• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model:a retrospective observational study

    2022-10-30 05:59:10MehmetTahirHuyutHilalstnda
    Medical Gas Research 2022年2期

    Mehmet Tahir Huyut,Hilal üstünda?

    1 Department of Biostatistics and Medical Informatics,Faculty of Medicine,Erzincan Binali Y?ld?r?m University,Erzincan,Turkey

    2 Department of Physiology,Faculty of Medicine,Erzincan Binali Y?ld?r?m University,Erzincan,Turkey

    Abstract The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world.The diff erent symptoms of COVID-19 made it diffi cult to understand which variables were more inf luential on the diagnosis,course and mortality of the disease.Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis,prognosis and mortality of diseases.Because of this advantage,the use of machine learning models as decision support systems in health services is increasing.The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model,one of the machine learning methods,which is a subf ield of artif icial intelligence.This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1,2020 and March 1,2021.Arterial blood gas values of all patients were obtained from the hospital registry system.While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease,it was 68.2% in the diagnosis of the disease.According to the results obtained,the low ionized-calcium value (< 1.10 mM) signif icantly predicted the need for intensive care of COVID-19 patients.At admission,low-carboxyhemoglobin (< 1.00%),high-pH (> 7.43),low-sodium (< 135.0 mM),hematocrit (< 40.0%),and methemoglobin(< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising.The f indings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe.The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.

    Key words: arterial blood gases;artif icial intelligence;carboxyhemoglobin;COVID-19;decision trees;ionized calcium;machine learning models;SARS-CoV-2

    INTRODUCTION

    The coronavirus disease 2019 (COVID-19) epidemic,which emerged in Wuhan,China at the end of 2019 and caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2),spread rapidly around the world and went down in history as the f irst pandemic caused by corona viruses1.The mechanism of damage caused by SARS-CoV-2 virus in cells,tissues and organs is not fully known.It is characterized by severely atypical respiratory distress in patients with COVID-19 and hypoxemia,which may precede radiological changes or other clinical symptoms,including dyspnea.2Hypoxemia in COVID-19 is severe and ultimately is the primary mechanism of multi-organ failure and death.3The underlying pathology is due to the entry of COVID-19 into cells via the angiotensin-converting enzyme 2 receptor.This receptor is expressed in many cells,including alveolar epithelial cells and vascular endothelium,which can result in a profound immune response and widespread endothelial dysfunction.4

    The diagnosis and treatment of most respiratory system diseases largely depends on an understanding of the basic physiological principles of respiration and gas exchange.

    While some of the respiratory system diseases are caused by ventilation failure;others result from the pulmonary membrane,diff usion disorders,or defects in the transport of gases in the blood between the lungs and tissues.5Blood partial pressure of oxygen,partial pressure of carbon dioxide,and pH values obtained from blood gases,which are frequently preferred in the diagnosis,treatment and follow-up of respiratory and metabolic diseases,are among the important tests used to examine lung functions.5-7

    Today,machine learning (ML) is used in many f ields such as object recognition,image processing,face recognition,virtual reality,augmented reality,voice recognition,iris recognition,marketing,health,customer service,satellite images,earth science,fraud detection (fraud).It is a subfield and the largest branch of artif icial intelligence (AI).It is seen that AI technologies,which have been used with great success in many f ields in recent years,have started to be used frequently in the diagnosis,prognosis and treatment processes of diseases,especially in the f ield of medicine.The most important reason for this is the power of ML algorithms,which are under AI technologies and accepted as an important part of data mining,to reveal hidden relationships between patterns.In this way,serious success can be achieved in the diagnosis of diseases that show similar symptoms,have intense uncertainties and are diffi cult to distinguish from each other.For example,data mining approaches applied to medical science topics are rapidly being developed due to their high performance in predicting outcomes,reducing drug costs,improving patient health,improving healthcare value and quality,and making real-time decisions to save people’s lives.8

    In developed countries,AI research departments associate with hospital to carry out the studies on the “Research and Development,” because the developments in AI technologies not only impacts patients and doctors but also the entire health system.In particular,“Artif icial Intelligence in Medicine” has been def ined as a branch of computer science it has the capacity to analyze complex medical data and help physicians improve patient outcomes.Considering the number of data constantly included in the system in hospitals,information processing capabilities beyond human capacity are needed in order to manage such large data.Today,thanks to AI technologies,predictive discoveries can be made by processing blood,urine and other laboratory samples from patients with powerful learning algorithms.Thanks to these discoveries,the physician can now be more accurate and safe when making a decision about the patient.

    In this study,the diagnosis and prognosis of COVID-19 disease was determined by blood gas data using decision trees model from ML algorithms,which is a subf ield of AI.It is thought that the results obtained from this study will guide clinicians in diagnosis and prediction of disease progression and useful strategic issues.

    MATERIALS AND METHODS

    This retrospective observational study was conducted taking into account theDeclaration of Helsinkiand was approved by the Ministry of Health of the Republic of Turkey and the Clinical Research Ethics Committee of Erzincan Binali Y?ld?r?m University Faculty of Medicine (Ruling No.E-21142744-804.99-70855) on March 23,2020.Between May 1,2020 and March 1,2021,data in accordance with our criteria were collected from the information system of Erzincan Binali Y?ld?r?m University Mengücek Gazi Training and Research Hospital and included in the study.The study only included individuals over the age of 18 years.The laboratory information of the patients participating in the study was the f irst blood values measured at hospital admission.Informed consent was obtained from all individuals included in this study.This study follows the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement (Additional f ile 1).

