Biomedical Physics & Engineering Express最新文献

{"title":"Full fine-tuning strategy for endoscopic foundation models with expanded learnable offset parameters.","authors":"Minghan Dong, Xiangwei Zheng, Xia Zhang, Xingyu Zhang, Mingzhe Zhang","doi":"10.1088/2057-1976/adaec3","DOIUrl":"10.1088/2057-1976/adaec3","url":null,"abstract":"<p><p>In the medical field, endoscopic video analysis is crucial for disease diagnosis and minimally invasive surgery. The Endoscopic Foundation Models (Endo-FM) utilize large-scale self-supervised pre-training on endoscopic video data and leverage video transformer models to capture long-range spatiotemporal dependencies. However, detecting complex lesions such as gastrointestinal metaplasia (GIM) in endoscopic videos remains challenging due to unclear boundaries and indistinct features, and Endo-FM has not demonstrated good performance. To this end, we propose a fully fine-tuning strategy with an Extended Learnable Offset Parameter (ELOP), which improves model performance by introducing learnable offset parameters in the input space. Specifically, we propose a novel loss function that combines cross-entropy loss and focal loss through a weighted sum, enabling the model to better focus on hard-to-classify samples during training. We validated ELOP on a private GIM dataset from a local grade-A tertiary hospital and a public polyp detection dataset. Experimental results show that ELOP significantly improves the detection accuracy, achieving accuracy improvements of 6.25 % and 3.75%respectively compared to the original Endo-FM. In summary, ELOP provides an excellent solution for detecting complex lesions in endoscopic videos, achieving more precise diagnoses.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051468","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":"Magnetic vector field mapping of the stimulated abductor digiti minimi muscle with optically pumped magnetometers.","authors":"Marlen Kruse, Simon Nordenström, Stefan Hartwig, Justus Marquetand, Victor Lebedev, Thomas Middelmann, Philip J Broser","doi":"10.1088/2057-1976/adaec5","DOIUrl":"10.1088/2057-1976/adaec5","url":null,"abstract":"<p><p><i>Objective.</i>Mapping the myomagnetic field of a straight and easily accessible muscle after electrical stimulation using triaxial optically pumped magnetometers (OPMs) to assess potential benefits for magnetomyography (MMG).<i>Approach.</i>Six triaxial OPMs were arranged in two rows with three sensors each along the abductor digiti minimi (ADM) muscle. The upper row of sensors was inclined by 45° with respect to the lower row and all sensors were aligned closely to the skin surface without direct contact. Then, the electromagnetic muscle activity was electrically evoked utilizing stepwise increasing currents at the cubital tunnel at the ulnar nerve. Evoked myomagnetic activity was recorded with 18 channels, three per sensor. As the measurements were performed in PTB's magnetically shielded room (BMSR-2) no averaging and only moderate filtering was applied.<i>Main results.</i>The myomagnetic vector field could be successfully mapped. The obtained spatial structure with a radial symmetry corresponds to the expectations from the ADM's parallel muscle architecture. The temporal evolution exhibits an up to four-phasic shape. Implications for future experiments are derived and needs for sensor performance improvements are identified<i>. Significance.</i>The use of an OPM array with small (∼3 mm edge length) sensing voxels enabled the mapping of the magnetic vector field of the ADM. This allowed visualization of the spatiotemporal evolution of the muscle's evoked magnetic field and gives implications for future experiments. In the future, high density OPM grids may enable high-accuracy determination of muscle parameters such as innervation zone position, pennation angle, and propagation velocities.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051475","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":"Experimental and GEANT4 simulated FEPE of NaI(Tl) detector for linear sources.","authors":"Shahid Mansoor, Azhar Hussain Malik, Khizar Hayat Satti, Misbah Ijaz, Muhammad Tariq Siddique","doi":"10.1088/2057-1976/adaec8","DOIUrl":"10.1088/2057-1976/adaec8","url":null,"abstract":"<p><p>The current study investigated the geometry, design and solid angle impacts on full energy peak efficiency (FEPE) of NaI(Tl) detectors for a line source. A line source is fabricated using^99mTc solution filled in a borosilicate glass tube of inner diameter 3 mm, tube wall thickness 2.5 mm and length 12.7 cm. The FEPE is measured for the fabricated linear source using 2″×2″ NaI(Tl) cylindrical detector at various source-detector distances. The experimental setup is simulated in GEANT4 and the computed FEPE values are compared with experimental values. The absolute error of 5% is observed between computed and measured FEPE values. Utilizing the advantages of MC simulations, the impact of numerous source parameters such as source length, diameter, source-detector distance, glass tube thickness and lead shield effects on FEPE are investigated to optimize the fabrication process of linear sources. A case study for current investigation has been analyzed by considering absolute FEPE of the NaI(Tl) system for a syringe filled with radioactive solution. This study provides an insight for the fabrication of standard linear sources by analyzing different source parameters and hence, may serve as a guideline to prepare standard linear sources for the calibration of radiation detectors.