SLAS Technology最新文献

{"title":"PRIMDEx: Prototyping rapid innovation of microfluidics devices for experimentation","authors":"Patrick B. Kruk,&nbsp;Jose A. Wippold","doi":"10.1016/j.slast.2025.100326","DOIUrl":"10.1016/j.slast.2025.100326","url":null,"abstract":"<div><div>Microfluidics has quickly become an established technology in the transformative fields that make up broader biotechnology. Microfluidics has applications spanning the entire breadth of the discipline, from chemical synthesis, environmental monitoring, biomedical diagnostics, to lab- and organ-on-a-chip. New demands for novel microfluidic chips have outpaced their contemporary manufacturing methods, thus limiting their scientific applicability. This predicament is particularly accentuated for R&amp;D and research laboratories where resources (time &amp; money) are limited. Manufacturing a microfluidic device (MFD) for mass production typically involves outsourcing a design for CNC machining of the negative mold, followed by Injection Molding (IM) the positive-feature consumables or MFDs. This process can cost ∼$1000-$5000 depending on complexity and can require a 1–2-week lead time. In comparison, 3D Printing (3DP) is limited by long print times, limited resolutions, and higher per unit material cost. This leaves traditional commercial fabrication processes impractical to implement into a typical biotech experimental procedure, where they could be subjected to constantly changing experimental demands and redesigns. Each redesign and subsequent round of fabrication demands greater cost and time investments. Here, we present PRIMDEx, or <u>P</u>rototyping <u>R</u>apid <u>I</u>nnovation of <u>M</u>icrofluidic <u>D</u>evices for <u>Ex</u>perimentation, to address this by integrating both 3DP and rapid IM into a single manufacturing workflow. PRIMDEx implemented the advantages of both manufacturing methods to establish an approach more conducive to the design-test-build cycles of biotech and biomedical research regimes.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100326"},"PeriodicalIF":2.5,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576957","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":"Data acquisition of exercise and fitness pressure measurement based on artificial intelligence technology","authors":"Ru Liu ,&nbsp;Wenxi Shen","doi":"10.1016/j.slast.2025.100328","DOIUrl":"10.1016/j.slast.2025.100328","url":null,"abstract":"<div><div>This project aims to improve the accuracy of fitness and physical pressure ratings, focusing on basketball, by integrating artificial intelligence (AI) into data collection and training. Athletes and fitness fanatics can benefit greatly from the data collected using complex AI algorithms to determine stress levels. This study employs the Intelligent Physiological Monitoring Framework for Exercise and Fitness Pressure Measurement (IPM-EFPM) to perform automated stress tests that employ AI to enhance the precision of exercise and fitness pressure measurements. Basketball training programs can benefit from this framework's utilization of state-of-the-art technology, meticulous monitoring of exercise-induced stress, and continuous validation and improvement. The IPM-EFPM system gathers data from wearable sensors, uses real-time location systems, and employs artificial intelligence's Long Short-Term Memory (LSTM) and machine learning algorithms to uncover new insights in healthcare and sports. To accurately record fitness strain, physical activity, exercise-induced stress, and sports like basketball, this system employs cutting-edge artificial intelligence technologies, such as wearable sensors and current gathering data methods. Placement of sensors, real-time data collecting, data preprocessing and integrating, evaluation of stress by artificial intelligence algorithms, discovery and application of new information, validation and improvement are all parts of an iterative method that has been fine-tuned for use in sports and fitness settings by the IPM-EFPM. Examining the intricate relationship between AI, physical activity, and psychological stress is the main objective of this research. This could have real-world uses tailored to the sports world, particularly for basketball players.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100328"},"PeriodicalIF":2.