MethodsX最新文献

{"title":"Optimizing merchant compliance: A system for product specific rule extraction using NLP.","authors":"Atharva Khewalkar, Anuj Walsepatil, Ritesh Phadtare, Sakshi Jadhav, Pratiksha Shinde, Archana Y Chaudhari","doi":"10.1016/j.mex.2026.103993","DOIUrl":"10.1016/j.mex.2026.103993","url":null,"abstract":"<p><p>Product quality and safety are crucial in the e-commerce market, particularly for regulated sectors like the food industry, for user protection, and it is the responsibility of the merchants who sell their products to ensure these are maintained. Here comes the role of the government, which lays down certain rules and regulations that need to be followed. These rules and regulations are laid down by the central or state government of India. All these rules are enforced by the authorities established under the respective ministries. All these rules and regulations are maintained in the form of government repositories on the websites of the respective government ministries. Accessing data that is present in these repositories is very difficult for users, as these rules, regulations, and amendments are made available in PDF format to the general public and are often in an unstructured format. This initial study validates the system's effectiveness using the specific domain of Indian food safety regulations. Validation of the system on food-specific regulations demonstrated promising performance, achieving a task accuracy between 82 and 89% over 10 runs and an NLP model F-score of 87.17%. The proposed system makes it easy for users to access this data, allowing them to search for rules and regulations related to their product by providing a search engine. The proposed system gathers data scattered across various websites using web scraping. This data is then automatically cleaned and organized using automation scripts before being presented to the users in a structured and informative format. The proposed system website delivers accurate, real-time data with high speed and reliability, ensuring users get up-to-date and precise information instantly. The proposed system provides product-specific output using NLP (Natural Language Processing).•Ensuring product quality and safety in e-commerce is crucial for user protection, and merchants must maintain these standards. The government enforces rules and regulations through various ministries.•Government rules and regulations are often stored in unstructured PDF formats on ministry websites, making it difficult for users to access and interpret the information.•The proposed system uses web scraping to gather regulatory data, cleans it using automated scripts, and stores it in text format. It employs Spacy natural language processing algorithms to provide product-specific search results.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103993"},"PeriodicalIF":2.9,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13264087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148251853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reproducible R workflow for harmonizing indoor air quality sensor data and cleaning activity logs in school-based field campaigns.","authors":"C Falzone, H Moujahid, N Redon, F Thevenet, M Verriele","doi":"10.1016/j.mex.2026.103995","DOIUrl":"10.1016/j.mex.2026.103995","url":null,"abstract":"<p><p>This article presents a reproducible workflow developed in R to process indoor air quality data by integrating wearable multi sensor measurements with tablet-based activity logs. The data come from two field campaigns in four primary schools and involve 20 female maintenance staff members. The method supports occupational exposure assessment under real world conditions, where sensor signals can be incomplete or noisy and self-reported activities may contain timing errors. The workflow organizes raw files by campaign, school and instrument, standardized timestamps (including daylight saving changes), and harmonizes quantitative signals (temperature, relative humidity, CO₂, PM_2.5 and VOC sensors) with qualitative descriptors from activity logs (location, activity, cleaning practice, product, time). It then builds an integrated dataset in which each sensor reading is linked to its activity context, making it possible to distinguish cleaning periods from daytime and night time storage of instruments. The approach produces graphical outputs and summary statistics that support sensor diagnostics, comparison of exposure profiles between micro environments, and communication of semi quantitative exposure patterns to researchers and stakeholders. • Reproducible R workflow integrating multisensor and activity data. • Standardized pipeline for data cleaning, temporal alignment, and data structuring. • Analysis-ready outputs linking exposure levels to activities and contexts.