Physiological measurement最新文献

{"title":"Comparison of feature-based indices derived from photoplethysmogram recorded from different body locations during lower body negative pressure.","authors":"Shrikant Chand, Neng-Tai Chiu, Yun-Hsin Chou, Aymen Alian, Kirk Shelley, Hau-Tieng Wu","doi":"10.1088/1361-6579/adf489","DOIUrl":"10.1088/1361-6579/adf489","url":null,"abstract":"<p><p><i>Objective.</i>Various time domain features, including dicrotic notch (<b>dic</b>), in photoplethysmogram (PPG), and the pulse transit time (PTT) determined using the simultaneously recorded electrocardiogram (ECG), are believed to have a critical role with many potential clinical applications. However, the dependence of these parameters on PPG sensor location is less well known.<i>Approach.</i>Three transmissive pulse oximetry probes (Xhale) were put simultaneously on the ear, nose, and finger of 36 healthy volunteers in the lower body negative pressure (LBNP) experiment. Various features of the recorded PPG signals were analyzed across different LBNP phases for each location. Simultaneously recorded finger PPG and ECG (Nellcor) were used to assess the dependence of PTT on PPG sensor location.<i>Main results.</i>PPG signal quality varies by measurement site, with nasal PPG showing the highest quality and ear PPG the lowest. Except pulse rate (PR), most feature-related indices differ across sites. Specifically, the ratios of detectable<b>dic</b>vary, highest in finger PPG and lowest in nasal PPG. When<b>dic</b>is detectable, the<i>e</i>point and<b>dic</b>are significantly different. PR variability indices and PTT also vary by location, though no clear conclusions can be drawn about PTT behavior across different LBNP phases.<i>Significance.</i>Various indices derived from PPG signals in a well-controlled study environment are influenced by sensor placement. Although not all possible indices are examined, the findings clearly illustrate the sensitivity of signal features to measurement location. While these results may not be directly generalizable to routine clinical settings, caution is warranted when extrapolating findings from one PPG site to another. This consideration is especially important in the digital health era, where mobile devices with PPG sensors are increasingly deployed at diverse body sites.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144718313","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":"Prescreening depression using wearable electrocardiogram and photoplethysmogram data from a psycholinguistic experiment.","authors":"Sajjad Karimi, Masoud Nateghi, Gabriela I Cestero, Lina Sophie Chitadze, Deepanshi Sharma, Yi Yang, Juhee H Vyas, Chuoqi Chen, Zeineb Bouzid, Cem Okan Yaldiz, Nicholas Harris, Rachel Bull, Bradly Stone, Spencer K Lynn, Bethany K Bracken, Omer T Inan, James Douglas Bremner, Reza Sameni","doi":"10.1088/1361-6579/adf6fe","DOIUrl":"https://doi.org/10.1088/1361-6579/adf6fe","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Depression is a prevalent mental health disorder that significantly impacts well-being and quality of life. This study investigates the relationship between depression and cardiovascular function, exploring time-series features derived from electrocardiogram (ECG) and photoplethysmogram (PPG) data as potential biomarkers for depression prescreening.&#xD;&#xD;Approach: &#xD;As part of a comprehensive psycholinguistic experiment, we collected data from 60 individuals, including both healthy participants and those with varying levels of depression, assessed using the Beck Depression Inventory-II (BDI-II) and the Patient Health Questionnaire-9 (PHQ-9). &#xD;&#xD;Bimodal features derived from both ECG and PPG data were used to develop machine learning models for depression risk classification, employing classifiers such as Random Forest, XGBoost, Logistic Regression, and Support Vector Machines (SVM). Additionally, regression models were built to predict depression severity based on ECG- and PPG-derived biomarkers.&#xD;&#xD;Main Results: &#xD;Key findings indicate that short-term variability (SD1) features in the ECG RR interval, peripheral systolic and diastolic phases from the PPG, and pulse duration significantly differ between healthy individuals and those at risk of depression. SVM achieved the best classification performance, with an AUROC of 0.83 ± 0.11 for BDI-II-based classification and 0.78 ± 0.11 for PHQ-9-based classification. SHAP analysis consistently identified systolic-SD1 and RR-SD1 as key predictors. Regression analysis further supported the role of cardiovascular features in assessing depression severity, yielding a mean absolute error (MAE) of 10.18 for BDI-II and 5.27 for PHQ-9 score regression.&#xD;&#xD;Significance: &#xD;This study demonstrates the feasibility of using wearable ECG and PPG technologies for depression prescreening. The findings suggest that cardiac activity-based biomarkers can contribute to the development of cost-effective, objective, and non-invasive tools for mental health assessment, complementing traditional diagnostic methods.