div>Following a simple on-plate extraction, infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) was employed to directly sample the cell lysate at a speed of one sample per second with high mass resolution. A549 cells were treated with compounds identified as hits in literature, including a recently reported glutaminase cellular screen. Among the test compounds were confirmed glutaminase inhibitors, proposed nuisance compounds, and cell-active but enzyme-inactive compounds. Filtered data were further processed in R for dimensionality reduction and unsupervised clustering. The general nature of dimensionality reduction enables the immediate use of this method in applications other than glutaminase inhibition.</div><div>Though we observed that all compounds affected the intracellular conversion of glutamine to glutamate, there were clear metabolic differences between the biochemically active compounds and the off-target false hits. Moreover, two nuisance compounds were observed to cluster separately from the confirmed glutaminase inhibitors in the observed metabolite fingerprints.</div><div>This proof-of-concept work establishes a workflow that enables high-throughput mass spectrometry-based phenotypic screening. The methods proposed herein, at the throughput enabled by IR-MALDESI, could offer a new avenue for the discovery of novel drugs.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100272"},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634981","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":"Transformative effects of fluorescence imaging technologies on current vascular surgical practices: An updated review","authors":"Tao Fang,&nbsp;Jianxin Dong,&nbsp;Zhilei Xie","doi":"10.1016/j.slast.2025.100270","DOIUrl":"10.1016/j.slast.2025.100270","url":null,"abstract":"<div><div>Fluorescence imaging technologies have revolutionized vascular surgery by enabling real-time visualization of vascular anatomy, blood circulation, and tissue perfusion, thus improving intraoperative decision-making. This review provides a comprehensive analysis of key fluorescence modalities, including Fluorescence-Guided Surgery (FGS), Near-Infrared (NIR) fluorescence imaging, and Indocyanine Green (ICG) angiography, highlighting their roles in optimizing tissue perfusion assessment, vessel patency evaluation, and identifying anatomical variations. Unlike existing literature, this review addresses critical gaps in current practices by comparing these technologies and exploring their applications across a range of vascular procedures such as peripheral vascular surgery, coronary artery bypass grafting, and oncological operations. The review further delves into the potential future directions for fluorescence imaging in vascular surgery, emphasizing emerging technologies, challenges in clinical implementation, and how these advancements can enhance surgical precision, patient outcomes, and intraoperative guidance. By synthesizing the latest developments, this review offers valuable insights into the evolving role of fluorescence imaging in vascular surgery and its potential to transform surgical practices.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100270"},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630981","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":"Computer interactive intelligent music therapy assisted student mental health information processing in life sciences","authors":"Fang Yang","doi":"10.1016/j.slast.2025.100269","DOIUrl":"10.1016/j.slast.2025.100269","url":null,"abstract":"<div><div>In order to explore music therapy methods suitable for different types of students, this paper analyzes students' daily behavioral habits, studies the motivations and sources of their mental health activities, and constructs a student mental health data processing system based on network application development tools. Using .net application development tools as the main tool, a student mental health data information processing system is simulated and designed. Based on its own understanding of usage, this paper analyzes several commonly used system function modules and supplements the IoT smart medical health monitoring architecture. The results show that music therapy has a certain therapeutic effect on students' mental health. The high school students surveyed in this questionnaire meet the scope of application of music therapy. Combined with the various advantages of modern computer technology and IoT sensors, the mental health status of students is analyzed to restore their mental health level.