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Objective To investigate the characteristic volatile organic compounds(VOCs)in exhaled breath and their diagnostic value in mice with early stage radiation injury.Methods The thermal desorption gas chromatography-mass spectrometry(TD-GC/MS)technique was used to analyze VOCs in exhaled breath of irradiated mice by 60Coγ-ray with 800 cGy.The characteristic VOCs in the early stage of radiation injury were identified,and a diagnostic model was established.Results The 30-day survival rate of mice was 4.2%.There were significant differences in characteristic VOCs at 7 hours after radiation injury,and thirty characteristic VOCs related to early-stage radiation injury were identified.The diagnostic value of differential metabolites in mice after irradiation was evaluated via the ROC curve,and the area under the ROC curve(AUC)of a single compound exceeded 0.8.The diagnostic model was constructed by screening 9 potential biomarkers of exhalation through Fisher discriminant analysis,and its sensitivity and specificity were close to 100%.Conclusion Analysis of VOCs in exhaled breath is expected to provide a non-invasive diagnostic method for early screening and diagnosis of radiation injury.
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Objective·To explore the relationship between osteoarthritis and nicotinamide metabolism-related genes using bioinformatics analysis,and identify key genes with diagnostic value and therapeutic potential.Methods·By using"Osteoarthritis"as a search term,GSE12021,GSE55235,and GSE55457 were obtained from the GEO database,with GSE55457 being used as the validation set.After removing batch effects from the GSE12021 and GSE55235 datasets,the standardized combined dataset was obtained and used as the training dataset.Differentially expressed genes(DEGs)were identified from the training dataset.All nicotinamide metabolism-related genes(NMRGs)were obtained from the GeneCards and MSigDB databases.The intersection of DEGs and NMRGs was taken to obtain nicotinamide metabolism-related differentially expressed genes(NMRDEGs).Gene set enrichment analysis(GSEA)was performed on the training dataset,while gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)analysis were performed on NMRDEGs.Key genes were selected by using least absolute shrinkage and selection operator(LASSO)and support vector machine(SVM)analysis in NMRDEGs to build an osteoarthritis diagnosis model which was validated by using the GSE55457 dataset.Single sample gene set enrichment analysis(ssGSEA)was used to analyze the immune cell infiltration type.Interactions networks and drug molecule predictions were obtained for these key genes'mRNA with the DGIdb,ENCORI,and CHIPBase databases.siRNA was used to knock down the key genes in chondrocytes,and then real-time fluorescence quantitative polymerase chain reaction(RT-qPCR)was used to detect the expression of chondrogenesis-related genes.Results·Seven NMRDEGs,including NAMPT,TIPARP,were discovered.GO and KEGG analysis enriched some signaling pathways,such as nuclear factor-κB signaling pathway and positive regulation of interleukin-1-mediated signaling pathway.GSEA enriched pathways such as Hif1 Tfpathway and syndecan 1 pathway.Key genes NPAS2,TIPARP,and NAMPT were identified through LASSO and SVM analysis,and used to construct an osteoarthritis diagnostic model.The validated results showed that the diagnostic model had high accuracy.Immune infiltration analysis results obtained by ssGSEA showed significant differences(all P<0.05)in 15 types of immune cells,including macrophages.Seven potential small molecules targeting key genes were identified,along with 19 miRNAs with the sum of upstream and downstream>10,19 transcription factors with upstream and downstream>7,and 27 RNA binding proteins with clusterNum>19.The results of RT-qPCR showed that knocking down key genes reduced the expression of chondrogenesis-related genes.Conclusion·Through bioinformatics analysis,key genes related to nicotinamide metabolism,NPAS2,TIPARP,and NAMPT,are discovered,and an osteoarthritis diagnostic model is constructed.