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576956","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":"Development and application of a customized online affinity selection mass spectrometry screening platform","authors":"Jun Zhang,&nbsp;Jerry Barney,&nbsp;Qingqing Shen,&nbsp;Anthony Paiva,&nbsp;Wilson Shou,&nbsp;Chris Barbieri,&nbsp;Nancy Huynh,&nbsp;Heidi L. Perez,&nbsp;Andrew F. Donnell,&nbsp;Cullen L. Cavallaro","doi":"10.1016/j.slast.2025.100329","DOIUrl":"10.1016/j.slast.2025.100329","url":null,"abstract":"<div><div>With the evolving landscape of small molecule modalities and drug target portfolios, affinity selection mass spectrometry (ASMS) has emerged as a preferred high-throughput screening approach, driven by the growing chemical space capable of regulating historically undruggable targets. To meet the ever-increasing demand for binding screens, the ASMS platform requires continuous enhancements in efficiency and scalability. In this study, we developed a customized online ASMS platform, featuring multiplexed two-dimensional LC/MS for sample analysis, ultrafast acoustic ejection mass spectrometry for reaction product scouting of high-throughput chemistry before subjecting them to direct ASMS screens, and an integrated tool for real-time data processing and cross-hit evaluation. With the quality controls we implemented to ensure operational robustness and quality, the multiplexed system has demonstrated significantly enhanced analytical speed, efficiency, and overall capacity. The integrated data processing tool enhanced data quality by incorporating advanced cross-hit assessments to reduce false positives, and streamlined the screening workflow through real-time data analysis, effectively eliminating the traditional bottleneck in ASMS screening. The established ASMS platform has been utilized for our internal screening campaigns generating tractable binding hits, and providing direct affinity ranking of hits from nanoscale synthesis, enabling hit optimization and chemotype expansion with demonstrated high platform performance.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100329"},"PeriodicalIF":2.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561960","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":"Effect and toxicity of PF chemotherapy combined with radiotherapy in the treatment of advanced cervical cancer: Medical thermography test","authors":"Caixia Gou ,&nbsp;Wang Qi ,&nbsp;Pengbing Han ,&nbsp;Chunlin Zhang","doi":"10.1016/j.slast.2025.100327","DOIUrl":"10.1016/j.slast.2025.100327","url":null,"abstract":"<div><div>Cancer is one of the leading causes of death worldwide, and treatment options and prognosis for patients with advanced cancer are particularly challenging. PF chemotherapy regimen (the combination of cisplatin and 5-fluorouracil) has been widely used in the treatment of a variety of cancers, and radiotherapy as a local treatment can effectively control tumor growth. This is a prospective clinical trial in which patients were treated with PF chemotherapy combined with radiotherapy. Medical thermal imaging was performed on all patients before, during and after treatment. The examination process involves recording the patient's body surface temperature using a highly sensitive infrared camera and analyzing temperature changes in the tumor area and surrounding tissue. Clinical data on patients were also collected, including treatment response, quality of life scores, and reports of toxic and side effects. Preliminary results showed that PF chemotherapy combined with radiotherapy showed a positive effect in controlling tumor growth, and most patients experienced a reduction in tumor volume. Medical thermal image examination revealed significant changes in tumor area temperature during treatment, which correlated with tumor reactivity. In some cases, thermal imagery shows potential skin and mucosal damage in advance, suggesting the need for early intervention. Thermal imagery also helped assess the impact of treatment on patients' quality of life, such as pain and discomfort by looking at changes in the patient's body surface temperature distribution.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100327"},"PeriodicalIF":2.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561961","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":"Classifying kidney disease using a dense layers deep learning model","authors":"Amal Al-Rasheed ,&nbsp;Sheikh Muhammad Saqib ,&nbsp;Muhammad Zubair Asghar ,&nbsp;Tehseen Mazhar ,&nbsp;Asim Seedahmed Ali Osman ,&nbsp;Mohammad Shahid ,&nbsp;Muhammad Iqbal ,&nbsp;Muhammad Amir Khan","doi":"10.1016/j.slast.2025.100324","DOIUrl":"10.1016/j.slast.2025.100324","url":null,"abstract":"<div><div>Early diagnosis and thorough management techniques are crucial for people with chronic kidney disease (CKD), a crippling and potentially fatal condition. Research has focused a lot on machine learning and deep learning systems for the detection of kidney diseases. Deep learning platforms like hidden layers, activation functions, optimizers, and epochs are also necessary for the automatic detection of these diseases. The proposed model achieved 99 % accuracy, with a precision, recall, and F1 score of 0.99, indicating highly reliable performance. Additionally, the model demonstrated strong agreement and robustness, as reflected in metrics such as the ROC AUC score of 0.9821 and Matthews Correlation Coefficient of 0.9727. The experiment used a publicly accessible dataset with 24 independent fields and independent values as chronic or not-chronic classes, building dense-layered deep neural networks based on an optimized architecture. The outcomes demonstrated that, when compared to the other models, the proposed model was the most accurate.