Medical & Biological Engineering & Computing最新文献

{"title":"Improved drowsiness detection in drivers through optimum pairing of EEG features using an optimal EEG channel comparable to a multichannel EEG system.","authors":"Riaz Minhas, Nur Yasin Peker, Mustafa Abdullah Hakkoz, Semih Arbatli, Yeliz Celik, Cigdem Eroglu Erdem, Yuksel Peker, Beren Semiz","doi":"10.1007/s11517-025-03375-1","DOIUrl":"https://doi.org/10.1007/s11517-025-03375-1","url":null,"abstract":"<p><p>Multichannel electroencephalography (EEG)-based drowsiness detection (DD) offers higher coverage but comes with increased computational demands, hardware requirements, and user discomfort, whereas single-channel devices are cost-effective and user-friendly but provide lower coverage. We hypothesized that an optimal channel with optimum paired EEG features could achieve coverage comparable to a multichannel system. Subject-specific, EEG-feature-specific thresholding techniques were introduced to classify 927 EEG epochs, derived from visual-based scoring through image processing of fifty drivers' facial expressions during a 50-min driving simulation, using six individual EEG channels with paired features. Ten normalized EEG features were extracted per epoch using discrete wavelet transform (DWT), and seven thresholding techniques were applied to identify the most consistent method across subjects. Epochs were classified as drowsy or wakeful based on whether their normalized values exceeded or fell below a specific threshold. We then assessed the coverage of each channel by comparing EEG patterns with visual-based scoring. To determine the optimal feature pair for classifying each epoch in alignment with visual-based scoring, 45 feature combinations were evaluated. The pairing of power spectral density (PSD) alpha and PSD theta in channels Frontal4 (F4) and Occipital2 (O2) yielded the highest coverage, achieving 96.1% and 95% with corresponding accuracies of 95.4% and 94.7%, respectively. These results slightly surpassed the coverage achieved using six channels with a single feature, with increases of 1.47% for F4 and 0.32% for O2. Our study demonstrates that an optimal EEG channel with optimum paired EEG features can reduce channels from six to one, lowering computational demands for wearable DD devices.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081640","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":"Analysis of anthropometric measurements in U-15 female weightlifters using Kinect camera and comparison with traditional methods.","authors":"Bülent Işik, Kenan Erdaği, Serkan Örücü, Usame Ömer Osmanoğlu, Erkan Özbay","doi":"10.1007/s11517-025-03373-3","DOIUrl":"https://doi.org/10.1007/s11517-025-03373-3","url":null,"abstract":"<p><p>Anthropometric measurements are important in a wide range of areas, from athlete selection to performance analysis and medical exercise applications. With its portable and cost-effective structure, Microsoft Kinect offers significant advantages in measuring human movements and provides valuable data in the fields of sports science and medicine. The aim of this study is to compare certain anthropometric measurements of adolescent female weightlifters in the U-15 age category with Kinect V2 and traditional methods. Twelve athletes who won medals in international weightlifting championships were included in the study. Anthropometric evaluations of each athlete were performed using Kinect V2 and the traditional method. Differences between measurements were analyzed with Bland-Altman plots and Pearson correlation coefficients. Kinect was found to exhibit less variability and higher coherency in measurements of humerus, forearm, trochanter-tibiale laterale, tibial lengths, and shoulder width. It has been observed that the traditional method provides more coherence results in hand length measurements. Kinect was found to exhibit less variability and higher coherency in measurements of forearm, trochanter-tibiale laterale, tibial lengths, and shoulder width, indicating its potential as a reliable tool for these parameters. Notably, Kinect demonstrated high reliability for tibial length (ICC 0.918) and moderate reliability for trochanter-tibiale laterale (ICC 0.737), showing its effectiveness in these measurements. Additionally, Kinect exhibited lower variation and higher coherency in most measurements compared to manual methods, supporting its consistency and repeatability in anthropometric assessments. These results indicate that Kinect may be a suitable tool for some measurements but that traditional methods may be preferable for hand length measurements. These findings suggest that Kinect can be used effectively for certain anthropometric measurements in sports and medical science.