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BACKGROUND:Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis.Artificial neural networks have powerful data computing and classification capabilities,which have shown better performance in disease diagnosis. OBJECTIVE:To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS:The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group.The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes.GO and KEGG enrichment analyses were performed on the differentially expressed genes.Through Lasso regression model,support vector machine model and random forest tree model,the key genes of osteoarthritis were further identified from the differentially expressed genes.The R software"Neuralnet"package was then used to construct the osteoarthritis diagnosis model based on artificial neural network,and the model performance was evaluated by the five-fold cross-validation.Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION:A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis,of which 33 were down-regulated and 57 were up-regulated.GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes,including leukocyte-mediated immunity,leukocyte migration in bone marrow and chemokine production.KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis,interleukin-17 signaling pathway and osteoclast differentiation pathway.Five key genes for the diagnosis of osteoarthritis,HMGB2,GADD45A,SLC19A2,TPPP3 and FOLR2,were identified by three machine learning methods.The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36%and the area under the curve was 0.997.The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness.Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively.Therefore,the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value.
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Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.
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Objective:To analyze the application value of non-invasive inflammation diagnosis model in the diagnosis of liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) combined with hepatitis B virus (HBV) infection.Methods:A total of 98 patients with NAFLD complicated with HBV infection admitted to some coastal China Coast Guard Hospital of People′s Armed Police from June 2019 to October 2021 were selected. Their liver stiffness (LSM), aspartate aminotransferase to platelet ratio (APRI), γ-glutamyltranspeptidase to platelet ratio (GPR), and fibrosis index based on factor 4 (FIB-4) were measured, The receiver operating characteristic (ROC) curve was used to analyze its clinical diagnostic efficacy for liver fibrosis caused by NAFLD combined with HBV infection.Results:Among 98 patients, there were 7 cases in S0 stage, 47 cases in S1 stage, 21 cases in S2 stage, 14 cases in S3 stage, and 9 cases in S4 stage; Including 35 cases of obvious liver fibrosis and 9 cases of cirrhosis. There was no statistically significant difference in gender and body mass index (BMI) among patients in different stages (all P>0.05). Age: the S0 group<the S1 group<the S2 group<the S3 group<the S4 group ( P<0.05); LSM, APRI, FIB-4: S0 group<S1 group<S2 group<S3 group<S4 group (all P<0.05); The GPR of the S0 and S1 groups was significantly lower than the other groups ( P<0.05), and the S2 group<the S3 group<the S4 group ( P<0.05). The sensitivity and specificity of LSM in diagnosing obvious liver fibrosis were 71.6% and 83.1%, respectively; The sensitivity and specificity of APRI were 61.4% and 80.9%, respectively; The sensitivity and specificity of GPR were 82.3% and 66.8%, respectively; The sensitivity and specificity of FIB-4 were 66.2% and 69.5%, respectively. The sensitivity and specificity of LSM in diagnosing liver cirrhosis were 86.1% and 67.5%, respectively; The sensitivity and specificity of APRI were 77.4% and 75.2%, respectively; The sensitivity and specificity of GPR were 79.6% and 75.3%, respectively; The sensitivity and specificity of FIB-4 were 81.2% and 60.4%, respectively. Conclusions:Patients with NAFLD combined with HBV infection show a significant increase in LSM, APRI, GPR, and FIB-4 as liver fibrosis worsens. These non-invasive inflammatory diagnostic models have certain diagnostic value for liver fibrosis, with LSM and GPR having higher diagnostic efficacy.
