Computers in biology and medicine最新文献

{"title":"A machine learning approach to differentiate stage IV from stage I colorectal cancer","authors":"Naim Abu-Freha ,&nbsp;Zaid Afawi ,&nbsp;Miar Yousef ,&nbsp;Walid Alamor ,&nbsp;Noor Sanalla ,&nbsp;Simon Esbit ,&nbsp;Malik Yousef","doi":"10.1016/j.compbiomed.2025.110179","DOIUrl":"10.1016/j.compbiomed.2025.110179","url":null,"abstract":"<div><h3>Background and aim</h3><div>The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in the diagnosis of stage IV CRC by utilizing supervised machine learning, analyzing clinical history, and laboratory values, comparing them with those of stage I CRC.</div></div><div><h3>Methods</h3><div>We conducted a respective study using patients diagnosed with stage I (n = 433) and stage IV CRC (n = 457). We employed supervised machine learning using random forest. The decision tree is used to visualize the model to identify key clinical and laboratory factors that differentiate between stage IV and stage I CRC.</div></div><div><h3>Results</h3><div>The decision tree classifier revealed that symptoms combined with laboratory values were critical predictors of stage IV CRC. Change in bowel habits was predictive for stage IV CRC among 14 of 22 patients (63 %). Weight loss, constipation, and abdominal pain in combination with different levels of carcinoembryonic antigen (CEA) were predictors for stage IV CRC. A CEA level higher than 260 was indicative for stage IV CRC in all observed patients (61 out of 61 patients). Additionally, a lower CEA level, in combination with hemoglobin, white blood cell count, and platelet count, also predicted stage IV CRC.</div></div><div><h3>Conclusions</h3><div>By applying a machine learning based approach, we identified symptoms and laboratory values (CEA, hemoglobin, white blood cell count, and platelet count), as crucial predictors for stage IV CRC diagnosis. This method holds potential for facilitating the diagnosis of stage IV CRC in clinical practice, even before imaging tests are conducted.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110179"},"PeriodicalIF":7.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photodegradation of psychotropic medications: Impact on efficacy, safety, and drug properties","authors":"Ana-Maria Udrea ,&nbsp;Catalin Buiu ,&nbsp;Angela Staicu ,&nbsp;Aurelia Nicoleta Dabu ,&nbsp;Speranta Avram","doi":"10.1016/j.compbiomed.2025.110115","DOIUrl":"10.1016/j.compbiomed.2025.110115","url":null,"abstract":"<div><div>Antipsychotics and antidepressants are essential psychotropic medications used for treating various mental health conditions such as depression, schizophrenia, and bipolar disorder. However, when exposed to light, these compounds are susceptible to photodegradation, potentially changing their biological activity and safety profiles. This study evaluates the pharmacokinetic and pharmacodynamic properties of several photoproducts derived from 13 psychotropic drugs. We used computational methods to predict the biological activity, toxicity, and drug-like properties of the photoproducts. Our results indicate that photoproducts such as amisulpride_TP166, TP246, quetiapine_D4, and quetiapine_PH1 show enhanced biological affinity and ADME-Tox profiles similar to their parent compounds, suggesting possible therapeutic advantages in their interaction with targeted receptors. However, some of the photocompounds exhibit lower predicted binding affinities when interacting with those receptors compared to their parent compounds, indicating a possible loss of function. These findings emphasize the need for further investigation into the effects and safety of drug photoproducts, particularly in the context of long-term pharmacotherapy.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110115"},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive diagnosis of lung diseases via multimodal feature extraction from breathing audio and chest dynamics","authors":"Alyaa Hamel Sfayyih ,&nbsp;Nasri Sulaiman ,&nbsp;Ahmad H. Sabry","doi":"10.1016/j.compbiomed.2025.110182","DOIUrl":"10.1016/j.compbiomed.2025.110182","url":null,"abstract":"<div><div>Early and accurate diagnosis of lung diseases is crucial for effective treatment. While traditional methods have limitations, audio analysis offers a promising non-invasive approach. However, existing studies often rely solely on acoustic features, neglecting valuable information contained in visual cues like chest wall dynamics. This research proposes a novel multimodal approach that integrates both audio and visual modalities to enhance lung disease detection. By extracting and fusing features from both modalities, we aim to capture a more comprehensive representation of lung health. The proposed deep learning model, trained on a dataset of audio and video recordings, achieved a validation accuracy of 92.02 %. The model effectively leverages features such as pitch, MFCCs, and breathing audio envelopes, along with visual cues from chest wall dynamics, to accurately classify different lung disease categories. This multimodal approach offers several advantages, including improved accuracy, robustness to noise and variability, and the potential for early disease detection. By addressing the limitations of single-modality approaches, this research contributes to the development of more effective and accessible lung disease diagnostic tools.