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768858","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":"Dynamic response of Bluetooth wearable heart rate monitors during rapid changes in heart rate.","authors":"Mariah Sabioni, Jonas Willén, Seraina A Dual, Martin Jacobsson","doi":"10.1088/1361-6579/adece4","DOIUrl":"10.1088/1361-6579/adece4","url":null,"abstract":"<p><p><i>Objectives.</i>To quantify and evaluate the dynamic response of RR intervals (RRI) and heart rate (HR) measurements of commercially available Bluetooth chest-worn HR monitors during induced rapid changes in HR.<i>Approach.</i>An arbitrary function generator created synthetic electrocardiogram signals simulating the heart activity. Different scenarios of rapid changes in HR were simulated several times using: (1) step responses; (2) exercise data (EX); and (3) intermittent EX data. RRI and HR were recorded using the standard Bluetooth HR service for four wearable monitors: Garmin HRM-Dual, Movesense active, Polar H10, and Wahoo TRACKR. RRI latency, HR latency, and agreement were evaluated from the reference signal.<i>Main results.</i>RRI latency (median and interquartile range) was 0.7(0.5,0.7) s for Garmin, 0.4(0.2,0.5) s for Movesense, 2.6(2.2,2.8) s for Polar, and 2.1(1.9,2.4) s for Wahoo, where results did not differ greatly between tests. HR response latency was different between devices and tests. During intermittent EX tests, HR latency was 3.3(3.0, 3.3) s for Garmin, 1.0(1.0,1.0) s for Movesense, 2.3(2.3,2.3) s for Polar, and 2.2(2.2,2.3) s for Wahoo, where all devices consistently underestimated HR peaks and overestimated HR valleys, with a greater discrepancy in HR valleys.<i>Significance.</i>Most validation protocols of RRI and HR measured by wearable monitors neglect their dynamic characteristics. The present study demonstrated that manufacturers implemented different digital filters to compute the HR values, limiting the devices' ability to capture rapid HR changes. Open documentation of the processing steps is advised, and use cases involving sharp HR changes-such as intermittent high-intensity training-should rely on beat-to-beat RRI recordings.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584545","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":"Physics-informed neural networks for physiological signal processing and modeling: a narrative review.","authors":"Anni Zhao, Davood Fattahi, Xiao Hu","doi":"10.1088/1361-6579/adf1d3","DOIUrl":"10.1088/1361-6579/adf1d3","url":null,"abstract":"<p><p>Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretability. Like many other fields in engineering and science, the analysis of physiological signals has been influenced by PINNs in recent years. This manuscript provides a comprehensive overview of PINNs from various perspectives in the physiological signal analysis domain. After exploring the literature and screening the search results, more than 40 key studies in the related domain are selected and categorized based on both practically and theoretically significant perspectives, including input data types, applications, physics-informed models, and neural network architectures. While the advantages of PINNs in tackling forward and inverse problems in physiological signal contexts are highlighted, challenges such as noisy inputs, computational complexity, loss function types, and overall model configuration are discussed, providing insights into future research directions and improvements. This work can serve as a guiding resource for researchers exploring PINNs in biomedical and physiological signal processing, paving the way for more precise, data-efficient, and clinically relevant solutions.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.","authors":"Ilaria Mentasti, Marta Carrara, Manuela Ferrario","doi":"10.1088/1361-6579/adf0bf","DOIUrl":"10.1088/1361-6579/adf0bf","url":null,"abstract":"<p><p><i>Objective.</i>The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical evolution of septic patients and to evaluate the predictive ability of features.<i>Approach</i>. Data from intensive care unit septic patients were extracted from the freely available HiRID database and a comprehensive pipeline for time series analysis of critical care data was developed. Predictive models of cardiovascular deterioration (based on mean pressure and lactate values) and global organ dysfunction (based on SOFA score) were developed, and the addition of variability, such as entropies, cross-entropies and cross-correlation of heart rate and blood pressure (BP), was tested against the use of standard metrics alone.<i>Main results.</i>The best model achieved an area under the ROC curve value of 0.9671, with SOFA score values and trends being the most important features in the model, followed by features related to lactate, fluid balance, therapy and entropy values of BP.<i>Significance.</i>The results show that the dynamics of vital signs and their cross-coupling, as captured by the proposed variability indices, can provide additional insights into the physiological responses to the therapy administered.