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Objective:Screening factors that might influence rheumatoid arthritis (RA) complicating interstitial lung diseases (ILD) by constructing and validating a model for early diagnostic.Methods:The study subjects were composed of 712 RA patients in the Department of Rheumatology and Immunology of the Second Hospital of Shanxi Medical University during December 2019 to October 2022. Fifty-two variables such as their demographic data, clinical symptoms, and laboratory indexes were collected. Patients were categorized into RA-only group and RA-ILD group with or without the occurrence of ILD disease. After data preprocessing, subjects were randomly assigned to the modeling and validation groups in a 7:3 ratio.Univariate analysis comparing baseline characteristics of the two groups of patients. Feature selection was performed using LASSO and SVM-RFE regression algorithms.Screening indicators were analyzed by logistic regression and the results were used to develop a nomograms model for the early diagnosis of RA complicating interstitial lung disease; and the modeling group was evaluated for its performance for internal assessment of the model and internal validation using data from the validation group.Results:A total of 712 subjects participated in the study, of which 498 in the modeling group and 214 in the validation group. Univariate analysis showed that the differences between the two groups were statistically significant ( P<0.05) in 18 characteristic indexes, including male, gender, age, smoking history, drinking history, number of swollen joints, number of painful joints, use of prednisone, WBC, ESR, CRP, IL-2, IL-10, IL-17, TNF-α, INF-γ, AFA family, APF, and serum albumin. The LASSO algorithm identified 13 risk variables for RA-ILD, the SVM-RFE algorithm identified 12 variables for RA-ILD, and the intersecting risk variables were male, age, history of alcohol consumption, number of painful joints, prednisone acetate, IL-2, AFA family, TNF-α, serum albumin, and IL-10. The results of multifactorial logistic regression analysis confirmed that the differences between males [ OR(95% CI)=3.61(2.11, 6.18)], gender, age [ OR(95% CI)=1.05(1.03, 1.08)], number of painful joints [ OR(95% CI)=1.03(1.01, 1.06)], IL-2 [ OR(95% CI)=0.91 (0.84, 0.99)], and TNF-α[ OR (95% CI)=1.06 (1.02, 1.10)] were statistically significant ( P<0.05) and were independently influences on ILD complicated by RA. The modeling and validation groups that were used to construct early diagnostic Nomograms had high calibration curve accuracies, and the model had a high diagnostic power, which was mainly demonstrated by the receiver operating characteristic (ROC) area under the curve (AUC) and decision curve analysis(DCA), the model modeling group had an AUC of 0.76 (95% CI=0.71, 0.81), with net benefit rates of 3%~82% and 93%~99%, whereas the model validation group had an AUC of 0.71 (95% CI=0.64, 0.79), with net benefit rates of 5%~11%, 14%~60% and 85%~89%. Conclusion:Male, gender, age, number of painful joints, IL-2, and TNF-α are independent factors for RA complicated with ILD, and the Nomogram model constructed has good performance in early diagnosis of the disease.
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Objective:To investigate the clinical characteristics of patients with acute aortic dissection (AAD) through a retrospective and observational study, and to construct an early warning model of AAD that could be used in the emergency room.Methods:The data of 11 583 patients in the Emergency Chest Pain Center from January to December 2019 were retrospectively collected from the Chest Pain Database of Zhongshan Hospital Affiliated to Fudan University. Inclusion criteria: patients with chest pain who attended the Emergency Chest Pain Center between January and December 2019. Exclusion criteria were 1) younger than 18 years, 2) no chest/back pain, 3) patients with incomplete clinical information, and 4) patients with a previous definite diagnosis of aortic dissection who had or had not undergone surgery. The clinical data of 9668 patients with acute chest/back pain were finally collected, excluding 53 patients with previous definite diagnosis of AAD and/or without surgical aortic dissection. A total of 9 615 patients were enrolled as the modeling cohort for early diagnosis of AAD. The patients were divided into the AAD group and non-AAD group according to whether AAD was diagnosed. Risk factors were screened by univariate and multivariate logistic regression, the best fitting model was selected for inclusion in the study, and the early warning model was constructed and visualized based on the nomogram function in R software. The model performance was evaluated by accuracy, specificity, sensitivity, positive likelihood ratio and negative likelihood ratio. The model was validated by a validation cohort of 4808 patients who met the inclusion/exclusion criteria from January 2020 to June 2020 in the Emergency Chest Pain Center of the hospital. The effect of early diagnosis and early warning model was evaluated by calibration curve.Results:After multivariate analysis, the risk factors for AAD were male sex ( OR=0.241, P<0.001), cutting/tear-like pain ( OR=38.309, P<0.001), hypertension ( OR=1.943, P=0.007), high-risk medical history ( OR=12.773, P<0.001), high-risk signs ( OR=7.383, P=0.007), and the first D-dimer value ( OR=1.165, P<0.001), Protective factors include diabetes( OR=0.329, P=0.027) and coronary heart disease ( OR=0.121, P<0.001). The area under the ROC curve (AUC) of the early diagnosis and warning model constructed by combining the risk factors was 0.939(95 CI:0.909-0.969). Preliminary validation results showed that the AUC of the early diagnosis and warning model was 0.910(95 CI:0.870-0.949). Conclusions:Sex, cutting/tear-like pain, hypertension, high-risk medical history, high-risk signs, and first D-dimer value are independent risk factors for early diagnosis of AAD. The model constructed by these risk factors has a good effect on the early diagnosis and warning of AAD, which is helpful for the early clinical identification of AAD patients.