    Study design,study workflow and participants criteria

    Three diff erent patient groups who applied to the Training and Research Hospital of our university between May 2020 and March 2021 were included in this study.First group:those diagnosed with COVID-19 and admitted to the intensive care unit (ICU);Second group:Those diagnosed with COVID-19 and treated in wards outside the intensive care unit (non-ICU);Third group:Patients who were not diagnosed with COVID-19 but had diff erent etiologies and respiratory distress (Control group).Blood gas laboratory data of all patient groups were retrospectively analyzed by f ile scanning.A total of 686 patients,including 131 treated in the ICU unit,212 treated in non-ICU units,and 343 individuals in the control group,were included in the study according to the criteria determined.COVID-19 was diagnosed in our hospital only in cases where SARS-CoV-2 was detected by real-time polymerase chain reaction in nasopharyngeal or oropharyngeal swabs during the dates covered by this study.

    The workf low of this study consists of two stages.In the f irst step,Chi-squared Automatic Interaction Detector (CHAID)decision tree algorithm was run to predict the prognosis of the disease (separating non-ICU from ICU) with blood gas parameters.In the second stage,CHAID decision tree algorithm was run to predict the diagnosis of the disease (diff erentiating patients with COVID-19 from patients with non-COVID-19)using blood gas parameters.Blood gas biomarkers that are eff ective in the diagnosis and prognosis of the disease were determined by decision tree regression analysis and cut-offvalues were calculated.

    Measurements

    Age,gender and arterial blood gas;P50 (oxygen tension when hemoglobin is 50% saturated with oxygen),bicarbonate plasma,bilirubin,deoxyhemoglobin,glucoce,HCO3,hematocrit,hemoglobin,carboxyhemoglobin,chlorine,lactate,methemoglobin,oxyhemoglobin,oxygen saturation,osmalarite,pH,potassium,sodium,standard base,total O2,partial pressure of carbon dioxide,partial pressure of oxygen,ionized calcium (iCa) values of the patient and control groups in this study were obtained.Arterial blood gas samples were analyzed and digitally recorded on ABL 700 (Radiometer,Copenhagen,Denmark).

    Outcome criterion

    The f irst aim of this study is to predict the prognosis of the disease (diff erentiating out of ICU and intensive care unit) by blood gas parameters.Our secondary aim is to predict the diagnosis of the disease (diff erentiating patients with COVID-19 from patients with non-COVID-19) with blood gas parameters.With this methodology,the diagnosis and prognosis of COVID-19 is determined by the blood gas values measured at the time of admission of the patients.

    CHAID decision tree analysis

    CHAID decision tree method is a non-parametric method that can analyze nominal and quantitative variables together,provides highly reliable predictions on large data sets,and can be used as an alternative to logistic regression models.Besides these benef its,it can detail the relationship structures between predictive variables and provide effi cient and understandable tree-like outputs even in complex data structures.With this superiorty,CHAID decision tree method has a large range of uses.9,10

    Preparation of the data set

    After data completion,training and test sets were determined by 10-fold cross validation on the measured properties dataset.In addition,the advantage of cross validation was used to minimize the risk of overf itting in the evaluation of training and test sets.This procedure allowed for unbiased generalization estimation while determining the parameters of the decision tree model.9Then,the optimum hyperparameters of the decision tree model were determined by grid search.Non-ICU and ICU patients with COVID-19 were selected as the dependent variable when predicting the prognosis of the disease.When determining the diagnosis of the disease,groups with COVID-19 and non-COVID-19 (Control group)were selected as dependent variables.In addition,a balanced data set was created to increase the success performance of the decision tree model in the diagnosis of COVID-19,and an equal number of patients were included in the COVID-19 group and the non-COVID-19 (Control) group.

    Statistical analysis

    SPSS Statistics 25 (IBM,Armonk,NY,USA) software was used for statistical analysis.Categorical variables were analyzed with the chi-square test and continuous variables were analyzed with the Mann-WhitneyUtest.The CHAID algorithm,one of the Decision Trees analysis methods,was used.P< 0.05 was considered statistically important.All blood gas parameters obtained as a result of the measurement and age and gender variables of the patients were included in the decision tree model,which was run to determine the variables aff ecting the diagnosis and prognosis of COVID-19.

    RESULTS

    Demographic data of patients

    The demographic data of this study population are summarized in Figure 1.Of the 131 ICU group patients included in the study,80 (61.1%) were male and 51 (38.9%) female;of the 212 non-ICU group patients,122 (57.5%) were male and 90(42.5%) female;of the 343 control group patients,209 (60.9%)were male and 134 (39.1%) female (Figure 1).

    Figure 1:Flowchart of demographic data of this study population.

    The gender variable was not found to be signif icant among the patient groups (P> 0.669).Data regarding the age distribution of the study groups are summarized in Table 1.The mean age of the patients in the ICU and non-ICU groups was higher than the control group (P< 0.05).However,there was no signif icant diff erence between the ages of the ICU and non-ICU groups (P> 0.15) (Table 1).

    Table 1:Demographic characteristics of the study groups

    Predictive blood gas variables affecting the prognosis of the disease

    In this research,the CHAID decision tree diagram obtained to identify the blood gas predictors that ?nf luenced the prognosis of COVID-19 disease (separating non-ICU from the ICU)is presented in Figure 2.When the decision tree diagram in Figure 2 was investigated,212 (61.8%) of the COVID-19 patients included in the study were non-ICU and 131 (38.2%)were ICU patients.The “ionized calcium” blood gas parameter was found to be the most effi cient predictor of the prognosis of the disease (χ2= 25.027,P< 0.001;Figure 2).Accordingly,COVID-19 patients are divided into two diff erent groups according to the ionized calcium variable whose cut-off value is determined.According to these f indings,1.10 and lower“ionized calcium” values increases the prognosis of patients COVID-19 signif icantly (27.1%vs.53.9%).In addition,constructing our decision tree with the “ionized calcium”variable to determine disease progression demonstrated the clinical accuracy of our interpretable decision tree (Figure 2).