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110182"},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease","authors":"Bingqing Long ,&nbsp;Rui Li ,&nbsp;Ronghua Wang ,&nbsp;Anyu Yin ,&nbsp;Ziyi Zhuang ,&nbsp;Yang Jing ,&nbsp;Linning E","doi":"10.1016/j.compbiomed.2025.110128","DOIUrl":"10.1016/j.compbiomed.2025.110128","url":null,"abstract":"<div><h3>Objectives</h3><div>To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).</div></div><div><h3>Materials and methods</h3><div>The data of 264 CTD-ILD patients were retrospectively collected. GAP Stage I, II, III patients are 195, 56, 13 cases respectively. The latter two stages were combined into one group. The patients were randomized into a training set and a validation set. Single-input models were separately constructed using the selected radiomics and DL features, while DLR model was constructed from both sets of features. For all models, the support vector machine (SVM) and logistic regression (LR) algorithms were used for construction. The nomogram models were generated by integrating age, gender, and DLR features.</div></div><div><h3>Results</h3><div>The DLR model outperformed the radiomics and DL models in both the training set and the validation set. The predictive performance of the DLR model based on the LR algorithm was the best among all the feature-based models (AUC = 0.923). The comprehensive models had even greater performance in predicting the GAP stage of CTD-ILD patients. The comprehensive model using the SVM algorithm had the best performance of the two models (AUC = 0.951).</div></div><div><h3>Conclusion</h3><div>The DLR model extracted from CT images can assist in the clinical prediction of the GAP stage of CTD-ILD patients. A nomogram showed even greater performance in predicting the GAP stage of CTD-ILD patients.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning","authors":"Myeongji Cho ,&nbsp;Nara Been ,&nbsp;Hyeon S. Son","doi":"10.1016/j.compbiomed.2025.110143","DOIUrl":"10.1016/j.compbiomed.2025.110143","url":null,"abstract":"<div><div>Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infectivity and host adaptation. This study employed machine-learning techniques to classify RVA genotypes based on the molecular and physicochemical properties of these proteins. A dataset of 94 VP7 and 68 VP4 protein sequences was collected from various host species. Seven machine-learning algorithms—Naïve Bayes (NB), logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbour (kNN), support vector machine (SVM), and artificial neural network (ANN)—were used for genotype classification. Feature subsets were configured using ranking-based attribute selection, and classification performance was evaluated using accuracy (ACC), precision, recall, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). kNN demonstrated the highest classification accuracy for both VP7 (ACC = 97.87 %) and VP4 (ACC = 100 %), outperforming NB, LR, DT, RF, SVM, and ANN. For VP7 sequences, key properties influencing genotype classification included hydrophobicity, normalised van der Waals volume, and leucine composition. For VP4, polarity, normalised van der Waals volume, and polarizability were the most significant factors. In summary, the genotype-specific molecular features of VP7 and VP4 proteins served as reliable markers for RVA classification. Our findings highlight the potential of machine-learning approaches to predict RVA genotypes based on the physicochemical properties of amino acids, providing valuable insights into the molecular mechanisms that drive viral evolution, host specificity, and immune evasion.