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Objective:To investigate the diagnostic accuracy of serological indicators and evaluate the diagnostic value of a new established combined serological model on identifying the minimal hepatic encephalopathy (MHE) in patients with compensated cirrhosis.Methods:This prospective multicenter study enrolled 263 compensated cirrhotic patients from 23 hospitals in 15 provinces, autonomous regions and municipalities of China between October 2021 and August 2022. Clinical data and laboratory test results were collected, and the model for end-stage liver disease (MELD) score was calculated. Ammonia level was corrected to the upper limit of normal (AMM-ULN) by the baseline blood ammonia measurements/upper limit of the normal reference value. MHE was diagnosed by combined abnormal number connection test-A and abnormal digit symbol test as suggested by Guidelines on the management of hepatic encephalopathy in cirrhosis. The patients were randomly divided (7∶3) into training set ( n=185) and validation set ( n=78) based on caret package of R language. Logistic regression was used to establish a combined model of MHE diagnosis. The diagnostic performance was evaluated by the area under the curve (AUC) of receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve. The internal verification was carried out by the Bootstrap method ( n=200). AUC comparisons were achieved using the Delong test. Results:In the training set, prevalence of MHE was 37.8% (70/185). There were statistically significant differences in AMM-ULN, albumin, platelet, alkaline phosphatase, international normalized ratio, MELD score and education between non-MHE group and MHE group (all P<0.05). Multivariate Logistic regression analysis showed that AMM-ULN [odds ratio ( OR)=1.78, 95% confidence interval ( CI) 1.05-3.14, P=0.038] and MELD score ( OR=1.11, 95% CI 1.04-1.20, P=0.002) were independent risk factors for MHE, and the AUC for predicting MHE were 0.663, 0.625, respectively. Compared with the use of blood AMM-ULN and MELD score alone, the AUC of the combined model of AMM-ULN, MELD score and education exhibited better predictive performance in determining the presence of MHE was 0.755, the specificity and sensitivity was 85.2% and 55.7%, respectively. Hosmer-Lemeshow test and calibration curve showed that the model had good calibration ( P=0.733). The AUC for internal validation of the combined model for diagnosing MHE was 0.752. In the validation set, the AUC of the combined model for diagnosing MHE was 0.794, and Hosmer-Lemeshow test showed good calibration ( P=0.841). Conclusion:Use of the combined model including AMM-ULN, MELD score and education could improve the predictive efficiency of MHE among patients with compensated cirrhosis.
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OBJECTIVE To utilize RNA sequencing(RNA-seq)data from the GEO database to identify genes with potential diagnostic value for eosinophilic chronic rhinosinusitis with nasal polyps(ECRSwNP).METHODS Three datasets were obtained,and samples were divided into ECRSwNP and nonECRSwNP groups based on the expression levels of CST1 and CLC.A diagnostic model for ECRSwNP was established using R software and algorithms,and its accuracy was assessed.RESULTS The samples were grouped as follows:GSE136825(ECRSwNP 7,nonECRSwNP 19),GSE72713(ECRSwNP 3,nonECRSwNP 3),and GSE179265(ECRSwNP 11,nonECRSwNP 2).The diagnostic performance of the upregulated gene model(ADH1C,CCL26,HRH1,NOS2)and the downregulated gene model(LCN2,MUC5B,PLAT,TMEM45A,XDH)constructed based on the support vector machine(SVM)algorithm for ECRSwNP was excellent.CONCLUSION The diagnostic genes identified by the SVM model may serve as biological markers for the non-invasive diagnosis of ECRSwNP and potentially play a crucial role in the pathogenesis of ECRSwNP.
