Healthcare Technology Letters最新文献

Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information. 生成人工智能在提供患者健康信息方面的伦理和法律挑战的证据综合。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-10-14 eCollection Date: 2026-01-01 DOI: 10.1049/htl2.70102

Krithika Anil, Inocencio Daniel Cortes Maramba, David Paul Cook, Daniel Cyrus, Zhamayne Fakharuzi, Amir Hossein Namdar, Timothy Owen West, Sandeep Shirgill

{"title":"Evidence Synthesis of the Ethical and Legal Challenges of Generative Artificial Intelligence in the Provision of Patient Health Information.","authors":"Krithika Anil, Inocencio Daniel Cortes Maramba, David Paul Cook, Daniel Cyrus, Zhamayne Fakharuzi, Amir Hossein Namdar, Timothy Owen West, Sandeep Shirgill","doi":"10.1049/htl2.70102","DOIUrl":"10.1049/htl2.70102","url":null,"abstract":"Large language model (LLM)-based generative AI offers the promise of responsive health information for patients and carers but presents ethical and legal challenges when used outside clinical oversight. This review maps these considerations for patients accessing information on long-term conditions in non-clinical settings. Following PRISMA guidelines, we searched databases (MEDLINE, CINAHL, EMBASE, and Web of Science) between April and May 2025. The review synthesises 24 cross-sectional studies regarding patient-facing LLMs, excluding clinician-support tools. Results indicate that LLMs perform well on general topics but struggle with specialised information, often generating complex responses with unreliable citations. Ethical concerns highlight inaccuracy, insufficient empathy, and the potential exacerbation of health inequalities, while analyses of legal challenges focus mainly on liability and consent. We conclude that current technical limitations and regulatory gaps regarding device classification and safety obligations could pose risks to patients. Consequently, stakeholders must establish clear accountability frameworks, and LLMs should currently function only as supplementary tools rather than replacements for expert clinical advice. Further research into the use of agent-based LLM architectures, where specialised LLM agents collaborate to verify information, reason symbolically, and interface with patient health records under strict data governance, may provide the solution to the current limitations of LLMs.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":"e70102"},"PeriodicalIF":2.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216964","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}

引用次数: 0

Designing a Mobile App for Post Traumatic Stress Disorder: Insights From Lived Experience and Clinical Practice 设计创伤后应激障碍的移动应用程序：来自生活经验和临床实践的见解。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-10-01 DOI: 10.1049/htl2.70098

Richard Laugharne, Michael Loizou, Marius Varga, Sivia Lin, Sarah Lennard, Bojana Daw Srdanovic, Roger Farrell, Rohit Shankar

{"title":"Designing a Mobile App for Post Traumatic Stress Disorder: Insights From Lived Experience and Clinical Practice","authors":"Richard Laugharne, Michael Loizou, Marius Varga, Sivia Lin, Sarah Lennard, Bojana Daw Srdanovic, Roger Farrell, Rohit Shankar","doi":"10.1049/htl2.70098","DOIUrl":"10.1049/htl2.70098","url":null,"abstract":"Post-traumatic stress disorder (PTSD) is a disabling anxiety disorder precipitated by severe trauma. Many people with this condition have little access to clinical interventions, especially in low-middle-income countries. For the development of any digital health intervention, it is vital to know the opinions of PLE and clinicians to guide the developers. We conducted two focus groups, one with clinicians (n = 5) and one with people with lived experience (PLE) (n = 11), to gain knowledge of their views on the development of a PTSD App. Transcripts of both focus groups were coded and thematically analysed. The key themes that emerged from the PLE group were the importance of ease of use with clear, brief, positive but not patronising communication; control over the App styling; and the desire for a variety of interventions and outcome measures. For the clinical group, main themes included bite-sized positive communication, App providing a choice of interventions and outcome measures while monitoring progress. Both groups talked positively about how gamification might be helpful. Our small study highlights that co-producing health Apps, such as the proposed PTSD one with lived experience and clinicians, ensures accessible, non-patronising design, meaningful choice, and scalable digital support to extend care where services remain limited.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628459/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115765","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}

引用次数: 0

ALSGate: An Efficient Gated Mixture-of-Experts Model for Reliable ALS Detection Using EMG Signals ALSGate：一种有效的门控混合专家模型，用于肌电图信号的可靠ALS检测。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-09-03 DOI: 10.1049/htl2.70097

