Journal of Medical Radiation Sciences最新文献

{"title":"Renal screening sonography—A comparative study in a Portuguese basic emergency service","authors":"Sérgio Miravent MSc,&nbsp;Carmen Jiménez MD, MSc,&nbsp;Narciso Barbancho MD, MSc,&nbsp;Manuel Duarte Lobo MSc,&nbsp;Teresa Figueiredo MD, PhD,&nbsp;Carla Gomes MD, MSc,&nbsp;Ion Ratusneac MD, MSc,&nbsp;João Mário Gonçalves MD, MSc,&nbsp;Corina Hasnas BSc,&nbsp;Rui de Almeida PhD","doi":"10.1002/jmrs.802","DOIUrl":"10.1002/jmrs.802","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This study intends to compare the accuracy and pertinence of sonographic findings obtained by a sonographer in a Basic Emergency Service (BES) with the imaging findings at the Referral Hospital (RH).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Thirty-one patients suspected of having renal pathology underwent initial renal sonography screening with sonographer reporting at the BES and were subsequently referred to the RH for additional imaging examinations. The results of both examinations were compared to verify whether the findings from the BES were confirmed by the radiologist in the RH and to ensure that the patient referrals from BES to RH were appropriate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In our sample, most patients (80%) exhibited varying degrees of pyelocaliceal distension, with nearly half (48%) presenting obstructions. A strong association between the sonographic findings in the BES and the RH was found in the variables “Dilatation of pyelocaliceal system” (V=0.895; <i>p</i>=0.000), “Simple cystic formation” (V=0.878; <i>p</i>=0.000), respectively. There was a statistically significant correlation between BES and RH findings, indicating a strong association between these two variables respectively (<i>k</i>=0.890; <i>p</i>=0.000) and (<i>k</i>=0.870; <i>p</i>=0.000). In this research, an achieved sensitivity of 96% and a specificity of 85% were demonstrated in the identification of pyelocaliceal dilatation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Renal sonographer reporting screening successfully detected abnormalities in the urinary system of patients suspected of having renal colic. The sonographic data obtained at the BES demonstrated a strong correlation with the additional imaging findings from the RH in Portugal. These results suggest that Radiographers/Sonographers can have an important role in the preliminary assessment of urgent renal pathology in remote areas, contributing to a correct referral and early treatment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"72 1","pages":"8-16"},"PeriodicalIF":1.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419512","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":"Continuing Professional Development - Medical Imaging","authors":"","doi":"10.1002/jmrs.805","DOIUrl":"10.1002/jmrs.805","url":null,"abstract":"<p>Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 71, Issue 4, December 2024.</p><p>Scan this QR code to find the answers.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"491"},"PeriodicalIF":1.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419511","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":"Continuing Professional Development - Radiation Therapy","authors":"","doi":"10.1002/jmrs.799","DOIUrl":"10.1002/jmrs.799","url":null,"abstract":"<p>Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 71, Issue 4 December 2024.</p><p>Scan this QR code to find the answers.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"319"},"PeriodicalIF":2.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141296267","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":"Multi-centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments","authors":"Daniel Serra MSc, BSc,&nbsp;Michael J Neep PhD, MSc, BApp Sci(Med Rad Tech),&nbsp;Elaine Ryan PhD, MSc, BSc (Hons), PGDip(IPEM)","doi":"10.1002/jmrs.796","DOIUrl":"10.1002/jmrs.796","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Reject analysis in digital radiography helps guide the training of staff to reduce patient radiation dose and improve department efficiency. The purpose of this study was to perform a multi-centre, vendor agnostic reject analysis across different room usage types, and to provide benchmarks for comparison.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Retrospective reject and exposure log data were collected via USB from fixed general X-ray systems across multiple Australian sites, for collation and analysis. The overall reject rate, local reject reference level, absolute and relative reject rates for body part categories, reject rates by room usage types and the reject rate for each reason of rejection were calculated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Data were collected from 44 X-ray systems, across 11 hospitals. A total of 2,031,713 acquired images and 172,495 rejected images were included. The median reject rate was 9.1%. The local reject reference level (LRRL), set as the 75<i>th</i> percentile of all reject rates, was 10.6%. Median reject rates by room type were emergency (7.4%), inpatients + outpatients (9.6%), outpatients (9.2%), and hybrid (10.1%). The highest absolute reject rates by body part were chest (2.1%) and knee (1.4%). The highest relative rates by body part were knee (18.1%) and pelvis (17.2%). The most frequent reasons for image rejection were patient positioning (76%) and patient motion (7.5%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The results compare well with previously published data. The range of reject rates highlights the need to analyse typical reject rates in different ways. With analysis feedback to participating sites and the implementation of standardised reject reasons, future analysis should monitor whether reject rates reduce.