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144650128","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":"Photoplethysmography imaging to assess facial perfusion under simulated hypovolemia.","authors":"Stefan Borik, Marguerite L Gilmore, Antonio J Gonzales-Fiol, James W Biondi, Hau-Tieng Wu, Kirk H Shelley, Aymen A Alian","doi":"10.1088/1361-6579/adece3","DOIUrl":"10.1088/1361-6579/adece3","url":null,"abstract":"<p><p><i>Objective.</i>This study evaluates the potential of photoplethysmography imaging (PPGI) with automated facial tracking for detecting hemodynamic and autonomic changes induced by lower-body negative pressure (LBNP). The goal is to assess whether PPGI-derived facial perfusion variations are related with stroke volume (SV), systemic vascular resistance (SVR), heart rate variability (HRV), and autonomic responses to progressive hypovolemia.<i>Approach.</i>Twenty-four healthy adults (8 females, 16 males; aged 28.7 ± 3.5 years) underwent a seven-stage LBNP protocol (-15 to -60 mmHg, recovery). Facial perfusion was recorded using cross-polarized PPGI, along with SV, SVR, HR, and mean arterial pressure. Facial landmark tracking (MediaPipe) was used to extract region-specific PPGI signals. Wavelet synchrosqueezing transform enabled spectral analysis, and HRV was assessed with NeuroKit2.<i>Main Results.</i>At -60 mmHg, the LBNP-intolerant group showed a 25.2% decrease in SV (<i>p</i>< 0.0001) and a 19% increase in SVR (<i>p</i>= 0.041). At -30 mmHg recovery, SV remained reduced by 21% (<i>p</i>< 0.001), with SVR elevated by 30.1% (<i>p</i>= 0.002). In contrast, the tolerant group exhibited SV increases of 12% and 18% at these stages (both<i>p</i>< 0.0001), and a HR reduction of up to 5% (<i>p</i>< 0.05), with a decreasing SVR trend. HRV analysis indicated greater sympathetic activation in the intolerant group, with reduced HF power (<i>p</i>= 0.037) and increased LF/HF ratio (3.5 at -60 mmHg,<i>p</i>= 0.020). First harmonic PPGI amplitudes significantly declined in the intolerant group, most notably in the cheeks (-44.2%,<i>p</i>= 0.005).<i>Significance.</i>These findings suggests that PPGI, combined with AI-based face tracking and wavelet analysis, enables non-invasive, spatially resolved monitoring of vascular and autonomic responses. PPGI differentiates tolerant and intolerant groups, supporting its potential for real-time cardiovascular assessment in critical care and emergency settings.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584547","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":"Clinical applications of thoracic electrical impedance tomography in China: an updated review on recent 5 years.","authors":"Jiali Yuan, Sini He, Ling Sang, Zhanqi Zhao","doi":"10.1088/1361-6579/adf16e","DOIUrl":"10.1088/1361-6579/adf16e","url":null,"abstract":"<p><p>Electrical impedance tomography (EIT) is an emerging imaging technology that has garnered increasing attention in recent years, particularly in the medical field and the diagnosis and treatment of respiratory diseases. Fascinating developments were achieved after the previous review focusing on clinical applications in Chinese hospitals. Over hundred publications in SCI journals related to thoracic EIT clinical research and daily applications have been recorded in the past five years. As EIT devices become more accessible and portable, clinical application scenarios include not only ICU, but also chronic disease management, and health screening. We were excited to welcome more than 10 local companies manufacturing their own EIT devices, which were exhibited during the 24th International Conference on Biomedical Applications of EIT in Hangzhou, China. This article systematically reviewed the applications of thoracic EIT in clinical research and routine use in Chinese hospitals over the past five years.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659878","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":"Innovative screening for lower extremity atherosclerotic disease in people with diabetes: using novel and multidimensional PPG features.","authors":"Shoutian Wu, Xiaowen Hou, Ting Sun, Zeyang Song, Liang Lu, Zuchang Ma","doi":"10.1088/1361-6579/adeb42","DOIUrl":"10.1088/1361-6579/adeb42","url":null,"abstract":"<p><p><i>Objective</i>. Diabetes mellitus presents a significant global health burden, with patients demonstrating high prevalence of lower extremity atherosclerotic disease (LEAD) and poor prognosis. Despite the crucial need for early screening, primary healthcare lacks accessible LEAD screening protocols for people with diabetes. This study proposed a photoplethysmography (PPG)-based approach to enhance detection sensitivity for this high-risk population.<i>Approach</i>. This study collected toe PPG signals from 104 participants with diabetes, including 54 participants with LEAD. PPG signals underwent preprocessing followed by extraction of 162 features from 7 dimensions. Through a hybrid feature selection framework integrating feature extraction rate filtering and embedded random forest (RF) algorithms, 6 key PPG features were identified for RF classification model construction. The model was evaluated using metrics including sensitivity, specificity, accuracy,<i>F</i>1 score and Kappa coefficient, with DUS results serving as the reference standard.