• <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.

    一区在线观看完整版| 久久久久久久久久久久大奶| 天天操日日干夜夜撸| 一级片免费观看大全| 亚洲精品,欧美精品| 亚洲色图 男人天堂 中文字幕| 26uuu在线亚洲综合色| 亚洲av成人精品一二三区| 亚洲av.av天堂| 午夜激情久久久久久久| 日韩中文字幕视频在线看片| 欧美日韩视频精品一区| 久久精品久久久久久噜噜老黄| 国产97色在线日韩免费| 国精品久久久久久国模美| 一边摸一边做爽爽视频免费| 亚洲男人天堂网一区| 欧美精品一区二区大全| 午夜久久久在线观看| 一本久久精品| 男女午夜视频在线观看| 国产熟女欧美一区二区| 色婷婷av一区二区三区视频| 毛片一级片免费看久久久久| 国产乱人偷精品视频| 精品酒店卫生间| 免费观看a级毛片全部| 久久久久人妻精品一区果冻| 一区二区三区激情视频| 久久精品国产亚洲av涩爱| 只有这里有精品99| 亚洲av电影在线观看一区二区三区| 欧美 亚洲 国产 日韩一| 亚洲欧美一区二区三区国产| 国产精品久久久久久av不卡| 日韩欧美精品免费久久| av电影中文网址| www日本在线高清视频| 在线免费观看不下载黄p国产| 国产精品香港三级国产av潘金莲 | 建设人人有责人人尽责人人享有的| 精品人妻在线不人妻| 在线看a的网站| 最近最新中文字幕免费大全7| 丝袜美足系列| 国产在线一区二区三区精| 岛国毛片在线播放| 日本午夜av视频| 午夜免费鲁丝| 亚洲欧美清纯卡通| 精品国产超薄肉色丝袜足j| 伊人久久大香线蕉亚洲五| 亚洲伊人色综图| 精品一区二区三卡| 久久亚洲国产成人精品v| 大香蕉久久网| 成人毛片60女人毛片免费| 成人国语在线视频| 下体分泌物呈黄色| a级片在线免费高清观看视频| 亚洲精品日韩在线中文字幕| 亚洲国产精品一区三区| 中文字幕制服av| 国产在线视频一区二区| 亚洲美女视频黄频| 超碰成人久久| 精品一区二区三卡| 成人毛片a级毛片在线播放| 中文精品一卡2卡3卡4更新| 老司机亚洲免费影院| 国产又爽黄色视频| 汤姆久久久久久久影院中文字幕| 人人妻人人添人人爽欧美一区卜| 久久人人97超碰香蕉20202| 亚洲经典国产精华液单| 菩萨蛮人人尽说江南好唐韦庄| 毛片一级片免费看久久久久| 巨乳人妻的诱惑在线观看| 男女下面插进去视频免费观看| 又粗又硬又长又爽又黄的视频| 老司机亚洲免费影院| 欧美在线黄色| 欧美日韩成人在线一区二区| 青春草亚洲视频在线观看| 久久人人爽av亚洲精品天堂| 菩萨蛮人人尽说江南好唐韦庄| 亚洲精品aⅴ在线观看| 女人精品久久久久毛片| 亚洲精品av麻豆狂野| 久久人人爽人人片av| 国产精品一二三区在线看| 女人高潮潮喷娇喘18禁视频| 热99久久久久精品小说推荐| 国产野战对白在线观看| 涩涩av久久男人的天堂| 午夜免费男女啪啪视频观看| 日韩在线高清观看一区二区三区| 熟女电影av网| 男女国产视频网站| 大片电影免费在线观看免费| 亚洲欧洲精品一区二区精品久久久 | 丰满乱子伦码专区| 999久久久国产精品视频| 色94色欧美一区二区| 极品人妻少妇av视频| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 日本av手机在线免费观看| 老汉色∧v一级毛片| 黄色配什么色好看| 日韩制服骚丝袜av| 午夜免费鲁丝| 一级毛片我不卡| 国产精品99久久99久久久不卡 | 在现免费观看毛片| 美女国产高潮福利片在线看| 