    Figure 2:Tree structure of predictive variables aff ecting the progression of COVlD-19.

    The classif ication success rate of the decision tree model,which was created to predict the prognosis of the disease with only the “ionized calcium” variable,was 65.0% and it was found to be statistically signif icant (Table 2).Accordingly,69.3% of non-ICU patients and 58.0% of ICU patients were predicted correctly by considering only the cut-off value of the “ionized calcium” variable (Table 2).

    Table 2:Prediction accuracy of “ionized calcium”parameter in prognosis according to CHAID decision tree model among COVID-19 patients

    Predictive blood gas variables affecting the detection of COVID-19

    In this manuscript,the CHAID decision tree diagram was constructed to identify the blood gas predictors that inf luence the detection of COVID-19 (separating the COVID-19 patients from the Control group) (Figure 3).When the decision tree diagram in Figure 3 was investigated,343 (50.0%) of 686 patients were in the COVID-19 group and 343 (50.0%) were in the Control group.It was seen that the most eff ective blood gas variable on the diagnosis of the disease is “carboxyhemoglobin” (χ2= 76.698,P< 0.001;Figure 3).The presence of Carboxyhemoglobin (and subsequently pH,sodium,hematocrit,methemoglobin) at the root of the decision tree ensures the clinical accuracy of our interpretable Decision Tree (Figure 3) regarding the robustness of the approach to determining the diagnosis of the disease.

    Figure 3:Tree structure of predictive variables affecting the diagnosis of the disease.

    According to the diagnosis of COVID-19,the “carboxyhemoglobin” variable was divided into three diff erent groups in our decision tree.While the “carboxyhemoglobin” value of 7.3% of the individuals in the study could not be reached,92.7% were classif ied according to cut-off values of ≤ 1.00,(1.00-1.60) and > 1.60.When Figure 3 was examined,62.0%of individuals with a “carboxyhemoglobin” value of ≤ 1.00,43.9% of (1.00-1.60),and 24.0% of individuals with > 1.60 were COVID-19 patients,respectively.According to these f indings,low Carboxyhemoglobin value was found to be an important biomarker in the diagnosis of the disease.In addition,the most inf luential variables in the diagnosis of COVID-19 in individuals with three diff erent “carboxyhemoglobin” values were pH (χ2= 21.765,P< 0.001),sodium (χ2= 10.574,P<0.05) and hematocrit (χ2= 10.574,P< 0.05).In three diff erent“carboxyhemoglobin” groups,individuals with > 7.43 pH,≤135.00 mM sodium and ≤ 40.0% hematocrit values,respectively,had more COVID-19 disease (84.7%,59.2%,37.0%,respectively).In addition,“methemoglobin” was the most inf luential variable in the diagnosis of COVID-19 diseases in individuals with a carboxyhemoglobin value of ≤ 1.00 and a pH value of ≤ 7.43 (χ2= 9.585,P< 0.05).In accordance with the f indings,the rate of having COVID-19 in individuals with a methemoglobin value of ≤ 1.30 was 65.9%,while this rate was 43.4% in individuals with a value of > 1.30.According to this,high pH,low sodium,hematocrit and methemoglobin values were found to be the most important biomarkers in the diagnosis of COVID-19 after low “carboxyhemoglobin” value.

    The classif ication success of the decision tree model obtained in order to predict the COVID-19 diagnosis of individuals within the scope of the study is presented in Table 3.The overall classif ication success rate of the decision tree model,which was created with only f ive variables,was 68.2% and it was statistically signif icant (Table 3).In addition,59.5% of 343 COVID-19 individuals and 77.0% of 343 non-COVID-19 individuals were predicted correctly,considering the cut-offvalue of carboxyhemoglobin,hematocrit,sodium,pH and methemoglobin variables with the decision tree model established for the diagnosis of the disease (Table 3).

    Table 3:Prediction accuracy in diagnosis according to five blood gas parameters of the CHAID decision tree model between COVID-19 (patient group) and non-COVID-19 (control group)

    DISCUSSION

    The symptoms of COVID-19 are very similar to the common f lu,which includes fever,cough and nasal congestion.11As the pandemic spread,other symptoms emerged,such as loss of taste and smell (anosmia).12,13Severe cases can lead to serious respiratory illness and pneumonia.Those most at risk are the elderly and people with underlying medical problems/comorbid diseases such as cardiovascular diseases and diabetes.14,15As the disease spreads around the world,more symptoms and features are being noticed that aff ect patient deaths.Having such a broad set of features aff ected by the disease makes it diffi cult to understand which variables have a greater impact on disease mortality.ML models can analyze large datasets to identify diseases,predict progression and mortality,and can be used to help accurately assess risk factors.