Sandipa Chowdhury, Sudipto Pramanik, Sudha Bhattacharjee, Motasim Billah

{"title":"ALSGate: An Efficient Gated Mixture-of-Experts Model for Reliable ALS Detection Using EMG Signals","authors":"Sandipa Chowdhury, Sudipto Pramanik, Sudha Bhattacharjee, Motasim Billah","doi":"10.1049/htl2.70097","DOIUrl":"10.1049/htl2.70097","url":null,"abstract":"Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting motor neurons, resulting in neuromuscular weakness and paralysis. Electromyography (EMG) is of vital importance for the detection of ALS. In this paper, a refined mixture of experts is proposed that automatically discriminates ALS patients from non-ALS cases using clinical EMG signals from the N2001 EMGLAB open-access dataset. The architecture consists of a 1D convolutional neural network, a temporal convolutional network and a spectrogram-based CNN to collectively learn localised temporal, long-range temporal and spectral features from EMG activity. A gating mechanism dynamically weights expert contributions and performs significantly better than equal-weight fusion. Training with focal loss and exponential moving average stabilisation addresses class imbalance and improves convergence. The proposed approach reached an AUROC of 0.9992, an F1-score of 0.9903 and a balanced accuracy of 0.9905, demonstrating strong discriminative performance and potential for real-time clinical applications.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148888877","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}

引用次数: 0

Impact of Aggregation Functions and Learning Settings in Privacy-Preserving Federated Learning for Skin Cancer Detection 聚合函数和学习设置在隐私保护联合学习中对皮肤癌检测的影响。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-08-31 DOI: 10.1049/htl2.70096

Bela Shrimali, Rebakah Geddam, Hemant Ghayvat, Rushi Shah, Mohit Ginglani

{"title":"Impact of Aggregation Functions and Learning Settings in Privacy-Preserving Federated Learning for Skin Cancer Detection","authors":"Bela Shrimali, Rebakah Geddam, Hemant Ghayvat, Rushi Shah, Mohit Ginglani","doi":"10.1049/htl2.70096","DOIUrl":"10.1049/htl2.70096","url":null,"abstract":"Skin cancer is among the most common malignant tumors worldwide, and early detection is essential to improve patient survival and recovery. Conventional AI-based diagnostic approaches rely on centralized data, which raises security and privacy concerns. This paper presents an implementation of federated learning that addresses these challenges by enabling collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential. A convolutional neural network architecture, ResNet50, is employed at client nodes, with preprocessing steps including image augmentation, contrast enhancement, and lesion segmentation to improve feature extraction on the International Skin Imaging Collaboration (ISIC) dataset. Multiple aggregation algorithms are implemented at the central server, along with both vertical and horizontal federated learning settings. Their performance is evaluated using metrics such as precision, accuracy, recall, and F1-score. In the horizontal federated learning setting, the FedNova aggregation approach outperformed other methods, achieving an accuracy of 76.45%, F1-score of 0.73, and a recall of 0.72, demonstrating enhanced overall classification performance among the evaluated aggregation algorithms and stable convergence across clients. These results highlight the impact of federated learning settings and aggregation strategies on overall skin cancer detection performance.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148866926","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}

引用次数: 0

RETRACTION: Autism Spectrum Disorder Detection Using Facial Images: A Performance Comparison of Pretrained Convolutional Neural Networks 使用面部图像检测自闭症谱系障碍：预训练卷积神经网络的性能比较。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-08-17 DOI: 10.1049/htl2.70095

{"title":"RETRACTION: Autism Spectrum Disorder Detection Using Facial Images: A Performance Comparison of Pretrained Convolutional Neural Networks","authors":"","doi":"10.1049/htl2.70095","DOIUrl":"10.1049/htl2.70095","url":null,"abstract":"RETRACTION: I. Ahmad, J. Rashid, M. Faheem, A. Akram, N. A. Khan, and R. Amin, “Autism Spectrum Disorder Detection Using Facial Images: A Performance Comparison of Pretrained Convolutional Neural Networks,” Healthcare Technology Letters 11, no. 1 (2024): 227–239, https://doi.org/10.1049/htl2.12073.The above article, published online on 08 January 2024 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Christopher James; The Institution of Engineering and Technology; and John Wiley & Sons Ltd. The retraction was agreed upon due to concerns regarding both the reliability of the study and significant privacy issues. The dataset used in the study contains images of children purported to have, or not have autism spectrum disorder (ASD), sourced from the internet without any documented clinical history or independent verification of an ASD diagnosis, raising concerns about the scientific validity of the dataset. In addition, the images of minors have been used and presented in the article without consent from the individuals or their parents/legal guardians. For these reasons, the article has been retracted, and the corresponding images of children have been redacted. The authors have been informed of the decision, and they disagree with the retraction.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799122","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}