妹子高潮喷水视频| 久久久久久久亚洲中文字幕| 欧美激情极品国产一区二区三区| 男女免费视频国产| 亚洲人成77777在线视频| 亚洲欧美精品综合一区二区三区 | 狂野欧美激情性bbbbbb| 韩国高清视频一区二区三区| 91成人精品电影| 十八禁网站网址无遮挡| 一级片免费观看大全| 中文字幕最新亚洲高清| 极品少妇高潮喷水抽搐| 纵有疾风起免费观看全集完整版| 久久久久久久精品精品| 日韩制服丝袜自拍偷拍| 久久人妻熟女aⅴ| 国产精品 欧美亚洲| 热re99久久精品国产66热6| 黄色一级大片看看| av免费在线看不卡| 日韩 亚洲 欧美在线| 熟女av电影| 免费在线观看黄色视频的| 性色avwww在线观看| 国产色婷婷99| 国产精品国产三级专区第一集| 亚洲,一卡二卡三卡| 精品人妻一区二区三区麻豆| 这个男人来自地球电影免费观看 | 久久久久久人人人人人| 中文天堂在线官网| 天堂8中文在线网| 日韩制服丝袜自拍偷拍| 2022亚洲国产成人精品| 免费观看性生交大片5| 99久久人妻综合| 亚洲欧美精品自产自拍| 黑人欧美特级aaaaaa片| tube8黄色片| 男女啪啪激烈高潮av片| 一级毛片我不卡| 午夜免费观看性视频| 亚洲美女黄色视频免费看| 午夜福利视频精品| 赤兔流量卡办理| 少妇被粗大猛烈的视频| 欧美日韩av久久| 99国产精品免费福利视频| 日韩一卡2卡3卡4卡2021年| 国产片特级美女逼逼视频| 18禁国产床啪视频网站| 日韩制服骚丝袜av| 热re99久久精品国产66热6| 久久国产精品大桥未久av| 日本猛色少妇xxxxx猛交久久| 妹子高潮喷水视频| 亚洲一码二码三码区别大吗| 91在线精品国自产拍蜜月| 国产亚洲精品第一综合不卡| 国产 一区精品| 性色avwww在线观看| 80岁老熟妇乱子伦牲交| 亚洲国产av影院在线观看| 国产精品久久久久久精品电影小说| 亚洲男人天堂网一区| 日本vs欧美在线观看视频| 最近2019中文字幕mv第一页| 免费高清在线观看日韩| 国产综合精华液| 国产日韩欧美亚洲二区| 欧美日韩视频精品一区| 亚洲欧美中文字幕日韩二区| 90打野战视频偷拍视频| 国产日韩一区二区三区精品不卡| tube8黄色片| 欧美日韩成人在线一区二区| 在线 av 中文字幕| 亚洲经典国产精华液单| 丰满乱子伦码专区| 亚洲欧美色中文字幕在线| 只有这里有精品99| 免费在线观看视频国产中文字幕亚洲 | 欧美精品一区二区免费开放| 亚洲综合精品二区| 捣出白浆h1v1| 欧美国产精品一级二级三级| av在线app专区| 国产黄色免费在线视频| 午夜日韩欧美国产| 深夜精品福利| 国产精品三级大全| 丝袜在线中文字幕| 老鸭窝网址在线观看| 看免费av毛片| 亚洲三级黄色毛片| 七月丁香在线播放| 久久久久久久国产电影| 伊人亚洲综合成人网| 久久久久久久精品精品| 18+在线观看网站| 久久这里只有精品19| 亚洲欧美精品综合一区二区三区 | 精品第一国产精品| 国产亚洲精品第一综合不卡| 免费在线观看黄色视频的| 97人妻天天添夜夜摸| 国产欧美亚洲国产| 久久ye,这里只有精品| 丝瓜视频免费看黄片| 电影成人av| 色播在线永久视频| 捣出白浆h1v1| 在线观看国产h片| 涩涩av久久男人的天堂| 两性夫妻黄色片| 久久久精品94久久精品| 两个人看的免费小视频| 久热久热在线精品观看| 黄片小视频在线播放| 超色免费av| 日韩中文字幕视频在线看片| 大片免费播放器 马上看| 久久精品久久久久久久性| 考比视频在线观看| 亚洲精品日韩在线中文字幕| 一级毛片电影观看| 