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Objective@#To explore the value of an oral squamous cell carcinoma (OSCC) diagnostic model constructed by using principal component analysis (PCA) to analyze a database of differentially expressed genes in OSCC and to provide a reference for clinical diagnosis and treatment.@*Methods@# RNA-seq expression data of OSCC and normal control samples were obtained from The Cancer Genome Atlas (TCGA) database, and then, normalized and differentially expressed genes (DEGs) were identified by R software. DEGs were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to identify their main biological characteristics. 70% of DEGs expression data in RNA-seq were randomly selected as the training set and 30% were selected as the test set. Then, the PCA method was applied to analyze the training set data and extract the principal components (PCs) related to the diagnosis of OSCC in order to construct a PCA model. Then, the receiver operating characteristic (ROC) curves of PCA models in the training set and the test set were respectively drawn, and the area under curve (AUC) was calculated to evaluate the accuracy of the PCA model in the diagnosis of OSCC.@*Results@#RNA-seq expression data of OSCC and normal control samples obtained from TCGA database included 330 samples and 32 samples, respectively. Using false discovery rate (FDR) <0.001 and |log2 fold change| (|log2FC|) >4 as the thresholds, a total of 159 downregulated and 248 upregulated DEGs were identified, which were mainly enriched in cellular components such as intermediate fiber and melanosomal membrane, pigment and salivation-related biological processes and mainly involved in salivary secretion and tyrosine metabolism pathways (P.adjust<0.05 and Q<0.05). The DEGs were proposed as tumor markers for OSCC, and PCA analysis of the training set showed that the cumulative ratio of variance of PC1, PC2 and PC3: [including submaxillary gland androgen regulated protein 3B (SMR3B), proline rich 27 (PRR27), histatin 3 (HTN3), statherin (STATH), cystatin D (CST5), BPI fold containing family A member 2 (BPIFA2), proline rich protein Hae Ⅲ subfamily 2 (PRH2), keratin 35(KRT35), histatin 1 (HTN1), amylase alpha 1B (AMY1B)] were 0.873, 0.100 and 0.023, respectively, and the total weight of the three was 0.996. The PCA diagnostic model of OSCC was further constructed by combining the eigenvectors of the above three components. The ROC curves of the training set and test set showed that the AUC values of the PCA model were 0.852 and 0.844, respectively, which were higher than those of other single genes.@*Conclusion @#The OSCC diagnostic model based on the expression levels of SMR3B, PRR27, HTN3, STATH, CST5, BPIFA2, PRH2, KRT35, HTN1 and AMY1B constructed with the PCA method and DEGs has a high diagnostic advantage. This study provides a theoretical basis for the early genetic diagnosis of OSCC and the application of the PCA model in clinical diagnosis.
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【Objective】 To evaluate the performance of SPINK1/SPP1 in diagnosis of hepatocellular carcinoma (HCC) alone or in combination. 【Methods】 A total of 419 serum samples were collected and divided into four groups: normal control (n=93), chronic hepatitis B (CHB) (n=72), HBC related liver cirrhosis (LC) (n=77), and hepatocellular carcinoma (HCC) (n=177). Serum concentrations of SPINK1 and SPP1 were determined by ELISA kits. All parameters were first analyzed by significance tests among the groups. To distinguish tumors from non-tumors, a combination model was generated by multivariable binary Logistic regression using a comprehensive control group which consisted of the normal, the CHB and the LC groups. The performance of each indicator was judged by comparison of AUC, sensitivity, specificity and accuracy. 【Results】 The serum levels of SPINK1 and SPP1 were both significantly higher in HCC group than in all the others (P0.05), was lower than that of the combination model (P=0.042 9 and P0.05). 【Conclusion】 The data identified SPINK1 and SPP1 as novel tumor biomarkers with greater robust efficiency than the currently used AFP for detection of hepatocellular carcinoma, alone or in combination.