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103995"},"PeriodicalIF":2.9,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13264079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148252267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical approach to a single-chain variable fragment (scFv) biotinylating for immunohistochemical analysis.","authors":"Cynthia Rodríguez-Nava, Carlos Ortuño-Pineda, Amalia Vences-Velázquez, Berenice Illades-Aguiar, Karen Cortés-Sarabia, Luz Del Carmen Alarcón-Romero","doi":"10.1016/j.mex.2026.103989","DOIUrl":"10.1016/j.mex.2026.103989","url":null,"abstract":"<p><p>Immunohistochemistry (IHC) is a widely used technique for detecting proteins in tissue sections; however, its performance is significantly influenced by the quality of antibodies and detection strategies. Conventional IgG or IgM-based antibodies often come with high production costs, variability between batches, and limited penetration into tissues, which can compromise the uniformity and reproducibility of staining. Recombinant single-chain variable fragments (scFvs) offer an attractive alternative due to their smaller size, defined molecular composition, and renewable production capabilities. In this work, we focus on optimizing IHC by chemically biotinylating scFvs, allowing for direct coupling to streptavidin-based detection systems. This method enhances signal sensitivity while eliminating the need for secondary antibodies or additional labeling steps. Biotinylated scFvs can diffuse more easily into tissue sections, streamline the staining workflow, and reduce both assay time and cost. Overall, integrating biotinylated scFvs into IHC protocols presents a robust, scalable, and reproducible strategy that addresses key limitations of conventional antibody-based methods, thus supporting more consistent and efficient protein detection in histological analyses.This protocol allowed the optimization of conventional IHC conditions by using biotinylating scFv to achieve precise, time and cost-efficient antigen visualization.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103989"},"PeriodicalIF":2.9,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148278073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reproducible EEG-ERP methodology for assessing cognitive decline: integrating P300 latency and resting-state EEG frequency.","authors":"Wojciech Derkowski, Piotr Derkowski","doi":"10.1016/j.mex.2026.103986","DOIUrl":"10.1016/j.mex.2026.103986","url":null,"abstract":"<p><p>Early detection of cognitive decline remains one of the major challenges in contemporary neurology. Although event-related potentials associated with cognitive processes, particularly the P300 component, as well as resting-state EEG analysis, have long been investigated as potential functional biomarkers of dementia, their clinical application is often limited by methodological heterogeneity and insufficient transparency of signal processing procedures. In this article, we present a reproducible EEG-ERP methodology based on proprietary software for stimulus generation, synchronization, and deterministic averaging of visual event-related potentials, combined with quantitative analysis of resting-state EEG frequency. The approach integrates precise stimulus timing, transparent offline synchronization using an audio marker recorded on the ECG channel, and a fully deterministic averaging procedure. Pilot clinical data obtained from patients with mild cognitive impairment and dementia are included to illustrate the feasibility of the proposed workflow. • Software-controlled visual stimulation with fixed inter-stimulus intervals and explicit offline synchronization using an audio marker. • Deterministic ERP averaging enabling reliable estimation of P300 latency in routine clinical EEG recordings. • Integration of resting-state EEG frequency and P300 latency as interpretable electrophysiological markers of cognitive decline.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103986"},"PeriodicalIF":2.9,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on hemodynamic state prediction based on feature-enhanced multi-model ensemble.","authors":"Shuai Yan, Minghua Liu, Xiaoyan Wang","doi":"10.1016/j.mex.2026.103985","DOIUrl":"10.1016/j.mex.2026.103985","url":null,"abstract":"<p><p>This study tackles the challenge of accurately inverting neural and vasodilatory signals from BOLD-fMRI data. We propose a novel framework that substantially enhances prediction performance through:•Multi-scale dynamic feature extraction to comprehensively characterize BOLD signal properties.•A stacking ensemble architecture that synergistically combines multiple heterogeneous base learners.•Hierarchical model fusion via a meta-learner to robustly integrate predictions and capture complex nonlinear mappings.Evaluated on synthetic data from the Balloon model, our method achieves an R² of 0.92 for the vasodilatory signal and 0.78 for the neural drive signal, outperforming existing benchmarks.Validation on real fMRI data shows successful reconstruction of neural activity, with reconstructed BOLD signals correlating with measured signals at levels up to 0.9931. This provides a new pathway for high-fidelity inversion of microscopic neural activity.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103985"},"PeriodicalIF":2.9,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the role of advanced MRI in understanding glioblastoma biology: A scoping review protocol.","authors":"James Brown-Miles, Oun Al-Iedani, Peter Greer, Michael Fay, Hubert Hondermarck, Saadallah Ramadan","doi":"10.1016/j.mex.2026.103984","DOIUrl":"10.1016/j.mex.2026.103984","url":null,"abstract":"<p><p>Advanced magnetic resonance imaging (MRI) offers unique opportunities to explore the biological complexity of glioblastoma, the most aggressive brain tumour. The 2021 World Health Organization reclassification of glioblastoma has obscured the interpretation of prior imaging research, necessitating a focussed synthesis to align it with current guidelines. This scoping review protocol aims to systematically map evidence from adult, preoperative, isocitrate dehydrogenase-wildtype glioblastoma to answer the question, \"can advanced MRI help understand the biology of glioblastoma?