少妇人妻精品综合一区二区| 精品亚洲成国产av| 丝袜美腿诱惑在线| 99久久中文字幕三级久久日本| 精品国产国语对白av| 亚洲国产精品国产精品| 大香蕉久久网| 26uuu在线亚洲综合色| 毛片一级片免费看久久久久| 亚洲图色成人| 人体艺术视频欧美日本| 国产成人91sexporn| 久久精品国产综合久久久| 精品亚洲乱码少妇综合久久| 99精国产麻豆久久婷婷| 国产成人91sexporn| 高清欧美精品videossex| 国产日韩欧美在线精品| 满18在线观看网站| 国产精品免费大片| 免费黄色在线免费观看| 99re6热这里在线精品视频| 免费在线观看黄色视频的| 日韩欧美精品免费久久| 精品亚洲成国产av| 香蕉丝袜av| av片东京热男人的天堂| 午夜福利影视在线免费观看| 精品亚洲成国产av| 满18在线观看网站| 国产精品亚洲av一区麻豆 | 中文乱码字字幕精品一区二区三区| 国产爽快片一区二区三区| 亚洲经典国产精华液单| 性少妇av在线| 亚洲综合精品二区| 国产成人精品一,二区| 欧美激情高清一区二区三区 | 国产视频首页在线观看| 国产片特级美女逼逼视频| 亚洲在久久综合| 国产精品亚洲av一区麻豆 | 国产午夜精品一二区理论片| 中文字幕色久视频| 精品亚洲成a人片在线观看| 日本色播在线视频| 热re99久久精品国产66热6| 纵有疾风起免费观看全集完整版| 欧美国产精品va在线观看不卡| 美女高潮到喷水免费观看| 国产精品一区二区在线不卡| 赤兔流量卡办理| 久久久久国产一级毛片高清牌| 精品99又大又爽又粗少妇毛片| 大香蕉久久成人网| 国产日韩欧美视频二区| 婷婷色麻豆天堂久久| 男人爽女人下面视频在线观看| 99精国产麻豆久久婷婷| 国产成人a∨麻豆精品| 午夜福利,免费看| 国产男女内射视频| 日韩精品免费视频一区二区三区| 国产精品人妻久久久影院| 日韩制服丝袜自拍偷拍| 搡女人真爽免费视频火全软件| 啦啦啦啦在线视频资源| 香蕉国产在线看| 一级黄片播放器| 满18在线观看网站| 久久久久网色| 久久精品国产亚洲av高清一级| 99久久综合免费| 久久久久国产网址| 日日摸夜夜添夜夜爱| 国产伦理片在线播放av一区| 9色porny在线观看| 国产伦理片在线播放av一区| 黑人欧美特级aaaaaa片| 1024香蕉在线观看| 国产日韩一区二区三区精品不卡| 亚洲视频免费观看视频| 青春草亚洲视频在线观看| 两个人免费观看高清视频| 国产男人的电影天堂91| 青春草视频在线免费观看| 成人国语在线视频| 在线观看一区二区三区激情| 在线观看三级黄色| 久久狼人影院| 大片电影免费在线观看免费| 精品一品国产午夜福利视频| 午夜久久久在线观看| 欧美97在线视频| 26uuu在线亚洲综合色| 蜜桃国产av成人99| 亚洲色图 男人天堂 中文字幕| 黄网站色视频无遮挡免费观看| 国产精品 国内视频| 女性被躁到高潮视频| 精品国产乱码久久久久久男人| 精品卡一卡二卡四卡免费| 五月开心婷婷网| 青青草视频在线视频观看| 亚洲精品一二三| 欧美另类一区| 国产精品不卡视频一区二区| 天堂8中文在线网| 久久av网站| 久久午夜综合久久蜜桃| 免费在线观看完整版高清| 国产精品一区二区在线不卡| 亚洲男人天堂网一区| 亚洲国产精品一区三区| 欧美黄色片欧美黄色片| 国产精品女同一区二区软件| 老汉色av国产亚洲站长工具| √禁漫天堂资源中文www| 久久精品国产a三级三级三级| 一级片免费观看大全| 99久久综合免费| 免费看不卡的av| 免费播放大片免费观看视频在线观看| 午夜激情av网站| 