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100269"},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630977","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":"Identifcation of the FGF family as therapeutic targets and prognostic biomarkers in the microenvironment of head and neck squamous cell carcinoma","authors":"Li Zhang ,&nbsp;Yingchun Gao ,&nbsp;Yumei Tian ,&nbsp;Jian Wei ,&nbsp;Yingjiao Xu ,&nbsp;Xuan Zhang ,&nbsp;Minhai Nie ,&nbsp;Xuqian Liu","doi":"10.1016/j.slast.2025.100271","DOIUrl":"10.1016/j.slast.2025.100271","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Almost 90 % of head and neck malignancies are malignant squamous cell cancers, making it the sixth most common malignancy in the developing countries, with an overall five-year overall survival rate about 40 %-50 %. Early diagnosis and treatment can bring a better prognosis. Fibroblast growth factor (FGF) is an important polypeptide &lt;em&gt;in vivo&lt;/em&gt;. Studies have found that FGF signal has carcinogenic potential and participates in a variety of carcinogenic behaviors. Some experiments have proved that FGF signal has the function of tumor inhibition in some cases, and the role of FGF signalling in tissue repair and homeostasis suggest a role for FGF in targeted therapy and prognosis. However, its manifestation and predictive role in HNSC have not been clearly defined.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Genome-wide expression analysis of Oncomine evaluated the evaluation of FGF family expression in HNSC. Expression analysis and HNSC data set were used to obtain FGF family expression data and T statistic was applied for analysis. The differential mRNA expression levels in tumor versus normal tissues, as well as the correlation with pathological staging and prognosis, were examined using the GEPIA single-gene analysis tool for the FGF family.FGF family altered CO expression and network modules were obtained from cBioportal and analyzed in 520 HNSC samples.Pro-protein interaction (PPI) flow network is performed on the differentially ordered FGF clusters using STRING, Gene Operating System (GO) domain domain enrichment as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis are performed on the FGF cluster and its neighbouring genes using DAVID6.8, key transcriptional factors (TF) of FGF family was analyzed by TRRUST, correlation between FGF family level and autoimmune cell migration was evaluated by TIMER, and biological analysis of FGF family kinase target enrichment was performed using LinkInterpreter.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Only the expression of FGF6 in HNSC was down-regulated in all FGF family(FC=2),Transcriptional level of FGF1, FGF2, FGF5, FGF7–14, FGF17–19, FGF21 and FGF22 was upregulated in HNSC .In terms of the relative level of FGF family in HNSC, the greatest amount of FGF11. In different pathological stages of HNSC, the expression of FGF was meaningless (&lt;em&gt;P&lt;/em&gt;&gt;0.05), and FGF3–6, FGF8–10, FGF14, FGF16, FGF17, FGF1921, FGF23 showed no significant difference in different HNSC stages. Low expression of FGF5 and high expression of FGF22 had low overall survival(OS) rate of HNSC(&lt;em&gt;P&lt;/em&gt; =0.012, &lt;em&gt;P&lt;/em&gt; =0.0015). In addition, enrichment analysis of FGF family in HNSC showed that it was highly abundant in PI3K-Akt signaling pathway, MAPK and rasper pathway. Our data showed that ATF4, STAT, RELA, NFKB1 are key transcription target of the FGF family, NLK, LOCK1, LYN, ZAP70, MAP2K3, RPS6KA4, AURKB, ATR, ROCK1, MYLK2, CAMK2A, EGFR, MAPK3, MAP3K8, SYK, LCK, HCK, PK","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100271"},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630979","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":"Digitalizing the design-make-test-analyze workflow in drug discovery with an electronic inventory platform","authors":"Ting Qin,&nbsp;Aparna Chandrasekaran,&nbsp;Jason Shiers,&nbsp;Matthew Crittall,&nbsp;Ciaran O’Reilly,&nbsp;Colin Sambrook Smith","doi":"10.1016/j.slast.2025.100262","DOIUrl":"10.1016/j.slast.2025.100262","url":null,"abstract":"<div><div>Drug discovery is a collaborative endeavor that often involves scientists from various disciplines and global collaborators. Efficient real-time sharing and updating of design-make-test-analyze (DMTA) information remains a challenge in drug discovery, hindering timely decision-making and project advancement. We propose a novel approach utilizing existing electronic inventory systems as DMTA workflow tracking platforms. Our approach at Sygnature Discovery leverages the inherent flexibility of these systems, allowing us to tailor stages and compound information to individual project needs, resulting in significant cost savings compared with building an in-house solution or purchasing a commercial solution. Given the wide adoption of electronic inventory platforms in drug discovery, our strategy holds immense potential for easy adoption and broad application across the industry.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100262"},"PeriodicalIF":2.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617787","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":"Tripartite game analysis of online public opinion evolution in major epidemics in the context of life sciences","authors":"Jinghua Zhao ,&nbsp;Shaoyun Cui ,&nbsp;Zhuang Wang","doi":"10.1016/j.slast.2025.100266","DOIUrl":"10.1016/j.slast.2025.100266","url":null,"abstract":"<div><div>In the face of a sudden major epidemic, people's panic may likely lead to the disruption of the public opinion ecosystem and the disorder of public opinion order. Therefore, clarifying the key main bodies and mechanisms in governing online public opinion is of crucial significance for effectively managing and guiding it. Firstly, based on the sentiment analysis of opinion leaders, an evolutionary game model involving