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051464","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":"Optimizing transformer-based network via advanced decoder design for medical image segmentation.","authors":"Weibin Yang, Zhiqi Dong, Mingyuan Xu, Longwei Xu, Dehua Geng, Yusong Li, Pengwei Wang","doi":"10.1088/2057-1976/adaec7","DOIUrl":"10.1088/2057-1976/adaec7","url":null,"abstract":"<p><p>U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. To address the challenges of scale and complexity in medical tasks, several variants of U-Net have been proposed. In particular, methods based on Vision Transformer (ViT), represented by Swin UNETR, have gained widespread attention in recent years. However, these improvements often focus on the encoder, overlooking the crucial role of the decoder in optimizing segmentation details. This design imbalance limits the potential for further enhancing segmentation performance. To address this issue, we analyze the roles of various decoder components, including upsampling method, skip connection, and feature extraction module, as well as the shortcomings of existing methods. Consequently, we propose Swin DER (i.e.,<b>Swin</b>UNETR<b>D</b>ecoder<b>E</b>nhanced and<b>R</b>efined), by specifically optimizing the design of these three components. Swin DER performs upsampling using learnable interpolation algorithm called offset coordinate neighborhood weighted up sampling (Onsampling) and replaces traditional skip connection with spatial-channel parallel attention gate (SCP AG). Additionally, Swin DER introduces deformable convolution along with attention mechanism in the feature extraction module of the decoder. Our model design achieves excellent results, surpassing other state-of-the-art methods on both the Synapse dataset and the MSD brain tumor segmentation task. Code is available at:<i>https://github.com/WillBeanYang/Swin-DER</i>.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051479","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":"External delay and dispersion correction of automatically sampled arterial blood with dual flow rates.","authors":"Benjamin Brender, Lubna Burki, Josefina Jeon, Alvina Ng, Nikta Yussefian, Carme Uribe, Emily Murrell, Isabelle Boileau, Kimberly L Desmond, Lucas Narciso","doi":"10.1088/2057-1976/adae13","DOIUrl":"10.1088/2057-1976/adae13","url":null,"abstract":"<p><p><i>Objective</i>. Arterial sampling for PET imaging often involves continuously measuring the radiotracer activity concentration in blood using an automatic blood sampling system (ABSS). We proposed and validated an external delay and dispersion correction procedure needed when a change in flow rate occurs during data acquisition. We also measured the external dispersion constant of [¹¹C]CURB, [¹⁸F]FDG, [¹⁸F]FEPPA, and [¹⁸F]SynVesT-1.<i>Approach</i>. External delay and dispersion constants were measured for the flow rates of 350, 300, 180, and 150 ml h^-1, using 1-minute-long rectangular inputs (<i>n</i>= 10;¹⁸F-fluoride in saline). Resulting constants were used to validate the external delay and dispersion corrections (<i>n</i>= 6;¹⁸F-fluoride in saline; flow rate change: 350 to 150 ml h^-1and 300 to 180 ml h^-1); constants were modelled to transition linearly between flow rates. Corrected curves were assessed using the percent area-under-the-curve (AUC) ratio and a modified model selection criterion (MSC). External delay and dispersion constants were measured for various radiotracers using a blood analog (i.e., similar viscoelastic properties).<i>Main results</i>. ABSS outputs were successfully corrected for external delay and dispersion using our proposed method accounting for a change in flow rate. AUC ratio reduced from ∼10% for the uncorrected 350-150 ml h^-1output (∼6% for the 300-180 ml h^-1) to < 1% after correction when compared to true input (511 keV energy window); approx. 5-fold increase in MSC. Assuming an internal dispersion constant of 5 s, the dispersion constant (internal + external) for [¹¹C]CURB, [¹⁸F]FDG, [¹⁸F]FEPPA, and [¹⁸F]SynVesT-1 was 13, 9, 16, and 10 s, respectively.