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100324"},"PeriodicalIF":2.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531106","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":"Leveraging FastViT based knowledge distillation with EfficientNet-B0 for diabetic retinopathy severity classification","authors":"Jyotirmayee Rautaray ,&nbsp;Ali B.M. Ali ,&nbsp;Meenakshi Kandpal ,&nbsp;Pranati Mishra ,&nbsp;Rzgar Farooq Rashid ,&nbsp;Farzona Alimova ,&nbsp;Mohamed Kallel ,&nbsp;Nadia Batool","doi":"10.1016/j.slast.2025.100325","DOIUrl":"10.1016/j.slast.2025.100325","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) remains a key contributor to eye impairment worldwide, requiring the development of efficient and accurate deep learning models for automated diagnosis. This study presents FastEffNet, a novel framework that leverages transformer-based knowledge distillation (KD) to enhance DR severity classification while reducing computational complexity. The proposed approach employs FastViT-MA26 as the teacher model and EfficientNet-B0 as the student model, striking the ideal mix between accuracy and computational efficiency. APTOS blindness detection dataset comprising 3662 images across five severity classes is collected, pre-processed, normalized, split and augmented to address class imbalance. The teacher model undergoes training and validation before transferring its knowledge to the student model, enabling the latter to approximate the teacher’s performance while maintaining a lightweight architecture. To comprehensively assess the efficacy of the proposed framework, additional student models—including HGNet, ResNet50, MobileNetV3, and DeiT—are analysed for comparative assessment. Model interpretability is enhanced through Grad-CAM++ visualizations, which highlight critical retinal regions influencing DR severity classification. Several measures are used to evaluate performance, including accuracy, precision, recall, F1-score, Cohen’s Kappa Score (CKS), Weighted Kappa Score (WKS), and Matthews Correlation Coefficient (MCC), ensuring a robust assessment. Among all student models, EfficientNet-B0 achieves the highest classification accuracy of 95.39 %, 95.43 % precision, recall of 95.39 %, F1-score of 95.37 %, CKS of 0.94, WKS of 0.97, MCC of 0.94, AUC of 0.99, and a KD loss of 0.17, with a computational cost of 0.38 G FLOPs. These results demonstrate its effectiveness as an optimized lightweight model for DR detection. The findings emphasize the potential of KD-based lightweight models in attaining high diagnostic accuracy while reducing computational complexity, paving the way for scalable and cost-effective DR screening solutions.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100325"},"PeriodicalIF":2.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523640","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":"Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematological parameters","authors":"Vinod Kumar ,&nbsp;Chander prabha ,&nbsp;Deepali Gupta ,&nbsp;Sapna Juneja ,&nbsp;Swati Kumari ,&nbsp;Ali Nauman","doi":"10.1016/j.slast.2025.100314","DOIUrl":"10.1016/j.slast.2025.100314","url":null,"abstract":"<div><div>LC continues to be the most prevalent cause of cancer deaths worldwide, which calls for sophisticated detection strategies. The present study investigates 34 demographic, behavioral, and hematological risk factors based on a sample of 2,000 patient data records. A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). Performance measures (accuracy, sensitivity, specificity, F1-score, AUC) identify quantitative differences: GB had the best F1-scores (0.953) and NB had the second-best F1-score (0.945), while GB had the best sensitivity (99.1 %). The KNN-AB hybrid model reported the highest accuracy with 99.5 %, while RF reported the highest AUC with a value of 0.92. Ensemble approaches (RF, GB) showed robust predictive performance across measures through integration of complementary strengths of base models. Lasso and ridge regression were able to minimize overfitting, making them easier to interpret. Therapeutic uses include integration into electronic health records (EHRs) for computerized risk stratification, LC screening earlier, and public health interventions in high-risk subjects (smokers with abnormal hematologic markers). The research highlights the value of hybrid ML models to integrate behavioral and biological data to effectively predict LC. Subsequent work can expand predictive capabilities through imaging data and genomics data incorporation, and continue to advance early identification and patient-specific therapy options. This is an intersection of computational advances and clinical translation, providing scalable solutions for global LC diagnosis.