    It was studied on patients who were hospitalized in the intensive care unit with the diagnosis of COVID-19,were diagnosed with COVID-19 and were treated in non-ICU services,were not diagnosed with COVID-19,but had a diff erent etiology and respiratory disease (control group).Based on the arterial blood gas test results of these three patient groups,decision trees (Figures 2 and 3) were obtained with the CHAID algorithm,which can be used for guidance in cases of doubt in the detection and progression of COVID-19,by evaluating predictors together.10

    In this study,COVID-19 patients were importantly older than the control group.The high correlation between advanced age and disease was found to be consistent with the literature.10In addition,age was included in the decision tree models in the study,as it was found to be an important predictor in the determination and progression of the disease.15-18

    Although iCa constitutes about half of the calcium level in the circulatory system,it is a free form that is metabolically active and not bound to proteins.Since the circulating ionized level is a better indicator than the total level,it is a more useful parameter in clinical terms.For this reason,it is requested by clinicians,especially to evaluate the follow-up or treatment of critically ill patients.19In this study,the most important arterial blood gas variable aff ecting the prognosis of COVID-19 disease was “ionized calcium” (Figure 2).The overall classif ication accuracy of the decision tree,which was modeled signif icantly only with the ionized calcium variable to predict the progression of COVID-19,was 65.0%.Accordingly,69.3% of non-ICU patients and 58.0% of ICU patients were predicted correctly (Table 2).Other arterial blood gas values were not found to be important in predicting the progression of COVID-19.In this manuscript,“ionized calcium” values (≤ 1.10 mM) signif icantly predicted the need for intensive care in COVID-19 patients.

    Although it was emphasized in one study that “methemoglobin” and “carboxyhemoglobin” levels may be associated with the severity of sepsis,these levels were not associated with the prognosis of COVID-19.20In another study,it was stated that carboxyhemoglobin alone cannot be used to diagnose pneumonia of COVID-19 or to predict disease severity.21In another study,low methemoglobin and carboxyhemoglobin values were observed at the beginning of the disease,and it was stated that these values were expected to increase with the progression of the disease.However,it was stated that both variables and their clinical consequences should be further investigated and followed in severe patients.22In addition,studies on intensive care patients indicated that low carboxyhemoglobin levels were associated with high mortality.In another study,it was stated that low carboxyhemoglobin level at admission in COVID-19 patients is a biomarker that can guide early follow-up and treatment planning to prevent severe acute respiratory distress syndrome and mortality.23

    In this study,the most substantial arterial blood gas variable in determining the disease was carboxyhemoglobin.After that,arterial blood values of sodium,pH,hematocrit and methemoglobin were found to be important in determining the disease.Low carboxyhemoglobin (< 1.00%),high pH(> 7.43),low sodium (< 135.0 mM),hematocrit (< 40.0%),and methemoglobin (< 1.30%) values were signif icant independent markers in detecting COVID-19.It was noteworthy that blood gas variables that were found to be important in determining COVID-19 were not considered signif icant in the progression of the disease.Arterial blood gas variables,which were found to be important in detecting COVID-19 and predicting its progression,were above the reference values at the time of admission and this was consistent with the literature.The overall classif ication accuracy of the decision tree modeled with f ive variables in determining COVID-19 was 68.2%.Accordingly,59.5% of patients with COVID-19 and 77.0% of individuals without COVID-19 were predicted correctly,and the results were promising.

    ML methods,which are increasingly used eff ectively in Medical Services,have the ability to distinguish useful patterns in large-scale data and can identify predictors that are expected to help decision-making process in studies.As a matter of fact,in many studies for the automatic detection of the diagnosis of COVID-19 with ML models,the routine hematochemical values of patients (white blood cell count and platelet,C-reactive protein (CRP),aspartate aminotransferase,alanine aminotransaminase,lactate dehydrogenase plasma levels) were used.In these studies,it was stated that AST,CRP and lymphocyte levels are parameters to be considered and important predictive features in the diagnosis of the disease.10,24-26

    In similar ML studies in the literature,it was reported that COVID-19 positivity was associated with lymphopenia,liver and muscle tissue damage,and signif icantly increased CRP.27In another study,a logistic regression ML model was run to identify risk factors for 4542 COVID-19 patients,and f inal predictors were identif ied and reported.In another study,in which the relationship between many laboratory values and disease was determined using data mining methods,aspartate aminotransferase,alanine aminotransaminase,calcium,sodium,potassium,creatinine and CRP were found to be associated with the risk of death.28Other studies using ML models found elevated neutrophil,CRP,lymphocyte and lactate dehydrogenase levels and advanced age variables as predictors of mortality associated with COVID-19 disease.29,30

    In many studies,CHAID analysis from decision tree ML models was used as a predictor of diagnosis,prognosis and mortality in the results of these studies may assist clinicians in the diagnosis and treatment of potential COVID-19 patients.Indeed,in one study,predictors of mortality in COVID-19 patients admitted to the emergency room were determined by CHAID analysis.As a result of the study,it was stated that a high Shock Index (SI) value is an important indicator of COVID-19 mortality.31The SI ratio,def ined as the ratio of heart rate to systolic blood pressure,is a biomarker that can be used to understand changes in cardiovascular status and tissue perfusion level.32Similarly,in another study using the CHAID decision tree model,the CIT (CRP × international normalized ratio × troponin) biomarker was found to be important in the diagnosis of COVID-19,while the CRP level was found to be important in predicting its prognosis.10

    The COVID-19 disease has turned into a worldwide health crisis and has caused signif icant problems in emergency rooms and intensive care units.Therefore,it is important to evaluate individuals who need intensive care and have high mortality expectations in the early stage of the disease (at admission),for the health system to work more effi ciently.Findings related to the decision trees obtained in our study may be helpful in the early diagnosis of the disease and in the intensive care treatment of severe patients.In addition,these results are important in terms of maintaining the effi ciency of the health system and reducing the pressure of time,cost and workload.

    Since this study is a retrospective study obtained from registries,the inaccessibility of patients’ comorbidity data and the fact that it is a single center are the limitations of this study.Since variables cannot be controlled in retrospective studies,our study data may need to be supported by prospective cohort studies.Moreover,the data used in the study were obtained from populations of COVID-19 patients in diff erent seasons.For this reason,the parameter values in this article may have shown seasonal discrepancy from analogous studies.Studies involving larger patient groups and diff erent centers will further clarify the importance of arterial blood gas laboratory values in the COVID-19 outbreak.

