SLAS Technology最新文献

{"title":"Application of 3D printing imaging technology in the treatment of bronchopleural fistula with individual customized bronchial occluder","authors":"Zelin Xiao ,&nbsp;Jianqi Gao ,&nbsp;Hui Zhang ,&nbsp;Linyao Wang","doi":"10.1016/j.slast.2025.100344","DOIUrl":"10.1016/j.slast.2025.100344","url":null,"abstract":"<div><h3>Background</h3><div>Bronchopleural fistula (BPF) is a common and severe complication in thoracic surgery, characterized by its complex nature and high mortality rate. Bronchial occluder is one of the effective methods for the interventional treatment of BPF under bronchoscopy.</div></div><div><h3>Objective</h3><div>This work was to evaluate the adoption and efficacy of individualized bronchial occluder designed with the assistance of 3D printing technology in the treatment of BPF.</div></div><div><h3>Material and Methods</h3><div>This single-center, retrospective study included 60 patients (35 males and 25 females, aged 40–78 years, without severe dysfunction of major organs or history of mental disorders) diagnosed with BPF between January 2020 and December 2023, who received different treatment and were divided into the Control Group (CG, receiving thoracotomy treatment), Therapy group-I (TG-I, receiving septal occluder treatment), and Therapy group-II (TG-II, receiving treatment with 3D-printed individualized bronchial occluder). Serum levels of high-sensitivity C-reactive protein (hs-CRP) and procalcitonin (PCT) were measured by enzyme-linked immunosorbent assay preoperatively and at 1, 4, and 12 weeks postoperatively. Daytime cough scores were assessed according to guidelines, pain scores were evaluated using a visual analog scale, clinical efficacy was determined based on consensus criteria, and complications were recorded. Quantitative variables were compared using one-way ANOVA with Bonferroni correction, while categorical variables were analyzed by chi-square or Fisher’s exact test.</div></div><div><h3>Results</h3><div>the overall clinical efficacy was higher in TG-I and TG-II versus CG, with TG-II showing a greatly higher efficacy after twelve weeks of treatment (<em>P</em> &lt; 0.05). Relative to CG, both TG-I and TG-II demonstrated lower serum levels of hs-CRP and PCT, markedly reduced cough and pain symptom scores, and markedly lower complication rates (<em>P</em> &lt; 0.05). Additionally, TG-II resulted in notably lower serum inflammatory markers, cough and pain symptom scores, and complication rates versus TG-I (<em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>The 3D-printed personalized occluder (TG-II) significantly reduced postoperative inflammatory responses and alleviated cough-associated pain, demonstrating superior short-term efficacy and safety compared to conventional treatment (CG) and commercially available occluders (TG-I). This approach offers distinct advantages for the personalized precision treatment of BPF. However, further clinical studies are required to validate its potential as an alternative to surgical repair.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100344"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978420","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":"Construction of a nomogram prediction model for screening of serum markers for lower extremity vasculopathy secondary to type 2 diabetes mellitus","authors":"Jingjing Yang ,&nbsp;Jinyan Chen ,&nbsp;Lanying Shen","doi":"10.1016/j.slast.2025.100352","DOIUrl":"10.1016/j.slast.2025.100352","url":null,"abstract":"<div><h3>Objective</h3><div>To screen serum markers for secondary lower extremity angiopathy (LEAD) in patients with type 2 diabetes mellitus (T2DM) and construct a nomogram prediction model accordingly.</div></div><div><h3>Methods</h3><div>The clinical data of 200 T2DM patients admitted to the hospital from December 2022 to October 2024 were retrospectively collected. It was also divided into modeling group (<em>n</em> = 160) and internal validation group (<em>n</em> = 40) in a 4:1 ratio by using the leave-out method. As the external validation group, clinical data from 100 T2DM patients who were admitted to other hospitals within the same time period were also gathered. Combined with previous reports of collecting serum marker data related to LEAD secondary to T2DM, key serum markers were screened using LASSO regression. Moreover, multifactorial analysis helped to clarify independent risk factors, and a nomogram prediction model was built and tested for accuracy.</div></div><div><h3>Results</h3><div>The incidence of LEAD in 200 T2DM patients in the hospital was 21.00 % (42/200). A total of 14 variables were screened by LASSO regression analysis. After multifactorial analysis, it was found that disease duration, history of alcohol consumption, mean platelet volume, fasting blood glucose, fibrinogen, high-sensitivity C-reactive protein, insulin-like growth factor 1, nucleotide binding oligomerization domain like receptor protein 3 were independent risk factors for LEAD secondary to T2DM.The results of model validation showed AUCs of 0.971, 0.900, and 0.959 for the modeling cohort, internal validation cohort, and external validation cohort, respectively. The Hosmer-Lemeshow test was <em>χ²</em>=6.607, 7.962, and 6.585 (<em>p</em> &gt; 0.05). Positive net benefits were obtained by intervening with patients using a nomogram model within the high-risk threshold of 0 to 0.9.