the government, netizens, and opinion leaders was constructed. It analyzed the gaming relationships among relevant stakeholders in the process of online opinion dissemination. Then, a simulation experiment is carried out to analyze the evolution of each stakeholder's strategy choice, and the effectiveness of the simulation scenario is verified by NLP technology. The research results show that when dealing with online public opinion during a major epidemic, the government should choose an appropriate time to intervene and reduce the cost of interfering with public opinion. The change in punishment intensity by the government has a greater impact on opinion leaders than on netizens. Additionally, when the government guides opinion leaders, increasing the degree of reward for opinion leaders is more effective than increasing the intensity of punishment.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100266"},"PeriodicalIF":2.5,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607032","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":"Advanced NLP-driven predictive modeling for tailored treatment strategies in gastrointestinal cancer","authors":"Zhaojun Ye ,&nbsp;Haibin Ban ,&nbsp;Cuihua Li ,&nbsp;Sufang Chen","doi":"10.1016/j.slast.2025.100264","DOIUrl":"10.1016/j.slast.2025.100264","url":null,"abstract":"<div><div>Gastrointestinal cancer represents a significant health burden, necessitating innovative approaches for personalized treatment. This study aims to develop an advanced natural language processing (NLP)-driven predictive modeling framework for tailored treatment strategies in gastrointestinal cancer, leveraging the capabilities of deep learning. The Resilient Adam Algorithm-driven Versatile Long-Short Term Memory (RAA-VLSTM) model is proposed to analyze comprehensive clinical data. The dataset comprises extensive electronic health records (EHRs) from multiple healthcare centers, focusing on patient demographics, clinical history, treatment outcomes, and genetic factors. Data preprocessing employs techniques such as tokenization, normalization, and stop-word removal to ensure effective representation of textual data. For feature extraction, state-of-the-art word embeddings are utilized to enhance model performance. The proposed framework outlines a comprehensive process: data collection from EHRs, preprocessing to prepare the data for analysis, and employing NLP techniques to extract meaningful features. The RAA optimization algorithm significantly improves training efficiency by adapting learning rates for each parameter, addressing common issues in gradient descent. This optimization enhances feature learning from sequential clinical data, enabling accurate predictions of treatment responses and outcomes. The overall performance in terms of F1-score (89.4%), accuracy (92.5%), recall (88.7%), and precision (90.1%). Preliminary results demonstrate the model's strong predictive capabilities, achieving high accuracy in predicting treatment outcomes, thereby suggesting its potential to improve individualized care. In conclusion, this study establishes a robust foundation for employing advanced NLP and machine learning techniques in the management of gastrointestinal cancer, paving the way for future research and clinical applications.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100264"},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588155","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":"Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data","authors":"Atta Ur Rahman ,&nbsp;Sania Ali ,&nbsp;Bibi Saqia ,&nbsp;Zahid Halim ,&nbsp;M.A. Al-Khasawneh ,&nbsp;Dina Abdulaziz AlHammadi ,&nbsp;Muhammad Zubair Khan ,&nbsp;Inam Ullah ,&nbsp;Meshal Alharbi","doi":"10.1016/j.slast.2025.100265","DOIUrl":"10.1016/j.slast.2025.100265","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a severe neurological illness that demolishes memory and brain functioning. This disease affects an individual's capacity to work, think, and behave. The proportion of individuals suffering from AD is rapidly increasing. It flatters a leading cause of disability and impacts millions of people worldwide. Early detection reduces disease expansion, provides more effective therapies, and leads to better results. However, predicting AD at an early stage is complex since its clinical symptoms match with normal aging, mild cognitive impairment (MCI), and neurodegenerative disorders. Prior studies indicate that early diagnosis is improved by the utilization of magnetic resonance imaging (MRI). However, MRI data is scarce, noisy, and extremely diverse among scanners and patient populations. The 2D CNNs analyze 3D data slices separately, resulting in a loss of inter-slice information and contextual coherence required to detect subtle and diffuse brain alterations. This study offered a novel 3Dimensional-Convolutional Neural Network (3D-CNN) and intelligent preprocessing pipeline for AD prediction. This work uses an intelligent frame selection and 3D dilated convolutions mechanism to recognize the most informative slices associated with AD disease. This enabled the model to capture subtle and diffuse structural changes across the brain visible in MRI scans. The proposed model examined brain structures by recognizing small volumetric changes associated with AD and acquiring spatial hierarchies within MRI data. After conducting various experiments, we observed that the proposed 3D-CNNs are highly proficient in capturing early brain changes. To validate the model's performance, a benchmark dataset called AD Neuroimaging Initiative (ADNI) is used and achieves a maximum accuracy of 92.89 %, outperforming state-of-the-art approaches.