SLAS Technology最新文献

{"title":"Editorial for the special issue: “Natural language processing and large language models in life sciences”","authors":"Akshi Kumar , MPS Bhatia","doi":"10.1016/j.slast.2025.100296","DOIUrl":"10.1016/j.slast.2025.100296","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100296"},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053489","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":"SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders","authors":"Samra Siddiqui ,&nbsp;Junaid A. Khan ,&nbsp;Tallha Akram ,&nbsp;Meshal Alharbi ,&nbsp;Jaehyuk Cha ,&nbsp;Dina A. AlHammadi","doi":"10.1016/j.slast.2025.100304","DOIUrl":"10.1016/j.slast.2025.100304","url":null,"abstract":"<div><div>With the intent of assisting gastroenterologists from all over the world, the proposed work aims to eliminate the effort required to achieve accurate diagnoses. Statistically, gastrointestinal diseases often result in fatal disorders, contributing to a significant number of fatalities. The upper gastrointestinal tract (GIT) includes the stomach, esophagus, and duodenum, while the lower one comprises a section of the small intestine, namely the ileum, as well as the large intestine, including the colon. The challenges associated with GIT tract issues are apparently complex. Therefore, multiple challenges exist regarding CAD (Computer-aided diagnosis) and endoscopy, including a lack of annotated images, a dark background, poor contrast, and an irregular pattern. The objective of this research is to develop a robust deep network, called SNet, that offers a solution to complex classification problems. Firstly, the endoscopic images undergo preprocessing before being subjected to feature extraction. This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. This facilitates cross-dataset system evaluation, resulting in an efficient system for an unseen image diagnosis. To avoid the problem of “<em>curse of dimensionality</em>”, the most discriminant feature information is selected based on proposed minimum redundancy maximum relevance (MRMR) algorithm. The proposed architecture has been evaluated using a range of performance metrics, such as accuracy, sensitivity, specificity, and Area under curve (AUC). With classification accuracy as the main metric, the achieved accuracy is 98.45% on the Kvasir v1, and 97.83% on the Kvasir v2 datasets.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100304"},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200837","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":"Therapeutic potential of PDA@MT in mitigating oxidative stress in obstructive sleep apnea based on biomedical images","authors":"Zeming Zhang ,&nbsp;Wenhui Wang ,&nbsp;Li Wei ,&nbsp;Li Han ,&nbsp;Zaiyan Wang ,&nbsp;Hao Chen","doi":"10.1016/j.slast.2025.100309","DOIUrl":"10.1016/j.slast.2025.100309","url":null,"abstract":"<div><div>Obstructive sleep apnea (OSA) is a common sleep disorder that affects breathing and is accompanied by increased oxidative stress, leading to multiple health problems. This study evaluated the therapeutic efficacy of PDA@MT on oxidative stress and OSA model, providing new ideas for the treatment of OSA. Firstly, PDA@MT nanoparticles were synthesized and their embedding efficiency and drug loading capacity were evaluated. The physicochemical properties of the particles were analyzed by means of particle size and ζ potential test, transmission electron microscope (TEM) imaging and sample stability test. Subsequently, cell viability assay, cell uptake assay and antioxidant assay were performed to evaluate the therapeutic effect of nanoparticles <em>in vitro</em>. OSA rat models were established, and histological analysis, immunofluorescence detection and reactive oxygen species (ROS) detection were performed to evaluate the efficacy of PDA@MT <em>in vivo</em>, and finally statistical analysis was performed. PDA@MT nanoparticles showed good cytocompatibility and significant antioxidant capacity, and could effectively reduce ROS levels <em>in vitro</em>. Multiple validated evaluations have shown that PDA@MT significantly improves respiratory status in model rats in OSA models, showing promising therapeutic potential. Biosafety evaluation results showed that PDA@MT is safe for use <em>in vivo</em>. Medical thermal images play a key role in evaluating the therapeutic effect of the nanoparticles and provide an important basis for further research and development.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100309"},"PeriodicalIF":2.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192528","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":"Enhanced high-throughput embryonic photomotor response assays in zebrafish using a multi-camera array microscope","authors":"Julia Jamison ,&nbsp;Thomas Jedidiah Jenks Doman ,&nbsp;Zoe Antenucci ,&nbsp;John Efromson ,&nbsp;Connor Johnson ,&nbsp;Michael T. Simonich ,&nbsp;Mark Harfouche ,&nbsp;Lisa Truong ,&nbsp;Robyn L. Tanguay","doi":"10.1016/j.slast.2025.100310","DOIUrl":"10.1016/j.slast.2025.100310","url":null,"abstract":"<div><div>Developing automated, high-throughput screening platforms for early-stage drug development and toxicology assessment requires robust model systems that can predict human responses. Zebrafish embryos have emerged as an ideal vertebrate model for this purpose due to their rapid development, genetic homology to humans, and amenability to high-throughput screening. However, existing commercial imaging platforms face significant technical limitations in capturing early developmental behaviors. We present the validation of the Kestrel™, a novel high-throughput imaging platform featuring a 24-camera array