</div></div><div><h3>Conclusion</h3><div>In this study, eight risk factors associated with LEAD secondary to T2DM are screened by LASSO regression and multifactorial analysis, and a nomogram prediction model is constructed.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100352"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115029","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":"Low code, high impact: Application of low-code platforms to enable and democratize the development of laboratory digitalization and automation applications","authors":"Jonas Austerjost ,&nbsp;Elias Knöchelmann ,&nbsp;Thomas Kruse ,&nbsp;Janina Kilian ,&nbsp;Bastian Quaas ,&nbsp;Michael W. Olszowy","doi":"10.1016/j.slast.2025.100353","DOIUrl":"10.1016/j.slast.2025.100353","url":null,"abstract":"<div><div>Conventionally, the initialization of new prototypes and concepts in laboratory automation and life science software applications has required close collaboration between hardware and software experts, as well as lab personnel such as biologists, chemists, biotechnologists, or process engineers. This setup - still common today - often means that the ideas and requests of lab personnel must be translated into software applications by software developers, which frequently results in long development times. Low-Code Development Platforms (LCDPs) seek to address this challenge by providing a way to accelerate application development by reducing dependence on traditional software development methods, empowering lab personnel to build applications without writing extensive amount of code. By offering a visual, drag-and-drop interface, lab personnel can actively participate in the software development process. This helps democratize application creation and can lead to the quick setup of software solutions tailored to laboratory needs.</div><div>This study demonstrates the implementation of four different use cases in a bioprocessing laboratory environment using an open-source LCDP and commercially available upstream and downstream equipment. The LCDP facilitated the integration and control of different device types with varying communication protocols also enabling dashboarding, monitoring and data processing capabilities. This methodology highlights the suitability of LCDPs for rapidly prototyping and evaluating laboratory and bioprocess automation pipelines, potentially expediting the development of biotechnological production processes and products. All developed components are made available through a publicly accessible repository, facilitating reuse and further development by the scientific community.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100353"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven transcriptomic biomarker discovery for early identification of pediatric deterioration in Acute Care","authors":"Qing Wang ,&nbsp;Lina Sun ,&nbsp;Wei Meng ,&nbsp;Chen Chen","doi":"10.1016/j.slast.2025.100357","DOIUrl":"10.1016/j.slast.2025.100357","url":null,"abstract":"<div><div>Early detection of juvenile clinical deterioration in acute care settings remains a significant problem in modern healthcare. This paper presents an AI-powered predictive analytics platform that combines transcriptome biomarker signals with structured vital signs, laboratory data, and unstructured clinical notes to improve early warning capabilities. The system uses ClinicalBERT to extract insights from clinical narratives, XGBoost to analyze tabular clinical information, and long short-term memory (LSTM) networks to simulate temporal dynamics. A meta-classifier combines multimodal data to produce real-time risk ratings for clinical deterioration. The performance evaluation utilizing five-fold cross-validation showed great accuracy, with an AUROC of 0.91, AUPRC of 0.83, and an average early warning lead time of 5.6 hours. Predictive markers included higher lactate levels, heart rate patterns, SpO₂ variability, and transcriptome signals indicating systemic inflammatory activation. Ablation investigations proved the importance of multimodal data fusion in increasing prediction robustness. The suggested strategy provides a scalable, interpretable, and high-performing hospital integration system that enables biomarker-informed, precision-based pediatric intervention options.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100357"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208410","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":"Single-step purification and formulation of antibody-drug conjugates using a miniaturized tangential flow filtration system","authors":"Muhammad Sajed ,&nbsp;Zahoor Khan ,&nbsp;Muhammad Usman Ashraf ,&nbsp;Hafsa Iftikhar ,&nbsp;Talha Bin Rahat ,&nbsp;Samia Falak ,&nbsp;Salman Fozail ,&nbsp;Quiterie Gue ,&nbsp;Raul Pardo ,&nbsp;Lance Ramsey ,&nbsp;Muhammad Saqib Shahzad","doi":"10.1016/j.slast.2025.100351","DOIUrl":"10.1016/j.slast.2025.100351","url":null,"abstract":"<div><div>Antibody-drug conjugates (ADCs) are a promising therapeutic modality that enables the delivery of cytotoxic drugs to the target cells that express the corresponding antigen. However, the purification of ADCs while ensuring product safety, homogeneity, and stability is a