<i>Significance</i>. This study presented an external delay and dispersion correction procedure needed when a change in flow rate occurs during ABSS data acquisition. Additionally, this is the first study to measure the external delay and dispersion constants using a blood analog solution, a suitable alternative to blood when estimating external dispersion.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143032212","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":"Quantitative assessment of delivered dose in carbon ion spatially fractionated radiotherapy (C-SFRT) and biological response to C-SFRT.","authors":"Toshiro Tsubouchi, Misato Umemura, Kazumasa Minami, Noriaki Hamatani, Naoto Saruwatari, Masashi Yagi, Keith M Furutani, Masaaki Takashina, Shinichi Shimizu, Tatsuaki Kanai","doi":"10.1088/2057-1976/ada964","DOIUrl":"10.1088/2057-1976/ada964","url":null,"abstract":"<p><p><i>Objective</i>. Applying carbon ion beams, which have high linear energy transfer and low scatter within the human body, to Spatially Fractionated Radiation Therapy (SFRT) could benefit the treatment of deep-seated or radioresistant tumors. This study aims to simulate the dose distributions of spatially fractionated beams (SFB) to accurately determine the delivered dose and model the cell survival rate following SFB irradiation.<i>Approach</i>. Dose distributions of carbon ion beams are calculated using the Triple Gaussian Model. The sensitive volume of the detector used in measurements was also considered. If the measurements and simulations show good agreement, the dose distribution and absolute dose delivered by SFB can be accurately estimated. Three types of dose distributions were delivered to human salivary gland cells (HSGc-C5): uniform dose distribution (UDD), and one-dimensional (1D) grid-like dose distributions (GDD) with 6 mm and 8 mm spacing. These provided high (Peak-to-Valley Dose Ratio, PVDR = 4.0) and low (PVDR = 1.64) dose differences between peak and valley doses, respectively. Linear-Quadratic (LQ) model parameters for HSGc-C5 were derived from the UDD and cell survival fractions (SF) were simulated for 1D GDD using these values.<i>Main results</i>. Good agreement was observed between measurements and simulations when accounting for detector volume. However, the TPS results overestimated dose in steep gradient region, likely due to the 2.0 mm calculation resolution. LQ parameters for HSGc-C5 were<i>α</i>= 0.34 and<i>β</i>= 0.057. The 1D GDD with 6 mm spacing showed good agreement between simulations and experiments, but the 8.0 mm spacing resulted in lower experimental cell survival.<i>Significance</i>. We successfully simulated the GDD and conducted SF simulations. The results suggest potential cell-killing effects due to high-dose differences in SFB.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999524","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":"Quantification of albumin and γ-globulin concentrations by multivariate regression based on admittance relaxation time distribution (<i>mr</i>ARTD).","authors":"Arbariyanto Mahmud Wicaksono, Daisuke Kawashima, Ryoma Ogawa, Shinsuke Akita, Masahiro Takei","doi":"10.1088/2057-1976/adabec","DOIUrl":"10.1088/2057-1976/adabec","url":null,"abstract":"<p><p>Albumin and<i>γ</i>-globulin concentrations in subcutaneous adipose tissues (SAT) have been quantified by multivariate regression based on admittance relaxation time distribution (<i>mr</i>ARTD) under the fluctuated background of sodium electrolyte concentration. The<i>mr</i>ARTD formulates<b>P</b>=<b>Ac</b>+<b>Ξ</b>(<b>P</b>: peak matrix of distribution function magnitudeγˆand relaxation timesτˆ,<b>c</b>: concentration matrix of albumin<i>c</i>_Alb,<i>γ</i>-globulin_Glo<i>c</i>, and sodium electrolyte^Na<i>c</i>,<b>A</b>: coefficient matrix of a multivariate regression model, and<b>Ξ</b>: error matrix). The<i>mr</i>ARTD is implemented by two processes which are: (1) the training process of<b>A</b>through the maximum likelihood estimation of<b>P</b>and (2) the quantification process of<i>c</i>_Alb,_Glo<i>c</i>, and^Na<i>c</i>through the model prediction. In the training process, a positive correlation is present between<i>c</i>_Alb,_Glo<i>c</i>, and^Na<i>c</i>toγˆ₁atτˆ₁= 0.1 as well asγˆ₂atτˆ₂= 1.40 μs as under a fixed concentration of proteins solution into a porcine SAT (<i>c</i>_Alb= 0.800-2.400 g/dL,_Glo<i>c</i>= 0.400-1.200 g dl^-1and^Na<i>c</i>= 0.700-0.750 g dl^-1). The<i>mr</i>ARTD method quantifies<i>c</i>_Alb,_Glo<i>c</i>, and^Na<i>c</i>in SAT with an absolute error of 33.79%, 44.60%, and 2.18%, respectively.