\" It will focus on peer-reviewed studies investigating associations between advanced MRI and tumour characteristics, including correlations with histopathological/molecular markers. Five databases will be searched for primary sources using advanced MRI, such as magnetic resonance spectroscopy, diffusion-, perfusion-, chemical exchange saturation transfer- and susceptibility-weighted imaging, to image glioblastoma. Findings will be stratified by biological domains, such as molecular factors, vascularisation and metabolism.•Databases: PubMed, Scopus, Cochrane, EBSCO, Embase.•Methodology: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews framework and Joanna Briggs Institute guidance.•Outcome: Evidence map and narrative synthesis of biological insights derived from advanced MRI, aligning literature with the current classification and highlighting methodological gaps and priorities for validation and clinical translation to guide future study design and standardisation efforts.</p>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"103984"},"PeriodicalIF":2.9,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148226712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining a clinical protocol using a computerized central visual processing battery","authors":"Marcelo Fernandes Costa ,&nbsp;Leonardo Dutra Henriques ,&nbsp;Givago Silva Souza","doi":"10.1016/j.mex.2026.103789","DOIUrl":"10.1016/j.mex.2026.103789","url":null,"abstract":"<div><div>This article presents the development and validation of a computerized clinical protocol for assessing Central Visual Processing (CVP). The protocol was designed to overcome limitations in current visual assessment tools by integrating sensory, perceptual, and cognitive visual functions within the dorsal and ventral processing streams. It comprises psychophysically controlled tasks measuring contrast sensitivity, texture perception, coherent motion, form integration, visual attention, reading-related eye movements, quantity estimation, and spatial-numerical mapping. Stimuli were developed using high-precision presentation software, and procedures were adapted to ensure both clinical feasibility and psychophysical validity.</div><div>Method validation was conducted with 41 healthy adults through test–retest analysis, Cronbach’s alpha, and Spearman–Brown split-half reliability. No significant differences were observed between first and second assessments (p &gt; 0.05), and reliability indices showed strong internal consistency across subtests. These findings confirm the reproducibility and methodological robustness of the protocol.<ul><li><span>•</span><span><div>A comprehensive computerized battery assessing central visual functions across dorsal and ventral streams</div></span></li><li><span>•</span><span><div>Psychophysical methods adapted for clinical precision and feasibility</div></span></li><li><span>•</span><span><div>Strong reliability demonstrated through test–retest, internal consistency, and split-half correlations</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103789"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USE-SVI: A reproducible pipeline for sampling, acquiring, and stitching Street View imagery to support urban analytics","authors":"Iuria Betco ,&nbsp;Cláudia M. Viana ,&nbsp;Jorge Rocha","doi":"10.1016/j.mex.2026.103785","DOIUrl":"10.1016/j.mex.2026.103785","url":null,"abstract":"<div><div>Street-level imagery (SLI) is increasingly used in urban analytics for tasks like estimating greenery, conducting transport audits, and assessing facades. However, inconsistent image quality, uneven spatial coverage, and non-standardized acquisition methods limit reproducibility. We introduce USE-SVI (Urban Sampling &amp; Extraction of Street View Imagery), a reproducible process to sample, acquire, and stitch street-view images for city-wide analysis. The protocol ensures regular spatial coverage sampling points at fixed intervals, generates four viewing directions per point to capture main views, acquires images through official Street View APIs or open-licence platforms (e.g., Mapillary or KartaView) with detailed metadata recording, and creates panoramas using OpenCV (e.g., ORB keypoints, FLANN matching, Stitcher). This approach produces evenly spaced images, clear provenance, and ready-to-use outputs (CSV, PNG, XLSX), supporting machine learning and visual checks. By standardizing key steps, sampling, acquisition, and stitching, USE-SVI enhances transparency and scalability, adheres to platform terms, and enables replication across cities and periods. Limitations involve variable provider coverage and occasional stitching failures in scenes with few features.