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"412-420"},"PeriodicalIF":1.8,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283893","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":"Evidence-based practice in radiography: A strategy for shifting our culture","authors":"Laura Di Michele GradDipHM, BMedRadSc(DR), SFHEA,&nbsp;Amani Bell PhD, BSc (Hons), GradCertHigherEd, SFHEA,&nbsp;Kate Thomson PhD, GradCertEdStudies (HigherEd), MIH (Dist), BPsych (Hons), FHERDSA, SFHEA,&nbsp;Warren Reed PhD, PGCert TLHE, BSc (Hons)","doi":"10.1002/jmrs.801","DOIUrl":"10.1002/jmrs.801","url":null,"abstract":"<p>Evidence-based practice (EBP) has a vital role to play in improving outcomes for patients, organisations and individual practitioners. Unfortunately, within diagnostic radiography, literature consistently demonstrates that positive EBP is not the norm. This editorial discusses a strategy for fostering cultural change within the profession to improve EBP.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"323-325"},"PeriodicalIF":1.8,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262028","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":"Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients","authors":"Ke Cao PhD (Melb),&nbsp;Josephine Yeung BPharm (Hons),&nbsp;Yasser Arafat MS (Usyd), FRACS,&nbsp;Jing Qiao MD,&nbsp;Richard Gartrell MS, FRACS,&nbsp;Mobin Master FRANZCR, MBBS,&nbsp;Justin M. C. Yeung DM, FRACS,&nbsp;Paul N. Baird PhD (Lond)","doi":"10.1002/jmrs.798","DOIUrl":"10.1002/jmrs.798","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012–2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients &gt;0.98, respectively) and test (Dice similarity coefficients &gt;0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944–0.999, <i>P</i> &lt; 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 4","pages":"519-528"},"PeriodicalIF":1.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081409","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":"Continuing Professional Development - Medical Imaging","authors":"","doi":"10.1002/jmrs.795","DOIUrl":"10.1002/jmrs.795","url":null,"abstract":"<p>Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 71, Issue 4 December 2024.</p><p>Scan this QR code to find the answers.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"318"},"PeriodicalIF":2.1,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912173","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":"ePortfolios: Enhancing confidence in student radiographers' communication of radiographic anatomy and pathology. A cross-sectional study","authors":"Magdalena Dolic BRadMedImag (Hons),&nbsp;Yaxuan Peng BRadMedImag (Hons),&nbsp;Keshav Dhingra BRadMedImag (Hons),&nbsp;Kristal Lee BRadMedImag (Hons),&nbsp;John McInerney HDipHPE. GCHPE, PGCertIV Leadership and Management, PGCert CT Imaging, PGDip IV cannulation, BSc(Rad) Hons","doi":"10.1002/jmrs.787","DOIUrl":"10.1002/jmrs.787","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In 2020, the Medical Radiation Practice Board of Australia made several revisions to its professional capabilities. To address this, medical radiation practitioners, including diagnostic radiographers, are required to escalate urgent findings in all radiographic settings. However, the confidence of radiographers in articulating descriptions of radiographic findings varies despite this requirement. This cross-sectional study explores how the implementation of eportfolio affects student self-perceived confidence in identifying and describing radiographic findings in both an academic and a clinical setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A Qualtrics survey was distributed to second-year radiography students who had used eportfolios. The survey comprised of four questions using a Likert-scale and one open-ended question. Quantitative data were analysed using the Wilcoxon signed-rank test and qualitative data was thematically assessed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Overall, 55 of 65 radiographic students (85%) completed the survey. Confidence (strongly agree and agree) decreased from 89% to 74% between academic and clinical environments when identifying abnormalities, and 89% to 73% when describing findings. This finding highlights the challenges students face when in the clinical environment. Wilcoxon signed rank test analysed a statistically significant relation between the two environments (<i>P</i> &lt; 0.05). However, the relationship between identifying and describing skills was not statistically significant (<i>P</i> &gt; 0.05). Following a review of the qualitative data, three recurring themes were identified among responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>ePortfolios assist in improving confidence in identification and description of radiographic abnormalities, particularly in an academic setting. The clinical environment presents unique challenges which may limit student clinical performance; however, this requires further investigation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"403-411"},"PeriodicalIF":1.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855114","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":"Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI","authors":"Zaixian Zhang PhD,&nbsp;Junqi Han MS,&nbsp;Weina Ji MS,&nbsp;Henan Lou MS,&nbsp;Zhiming Li PhD,&nbsp;Yabin Hu PhD,&nbsp;Mingjia Wang PhD,&nbsp;Baozhu Qi MS,&nbsp;Shunli Liu PhD","doi":"10.1002/jmrs.794","DOIUrl":"10.1002/jmrs.794","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (<i>n</i> = 45) and a validation cohort (<i>n</i> = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 4","pages":"509-518"},"PeriodicalIF":1.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140660443","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":"The importance of quality management systems in nuclear medicine departments","authors":"Kunthi Pathmaraj MSc (Radiation Physics), BSc Applied Science (Medical Radiations), Grad Dip Computer Science","doi":"10.1002/jmrs.793","DOIUrl":"10.1002/jmrs.793","url":null,"abstract":"<p>Quality management systems (QMS) in nuclear medicine is an essential component of the Quality program and is instrumental in the safe delivery of a high standard clinical service. The IAEA QUANUM program is a nuclear medicine specific audit program that can be used to assess the standards of a nuclear medicine department and its service delivery. Regular internal and external audits are encouraged as part of the QMS.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"167-169"},"PeriodicalIF":2.1,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682113","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}