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081639","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":"Few-shot small vessel segmentation using a detail-preserving network enhanced by discriminator.","authors":"Yan Huang, Jinzhu Yang, Qi Sun, Yuliang Yuan, Yang Hou, Jin Shang","doi":"10.1007/s11517-025-03368-0","DOIUrl":"https://doi.org/10.1007/s11517-025-03368-0","url":null,"abstract":"<p><p>Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039679","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":"Towards biomimetic evolution of artificial intervertebral disc: a review.","authors":"Ashutosh Khanna, Pushpdant Jain, C P Paul","doi":"10.1007/s11517-025-03371-5","DOIUrl":"https://doi.org/10.1007/s11517-025-03371-5","url":null,"abstract":"<p><p>In lumbar total disc replacement, artificial disc implants are utilized to cure degenerative disc disease and restore natural motion. Human intervertebral discs (IVD) are part of the spine and contribute to delivering six degrees of freedom, elastic deformation, and shock absorption and act differently under different load conditions. Despite advancements in spinal fixation systems and IVD replacement techniques, achieving long-term segmental stability while preserving physiological motion remains a significant challenge. To overcome this issue, the proposed work aims to identify the biomechanics of artificial IVD implants through rigorous analysis. The ultimate goal is to provide the information to explore the design and develop novel implants that seamlessly integrate with the spine, restoring natural spine function and providing long-term, sustainable load-bearing properties, mimicking the resilience and longevity of the natural IVD. To address all these issues, a comprehensive review of the literature was conducted, organizing findings based on body structure, associated diseases, biomechanics, and various IVD development models. The present endeavour involves a critical analysis with the aim of facilitating the input to the design and development of novel IVD implants in the future.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052040","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":"Predicting fixations and gaze location from EEG.","authors":"Yoelvis Moreno-Alcayde, V Javier Traver, Luis A Leiva","doi":"10.1007/s11517-025-03362-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03362-6","url":null,"abstract":"<p><p>Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057225","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":"Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer.","authors":"Saeideh Khorshid Sokhangouy, Mohsen Zeinali, Sina Fathi, Elham Nazari","doi":"10.1007/s11517-025-03365-3","DOIUrl":"https://doi.org/10.1007/s11517-025-03365-3","url":null,"abstract":"<p><p>Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the development of more precise and specific methods for diagnosis. In this study, a deep learning model was applied to identify ten key genes from a pool of 68 common differentially expressed genes in the three transcriptomic datasets. The model demonstrated high performance, with the accuracy of 0.969, R² of 0.88, and PR-AUC of 0.98. Notably, selected genes have been previously reported as functionally important in various cancers. Among them, SCUBE2 stands out as a novel potential diagnostic biomarker in prostate cancer, showing a strong diagnostic performance in the TCGA dataset with AUC = 0.84, sensitivity = 0.76, and specificity = 0.84. SCUBE2 is a secreted glycoprotein known for its ability to suppress tumor growth, cell migration, and epithelial-mesenchymal transition (EMT) in several cancer types, including gliomas, breast, and colorectal cancers, mainly through its regulation of signaling pathways such as Hedgehog (Shh). Although its role in prostate cancer (PCa) has not been previously explored, its consistent downregulation across multiple PCa datasets in this study suggests it may act as a tumor suppressor, warranting further investigation. Another candidate, SLC16A5, showed moderate performance individually (AUC = 0.62, SP = 0.81, SE = 0.42 in GSE88808), but its combination with SCUBE2 significantly enhanced diagnostic accuracy (combined AUC = 0.76, SE = 0.75, SP = 0.71). SLC16A5 is a monocarboxylate transporter involved in metabolic reprogramming, and prior studies have linked its downregulation to immune infiltration and poor prognosis in PCa. Functional enrichment analysis of the ten identified genes revealed strong involvement of these genes in cancer-related processes, including gap junction assembly, tight junction formation, efflux transporter activity, and pathways such as Hedgehog signaling, leukocyte transendothelial migration, and cell-cell adhesion. Hub gene analysis further confirmed the central roles of identified genes such as CAV1, GJA1, AMACR, and CLDN8, which are well-documented in cancer progression, metastasis, or therapeutic resistance. In summary, this study identifies SCUBE2 as a novel potential diagnostic biomarker for prostate cancer and supports the use of AI-driven gene discovery in identifying key players in tumor biology. The combination of SCUBE2 with SLC16A5 not only enhances diagnostic precision but also opens new avenues for functional and clinical validation, ultimately contributing to the development of more accurate, multi-gene diagnostic panels for PCa.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993781","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":"Using baseline MRI radiomic features to predict the efficacy of repetitive transcranial magnetic stimulation in Alzheimer's patients.","authors":"Chandan Saha, Chase R Figley, Brian Lithgow, Xikui Wang, Paul B Fitzgerald, Lisa Koski, Behzad Mansouri, Zahra Moussavi","doi":"10.1007/s11517-025-03366-2","DOIUrl":"https://doi.org/10.1007/s11517-025-03366-2","url":null,"abstract":"<p><p>The efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for Alzheimer's disease (AD) is uncertain at baseline. Herein, we aimed to investigate whether radiomic features from the pre-treatment MRI data could predict rTMS efficacy for AD treatment. Out of 110 participants with AD in the active (n = 75) and sham (n = 35) rTMS treatment groups having T1-weighted brain MRI data, we had two groups of responders (active = 55 and sham = 24) and non-responders (active = 20 and sham = 11). We