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103785"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph neural network-based mutation-aware regression test ordering using code dependency graphs and execution traces","authors":"S Sowmyadevi,&nbsp;Anna Alphy","doi":"10.1016/j.mex.2025.103782","DOIUrl":"10.1016/j.mex.2025.103782","url":null,"abstract":"<div><div>The mutation-aware test prioritisation system in this paper uses Graph Neural Networks (GNNs) to combine static program structure, dynamic execution traces, and mutation coverage into a hybrid graph representation to enhance regression testing. The framework embeds higher-order dependencies in test cases using GCN, GAT, and GraphSAGE variations and ranks them using a multi-objective optimisation function that balances fault detection, execution cost, and mutation coverage. On benchmark datasets like Defects4J and ManySStuBs4J, the proposed approach consistently outperforms traditional baselines (coverage-based APFD = 72.4 %, cost-based = 74.5 %) and ML baselines (LSTM = 80.1 %, RL = 82.7 %), achieving an average APFD of 88.9 % and mutation score of 84.6 % with a 16.1-second execution overhead. Statistical tests (Wilcoxon signed-rank, <em>p</em> &lt; 0.05) indicate the robustness of these gains. Ablation experiments show that removing execution traces or mutation characteristics reduces APFD by 5–8 %, emphasising their relevance. Qualitative research shows that GNN embeddings cluster fault-related test cases for interpretable prioritisation. The suggested paradigm for contemporary regression testing is scalable, accurate, and mutation-driven.<ul><li><span>•</span><span><div><strong>Multi-Tiered Graph-Based Architecture</strong>: The method transforms raw program artifacts (codebase, mutants, test traces) into Program Dependence Graphs and Call Graphs, where nodes represent program elements and edges capture dependencies enriched with runtime characteristics.</div></span></li><li><span>•</span><span><div><strong>GNN-Powered Multi-Objective Optimization</strong>: Core innovation uses Graph Neural Networks (GCN, GAT, GraphSAGE) to create enriched embeddings through iterative neighborhood aggregation, feeding into a scoring function that balances fault detection potential, execution cost, and mutation coverage.</div></span></li><li><span>•</span><span><div><strong>Superior Validated Performance</strong>: Achieves 88.9 % APFD compared to 82.7 % for best baseline methods on real-world datasets, with statistical significance confirmed through Wilcoxon signed-rank tests across multiple evaluation metrics.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103782"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting upwelling dynamics in the South Sea of Java, Indonesia: A deep learning approach with ConvLSTM and 3D-CNN","authors":"Dwi Rantini ,&nbsp;Rumaisa Kruba ,&nbsp;Yudi Haditiar ,&nbsp;Muhammad Ikhwan ,&nbsp;Yusuf Jati Wijaya ,&nbsp;Aris Ismanto ,&nbsp;Muhammad Mahdy Yandra ,&nbsp;Hafiz Rahman ,&nbsp;Arip Ramadan ,&nbsp;Fazidah Othman","doi":"10.1016/j.mex.2026.103802","DOIUrl":"10.1016/j.mex.2026.103802","url":null,"abstract":"<div><div>Oceans exhibit complex dynamics influenced by climate change, anthropogenic activities, and natural phenomena. Understanding these dynamics is critical for ensuring the sustainability of marine environments and their optimal utilization. This research aims to study and monitor upwelling phenomena in the South Sea of Java. Upwelling, the exchange of nutrient-rich, cold water from deeper layers to the surface, enhances marine biological productivity; Sea Surface Temperature (SST) serves as a key indicator for its detection. To achieve these objectives, this study employs both ConvLSTM and 3D-CNN. ConvLSTM, a deep learning architecture that integrates convolutional structures within LSTM units, effectively captures spatiotemporal dependencies in sequential data. 3D-CNN, a deep learning model extending traditional 2D convolutional neural networks, processes volumetric data, enabling the extraction of spatial features across three dimensions. Analysis reveals that ConvLSTM outperforms 3D-CNN in modeling upwelling data in the South Sea of Java. This is evidenced by lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The ConvLSTM method was then used for forecasting, and the results were validated with data obtained from local fishermen regarding their fishing expeditions. Visual analysis confirms that the ConvLSTM method accurately models upwelling data in the South Sea of Java with fishermen's schedules.</div><div>ConvLSTM and 3D-CNN methods were comparatively evaluated for modeling Sea Surface Temperature (SST) data, considering wind speed, sea surface salinity, and the El Niño-Southern Oscillation (ENSO) phase as influential factors.</div><div>Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, the ConvLSTM method exhibited lower values, indicating superior performance compared to the 3D-CNN approach. Specifically, RMSE and MAE values for ConvLSTM were 0.4161 and 0.3017, respectively, while for 3D-CNN, the corresponding values were 0.6095 and 0.4259.</div><div>Upwelling data forecasting results were validated against local fishermen's schedules, with data collected in July 2022. Visual inspection confirmed alignment between the forecasted upwelling patterns and the fishermen's activity.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103802"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}