引用次数: 0

InteractOR: Interacting with Images during Surgery in Mixed Reality Through Real-Time Instrument Segmentation InteractOR：在混合现实手术中通过实时仪器分割与图像交互。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-08-15 DOI: 10.1049/htl2.70094

Nour Karoui, Isabelle Bloch, Ignacio Avellino

引用次数: 0

User-Centred Design of Multi-DoF Prosthetic Wrist Controllers: Performance and Preference in Daily Living Tasks 以用户为中心的多自由度假肢手腕控制器设计：日常生活任务中的性能和偏好。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-07-31 DOI: 10.1049/htl2.70092

Deepti Bharadwaj, Ingyun Ahn, Saurabh S. Deshmukh, Jiyeon Kang

{"title":"User-Centred Design of Multi-DoF Prosthetic Wrist Controllers: Performance and Preference in Daily Living Tasks","authors":"Deepti Bharadwaj, Ingyun Ahn, Saurabh S. Deshmukh, Jiyeon Kang","doi":"10.1049/htl2.70092","DOIUrl":"10.1049/htl2.70092","url":null,"abstract":"This study presents a practical, IMU-based control framework for 2-DoF prosthetic wrists, designed to explore the feasibility of supporting functional wrist control in daily tasks. The system employs movement synergies inherent to the upper limb and offers three controller configurations: pronation–supination (PS), radial-ulnar deviation (RUD) and a combined PS-RUD mode. Ten healthy participants, simulating upper-limb amputees, performed four representative activities of daily living in a virtual environment, during which both kinematic performance metrics and user-perceived effort were recorded. Task-specific controllers led to significant reductions in compensatory limb movements, and overall, the combined PS-RUD controller was preferred by users regardless of task completion time. Participants reported lower physical and cognitive effort, underscoring the intuitiveness and usability of the suggested controller. These preliminary findings suggest the potential of a user-centred control strategy for prosthetic wrists that could reduce training burden and facilitate seamless integration into everyday functional use.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13425627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654596","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}

引用次数: 0

A Privacy-Preserving Markov Chain-Based Framework for Robust Motor Imagery EEG Classification in Brain–Computer Interfaces 基于马尔可夫链的脑机接口鲁棒运动图像脑电分类框架。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-07-22 DOI: 10.1049/htl2.70093

Taslima Khanam, Siuly Siuly, Hua Wang

{"title":"A Privacy-Preserving Markov Chain-Based Framework for Robust Motor Imagery EEG Classification in Brain–Computer Interfaces","authors":"Taslima Khanam, Siuly Siuly, Hua Wang","doi":"10.1049/htl2.70093","DOIUrl":"10.1049/htl2.70093","url":null,"abstract":"Accurate classification of motor imagery (MI)-based electroencephalogram (EEG) signals is often challenged by signal non-stationarity, subject-specific variability, and privacy concerns associated with sharing raw neural data. To address these challenges, this study proposes a hybrid Markov chain–spatial statistical (MCSS) machine learning framework for privacy-preserving MI–EEG classification. Spatially filtered EEG signals were discretised into six symbolic amplitude states representing progressively increasing signal intensities. These symbolic sequences were modelled as stochastic processes, from which transition probability matrices (TPMs) were constructed as the primary feature representation. This TPM-based abstraction provides a non-invertible and privacy-friendly feature space, substantially reducing the ability to reconstruct the original neural waveform. To enhance discriminative performance, the Markov features were combined with seven statistical descriptors. The framework was evaluated using three machine learning algorithms on the brain–computer interface (BCI) Competition III Datasets IVa and IVb. The proposed MCSS + support vector machine framework achieved consistently high classification accuracy (>98%) across all subjects, demonstrating strong robustness and cross-subject stability. Privacy robustness was further validated through empirical threat-model evaluation, where membership inference attacks remained close to chance level, and feature inversion attacks showed low reconstruction similarity. Overall, the framework provides an accurate, computationally efficient, and privacy-preserving solution for scalable BCI development.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13390764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563108","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}