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100314"},"PeriodicalIF":2.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499332","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":"Clinical effect of CGF blood concentration factor in extracting supernumerary teeth in the middle and high positions of the upper palate","authors":"Bing Yuan ,&nbsp;Chunyan He ,&nbsp;Weidong Lai","doi":"10.1016/j.slast.2025.100321","DOIUrl":"10.1016/j.slast.2025.100321","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;this article aims to compare the postoperative clinical effects and analysis of using patient’s autologous blood extracted CGF (concentrated growth factors) blood concentration factor to fill the extraction wound of supernumerary teeth (ST) in patients with maxillary palatal type III high buried supernumerary teeth.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;108 patients (a total of 173 supernumerary teeth) with maxillary palatal bone buried supernumerary teeth who visited the Department of Stomatology at Handan Stomatological Hospital from September 2022 to September 2024 were selected as the study subjects. Preoperative images were taken for curved surface tomography and CBCT (Cone Beam Computed Tomography) diagnosis. By analyzing the sample population for clinical classification, 60 patients (a total of 94 supernumerary teeth) who met the inclusion criteria were randomly divided into two groups and underwent minimally invasive surgery under general anesthesia to remove supernumerary teeth. The experimental group used autologous blood to extract CGF blood concentration factor through a blood centrifuge to fill the extraction socket wound, while the control group did not use it. The postoperative infection, pain level, swelling degree, wound healing after suture removal were observed in both groups of patients, as well as the comparison of alveolar bone recovery and bone density changes between CBCT taken after surgery and follow-up 3 months later.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;infection situation: after the extraction of type III high buried supernumerary teeth on the maxillary palatal side, there were no cases of infection in the experimental group and the control group after surgery, and there was no statistically significant difference (&lt;em&gt;P&lt;/em&gt; &gt; 0.05). Postoperative pain level: after the extraction of type III buried supernumerary teeth on the maxillary palatal side, the pain level in the control group was higher than that in the experimental group on days 1, 2, and 3 after surgery (&lt;em&gt;P&lt;/em&gt; &lt; 0.05), while there was no statistically significant difference in pain level between the two groups on days 5 and 7 after surgery (&lt;em&gt;P&lt;/em&gt; &gt; 0.05). Degree of postoperative swelling: on postoperative days 1, 2 and 3, the degree of swelling in the control group was significantly higher than that of the experimental group (&lt;em&gt;P&lt;/em&gt; &lt; 0.05), but on postoperative days 5 and 7, the degree of swelling in the two groups was comparable, with no significant difference (&lt;em&gt;P&lt;/em&gt; &gt; 0.05). Wound healing: when the stitches were removed on the 7th postoperative day, all the wounds in the experimental group reached II-A healing; 2 cases in the control group were II-B, and the rest were II-A. The healing situation of the experimental group was better, but the statistical difference was not significant (&lt;em&gt;P&lt;/em&gt; &gt; 0.05). Maxillary alveolar bone recovery and bone density change value: immediate postoperative C","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100321"},"PeriodicalIF":2.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340586","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":"Prospective analysis of quantitative EEG indices for predicting functional outcomes in acute ischemic stroke","authors":"Yuan Zhang ,&nbsp;Hongshan Chu ,&nbsp;Qi Qiao ,&nbsp;Shibo Dong ,&nbsp;Jing Liu ,&nbsp;Hebo Wang","doi":"10.1016/j.slast.2025.100317","DOIUrl":"10.1016/j.slast.2025.100317","url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to assess the effectiveness of quantitative electroencephalography (qEEG) in determining the prognosis of acute ischemic stroke, thus offering a scientific foundation for early prognostic evaluation.