    Author contributions

    MTH:Conceived the ideas or experimental design of the study,organized the material and methodology,applied the analyses,interpreted,discussed and written,f ixed the revisions;Hü:scanned the literature,contributed to the introduction and discussion,collected the material.

    Conflicts of interest

    Authors declare no conf lict of interest.

    Financial support

    The authors did not receive any f inancial support for this study.

    Institutional review board statement

    This retrospective observational study was conducted in accordance with the 1989Declaration of Helsinkiand was approved by the Ministry of Health of the Republic of Turkey and the Clinical Research Ethics Committee of Erzincan Binali Y?ld?r?m University Faculty of Medicin.

    Declaration of participant consent

    Informed consent was obtained from all individuals included in this study.

    Reporting statement

    This study follows the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement.

    Biostatistics statement

    The statistical methods of this study were reviewed by the biostatistician of Faculty of Medicine,Erzincan Binali Y?ld?r?m University.

    Copyright license agreement

    The Copyright License Agreement has been signed by both authors before publication.

    Data sharing statement

    Datasets analyzed during the current study are available from the corresponding author on reasonable request.

    Plagiarism check

    Checked twice by iThenticate.

    Peer review

    Externally peer reviewed.

    Open access statement

    This is an open access journal,and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License,which allows others to remix,tweak,and build upon the work non-commercially,as long as appropriate credit is given and the new creations are licensed under the identical terms.

    Additional file

    Additional f le 1:STROBE checklist.