引用次数: 0

Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification 基于染色归一化和深度特征提取的可解释集成学习用于急性淋巴细胞白血病分类。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-07-14 DOI: 10.1049/htl2.70091

Faysal Ahmmed, Md. Sadi Al Huda, Md. Asraf Ali, M. M. Manjurul Islam

{"title":"Explainable Ensemble Learning With Stain Normalization and Deep Feature Extraction for Acute Lymphoblastic Leukaemia Classification","authors":"Faysal Ahmmed, Md. Sadi Al Huda, Md. Asraf Ali, M. M. Manjurul Islam","doi":"10.1049/htl2.70091","DOIUrl":"10.1049/htl2.70091","url":null,"abstract":"Acute lymphoblastic leukaemia (ALL), a common form of cancer, remains a life-threatening condition that affects individuals worldwide, including both adults and children. The prognosis is significantly worse when the disease is identified at an advanced stage, as the chances of successful treatment diminish. Diagnosis typically requires invasive and costly procedures, which can delay timely intervention. Images from peripheral blood smears (PBS) have been useful for the preliminary screening of ALL in probable cases. However, due to the nonspecific presentation of the disease, interpreting these images poses significant challenges, increasing the risk of misdiagnosis. We have proposed an approach that uses 20,000 PBS images to precisely enable the early diagnosis of ALL and its distinct subtypes (benign, malignant-early Pro-B, malignant-Pro-B, and malignant-Pre-B) in order to overcome these problems. Our proposed system uses colour normalized PBS images using Vahadane method along with the feature extraction capabilities of deep neural networks combined with a stacking ensemble learning approach that integrates various machine learning algorithms for classification. This system is capable of distinguishing ALL cases from haematogones and accurately identifying its subtypes. By streamlining the diagnostic process, the platform aims to reduce the effort and time required by clinicians and patients. Our proposed model highlight the system's effectiveness, with a comparative analysis of established machine learning algorithms showcasing its superior performance. The proposed model achieved exceptional accuracy (99.95%), recall (99.95%), precision (99.95%) and F1-score (99.95%) for the early detection of ALL and its variants, indicating its potential as a proof-of-concept system, while requiring further validation before clinical deployment.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449612","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}

引用次数: 0

Driving Innovation: Transatlantic Attitudes to the Bionics Bus as a Vehicle for Health Transformation and STEM Engagement 推动创新：跨大西洋对仿生学总线作为健康转型和STEM参与载体的态度。

IF 2.4

Healthcare Technology Letters Pub Date : 2026-06-18 DOI: 10.1049/htl2.70090

Christopher James, Sarang Shankar, Madeleine Dale, Bhathika Perera, Richard Laugharne, Matthew Allen, Maria Feijoo-renones, Rohit Shankar

{"title":"Driving Innovation: Transatlantic Attitudes to the Bionics Bus as a Vehicle for Health Transformation and STEM Engagement","authors":"Christopher James, Sarang Shankar, Madeleine Dale, Bhathika Perera, Richard Laugharne, Matthew Allen, Maria Feijoo-renones, Rohit Shankar","doi":"10.1049/htl2.70090","DOIUrl":"10.1049/htl2.70090","url":null,"abstract":"Emerging mobile health and STEM engagement platforms are increasingly recognised as catalysts for improving public understanding of biomedical technologies and supporting digitally enabled models of healthcare delivery. The Bionics Bus (https://bionicsbus.org/) is a novel mobile laboratory designed to democratise access to robotics, artificial intelligence, prosthetics and digital diagnostics by delivering hands-on experiences directly to schools, communities and healthcare settings. This study provides the first transatlantic comparison of public attitudes toward the Bionics Bus within the United Kingdom and the United States of America (USA), two countries with contrasting healthcare systems and digital transformation priorities. Using a mixed-methods cross-sectional design, 78 participants completed a survey assessing technology perceptions, healthcare accessibility, enthusiasm for STEM and views on the usefulness and potential of the Bionics Bus. Quantitative analysis demonstrated high levels of excitement and strong recognition of the relevance of technology in healthcare across both countries, with USA respondents more likely to report geographical barriers to accessing healthcare. Thematic analysis revealed shared optimism for the Bus as a tool for education, accessibility and community-based innovation. These findings highlight the potential of mobile biomedical platforms to support digital inclusivity, strengthen science capital and enhance public engagement with technology-enabled healthcare.","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"13 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13279338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296667","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}

引用次数: 0