challenging task due to their complex and fragile structure. Size exclusion chromatography (SEC), the conventional method for ADC purification, is time-consuming as it requires multiple column washes and equilibration steps. Moreover, subsequent formulation of ADCs, typically using dead-end filtration (DEF), further complicates the production workflow. We compared SEC+DEF with the µPulse®, a miniaturized and automated tangential flow filtration system, for purification and formulation of ADCs. Quality analysis revealed that both approaches were equally gentle as comparable drug-to-antibody ratios (DARs) and monomer purities were observed in the purified samples. Most importantly, both methods exhibited equivalent cleanup efficiency with a 99.8% reduction in free linker-drug concentration. The endotoxin loads comprised 0.11 EU mg^-1 for the µPulse and 0.07 EU mg^-1 for SEC+DEF, ensuring validation of the safe application of purified ADCs in living systems. However, the µPulse performed purification and formulation of ADCs simultaneously as compared to SEC+DEF, which required multiple manual interventions. Our results indicate that the µPulse is a gentle, single-step, and walk-away approach for the purification of ADCs.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100351"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098086","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":"The RoboSeed facilitates automated extraction of cereal mature embryos","authors":"R. Berenstein ,&nbsp;V. Bloch ,&nbsp;A. Beery ,&nbsp;M.R. Prusty ,&nbsp;J. Awwad ,&nbsp;O. Amir-Segev ,&nbsp;S. Miterani ,&nbsp;M. Barak ,&nbsp;G. Lidor ,&nbsp;E. Fridman","doi":"10.1016/j.slast.2025.100355","DOIUrl":"10.1016/j.slast.2025.100355","url":null,"abstract":"<div><div>To overcome a critical bottleneck in plant biotechnology workflows, a semiautomated system RoboSeed was developed to extract mature embryos from cereal grains such as barley. In contrast to the commonly used manual extraction, the robot employs a precision-controlled pressing rod which applies mechanical force along an optimal trajectory and angle to detach intact embryos. A custom image-processing pipeline determines grain orientation and morphology, enabling precise rod alignment at the optimal force application point. Validation experiments using two barley cultivars (Noga and Golden Promise) and soaking duration of 10 and 20 h revealed optimal force application point relative location in the range 0.5–0.6, achieving maximum extraction success rates of 56.2 % (Noga) and 36 % (GP) after 20 h soaking. RoboSeed operated with a median cycle time of 20.9 s per extraction, translating to 37.2 s per successful embryo, compared to 27.9 s with expert manual extraction. While current throughput is lower than conventional methods, RoboSeed offers significant advantages in consistency, reduced reliance on operator skill, and potential for scaling. Future improvements include full automation of grain singulation, robotic arms for post-extraction handling, and expanded testing across additional genotypes. RoboSeed’s modular design provides a robust foundation for scalable, high-throughput embryo extraction, with potential to accelerate cereal transformation, gene mapping studies, and tissue culture-based research.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100355"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139404","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 study on drug balloon therapy for left main coronary bifurcation lesions based on thermal infrared imaging","authors":"Ande Jiao ,&nbsp;Xin Zhang ,&nbsp;Lili Wang ,&nbsp;Yan Wang ,&nbsp;Gang Xu ,&nbsp;Yanping Huo","doi":"10.1016/j.slast.2025.100358","DOIUrl":"10.1016/j.slast.2025.100358","url":null,"abstract":"<div><div>Left main coronary artery bifurcation disease is a treatment challenge in the field of coronary heart disease, characterized by high risk, poor prognosis, poor long-term efficacy of drug therapy, and high mortality rate. Although traditional interventional treatment strategies such as single stent and double stent procedures are widely used, the high rate of restenosis of the left circumflex branch (LCX) opening after the placement of the main stent remains a major problem. Thermal infrared imaging can monitor tissue blood flow perfusion and metabolic status in real-time and non invasively, providing a new evaluation method for coronary intervention therapy. The aim of this study is to explore the application value of thermal infrared imaging in the treatment of left main coronary bifurcation lesions with drug balloon (DCB), evaluate its effectiveness and safety in optimizing treatment parameters, reducing restenosis rate, and minimizing adverse cardiac events, and compare it with traditional double stent strategy. All patients in this article underwent preoperative and postoperative thermal infrared imaging to monitor changes in vascular wall temperature and blood flow perfusion. Using a high-sensitivity infrared thermal imager, real-time imaging and postoperative follow-up imaging are used to analyze drug release, vascular response, and long-term efficacy. The main outcome measures include immediate postoperative angiography results, incidence of major adverse cardiac events (MACE) within 1 year after surgery, intravascular ultrasound (IVUS) parameters, and thermal infrared imaging features. The results showed that the drug balloon group was significantly better than the double stent group in reducing the rate of left circumflex branch restenosis, late lumen loss (LLL), and MACE (<em>P</em> &lt; 0.05). Thermal infrared imaging shows that the temperature changes of the blood vessel wall during drug balloon dilation are related to good treatment response, and the blood flow perfusion and metabolic status of the drug balloon group are better during postoperative follow-up. The thermal infrared imaging features are significantly correlated with vascular angiography and IVUS results, and can effectively predict vascular restenosis and adverse events.