<i>Results.</i>The model achieved 85% sensitivity and 79% specificity, with 82% accuracy and<i>F</i>1-score, indicating good overall performance. The model's Kappa coefficient was 0.63, indicating good agreement with the DUS.<i>Significance</i>. This work demonstrates the feasibility of PPG-based method for screening LEAD in people with diabetes.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554150","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":"Early impairment of two arms of the baroreflex response in young normotensive patients with obesity.","authors":"Jana Cernanova Krohova, Barbora Czippelova, Zuzana Turianikova, Miriam Kuricova, Jana Tuzakova, Daniel Cierny, Luca Faes, Michal Javorka","doi":"10.1088/1361-6579/adea0a","DOIUrl":"10.1088/1361-6579/adea0a","url":null,"abstract":"<p><p><i>Objective</i>. Hypertension increasingly affects younger populations alongside rising obesity rates, and impaired baroreflex (BR) function could contribute to its development. This study investigated changes in BR control of the cardiac chronotropic (ccBR) and vascular resistance (vrBR) arms in young normotensive patients with obesity and explored associations with sex- and age-independent anthropometric measures (body mass index (iso-BMI) and waist to hip ratio (OSS of WHR)), insulin resistance (HOMA_IR), and arterial stiffness index CAVI₀.<i>Approach.</i>Twenty-three normotensive adolescents and young adults with obesity (17 females, median age: 17.1 years) and twenty-two sex- and age-matched healthy lean participants (16 females, median age: 17.4 years) were examined during four phases: supine rest, head-up-tilt (HUT), supine recovery, and mental arithmetics task (MA). The causal coupling and gain in the frequency-domain of the ccBR and vrBR arms were assessed non-invasively from the spontaneous variability series of arterial pressure, heart period, and peripheral vascular resistance using a partial spectral decomposition method in the low frequency band (0.04-0.15 Hz).<i>Main results.</i>Patients with obesity showed lower ccBR gain during HUT and persistently lower vrBR gain during supine rest and HUT. No significant associations were found between iso-BMI, OSS of WHR, HOMA_IR, CAVI₀, and spectral parameters during supine rest, except for a significant negative correlation between iso-BMI and changes in ccBR spectral gain as a response to MA.<i>Significance.</i>Advanced non-invasive methods accounting for causality in evaluating two BR arms revealed early BR impairment in young participants with obesity, affecting both the ccBR arm and the less-explored vrBR arm.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529232","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":"Automated detection of air trapping from mechanical ventilation waveform through interpretable dual-channel 1D convolutional neural network.","authors":"Chengxuan Zhang, Lifeng Gu, Weimin Shen, Kai Wang, Xiaoli Qian, Yuejia Ding, Lingwei Zhang, Fei Lu, Yuanjing Feng, Luping Fang, Huiqing Ge, Qing Pan","doi":"10.1088/1361-6579/adea2c","DOIUrl":"10.1088/1361-6579/adea2c","url":null,"abstract":"<p><p><i>Objective</i>. Air trapping is a major symptom of respiratory diseases like chronic obstructive pulmonary disease and asthma, and has always been a significant problem in treating patients using mechanical ventilation. If not handled timely, it can pose risk of severe respiratory dysfunction and potential life-threatening complications. Currently, the assessment of air trapping for ventilated patients largely relies on clinical experience of medical staffs.<i>Approach</i>. We introduced an interpretable dual-channel one-dimensional convolutional neural network (DC-1DCNN) with a simple structure, which enables fast inference. This model is designed to classify whether a respiratory waveform is indicative of air trapping. A global average pooling layer was integrated into the DC-1DCNN model to highlight the segments of the respiratory waveform that the model focused on when making a classification. An air trapping index (ATI) was introduced to quantify the condition of air trapping in the ventilated patients and to evaluate the effectiveness of bronchodilator nebulized treatments.<i>Main results</i>. The results demonstrate a satisfactory accuracy of 96.4% in identifying air trapping breath cycles, with highlighted critical sections in breath cycles that match the understanding of clinical experts for air trapping. The efficacy of bronchodilators can be well assessed by the ATI.<i>Significance</i>. The findings suggest that the proposed DC-1DCNN can help detect air trapping in real-time, and help the clinicians better monitor the airway condition of the ventilated patients.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529231","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}