extracted histogram-based radiomic features from MRI data using 3D Slicer software; the most important features were selected utilizing a combination of a two-sample t-test, correlation test, least absolute shrinkage, and selection operator. The support vector machine classified rTMS responders and non-responders with a cross-validated mean accuracy/AUC of 81.9%/90.0% in the active group and 87.4%/95.8% in the sham group. Further, the radiomic features of the active group significantly correlated with participants' AD assessment scale-cognitive subscale (ADAS-Cog) change after treatment (false discovery rate corrected p < 0.05). Given that baseline radiomic features were able to accurately predict AD patients' responses to rTMS treatment, these radiomic features warrant further investigation for personalizing AD therapeutic strategies.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046643","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":"Prediction of bone ingrowth into the porous swelling bone anchors using an osteoconnectivity-based adaptive finite element algorithm.","authors":"Amirreza Sadighi, Nolan Black, Mehrangiz Taheri, Moein Taghvaei, Sorin Siegler, Thomas P Schaer, Ahmad R Najafi","doi":"10.1007/s11517-025-03370-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03370-6","url":null,"abstract":"<p><p>In this study, a bone ingrowth framework was developed, which was integrated with a hygro-elastic swelling simulation, to evaluate the ingrowth of bone into porous co-polymeric swelling bone anchors. The aim was to investigate the impact of swelling-induced radial stress on bone ingrowth and the improvement in the mechanical properties and fixation strength of the anchors. Using the finite element method coupled with the osteoconnectivity matrix, the model successfully predicted the sequential bone formation within the porous bone anchor. The bone ingrowth framework was validated based on available experimental data, closely aligning with empirical observations. The results show that owing to radial stresses generated in the bone-anchor interface by swelling, considerable bone ingrowth could be stimulated. Moreover, among the three finite element models incorporating porosity within the recommended pore size range (300-600 <math><mrow><mi>μ</mi> <mi>m</mi></mrow> </math> ), smaller pore sizes seem to promote faster and more extensive bone ingrowth, while larger pores exhibit slower ingrowth rates. Regardless of the pore sizes, the mechanical integrity and fixation strength of the anchors significantly improved. These findings strengthen the hypotheses that swelling of such anchors can stimulate bone ingrowth, and highlight the importance of pore geometry, size and interconnectivity in optimizing bone ingrowth and improving their performance.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055976","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":"Deep learning-based prognostic assessment of polyploid giant cancer cells and mitotic figures in liver cancer.","authors":"Jingying Yang, Cuimin Chen, Qiming He, Jiayi Li, Houqiang Li, Jing Peng, Junru Cheng, Meihui Li, Xiaozhuan Zhou, Yonghong He, Tian Guan, Xi Li, Danling Jiang","doi":"10.1007/s11517-025-03360-8","DOIUrl":"https://doi.org/10.1007/s11517-025-03360-8","url":null,"abstract":"<p><p>Primary liver cancer is among the most lethal malignancies, with cell-level structural features such as polyploid giant cancer cells and mitotic figures strongly associated with poor patient prognosis. However, the quantification of these features is hindered by a shortage of pathologists, high workloads, and subjective discrepancies. To address these challenges, we leverage deep learning algorithms to enable the rapid detection of cell-level features, combining this capability with survival analysis to establish a novel, practical prognostic risk assessment system for liver cancer diagnosis and treatment. In collaboration with Peking University Shenzhen Hospital, we collected 172 liver cancer cases, comprising 340 pathology images, to construct the HCCP&M dataset. Our full-process calculation system integrates cell-level feature detection and survival analysis. During the detection phase, the CellFDet framework achieves F1 scores of 0.814, 0.819, and 0.935 for detecting polyploid giant cancer cells, mitotic figures, and general cells, respectively. In the survival analysis phase, patients were stratified into high-risk and low-risk groups based on the polyploid giant cancer cell index (P < 0.0001) and the mitotic index (P = 0.0025), with both indices demonstrating significant survival differences. Correlation analysis further confirmed these features as independent prognostic indicators for liver cancer. Our proposed system not only enables accurate detection of cell-level structural features but also provides reliable survival predictions, offering a valuable tool for improving the prognosis and treatment planning for liver cancer patients.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047307","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":"Corticospinal tract reconstruction with tumor by using a novel direction filter based tractography method.","authors":"Qingrun Zeng, Ze Xia, Jiahao Huang, Lei Xie, Jiawei Zhang, Shengwei Huang, Zhengqiu Xing, Qichuan Zhuge, Yuanjing Feng","doi":"10.1007/s11517-025-03357-3","DOIUrl":"https://doi.org/10.1007/s11517-025-03357-3","url":null,"abstract":"<p><p>The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010712","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}