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100358"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416988","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":"Automated endpoint titer ELISAs for high-throughput immunogenicity evaluations using a BioMek i7 liquid handler","authors":"Barbara Theriot ,&nbsp;Andrew N. Macintyre","doi":"10.1016/j.slast.2025.100368","DOIUrl":"10.1016/j.slast.2025.100368","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100368"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566477","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":"IoT-based approach for diabetes patient monitoring using machine learning","authors":"Sarra Ayouni ,&nbsp;Muhammad Hamza Khan ,&nbsp;Muhammad Ibrahim ,&nbsp;Mohamed Maddeh ,&nbsp;Nadeem Sarwar ,&nbsp;Nazik Alturki","doi":"10.1016/j.slast.2025.100348","DOIUrl":"10.1016/j.slast.2025.100348","url":null,"abstract":"<div><div>This study presents an IoT-based framework for real-time diabetes monitoring and management, addressing key limitations identified in previous studies by integrating four datasets: BVH Dataset, PIMA Diabetes Dataset, Simulated Dataset, and an Integrated Dataset. The proposed approach ensures diverse demographic representation and a wide range of features including real-time vital signs (e.g., oxygen saturation, pulse rate, temperature) and subjective variables (e.g., skin color, moisture, consciousness level). Advanced preprocessing techniques, including Kalman Filtering for noise reduction, KNN imputation for addressing missing data, and SMOTE-ENN for improving data quality and class balance, were employed. These methods resulted in a 25 % improvement in Recall and a 20 % increase in the F1-score, demonstrating the model's effectiveness and robustness.</div><div>By applying PCA and SHAP for feature engineering, high-impact features were identified, enabling the tuning of models such as Random Forest, SVM, and Logistic Regression, which achieved an accuracy of 97 % and an F1-score of 0.98. A novel triage system, integrated with edge and cloud computing, classifies health status in real-time (Green, Yellow, Red, Black), reducing latency by 35 %. The proposed system sets a new benchmark for scalable, individualized diabetes care in IoT-based healthcare solutions, demonstrating significant improvements in accuracy, response time, and feature incorporation compared to prior works.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100348"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071195","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":"Applications of artificial intelligence in rehabilitation: technological innovation and transformation of clinical practice","authors":"Haoyang Liu ,&nbsp;Qiurong Xie","doi":"10.1016/j.slast.2025.100360","DOIUrl":"10.1016/j.slast.2025.100360","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) into rehabilitation science is revolutionizing traditional therapeutic models, offering innovative solutions that enhance the precision, efficiency, and accessibility of rehabilitation services. This review explores the diverse applications of AI in rehabilitation, focusing on key technologies such as machine learning, deep learning, computer vision, natural language processing, and robotics. A key innovation is the proposed AI-empowered rehabilitation model, which transforms fragmented processes into an interactive, adaptive system with real-time assessment during interventions. AI-driven advancements in impairment assessment, intervention planning and delivery, post-discharge care, and patient education are driving a shift from experience-driven to data-model-driven rehabilitation systems. Notable AI-driven applications include AI-powered exoskeletons for motor rehabilitation (e.g., in stroke recovery), NLP-driven cognitive therapy, and tele-rehabilitation platforms that enable remote monitoring and adaptive interventions. Despite these advancements, challenges remain, including data limitations, ethical concerns, regulatory requirements, and clinical integration barriers. Addressing these challenges requires interdisciplinary collaboration to ensure AI's responsible and effective deployment in rehabilitation. This review highlights the transformative potential of AI in rehabilitation and emphasizes the need for continued research and validation to optimize patient outcomes and accessibility.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"35 ","pages":"Article 100360"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402667","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}