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999522","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":"Modelling and validation of a 6 MV compact linear accelerator via Monte Carlo simulation using Geant4 Application for Tomographic Emission (GATE).","authors":"Maynard E Limbaco, Franklin U Toledo, Renna Mae V Tondo, Salasa A Nawang","doi":"10.1088/2057-1976/ada9ef","DOIUrl":"10.1088/2057-1976/ada9ef","url":null,"abstract":"<p><p><b>Objective</b>. To accurately model and validate the 6 MV Elekta Compact linear accelerator using the Geant4 Application for Tomographic Emission (GATE). In particular, this study focuses on the precise calibration and validation of critical parameters, including jaw collimator positioning, electron source nominal energy, flattening filter geometry, and electron source spot size, which are often not provided in technical documentation.<b>Methods</b>. Simulation of the Elekta Compact^TM6 MV linear accelerator was performed using the Geant4 Application for Tomographic Emission (GATE) v.9.1. A 50 cm × 50 cm × 50 cm water phantom was irradiated with a source-to-surface distance of 100 cm. Percentage Depth Dose Profile (PDD) and Lateral Dose Profile (Crossplane and Inplane) were assessed as reference dose measurements. The half-length field difference (FHLD) method was introduced to optimize the jaw collimator setup. Gamma index analysis was used to quantitatively assess the accuracy of the simulated dosimetry data in relation to the actual dose measurements.<b>Results</b>. Crucial parameters of the Linac Head have been successfully optimized. The validation achieved Gamma-Index acceptance rates of 97.93% for the Depth Dose profile, 100% for the Crossplane (X) Dose Profile, and 93.98% for the Inplane (Y) Dose Profile, all meeting the 1%/1 mm Gamma-Index criteria.<b>Conclusion</b>. The simulation and calibration of the Elekta Compact Linac have achieved a reliable model with high fidelity in dosimetry calculations which could pave the way for the future development and application of new techniques using Elekta Compact^TMLinear Accelerator.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982478","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":"Hybrid data augmentation strategies for robust deep learning classification of corneal topographic maptopographic map.","authors":"Abir Chaari, Imen Fourati Kallel, Sonda Kammoun, Mondher Frikha","doi":"10.1088/2057-1976/adabea","DOIUrl":"10.1088/2057-1976/adabea","url":null,"abstract":"<p><p>Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification. We propose a hybrid data augmentation approach that combines traditional transformations, generative adversarial networks, and specific generative models. Experimental results demonstrate that the hybrid data augmentation method, achieves the highest accuracy of 99.54%, significantly outperforming individual data augmentation techniques. This hybrid approach not only improves model accuracy but also mitigates overfitting issues, making it a promising solution for medical image classification tasks with limited data availability.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999520","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":"A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.","authors":"Asifa Nazir, Ahsan Hussain, Mandeep Singh, Assif Assad","doi":"10.1088/2057-1976/ad9eb7","DOIUrl":"10.1088/2057-1976/ad9eb7","url":null,"abstract":"<p><p>Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ultrasound, and Positron Emission Tomography (PET), offer vital insights into three-dimensional structures but frequently fall short of delivering a comprehensive and detailed anatomical analysis, capturing only amplitude details. Three-dimensional holography microscopic medical imaging provides a promising solution by capturing the amplitude (brightness) and phase (structural information) details of biological structures. In this study, we investigate the novel collaborative potential of Deep Learning (DL) and holography microscopic phase imaging for cancer diagnosis. The study comprehensively examines existing literature, analyzes advancements, identifies research gaps, and proposes future research directions in cancer diagnosis through the integrated Quantitative Phase Imaging (QPI) and DL methodology. This novel approach addresses a critical limitation of traditional imaging by capturing detailed structural information, paving the way for more accurate diagnostics. The proposed approach comprises tissue sample collection, holographic image scanning, preprocessing in case of imbalanced datasets, and training on annotated datasets using DL architectures like U-Net and Vision Transformer(ViT's). Furthermore, sophisticated concepts in DL, like the incorporation of Explainable AI (XAI) techniques, are suggested for comprehensive disease diagnosis and identification. The study thoroughly investigates the advantages of integrating holography imaging and DL for precise cancer diagnosis. Additionally, meticulous insights are presented by identifying the challenges associated with this integration methodology.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821731","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}