that enables simultaneous acquisition of high-resolution video data across 96-well plates. This system overcomes key technical limitations through its unique optical design and automated image processing pipeline. Unlike current commercial systems, which require specialized setup and can only image subsets of wells, the Kestrel provides comprehensive plate imaging at 9.6 µm resolution with 10+ Hz video capture across an 8 × 12 cm field of view. We validated the system using zebrafish embryonic photomotor response (EPR) assays, demonstrating its ability to track behavioral responses in chorionated and dechorionated embryos without workflow modifications. The system successfully detected concentration-dependent responses to ethanol, methanol, and bisphenol A across different plate formats and well volumes. Notably, the Kestrel enabled equivalent detection of behavioral responses in chorionated and dechorionated embryos, eliminating the need for the dechorionation process while maintaining assay sensitivity. This technological advancement provides a robust platform for high-throughput chemical screening in drug discovery and toxicology applications, offering significant improvements in throughput, sensitivity, and reproducibility with a highly relevant vertebrate model.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100310"},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188545","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":"ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach","authors":"Mustafa Lateef Fadhil Jumaili ,&nbsp;Emrullah Sonuç","doi":"10.1016/j.slast.2025.100298","DOIUrl":"10.1016/j.slast.2025.100298","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. This study proposes an ensemble learning framework that combines five deep learning architectures (VGG16, VGG19, ResNet50, InceptionV3, and EfficientNetB7) to improve the accuracy of AD diagnosis. We use a comprehensive dataset of 3,714 MRI brain scans collected from specialized clinics in Iraq, categorized into three classes: NonDemented (834 images), MildDemented (1,824 images), and VeryDemented (1,056 images). The proposed voting ensemble model achieves a diagnostic accuracy of 99.32% on our dataset. The effectiveness of the model is further validated on two external datasets: OASIS (achieving 86.6% accuracy) and ADNI (achieving 99.5% accuracy), demonstrating competitive performance compared to existing approaches. Moreover, the proposed model exhibits high precision and recall across all stages of dementia, providing a reliable and robust tool for early AD detection. This study highlights the effectiveness of ensemble learning in AD diagnosis and shows promise for clinical applications.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100298"},"PeriodicalIF":2.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084494","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":"Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury","authors":"Hui Zhao ,&nbsp;Na Li ,&nbsp;Jianmei Zhang","doi":"10.1016/j.slast.2025.100299","DOIUrl":"10.1016/j.slast.2025.100299","url":null,"abstract":"<div><div>According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the rate of hospital visits and hospitalizations. In this study, patients in a local hospital of third-grade class-A hospital were used as cases. Artificial intelligence was used to conduct continuous nursing intervention for the patients who were discharged from the stroke by using the WeChat platform, regular follow-up and home care. Afterwards, the collected data were given a post-processing, independent-samples <em>t</em>-test for two groups. After 3 months of extended care, the BI (Barthel Index) score of the intervention group has increased by 23.87 points, and the depression self-rating scale score has decreased by 9.12 points. Compared with the control group, the patients' self-care ability, depression state, compliance with health guidance and laboratory indicators were also better than those in the control group, and the differences were statistically significant (<em>P</em> &lt; 0.05). Compared with the control group, the trend of increasing the scores of each index was more significant in the intervention group.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100299"},"PeriodicalIF":2.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948268","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 scalable deep attention mechanism of instance segmentation for the investigation of chromosome","authors":"Neelam Umbreen ,&nbsp;Sara Ali ,&nbsp;Hasan Sajid ,&nbsp;Yasar Ayaz ,&nbsp;Shrooq Alsenan ,&nbsp;Yunyoung Nam ,&nbsp;So Yeon Kim ,&nbsp;Muhammad Baber Sial","doi":"10.1016/j.slast.2025.100306","DOIUrl":"10.1016/j.slast.2025.100306","url":null,"abstract":"<div><div>Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imaging.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100306"},"PeriodicalIF":2.5,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999913","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":"Design and optimization of a fluid flow splitting device for low-flow applications","authors":"Alexis K. Yates ,&nbsp;Heather N. Murray ,&nbsp;Ethan S. Lippmann","doi":"10.1016/j.slast.2025.100305","DOIUrl":"10.1016/j.slast.2025.100305","url":null,"abstract":"<div><div>Microfluidic devices are defined by the application of fluid flow to micron-scale features. Inherent to most experiments involving microfluidic devices is the need to precisely and reproducibly control fluid flow at the microliter scale, often through multiple inlet ports on a single device. While the number of fluid channels per device varies, perfusing multiple inputs requires either the