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Objective:To analyze the liver pathological characteristics of chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) and negative hepatitis B e antigen (HBeAg), and to evaluate the diagnostic value of different serological models for liver fibrosis.Methods:Retrospective analysis was conducted on the patients with HBeAg-negative CHB who had normal ALT and underwent liver biopsy from August 2016 to December 2019 in the Department of Infectious Diseases, Henan Provincial People′s Hospital. The clinical data, serum indicators of hepatitis B virus (HBV) and HBV DNA were collected. The liver fibrosis stages (S) was assessed by pathological examination. The diagnostic efficacies of gamma-glutamyl transpeptidase to platelet ratio (GPR), fibrosis 4 score (FIB-4), S index, aspartate aminotransferase to platelet ratio index (APRI) and gamma-glutamyl transpeptidase to albumin ratio (γ-GT/ALB) for liver pathological fibrosis were analyzed by the receiver operating characteristic curves. Two variable correlation test was used to explore the relationship between the different models and pathological fibrosis of liver tissue. Chi-square test was used for statistical comparison.Results:The age of 448 patients was (37.98±9.82) years, and the male to female ratio was 1.286 ∶1. The proportions of S≥2 in patients with age>30 years, hepatitis B surface antigen (HBsAg)<2 000 IU/mL and HBV DNA≥2 000 IU/mL were higher than those in patients with age ≤30 years, HBsAg ≥2 000 IU/mL and HBV DNA<2 000 IU/mL, respectively, and the differences were all statistically significant ( χ2=7.68, P=0.006; χ2=11.44, P=0.001; χ2=9.12, P=0.003, respectively). There were 250 cases with pathological fibrosis stage S<2, 162 cases with S=2 and 36 cases with S≥3. FIB-4 (correlation coefficient 0.250), APRI (correlation coefficient 0.218), GPR (correlation coefficient 0.186), S index (correlation coefficient 0.184) and γ-GT/ALB (correlation coefficient 0.127) were positively correlated with the severity of liver fibrosis (all P<0.050). S index had the highest sensitivity (64.1%) in the diagnosis of significant liver fibrosis (S≥2), while γ-GT/ALB had the highest specificity (80.8%). In the diagnosis of severe liver fibrosis (S≥3), γ-GT/ALB had the highest sensitivity (77.8%), while APRI had the highest specificity (78.6%). Conclusions:The incidence of liver fibrosis in CHB patients with normal ALT and negative HBeAg is relatively high. The current serological diagnostic models are not suitable for the evaluation of liver fibrosis in these patients, and timely liver puncture is still necessary.
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Objective:To analyze the MRI findings of solid pseudopapilloma of the pancreas (SPTs) and nonfunctional pancreatic neuroendocrine tumors (PNETs), and to establish and verify the prediction model of SPTs and PNETs.Methods:The clinical and MRI data of 142 patients with SPTs and 137 patients with PNETs who underwent surgical resection and were confirmed by pathology in the First Affiliated Hospital of Naval Medical University from January 2013 to December 2020 were collected continuously. Age, gender, body mass index (BMI), lesion size, location, shape, boundary, cystic change, T 1WI signal, T 2WI signal, enhancement peak phase, whether the enhancement degree was higher than that of pancreatic parenchyma in the enhancement peak phase, enhancement pattern, whether pancreatic duct and common bile duct were dilated, whether the pancreas shrank, and whether it invaded adjacent organs and vessels were recorded. According to the international consensus on prediction model modeling, patients were divided into training set (106 SPTs and 100 PNETs between January 2013 and December 2018), and validation set (36 SPTs and 37 PNETs between January 2019 and December 2020). The above characteristics of patients in training and validation set were analyzed by univariate and multivariate logistic regression, and a prediction model was established to distinguish SPTs and PNETs, and then visualized as a nomogram. The receiver operating characteristic curve (ROC) of the nomogram of training set and verification set was drawn, and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the prediction efficiency of the model, and the clinical application value of the prediction model was evaluated by decision curve analysis (DCA). Results:Univariate regression analysis showed that there were significant differences on age, gender, lesion size, shape, cystic change, T 1WI signal, peak phase of enhancement, degree of enhancement in peak phase, pattern of enhancement and invasion of adjacent organs between SPTs group and PNETs group (all P value <0.05). Multivariate regression analysis showed that the older age, male patients, the smaller lesion, no high signal on T 1WI, the enhancement peak phase located in arterial phase or venous phase, and the enhancement degree in peak phase higher than that of pancreatic parenchyma were the six independent predictors of PNETs. The prediction model was established by using these six factors and visualized as a nomogram. The formula for predicting PNETs probability was 4.31+ 1.13×age+ 1.31×tumor size-1.29×female-4.18×high T 1WI signal+ 1.28×the enhancement degree higher than that of pancreatic parenchyma -4.69 ×enhancement peak in delay phase. The prediction model was visualized as a nomogram. The AUC values in the training set and validation set were 0.99(95% CI0.977-1.000) and 0.97 (95% CI 0.926-1.000), respectively. The sensitivity, specificity and accuracy in the training set are 98.00%, 94.34% and 96.12% and in the validation set were 86.49%, 97.22% and 91.78% respectively. The results of decision curve analysis show that the prediction model can accurately diagnose SPTs and PNETs. Conclusions:The prediction model established in this study can accurately differentiate SPTs from PNETs, and can provide important information for clinical decision and prognosis.