亚洲久久久国产精品| 久久这里只有精品19| av电影中文网址| 成人午夜精彩视频在线观看| 2022亚洲国产成人精品| 亚洲精品av麻豆狂野| 亚洲国产精品国产精品| 肉色欧美久久久久久久蜜桃| 国产日韩一区二区三区精品不卡| 啦啦啦啦在线视频资源| 亚洲人成77777在线视频| 男女边吃奶边做爰视频| 中文字幕制服av| 免费高清在线观看日韩| √禁漫天堂资源中文www| 纯流量卡能插随身wifi吗| 天天操日日干夜夜撸| 亚洲精品av麻豆狂野| 亚洲视频免费观看视频| 国产黄频视频在线观看| 亚洲色图综合在线观看| 九草在线视频观看| av国产久精品久网站免费入址| 夫妻性生交免费视频一级片| 亚洲精品国产av成人精品| 中文字幕另类日韩欧美亚洲嫩草| a级毛片黄视频| 老鸭窝网址在线观看| 国产精品免费视频内射| 大香蕉久久成人网| 午夜精品国产一区二区电影| 精品一区二区免费观看| 国产亚洲精品第一综合不卡| 大片电影免费在线观看免费| 国产成人午夜福利电影在线观看| 日韩成人av中文字幕在线观看| 日韩,欧美,国产一区二区三区| 一区二区三区四区激情视频| 成人亚洲精品一区在线观看| 性色avwww在线观看| 免费观看在线日韩| 美女国产高潮福利片在线看| 日韩人妻精品一区2区三区| 亚洲五月色婷婷综合| 免费人妻精品一区二区三区视频| 欧美成人精品欧美一级黄| 又大又黄又爽视频免费| 女人高潮潮喷娇喘18禁视频| 大片电影免费在线观看免费| 18禁国产床啪视频网站| 国精品久久久久久国模美| 成人亚洲精品一区在线观看| 免费在线观看视频国产中文字幕亚洲 | 精品一区二区免费观看| 久久久a久久爽久久v久久| 欧美精品国产亚洲| 久久久欧美国产精品| 人人澡人人妻人| 黄色一级大片看看| 婷婷色综合www| 国产一区二区三区综合在线观看| av网站在线播放免费| 国产老妇伦熟女老妇高清| 亚洲成av片中文字幕在线观看 | 国产高清国产精品国产三级| 男女午夜视频在线观看| 如日韩欧美国产精品一区二区三区| a级毛片黄视频| 免费高清在线观看日韩| 黄片播放在线免费| 国产精品麻豆人妻色哟哟久久| 日韩中字成人| 亚洲精品第二区| 亚洲第一区二区三区不卡| 美女xxoo啪啪120秒动态图| 高清在线视频一区二区三区| 国产精品 国内视频| 2021少妇久久久久久久久久久| 久久精品人人爽人人爽视色| 亚洲情色 制服丝袜| 日韩电影二区| 日本色播在线视频| 少妇 在线观看| 制服丝袜香蕉在线| 丝袜美足系列| 精品人妻熟女毛片av久久网站| 又粗又硬又长又爽又黄的视频| 久久久久久人妻| 欧美日韩成人在线一区二区| 曰老女人黄片| 精品国产一区二区三区久久久樱花| 日本av手机在线免费观看| 国产淫语在线视频| 爱豆传媒免费全集在线观看| 精品亚洲成a人片在线观看| 午夜激情久久久久久久| 亚洲欧美成人精品一区二区| 久久 成人 亚洲| 狠狠婷婷综合久久久久久88av| 午夜激情av网站| 如日韩欧美国产精品一区二区三区| www.自偷自拍.com| 啦啦啦在线观看免费高清www| 国产成人午夜福利电影在线观看| 黄色毛片三级朝国网站| 久久精品国产亚洲av高清一级| 亚洲五月色婷婷综合| 99香蕉大伊视频| 涩涩av久久男人的天堂| 男女国产视频网站| 女性生殖器流出的白浆| 一级毛片我不卡| 久久久久久久久久久久大奶| 欧美 日韩 精品 国产| 美女国产视频在线观看| 秋霞伦理黄片| 久久97久久精品| 狠狠婷婷综合久久久久久88av| 久久久久国产精品人妻一区二区| 亚洲天堂av无毛| 婷婷成人精品国产| 国产精品成人在线| 