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100265"},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588158","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":"NLP-driven integration of electrophysiology and traditional Chinese medicine for enhanced diagnostics and management of postpartum pain","authors":"Yaning Wang","doi":"10.1016/j.slast.2025.100267","DOIUrl":"10.1016/j.slast.2025.100267","url":null,"abstract":"<div><div>Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, physical recovery, and individual psychological states. The complex interactions between the variables can make it difficult for traditional diagnostic techniques to fully capture, creating inadequacies and inefficient management techniques. The aims to develop a comprehensive diagnostic and management framework for postpartum pain by integrating Natural Language Processing (NLP), electrophysiological data, and Traditional Chinese Medicine (TCM) principles. The seeks to enhance the accuracy of postpartum pain diagnosis, uncover meaningful correlations between TCM diagnoses and physiological markers, and optimize personalized treatment strategies. The focuses on analyzing textual data from patient-reported symptoms, medical records, and TCM diagnosis notes. Data pre-processing involves text cleaning and tokenization, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) to capture meaningful patterns. For diagnostics and management, a Refined Coyote Optimized Deep Recurrent Neural Network (RCO-DRNN) is employed to analyze and predict pain profiles, combining insights from TCM diagnoses with physiological markers. The results highlight the effectiveness of RCO-DRNN in accurately diagnosing pain types and offering personalized and holistic management strategies. This approach represents a significant advancement in integrating data-driven methodologies with traditional medical practices, providing a more comprehensive framework for postpartum pain management. The RCO-DRNN continuously beats the other models after thorough evaluation using metrics like MSE, MAE, and R², obtaining the lowest MSE (0.005), the smallest MAE (0.04), and the highest R² (0.98).</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100267"},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588170","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":"Image classification-driven speech disorder detection using deep learning technique","authors":"Nasser Ali Aljarallah ,&nbsp;Ashit Kumar Dutta ,&nbsp;Abdul Rahaman Wahab Sait","doi":"10.1016/j.slast.2025.100261","DOIUrl":"10.1016/j.slast.2025.100261","url":null,"abstract":"<div><div>Speech disorders affect an individual's ability to generate sounds or utilize the voice appropriately. Neurological, developmental, physical, and trauma may cause speech disorders. Speech impairments influence communication, social interaction, education, and quality of life. Successful intervention entails early and precise diagnosis to allow for prompt treatment of these conditions. However, clinical examinations by speech-language pathologists are time-consuming, subjective, and demand an automated speech disorder detection (SDD) model. Mel-spectrogram images present a visual representation of multiple speech disorders. By classifying Mel-Spectrogram, various speech disorders can be identified. In this study, the authors proposed an image classification-based automated SDD model to classify Mel-Spectrograms to identify multiple speech disorders. Initially, Wavelet Transform (WT) hybridization technique was employed to generate Mel-Spectrogram using the voice samples. A feature extraction approach was developed using an enhanced LEVIT transformer. Finally, the extracted features were classified using an ensemble learning (EL) approach, containing CatBoost and XGBoost as base learners, and Extremely Randomized Tree as a meta learner. To reduce the computational resources, the authors used quantization-aware training (QAT). They employed Shapley Additive Explanations (SHAP) values to offer model interpretability. The proposed model was generalized using Voice ICar fEDerico II (VOICED) and LANNA datasets. The exceptional accuracy of 99.1 with limited parameters of 8.2 million demonstrated the significance of the proposed approach. The proposed model enhances speech disorder classification and offers novel prospects for building accessible, accurate, and efficient diagnostic tools. Researchers may integrate multimodal data to increase the model's use across languages and dialects, refining the proposed model for real-time clinical and telehealth deployment.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100261"},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588160","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}