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Objective:To analyze the clinical features of latent autoimmune diabetes (LADA) in adults among newly diagnosed type 2 diabetes mellitus (T2DM), and to explore whether LADA diagnostic models can be established based on this.Methods:From May 2016 to January 2017, 302 patients with newly diagnosed T2DM in the outpatient and inpatient department of metabolism and endocrinology of Yueyang Central Hospital were analyzed. All of them were tested for glutamic acid decarboxylase antibody (GADA). According to the consensus of the Chinese Medical Association Diabetes Association (CDS) LADA diagnosis and treatment, they were divided into LADA group (18 cases) and T2DM group (284 cases). The general clinical data and clinical biochemical indexes of the two groups were analyzed; Multiple linear regression method was used to evaluate the feasibility of establishing LADA diagnostic model.Results:⑴ Compared with patients in the T2DM group, the patients in the LADA group had a younger age of onset, and " three more and one less" symptoms were more common ( P<0.05); the weight, body mass index (BMI), waist circumference, waist-to-hip ratio (WHR), triglycerides (TG), fasting C peptide (FCP), postprandial 2 h C peptide (2 h-CP), modified islet function index HOMA-islet (CP-DM), and modified insulin resistance index HOMA-IR (CP) in the LADA group were all lower, while high-density lipoprotein cholesterol (HDL-C) and HbA1c were higher ( P<0.05). ⑵ the linear regression method was used to analyze the multicollinearity of patients in LADA group and T2DM group. The biochemical indexes with statistically significant difference were selected as independent variables through correlation analysis, and the GADA value was used as dependent variable. The statistical results showed that the independent variables could not fully meet the conditions of multicollinearity regression analysis. Conclusions:⑴ Related clinical features and glucose metabolism indicators have differential diagnosis significance for LADA, but this study cannot be used for multiple linear regression analysis, and it is difficult to establish a diagnostic model for LADA. ⑵ LADA diagnosis is a comprehensive diagnosis, which should be combined with the results of islet autoantibody and clinical features.
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Objective: To compare the qualitative diagnostic value of different Methods: based on PET/CT for solitary pulmonary nodules (SPN). Methods: Data of 161 SPN patients who underwent PET/CT were collected. Clinical information, high resolution CT (HRCT) signs and maximum standard uptake value (SUVmax) were compared between benign and malignant SPN patients. The mathematical diagnostic model of SPN was constructed by using binary Logistic regression, and the diagnostic efficiencies were compared among diagnostic model, PET/CT and HRCT. Results: Among 161 patients, malignant SPN were pathologically diagnosed in 131 cases and benign in 30 cases. The sensitivity, specificity and accuracy of PET/CT in diagnosing malignant SPN was 98.47% (129/131), 76.67% (23/30) and 94.41% (152/161), of HRCT was 59.54% (78/131), 83.33% (25/30) and 63.98%(103/161), respectively. After univariate and multivariate analysis, SUVmax, patient's age, calcification and tracheal vascular bundles were incorporated into the regression equation, and a model was then established. The sensitivity, specificity and accuracy of the model for diagnosing malignant SPN was 82.44% (108/131), 86.67% (26/30) and 83.23% (134/161), respectively. The AUC of the model, PET/CT and HRCT for diagnosis of malignant SPN was 0.909, 0.876 and 0.714, respectively. The AUC of the model and PET/CT were both higher than HRCT (all P<0.001). There was no significant difference between the model and PET/CT (P=0.468). Conclusion: PET/CT-based Logistic regression model and PET/CT are better than HRCT in qualitative diagnosis of SPN, and the specificity of the model is higher than that of PET/CT.