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision medicine approach for in vitro modeling and computational screening of anti-epileptic drugs in pediatric epilepsy patients with SCN2A (R1629L) mutation","authors":"Jihun Kim ,&nbsp;Bilal Shaker ,&nbsp;Ara Ko ,&nbsp;Sunggon Yoo ,&nbsp;Dokyun Na ,&nbsp;Hoon-Chul Kang","doi":"10.1016/j.compbiomed.2025.110100","DOIUrl":"10.1016/j.compbiomed.2025.110100","url":null,"abstract":"<div><div>This study aimed to develop personalized anti-epileptic drugs for pediatric patients with an <em>SCN2A</em> (R1629L) mutation, which is unresponsive to conventional sodium channel blockers. The mutation was identified using genomic DNA sequencing, and patient-derived induced pluripotent stem cells (iPSCs) were differentiated into the neuronal network to mimic seizure activity. A total of 1.6 million compounds were screened using computational methods, identifying five candidates with high affinity to the mutant <em>SCN2A</em> protein, low potential toxicity, and high blood–brain barrier permeability. These compounds were pharmacologically evaluated using the patient-derived <em>in vitro</em> seizure model, which replicated the abnormal electrophysiological characteristics of epilepsy. Two of the five candidate compounds effectively modulated electrophysiological activities; moreover, these compounds were 100 times more potent than phenytoin. Therefore, this study demonstrates the feasibility of precision medicine in epilepsy treatment, emphasizing the benefits of patient-derived <em>in vitro</em> seizure models and computational drug screening. Additionally, this study highlights the potential of targeted therapeutic development for patients unresponsive to conventional therapies, showcasing a promising approach for personalized medical interventions in epilepsy.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-invasive blood glucose monitoring system","authors":"Mohammed H. Al-Jammas,&nbsp;Abdalrhman S. Iobaid,&nbsp;Mustafa M.N. Al-Deen,&nbsp;Yahya Wesam Aziz","doi":"10.1016/j.compbiomed.2025.110133","DOIUrl":"10.1016/j.compbiomed.2025.110133","url":null,"abstract":"<div><div>Diabetes is a chronic and common disease that requires regular monitoring of blood sugar levels. The usual way to measure blood sugar includes finger tingling to take a blood sample and this method can be painful or cause a health risk if the finger is not sterilized. Researchers were interested in developing painless methods to measure blood sugar levels. Infrared technology has shown promising results in painlessly measuring blood sugar levels by analyzing the sugar level through external sensors. Thus, the researchers have recently shifted to employ infrared sensors to measure blood sugar levels. This is due to these sensing devices offering promising results, however, further improvement to enhance their prediction outcomes is still demanded to replace the current figure prick gauges. This paper presents a system designed to measure blood sugar levels in a non-invasive manner through near-infrared technology. An infrared-emitting diode with a wavelength of 940 nm was used as a transmitter and a photodetector (BPW41N-ND) as a receptor of radiation by measuring infrared changes after passing through the tip of the index finger. The sensor measures blood sugar by developing a regression equation to analyze the collected data. Hence, a tailored equation was selected for Males and another model for Females. For males, an accuracy of 81.25 % was achieved, whereas 82.6 % was realized for females and 74.28 % for all. While the precision of 92.5 % and 94.1 % of males and females, and for all data, 91.07 %, was achieved. The device is designed to be simple and compact to be utilized by diabetic patients as self-monitoring blood glucose (SMBG) and display the results on an OLDE screen simply and clearly.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized fine-tuned ensemble classifier using Bayesian optimization for the detection of ear diseases","authors":"Israa Elmorsy ,&nbsp;Waleed Moneir ,&nbsp;Ahmed I. Saleh ,&nbsp;Abeer Twakol Khalil","doi":"10.1016/j.compbiomed.2025.110092","DOIUrl":"10.1016/j.compbiomed.2025.110092","url":null,"abstract":"<div><div>External and middle ear diseases are common disorders, especially in children, and can be examined using a digital otoscope. Hearing loss can result from delayed diagnosis and treatment which is subjective and error-prone depending on the expertise of the otolaryngologist. For these reasons, deep learning-based automated diagnostic systems are highly needed. In this study, a novel weighted average voting ensemble classifier between MobileNet and DenseNet169 has been developed to diagnose and detect different ear conditions. Bayesian optimization was used to select hyperparameters that gave the best results during the training process. MobileNet and DenseNet169 were fine-tuned by updating the weights of all layers in addition to the newly added layers before fusing them into one ensemble classifier to improve the classification ability of the model and be more specific to our task. This study was performed on a public dataset consisting of 282 otoscopic images. All classes were considered except the Tympanostomy Tubes class for having only two samples. Consequently, the proposed model demonstrated promising results of 99.54 % accuracy and an AUC of 1. Grad-CAM++ saliency maps were employed to highlight the affected area and pertinent features of the otoscopic image. The proposed approach contributes to improving accuracy, decreasing the misdiagnosis rate, and developing an automatic ear disease classification tool.