日韩一区二区三区影片| 亚洲熟女精品中文字幕| 国产野战对白在线观看| 国产高清国产精品国产三级| 久久女婷五月综合色啪小说| 丝袜脚勾引网站| 两性夫妻黄色片| 伊人久久国产一区二区| av一本久久久久| 精品酒店卫生间| 在现免费观看毛片| 欧美日韩视频精品一区| 少妇精品久久久久久久| 国产精品女同一区二区软件| 叶爱在线成人免费视频播放| 韩国av在线不卡| 日本wwww免费看| 久久婷婷青草| 国产av国产精品国产| 寂寞人妻少妇视频99o| 在线观看www视频免费| 极品人妻少妇av视频| 亚洲精品久久成人aⅴ小说| 人人妻人人添人人爽欧美一区卜| 国产av码专区亚洲av| 99热全是精品| 美女国产视频在线观看| 亚洲精华国产精华液的使用体验| 9191精品国产免费久久| 亚洲精品日韩在线中文字幕| 亚洲精品一区蜜桃| 国产人伦9x9x在线观看 | 久久久久网色| 美女主播在线视频| 男人添女人高潮全过程视频| 日韩成人av中文字幕在线观看| 中文字幕人妻丝袜制服| 青春草国产在线视频| 精品久久久久久电影网| 免费看不卡的av| 9191精品国产免费久久| 丝袜人妻中文字幕| 亚洲精品一区蜜桃| 久久久久人妻精品一区果冻| 人体艺术视频欧美日本| 天堂俺去俺来也www色官网| 一级毛片 在线播放| kizo精华| 国产在视频线精品| 香蕉国产在线看| 一级爰片在线观看| 日本爱情动作片www.在线观看| 中文字幕人妻丝袜制服| 蜜桃国产av成人99| 又粗又硬又长又爽又黄的视频| 不卡视频在线观看欧美| 成年人午夜在线观看视频| 美女主播在线视频| 在线观看免费视频网站a站| 日日爽夜夜爽网站| 久久免费观看电影| 伊人久久国产一区二区| 亚洲国产精品一区三区| 黄频高清免费视频| 欧美 日韩 精品 国产| 日韩av免费高清视频| 岛国毛片在线播放| 曰老女人黄片| 久久久精品国产亚洲av高清涩受| 亚洲视频免费观看视频| 一本大道久久a久久精品| 男人操女人黄网站| 男女无遮挡免费网站观看| 久久 成人 亚洲| 亚洲激情五月婷婷啪啪| 精品亚洲成a人片在线观看| 人人澡人人妻人| 满18在线观看网站| 伊人久久大香线蕉亚洲五| 91久久精品国产一区二区三区| 精品国产一区二区三区久久久樱花| 一区二区三区激情视频| 国产精品人妻久久久影院| 久久久久国产网址| 一级a爱视频在线免费观看| 99热网站在线观看| 中文字幕人妻丝袜一区二区 | 精品视频人人做人人爽| 亚洲精品av麻豆狂野| 一级,二级,三级黄色视频| 极品少妇高潮喷水抽搐| 国产精品女同一区二区软件| 久久99一区二区三区| 熟妇人妻不卡中文字幕| 国产亚洲最大av| 国产av精品麻豆| 韩国高清视频一区二区三区| xxx大片免费视频| 黄色一级大片看看| 亚洲五月色婷婷综合| 亚洲精品乱久久久久久| 日韩中文字幕欧美一区二区 | 中文字幕人妻熟女乱码| 亚洲第一av免费看| 91精品国产国语对白视频| 男的添女的下面高潮视频| 欧美成人午夜免费资源| 美女xxoo啪啪120秒动态图| 亚洲av日韩在线播放| 在线亚洲精品国产二区图片欧美| 男女边吃奶边做爰视频| 熟女电影av网| 女人被躁到高潮嗷嗷叫费观| 激情五月婷婷亚洲| 免费女性裸体啪啪无遮挡网站| 欧美成人精品欧美一级黄| 激情五月婷婷亚洲| 天堂中文最新版在线下载| 国产爽快片一区二区三区| 激情五月婷婷亚洲| 天堂中文最新版在线下载| 久久人人97超碰香蕉20202| 精品第一国产精品| 天天操日日干夜夜撸| 老熟女久久久| 黑人欧美特级aaaaaa片| 高清黄色对白视频在线免费看|