use of multiple flow controllers (often syringe or peristaltic pumps) or the ability to evenly divide fluid across outlets. Towards the latter approach, while a handful of commercial systems exist for splitting fluid flow, these set-ups require significant financial investment, multiple flow control and sensing components, and restrict the user to a predetermined perfusion control system. Simple in-line splitting devices, such a manifolds or T junctions, fail to achieve flow splitting at low flow rates often used in microfluidic systems. To increase capabilities for flow-controlled experiments, we performed experimental analyses of the physical considerations governing even flow splitting under low flow, leading to the design of a microdevice (µ-Split) that can be directly inserted into existing microfluidic set-ups. The µ-Split allows for reproducible, even flow splitting from 10 uL/min to &gt; 2.5 mL/min. By testing multiple device geometries in combination with multiphysics simulations, we identified the design and fabrication features underlying the splitting precision achieved by the µ-Split. Overall, this work provides a useful tool to simplify microfluidic experiments that require evenly divided flow streams, while minimizing the overall device footprint.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100305"},"PeriodicalIF":2.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943655","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":"CirnetamorNet: An ultrasonic temperature measurement network for microwave hyperthermia based on deep learning","authors":"Fanbing Cui ,&nbsp;Yongxing Du ,&nbsp;Ling Qin ,&nbsp;Baoshan Li ,&nbsp;Chenlu Li ,&nbsp;Xianwei Meng","doi":"10.1016/j.slast.2025.100297","DOIUrl":"10.1016/j.slast.2025.100297","url":null,"abstract":"<div><h3>Objective</h3><div>Microwave thermotherapy is a promising approach for cancer treatment, but accurate noninvasive temperature monitoring remains challenging. This study aims to achieve accurate temperature prediction during microwave thermotherapy by efficiently integrating multi-feature data, thereby improving the accuracy and reliability of noninvasive thermometry techniques.</div></div><div><h3>Methods</h3><div>We proposed an enhanced recurrent neural network architecture, namely CirnetamorNet. The experimental data acquisition system is developed by using the material that simulates the characteristics of human tissue to construct the body model. Ultrasonic image data at different temperatures were collected, and 5 parameters with high temperature correlation were extracted from gray scale covariance matrix and Homodyned-K distribution. Using multi-feature data as input and temperature prediction as output, the CirnetamorNet model is constructed by multi-head attention mechanism. Model performance was evaluated by analyzing training losses, predicting mean square error and accuracy, and ablation experiments were performed to evaluate the contribution of each module.</div></div><div><h3>Results</h3><div>Compared with common models, the CirnetamorNet model performs well, with training losses as low as 1.4589 and mean square error of only 0.1856. Its temperature prediction accuracy of 0.3 °C exceeds that of many advanced models. Ablation experiments show that the removal of any key module of the model will lead to performance degradation, which proves that the collaboration of all modules is significant for improving the performance of the model.</div></div><div><h3>Conclusion</h3><div>The proposed CirnetamorNet model exhibits exceptional performance in noninvasive thermometry for microwave thermotherapy. It offers a novel approach to multi-feature data fusion in the medical field and holds significant practical application value.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100297"},"PeriodicalIF":2.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935821","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":"Changes of peripheral blood lymphocyte population in patients with head and neck squamous cell carcinoma undergoing concurrent chemotherapy","authors":"Guodong Man,&nbsp;Jianli Wang","doi":"10.1016/j.slast.2025.100303","DOIUrl":"10.1016/j.slast.2025.100303","url":null,"abstract":"<div><div>Although medical technology has improved the quality of life of HNSCC patients, their survival rate has not increased significantly, and their life and health continue to be threatened. With the help of modern medical technology, medical institutions can confirm the diagnosis of HNSCC patients in a timely manner, so as to take appropriate surgical treatment plans. However, surgery is difficult for patients with recurrent or metastatic HNSCC, and concurrent chemotherapy alone is not effective for HNSCC patients. Therefore, in this paper, the chemotherapy and immunotherapy of HNSCC patients were studied, and the following conclusions were drawn by detecting the peripheral blood lymphocytes of HNSCC patients. The NK cell (natural killer cell) index of healthy people was 6.64% higher than that of HNSCC patients. The CD4+/CD8+ ratio (CD4+ is inductive T cells and helper T cells, CD8+ is suppressor T cells and cytotoxic T cells) in HNSCC patients is in a downward trend during concurrent chemotherapy. Concurrent chemotherapy not only kills HNSCC cells, but also inhibits normal immune cells. The detection of peripheral blood lymphocytes of HNSCC patients has reference value for judging the immune function and curative effect of patients.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"32 ","pages":"Article 100303"},"PeriodicalIF":2.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996424","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}