    在线看a的网站| 夜夜骑夜夜射夜夜干| 亚洲国产欧美在线一区| 亚洲国产av影院在线观看| 男人操女人黄网站| 汤姆久久久久久久影院中文字幕| 51午夜福利影视在线观看| 黄频高清免费视频| 国产免费现黄频在线看| 大香蕉久久网| 91精品三级在线观看| 午夜福利,免费看| 精品乱码久久久久久99久播| 美女高潮喷水抽搐中文字幕| 少妇精品久久久久久久| 一进一出好大好爽视频| 国产精品亚洲av一区麻豆| 亚洲第一av免费看| 欧美激情极品国产一区二区三区| 老司机靠b影院| 亚洲一区中文字幕在线| 亚洲午夜精品一区,二区,三区| 手机成人av网站| 亚洲熟女毛片儿| 日韩一区二区三区影片| 国产成人欧美| 国产男靠女视频免费网站| 亚洲第一av免费看| 午夜福利欧美成人| 一区二区日韩欧美中文字幕| 一进一出好大好爽视频| 老汉色av国产亚洲站长工具| 日韩熟女老妇一区二区性免费视频| 久久国产精品大桥未久av| 午夜福利免费观看在线| 国产精品自产拍在线观看55亚洲 | 免费一级毛片在线播放高清视频 | 男男h啪啪无遮挡| 看免费av毛片| 久久 成人 亚洲| 咕卡用的链子| 午夜福利影视在线免费观看| 天天躁日日躁夜夜躁夜夜| 国产在视频线精品| 香蕉久久夜色| 激情在线观看视频在线高清 | 国产精品一区二区精品视频观看| 亚洲精品一卡2卡三卡4卡5卡| 久热爱精品视频在线9| 一级a爱视频在线免费观看| 国产成人欧美| 久久久久久久大尺度免费视频| 中文字幕制服av| 精品卡一卡二卡四卡免费| 日日爽夜夜爽网站| 日韩一卡2卡3卡4卡2021年| 欧美日韩视频精品一区| 日韩有码中文字幕| 日本vs欧美在线观看视频| 成人国语在线视频| 2018国产大陆天天弄谢| 精品第一国产精品| 久久精品国产综合久久久| 我要看黄色一级片免费的| 久久久久网色| 天堂俺去俺来也www色官网| 亚洲少妇的诱惑av| 日韩视频在线欧美| 亚洲成人国产一区在线观看| 18禁国产床啪视频网站| 国产在线免费精品| 国产福利在线免费观看视频| 在线看a的网站| 最近最新免费中文字幕在线| tocl精华| 黄色片一级片一级黄色片| 最新在线观看一区二区三区| 午夜激情久久久久久久| 国产精品一区二区免费欧美| 亚洲色图综合在线观看| 老司机亚洲免费影院| 亚洲欧美日韩另类电影网站| 激情视频va一区二区三区| 蜜桃国产av成人99| 自线自在国产av| 久久狼人影院| 成年人免费黄色播放视频| 欧美激情高清一区二区三区| 久久亚洲真实| 欧美日韩国产mv在线观看视频| 日日爽夜夜爽网站| 老司机亚洲免费影院| 日韩一区二区三区影片| av线在线观看网站| 亚洲精品久久成人aⅴ小说| 国产一区二区三区综合在线观看| 91av网站免费观看| 18禁美女被吸乳视频| 性色av乱码一区二区三区2| 桃红色精品国产亚洲av| 国产成人av教育| www.精华液| 高潮久久久久久久久久久不卡| 国产精品电影一区二区三区 | 国产成人一区二区三区免费视频网站| 男女高潮啪啪啪动态图| 成人国语在线视频| 亚洲第一欧美日韩一区二区三区 | 国产精品熟女久久久久浪| 在线观看66精品国产| 成人av一区二区三区在线看| 99国产极品粉嫩在线观看| 国产在线免费精品| 99国产精品一区二区蜜桃av | 日韩有码中文字幕| 老司机福利观看| 成人18禁在线播放| 一级毛片精品| 国产精品国产高清国产av | 成年人午夜在线观看视频| 中文字幕制服av| 日韩欧美一区视频在线观看| 欧美午夜高清在线| 成人亚洲精品一区在线观看| 18禁美女被吸乳视频| 制服诱惑二区| 自拍欧美九色日韩亚洲蝌蚪91| 精品欧美一区二区三区在线| 一级毛片电影观看| 久久久久久久久免费视频了| 国产亚洲精品一区二区www | 麻豆成人av在线观看| 女人高潮潮喷娇喘18禁视频| 久热这里只有精品99| 亚洲第一青青草原| 久久精品人人爽人人爽视色| 国产成人精品久久二区二区91| 制服诱惑二区| 成人国产av品久久久| 无遮挡黄片免费观看| 欧美黑人精品巨大| 国产男女超爽视频在线观看| 国产色视频综合| 欧美日韩亚洲国产一区二区在线观看 | 国产成人免费无遮挡视频| 在线十欧美十亚洲十日本专区| 悠悠久久av| 国产91精品成人一区二区三区 | 久久久精品区二区三区| 国产精品久久电影中文字幕 | 高清黄色对白视频在线免费看| 国产成人影院久久av| 99精品久久久久人妻精品| 久久久久精品国产欧美久久久| 亚洲午夜理论影院| 久热爱精品视频在线9| 亚洲伊人色综图| av视频免费观看在线观看| 国产男女超爽视频在线观看| 亚洲人成电影观看| 首页视频小说图片口味搜索| 久久人人爽av亚洲精品天堂| 高清欧美精品videossex| 婷婷成人精品国产| 国产主播在线观看一区二区| 91成年电影在线观看| 欧美精品一区二区免费开放| 首页视频小说图片口味搜索| 中文字幕高清在线视频| 成人特级黄色片久久久久久久 | 成在线人永久免费视频| 超色免费av| 国产精品一区二区在线不卡| 99国产精品一区二区三区| 免费黄频网站在线观看国产| 久久久水蜜桃国产精品网| 亚洲精华国产精华精| 欧美成人免费av一区二区三区 | 亚洲成a人片在线一区二区| 日日摸夜夜添夜夜添小说| 亚洲五月色婷婷综合| 亚洲第一av免费看| 欧美激情高清一区二区三区| 精品国产乱码久久久久久小说| 大码成人一级视频| 9191精品国产免费久久| 在线观看66精品国产| 涩涩av久久男人的天堂| 国产伦理片在线播放av一区| 少妇裸体淫交视频免费看高清 | 激情视频va一区二区三区| 日韩欧美一区视频在线观看| 99国产综合亚洲精品| 91国产中文字幕| 美女扒开内裤让男人捅视频| 久久精品亚洲精品国产色婷小说| 亚洲成人免费电影在线观看| 欧美在线黄色| 久久中文字幕人妻熟女| 欧美日韩视频精品一区| 久久国产精品人妻蜜桃| 久久国产精品影院| 