</div></div><div><h3>Methods</h3><div>One hundred sixty-seven patients with acute ischemic stroke were admitted to the Neurology Department of Hebei Provincial People's Hospital between May 2022 and October 2023. All patients received standard treatments, including antiplatelet aggregation and lipid-lowering therapy to stabilize plaques. EEG data, mRS scores after three months, NIHSS scores before treatment, and basic patient information were all documented. Patients with a poor prognosis (mRS &gt; 3) and those with a good prognosis (mRS &lt; 3) were separated into two groups. The impact of EEG parameters on stroke prognosis was assessed. These indices included relative Alpha power (RAP), relative beta power (RBP), relative Theta power (RTP), relative Delta power (RDP), (δ+θ)/(α+β) value (DTABR), δ/θ value (DTR), α/β value (ABR), δ/α value (DAR), and α/(θ+δ) value (ATDR).</div></div><div><h3>Results</h3><div>RAP, RTP, RDP, DTABR, DTR, ABR, DAR, and ATDR were significantly correlated with mRS scores after three months. Univariate logistic regression analysis of the groups with good and poor prognoses revealed that NIHSS scores and EEG parameters, including α %, θ %, δ %, DTABR, ABR, DAR, and ATDR, were associated with functional outcomes. Following adjustment for NIHSS scores, multivariate logistic regression identified DTABR and DAR as predictors of functional outcomes. The optimal threshold for DTABR was 0.810, yielding a sensitivity of 0.848 and specificity of 0.864, while the cutoff value for DAR was 0.665, with a sensitivity of 0.759 and specificity of 0.955, as determined by ROC curve analysis assessing the sensitivity and specificity of DTABR and DAR in forecasting poor prognosis.</div></div><div><h3>Conclusion</h3><div>This study confirmed that NIHSS scores are reliable indicators of stroke severity for prognosis prediction. After accounting for NIHSS scores, it was further established that EEG indices could predict functional outcomes three months post-acute ischemic stroke, with DTABR and DAR demonstrating high sensitivity and specificity.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100317"},"PeriodicalIF":2.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340589","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":"Causal role of endothelial dysfunction in ischemic stroke and its subtypes: A two-stage analysis","authors":"Qian Wu ,&nbsp;Jiabo Cui ,&nbsp;Yao Jiang ,&nbsp;Xiaoxin Li ,&nbsp;Chongge You","doi":"10.1016/j.slast.2025.100322","DOIUrl":"10.1016/j.slast.2025.100322","url":null,"abstract":"<div><h3>Objective</h3><div>Endothelial dysfunction is implicated in the pathogenesis of ischemic stroke (IS), but its causal role remains unclear. This study systematically investigates the causal relationship between endothelial dysfunction proteins and IS and its subtypes through integrated observational and genetic evidence.</div></div><div><h3>Methods</h3><div>A two-stage study was conducted combining systematic meta-analysis and Mendelian randomization (MR). The meta-analysis integrated data from 29 observational studies to assess associations between endothelial dysfunction proteins (vWF, sE-selectin, sP-selectin, ICAM-1, VCAM-1, sLOX-1, VEGF, ET-1, SDF-1) and IS. This meta-analysis was registered online (PROSPERO ID: CRD42023461783). Subsequent MR was applied to discern the causal effects of the endothelial dysfunction proteins on IS and its subtypes, utilizing genetically instrumental variants.</div></div><div><h3>Results</h3><div>A meta-analysis demonstrated significant correlations with IS for vWF, sE-selectin, ICAM-1, sP-selectin, sLOX-1, and VEGF (all <em>p</em> &lt; 0.05). Furthermore, MR analysis showed that genetically elevated vWF increased the risk for any IS and cardioembolic stroke (CES), while E-selectin was causally linked to large-artery atherosclerosis stroke (LAS).</div></div><div><h3>Conclusion</h3><div>This work offers causal evidence that endothelial dysfunction significantly contributes to IS, highlighting the thrombotic activity of vWF in CES and the inflammatory function of E-selectin in LAS. These findings not only offer valuable insights into the mechanisms underlying IS and its subtypes but also help inform personalized stroke prevention strategies.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"33 ","pages":"Article 100322"},"PeriodicalIF":2.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340638","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}