SLAS Technology最新文献

{"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}

{"title":"AI-driven predictive modeling for disease prevention and early detection","authors":"Bikash Behera ,&nbsp;Azeem Irshad ,&nbsp;Imad Rida ,&nbsp;Mohammad Shabaz","doi":"10.1016/j.slast.2025.100263","DOIUrl":"10.1016/j.slast.2025.100263","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"Article 100263"},"PeriodicalIF":2.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544220","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":"Association between dietary fiber intake and gallstone disease in US adults: Data from NHANES 2017–2020","authors":"Tian Liu,&nbsp;Huimin Lv,&nbsp;Jia Li,&nbsp;Yusheng Chen,&nbsp;Mengnan Chen","doi":"10.1016/j.slast.2025.100259","DOIUrl":"10.1016/j.slast.2025.100259","url":null,"abstract":"<div><h3>Background</h3><div>Gallstone disease is a widespread condition affecting the gastrointestinal tract, and poor diet is believed to be one of the reasons for its occurrence. Previous studies of dietary fiber intake and gallstones have limitations. The study's goal is to investigate the relationship between dietary fiber intake and gallstone prevalence in US adults.</div></div><div><h3>Materials and methods</h3><div>Data from NHANES 2017 to March 2020 is used for the study. The association between fiber intake and gallstone prevalence was analyzed using multivariate logistic regression. To confirm the results’ robustness, we performed sensitivity analyses also.</div></div><div><h3>Results</h3><div>Among the 6,051 U.S. adults aged over 20 with complete information, the prevalence of gallstones was 10.8 % (651/6051). After adjusting for relevant covariates, an increase in fiber intake of 5 g/day was associated with an 11 % decrease in the prevalence of gallstones (fully adjusted OR = 0.89, 95 % CI: 0.83–0.95). Participants were divided into high (&gt;25 g/d) and low (≤25 g/d) fiber intake groups. Still significant negative association between dietary fiber intake and gallstones (fully adjusted OR = 0.66, 95 % CI: 0.49–0.91). Further dividing dietary fiber intake level into quintiles sustained this negative relationship, particularly showing the lowest gallstone occurrence in the highest dietary fiber group (OR = 0.63, 95 % CI: 0.44–0.91). While the stratified analyses indicated variability in the relationship between dietary fiber intake and the prevalence of gallstones, no interactive effects were identified in this association according to the interaction analysis.</div></div><div><h3>Conclusions</h3><div>This study confirms that dietary fiber intake is negatively associated with the prevalence of gallstones. Sufficient dietary fiber intake might protect from gallstones. In order to formulate dietary recommendations, it is important to carry out prospective studies to validate the observed associations.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"Article 100259"},"PeriodicalIF":2.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538171","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":"MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume","authors":"Dacong Zhao ,&nbsp;Jiang Guo ,&nbsp;Guanghua Lu ,&nbsp;Rui Jiang ,&nbsp;Chao Tian ,&nbsp;Xu Liang","doi":"10.1016/j.slast.2025.100260","DOIUrl":"10.1016/j.slast.2025.100260","url":null,"abstract":"<div><h3>Background</h3><div>The underlying mechanism of Parkinson's disease (PD) is associated with the neurodegeneration of the dopaminergic neurons, and the cerebellum plays a significant role together in non-motor and motor functions in PD progression. Morphological changes in the cerebellum can greatly impact patients' clinical symptoms, especially motor control symptoms, and may also help distinguish patients from healthy subjects. This study aimed to explore the potential of cerebellar gray matter volume, related to motor control function, as a neuroimaging biomarker to classify patients with PD and healthy controls (HC) by using voxel-based morphometric (VBM) measurements and support vector machine (SVM) methods based on independent component analysis (ICA).</div></div><div><h3>Methods</h3><div>Cerebellar gray matter volume was measured using VBM in patients with PD (n = 27) and HC (n = 16) from the Neurocon dataset. ICA analysis was performed on the gray matter volume of all subregions, resulting in 7 independent components. These independent components were then utilized for correlation analysis with clinical scales and trained as input features for the SVM model. PD patients (n = 20) and HC (n = 20) from the TaoWu dataset were used as test data to validate our SVM model.</div></div><div><h3>Results</h3><div>Among patients with PD, 3 out of the 7 independent components showed a significant correlation with clinical scales. The SVM model achieved an accuracy of 86 % in classifying PD patients and HC, with a sensitivity of 72.2 %, specificity of 88 %, and F1 Score of 76.5 %. The accuracy of the SVM model verification analysis using the TaoWu dataset was 70 %, with a sensitivity of 62.5 %, a specificity of 100 %, and the F1 Score was 76.9 %.</div></div><div><h3>Conclusions</h3><div>The results suggest that abnormal cerebellar gray matter volume, which is highly correlated with motor control function in Parkinson's patients, may serve as a valuable neuroimaging biomarker capable of distinguishing Parkinson's patients from healthy individuals. We observed that the combination of the ICA method and the SVM method produced an improved classification model. This model may function as an early warning tool that enables clinicians to conduct preliminary identification and intervention for patients with PD.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"Article 100260"},"PeriodicalIF":2.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538172","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":"LDM: A web application for automated management and visualization of laboratory screening data","authors":"David Meyer ,&nbsp;Anastasia Escher ,&nbsp;Eva Riegler ,&nbsp;David Keller ,&nbsp;Michael Prummer ,&nbsp;Stephanie Huber ,&nbsp;Tijmen Booij","doi":"10.1016/j.slast.2025.100258","DOIUrl":"10.1016/j.slast.2025.100258","url":null,"abstract":"<div><div>High-throughput screening (HTS) is essential in preclinical research to identify new drug candidates for specific diseases. This process typically generates large amounts of data that require effective storage, management, and analysis. Traditional methods for handling HTS data involve several standalone solutions, which can present challenges regarding data accessibility and reproducibility. We introduce Lab Data Management (LDM), an open-source web application developed to automate the management and visualization of HTS data. LDM provides a highly customizable data management system with an intuitive user interface for handling output data from various laboratory instruments, such as plate readers, microscopes, liquid handlers, and barcode readers. The app allows for results visualization and calculation of quality control metrics. An integrated Jupyter notebook can be used to retrieve the stored data and proceed with a more detailed analysis.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"31 ","pages":"Article 100258"},"PeriodicalIF":2.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484654","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}