欧美精品高潮呻吟av久久| 女人久久www免费人成看片| 亚洲色图 男人天堂 中文字幕| 欧美一级毛片孕妇| 王馨瑶露胸无遮挡在线观看| 国产成人欧美| 99re6热这里在线精品视频| 精品福利永久在线观看| 亚洲伊人久久精品综合| 亚洲欧美日韩高清在线视频 | 黄频高清免费视频| 国产成人免费观看mmmm| 91老司机精品| 国产精品免费视频内射| 99久久人妻综合| 99在线人妻在线中文字幕 | 人人妻人人澡人人看| 看免费av毛片| 亚洲三区欧美一区| 国产高清国产精品国产三级| 无限看片的www在线观看| 纯流量卡能插随身wifi吗| 久久精品国产亚洲av香蕉五月 | 男女免费视频国产| 精品久久久久久电影网| 在线观看免费高清a一片| 另类精品久久| 他把我摸到了高潮在线观看 | 丰满少妇做爰视频| 欧美日韩黄片免| 久久久欧美国产精品| 51午夜福利影视在线观看| 国产国语露脸激情在线看| √禁漫天堂资源中文www| 亚洲午夜理论影院| a级毛片黄视频| 天天躁狠狠躁夜夜躁狠狠躁| 欧美日韩亚洲综合一区二区三区_| a在线观看视频网站| 亚洲国产av新网站| 久久热在线av| 亚洲色图av天堂| 新久久久久国产一级毛片| 国产欧美日韩精品亚洲av| 国产成人影院久久av| 国产单亲对白刺激| 91精品国产国语对白视频| 成人特级黄色片久久久久久久 | 老司机影院毛片| 高清毛片免费观看视频网站 | 9热在线视频观看99| 久久久国产欧美日韩av| 国产主播在线观看一区二区| 男女免费视频国产| 国产成人一区二区三区免费视频网站| 最新美女视频免费是黄的| 欧美精品亚洲一区二区| 黑人巨大精品欧美一区二区蜜桃| 99精品欧美一区二区三区四区| 久久99一区二区三区| 久久这里只有精品19| 精品高清国产在线一区| 亚洲色图av天堂| 亚洲第一av免费看| 亚洲九九香蕉| 国产国语露脸激情在线看| 日日爽夜夜爽网站| 亚洲全国av大片| 日本精品一区二区三区蜜桃| 日韩人妻精品一区2区三区| av片东京热男人的天堂| 午夜免费鲁丝| 亚洲欧美激情在线| 午夜福利一区二区在线看| 国产伦人伦偷精品视频| 人人妻人人澡人人看| 肉色欧美久久久久久久蜜桃| 啪啪无遮挡十八禁网站| 久久久久视频综合| 中文字幕高清在线视频| 午夜免费成人在线视频| 丰满饥渴人妻一区二区三| 国产免费av片在线观看野外av| 天天操日日干夜夜撸| www.熟女人妻精品国产| 精品福利观看| 黑人巨大精品欧美一区二区mp4| 国产av一区二区精品久久| 汤姆久久久久久久影院中文字幕| 最新美女视频免费是黄的| 大型黄色视频在线免费观看| 久久中文字幕人妻熟女| 亚洲avbb在线观看| 老司机在亚洲福利影院| 亚洲精品久久成人aⅴ小说| 国产成人精品无人区| 一本—道久久a久久精品蜜桃钙片| 咕卡用的链子| 91成人精品电影| 国产av精品麻豆| 亚洲人成伊人成综合网2020| 香蕉久久夜色| 国产精品国产av在线观看| 欧美日韩视频精品一区| 亚洲成人手机| 极品少妇高潮喷水抽搐| cao死你这个sao货| 免费在线观看日本一区| 国产深夜福利视频在线观看| 国产伦人伦偷精品视频| 国产高清videossex| 美女视频免费永久观看网站| 极品教师在线免费播放| 欧美一级毛片孕妇| 在线观看66精品国产| 99re6热这里在线精品视频| 国产麻豆69| 中文字幕精品免费在线观看视频| 久久久精品免费免费高清| 操出白浆在线播放| 99九九在线精品视频| av在线播放免费不卡| 久久精品成人免费网站| 人人妻人人添人人爽欧美一区卜| 伊人久久大香线蕉亚洲五| 国产成人精品久久二区二区91| 国产aⅴ精品一区二区三区波| 亚洲中文字幕日韩| 露出奶头的视频| 亚洲成国产人片在线观看| 久热爱精品视频在线9| 人人妻人人爽人人添夜夜欢视频| 无人区码免费观看不卡 | 国产av精品麻豆| 大片电影免费在线观看免费| 麻豆成人av在线观看| 国产一区二区三区综合在线观看| 桃花免费在线播放| 国产老妇伦熟女老妇高清| 动漫黄色视频在线观看| 宅男免费午夜| 欧美日韩福利视频一区二区| 91九色精品人成在线观看| 午夜福利乱码中文字幕| 免费人妻精品一区二区三区视频| 啦啦啦在线免费观看视频4| 久久狼人影院| 69精品国产乱码久久久| 超碰成人久久| 国产精品国产av在线观看| 日本vs欧美在线观看视频| 老熟妇乱子伦视频在线观看| 99国产精品免费福利视频| 欧美一级毛片孕妇| 妹子高潮喷水视频| 免费女性裸体啪啪无遮挡网站| 高清在线国产一区| 国产深夜福利视频在线观看| 黄频高清免费视频| 青青草视频在线视频观看| 日韩成人在线观看一区二区三区| 日韩大码丰满熟妇| 精品国产国语对白av| 欧美午夜高清在线| 视频区图区小说| 老司机亚洲免费影院| 欧美日本中文国产一区发布| 国产欧美日韩一区二区精品| 日韩免费高清中文字幕av| 国产野战对白在线观看| 一区二区三区精品91| 亚洲精品av麻豆狂野| 国产av一区二区精品久久| 性少妇av在线| 一级黄色大片毛片| 啦啦啦 在线观看视频| av网站在线播放免费| 国产成人欧美| 满18在线观看网站| 黄片播放在线免费| 中文字幕制服av| 日本a在线网址| 天堂8中文在线网| 日韩视频在线欧美| 一区二区av电影网| 天堂俺去俺来也www色官网| 精品国产乱码久久久久久男人| 国产日韩一区二区三区精品不卡| 美女午夜性视频免费| 制服诱惑二区| 又黄又粗又硬又大视频| 美女高潮喷水抽搐中文字幕| 国产麻豆69| 麻豆成人av在线观看| 国产一区二区三区在线臀色熟女 | 十八禁高潮呻吟视频| 国产色视频综合| 免费日韩欧美在线观看| 99re6热这里在线精品视频| 自拍欧美九色日韩亚洲蝌蚪91| 热re99久久精品国产66热6| 久久国产精品大桥未久av| 法律面前人人平等表现在哪些方面| 中文字幕制服av| 亚洲一区二区三区欧美精品| 一区二区三区乱码不卡18| 久久中文看片网| 久久这里只有精品19| 777久久人妻少妇嫩草av网站| 亚洲,欧美精品.