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110092"},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel covariate adjustment method for spatial scan statistics based on outlier removal","authors":"Sheng Li ,&nbsp;Xuelin Li ,&nbsp;Wei Wang ,&nbsp;Menghan Yao ,&nbsp;Junyu Wang ,&nbsp;Qianqian Du ,&nbsp;Xinyue Tian ,&nbsp;Tao Zhang ,&nbsp;Fei Yin ,&nbsp;Yue Ma","doi":"10.1016/j.compbiomed.2025.110152","DOIUrl":"10.1016/j.compbiomed.2025.110152","url":null,"abstract":"<div><div>Spatial scan statistics (SSS) have been widely used in disease surveillance and epidemiology to detect geographical clusters. These applications make accuracy highly important. The accuracy of SSS detection can be affected by nonrandomly distributed covariates. To accurately detect clusters, previous studies adjusted the effects of covariates in statistical models to calculate the covariate-adjusted expected number of cases. However, these methods ignore the difference in the covariate effect on the expected number of cases inside and outside clusters and thus may lead to inaccurate adjustment and cluster detection. In this study, we developed a novel clustering outlier-based covariate adjustment method (COCA) to mitigate such differences. In COCA, the clusters detected by the traditional covariate adjustment method (TRA-CA) were removed as outliers, and the coefficients of the covariates were re-estimated to update the covariate-adjusted expected cases of all regions and then to detect the clusters again. The simulation results suggested that COCA outperformed TRA-CA and the latest GLM-based SSS in terms of accuracy measures, including sensitivity, specificity, PPV, and misclassification. COCA improved the accuracy of detected clusters and can be easily implemented by the combination of the conventional regression method with any other statistical software, such as R, and the efficient SatScan software. Thus, when covariates exist, COCA should be employed to obtain more accurate clusters.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of personalized synthetic 3-dimensional inlet velocity profiles for computational fluid dynamics simulations of type B aortic dissection","authors":"Kaihong Wang ,&nbsp;Chlӧe H. Armour ,&nbsp;Lydia Hanna ,&nbsp;Richard Gibbs ,&nbsp;Xiao Yun Xu","doi":"10.1016/j.compbiomed.2025.110158","DOIUrl":"10.1016/j.compbiomed.2025.110158","url":null,"abstract":"<div><h3>Background</h3><div>Computational fluid dynamics (CFD) simulations have shown promise in assessing type B aortic dissection (TBAD) to predict disease progression, and inlet velocity profiles (IVPs) are essential for such simulations. To truly capture patient-specific hemodynamic features, 3D IVPs extracted from 4D-flow magnetic resonance imaging (4D MRI) should be used, but 4D MRI is not commonly available.</div></div><div><h3>Method</h3><div>A new workflow was devised to generate personalized synthetic 3D IVPs that can replace 4D MRI-derived IVPs in CFD simulations. Based on 3D IVPs extracted from 4D MRI of 33 TBAD patients, statistical shape modelling and principal component analysis were performed to generate 270 synthetic 3D IVPs accounting for specific flow features. The synthetic 3D IVPs were then scaled and fine-tuned to match patient-specific stroke volume and systole-to-diastole ratio. The performance of personalized synthetic IVPs in CFD simulations was evaluated against patient-specific IVPs and compared with parabolic and flat IVPs.</div></div><div><h3>Results</h3><div>Our results showed that the synthetic 3D IVP was sufficient for faithful reproduction of hemodynamics throughout the aorta. In the ascending aorta (AAo), where non-patient-specific IVPs failed to replicate <em>in vivo</em> flow features in previous studies, the personalized synthetic IVP was able to match not only the flow pattern but also time-averaged wall shear stress (TAWSS), with a mean TAWSS difference of 5.9 %, which was up to 36.5 % by idealized IVPs. Additionally, the predicted retrograde flow index in both the AAo (8.36 %) and descending aorta (8.17 %) matched closely the results obtained with the 4D MRI-derived IVP (7.36 % and 6.55 %). The maximum false lumen pressure difference was reduced to 11.6 % from 68.8 % by the parabolic IVP and 72.6 % by the flat IVP.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the superiority of personalized synthetic 3D IVPs over commonly adopted parabolic or flat IVPs and offers a viable alternative to 4D MRI-derived IVP for CFD simulations of TBAD.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"191 ","pages":"Article 110158"},"PeriodicalIF":7.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}