| 国产区一区二久久| 国产成人欧美| 夜夜爽天天搞| 国产亚洲精品一区二区www | www.999成人在线观看| 亚洲综合色网址| 日韩免费av在线播放| 麻豆av在线久日| 精品久久久精品久久久| 欧美老熟妇乱子伦牲交| 两人在一起打扑克的视频| 亚洲精品中文字幕在线视频| 少妇裸体淫交视频免费看高清 | 两性午夜刺激爽爽歪歪视频在线观看 | 日韩欧美一区视频在线观看| 三级毛片av免费| 一边摸一边做爽爽视频免费| 亚洲av第一区精品v没综合| 亚洲精品av麻豆狂野| 久久久精品免费免费高清| 最新在线观看一区二区三区| 久久婷婷成人综合色麻豆| 日韩人妻精品一区2区三区| 国产在视频线精品| 岛国毛片在线播放| 国产激情久久老熟女| 免费看a级黄色片| 后天国语完整版免费观看| 757午夜福利合集在线观看| 黄色视频不卡| 欧美精品av麻豆av| av天堂在线播放| 国产黄频视频在线观看| 国产精品一区二区精品视频观看| 免费高清在线观看日韩| 亚洲色图综合在线观看| 欧美黑人欧美精品刺激| 国产精品欧美亚洲77777| 一个人免费看片子| 国产成人一区二区三区免费视频网站| 一本久久精品| 女人高潮潮喷娇喘18禁视频| 亚洲熟女精品中文字幕| 亚洲精品成人av观看孕妇| cao死你这个sao货| 超碰成人久久| 久久中文字幕人妻熟女| 国精品久久久久久国模美| 欧美老熟妇乱子伦牲交| 亚洲欧洲日产国产| 男女午夜视频在线观看| 男人操女人黄网站| 亚洲中文字幕日韩| a级毛片在线看网站| 757午夜福利合集在线观看| 狠狠婷婷综合久久久久久88av| 啦啦啦在线免费观看视频4| 一区二区三区乱码不卡18| 中文字幕人妻丝袜一区二区| 色尼玛亚洲综合影院| 欧美在线黄色| aaaaa片日本免费| 每晚都被弄得嗷嗷叫到高潮| 久久性视频一级片| 变态另类成人亚洲欧美熟女 | 操美女的视频在线观看| 黑人巨大精品欧美一区二区蜜桃| 成人手机av| 九色亚洲精品在线播放| 国产免费福利视频在线观看| 国产成人精品久久二区二区免费| 19禁男女啪啪无遮挡网站| 看免费av毛片| 91精品三级在线观看| 国产aⅴ精品一区二区三区波| av视频免费观看在线观看| 欧美一级毛片孕妇| 无遮挡黄片免费观看| 一区二区av电影网| 狠狠精品人妻久久久久久综合| 欧美乱妇无乱码| 亚洲精品成人av观看孕妇| 夜夜爽天天搞| 欧美日韩精品网址| 亚洲国产欧美一区二区综合| 国产精品免费视频内射| 俄罗斯特黄特色一大片| 色播在线永久视频| 亚洲av日韩精品久久久久久密| 国产精品一区二区免费欧美| 久久av网站| 久久影院123| av在线播放免费不卡| 在线av久久热| 97在线人人人人妻| 国产成人一区二区三区免费视频网站| 成人av一区二区三区在线看| 别揉我奶头~嗯~啊~动态视频| 国产精品久久久久久精品古装| 一边摸一边抽搐一进一小说 | 欧美成人午夜精品| 久久亚洲真实| 一二三四在线观看免费中文在| 人成视频在线观看免费观看| 国产精品熟女久久久久浪| 性色av乱码一区二区三区2| 亚洲自偷自拍图片 自拍| 国产av精品麻豆| 美女福利国产在线| 女性生殖器流出的白浆| 国产1区2区3区精品| 亚洲中文av在线| 亚洲成人手机| 亚洲人成77777在线视频| 亚洲av成人一区二区三| 国产一区二区激情短视频| 免费在线观看日本一区| 天堂动漫精品| 一级毛片女人18水好多| 久久久水蜜桃国产精品网| 色视频在线一区二区三区| 国产在线视频一区二区| 日日摸夜夜添夜夜添小说| av有码第一页| 精品高清国产在线一区| 免费高清在线观看日韩| 香蕉久久夜色| 男女之事视频高清在线观看| 欧美性长视频在线观看| 久久精品国产亚洲av高清一级| 欧美 亚洲 国产 日韩一| 视频区欧美日本亚洲| 99久久99久久久精品蜜桃| 国产精品一区二区在线观看99| 丰满少妇做爰视频| 天堂中文最新版在线下载| 欧美 亚洲 国产 日韩一| 大型av网站在线播放| 精品福利观看| 99riav亚洲国产免费| 久久久久久免费高清国产稀缺| tocl精华| 日韩免费av在线播放| 国产国语露脸激情在线看| 嫩草影视91久久| 十分钟在线观看高清视频www| 无遮挡黄片免费观看| 久久久国产一区二区| h视频一区二区三区| 一级毛片电影观看| 久久久欧美国产精品| 欧美大码av| 在线天堂中文资源库| 国产免费视频播放在线视频| 久久中文字幕人妻熟女| 美国免费a级毛片| 51午夜福利影视在线观看| 久久久久网色| 亚洲黑人精品在线| 亚洲成人免费电影在线观看| 国产av又大| 欧美+亚洲+日韩+国产| 国产精品亚洲av一区麻豆| 天天影视国产精品| 久久久精品94久久精品| 国产精品国产av在线观看| 欧美久久黑人一区二区| 亚洲精品粉嫩美女一区| 精品高清国产在线一区| 欧美激情高清一区二区三区| 在线十欧美十亚洲十日本专区| 夜夜爽天天搞| 国产视频一区二区在线看| 一区在线观看完整版| 欧美+亚洲+日韩+国产| 成人国产av品久久久| 制服诱惑二区| 日韩欧美国产一区二区入口| 成人国语在线视频| av视频免费观看在线观看| 成人18禁高潮啪啪吃奶动态图| 大香蕉久久网| 我要看黄色一级片免费的| 电影成人av| 国产精品久久久久久精品古装| 国产成人一区二区三区免费视频网站| videos熟女内射| 国产亚洲午夜精品一区二区久久| 亚洲第一青青草原| 国产成人免费无遮挡视频| 国内毛片毛片毛片毛片毛片| 老司机在亚洲福利影院| 欧美成狂野欧美在线观看| 亚洲av成人不卡在线观看播放网| 少妇的丰满在线观看| 久久毛片免费看一区二区三区| 亚洲 欧美一区二区三区| 国内毛片毛片毛片毛片毛片| 曰老女人黄片| 欧美人与性动交α欧美精品济南到| 亚洲精品在线观看二区| 欧美另类亚洲清纯唯美| 深夜精品福利|