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@#Objective To investigate the characteristic volatile organic compounds (VOCs) in exhaled breath and their diagnostic value in patients with early stage lung cancer. Methods Solid-phase micro-extraction combined with gas chromatography mass spectrometry was used to analyze exhaled breath VOCs of 117 patients with early stage lung cancer (54 males and 63 females, with an average age of 61.9±6.8 years) and 130 healthy subjects (79 males and 51 females, with an average age of 63.3±6.6 years. The characteristic VOCs of early stage lung cancer were identified, and a diagnostic model was established. Results Ten characteristic VOCs of early stage lung cancer were identified, including acetic acid, n-butanol, dimethylsilanol, toluene, 2,3,4-trimethylheptane, 3,4-dimethylbenzoic acid, 5-methyl-3-hexene-2-ketone, n-hexanol, methyl 2-oxoglutarate and 4-methoxyphenol. Gender and the 10 characteristic VOCs were included in the diagnostic model, with a sensitivity of 83.8% and a specificity of 96.2%. Conclusion Analysis of exhaled breath VOCs is expected to be one of the potential methods used for early stage lung cancer diagnosis.
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Objective@#To establish and evaluate diagnostic efficacy and applicability of serum Golgi protein (GP) 73 based non-invasive diagnostic model with other conventional serological indicators for compensated stage hepatitis B cirrhosis.@*Methods@#666 cases with chronic hepatitis B (CHB) who had visited to the Fifth Medical Center of People’s Liberation Army General Hospital from January 2010 to December 2017 were selected as the study subjects, and were classified according to compensated stage cirrhosis into clinical and pathological diagnosis group based on whether or not the liver histological examination was performed. A diagnostic model of compensated stage hepatitis B cirrhosis in the clinical diagnosis group was established. The current clinically used diagnostic model of liver cirrhosis, aspartate aminotransferase/platelet ratio index (APRI), fibrosis index (FIB)-4 and liver stiffness measurement (LSM) were compared. Eventually, the diagnostic model was verified step by step by pathological diagnosis group.@*Results@#The area under the receiver operating characteristic curve (AUC) of GP73 and APRI, FIB-4, and LSM for cirrhosis patients in the clinical diagnosis group were 0.842, 0.857, 0.864, and 0.832, respectively. The diagnostic efficiency of the four indicators were of similar (P value > 0.05). A diagnostic model of compensated stage hepatitis B cirrhosis (GAPA) using logistic regression analysis was established: LogitP = 1/ [1 + exp (1.614-0.054 × GP73-0.045 × Age + 0.030 × PLT-0.015 × ALP)]. The AUC of the model was as high as 0.940 and the optimal cut-off value were 0.41. The corresponding diagnostic sensitivity and specificity were 0.92 and 0.82, respectively. The diagnostic efficiency was better than that of APRI, FIB-4, LSM and GP73 alone (P < 0.05). The AUC of GAPA was 0.877 in the pathological diagnosis group, which was similar to the diagnostic efficacy of LSM (0.891) and FIB-4 (0.847) (P > 0.1), but still superior to that of APRI (0.811) and GP73 alone (0.780) (P < 0.001).@*Conclusion@#GAPA, a diagnostic model for compensated stage hepatitis B cirrhosis established in this study, has a good diagnostic efficacy in both the clinical and pathological diagnosis group, and has certain auxiliary diagnostic value in the areas where resources are relatively scarce or where LSM has not been developed.
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Individual dental age is used as an index of chronological age estimation and is an important indicator of the child's growth stage. Dental age does change greatly over time, but it changes constantly. And updating information about this change is important. The purpose of this study was to provide information about tooth eruption stage using diagnostic model analysis and to investigate tooth eruption sequence and estimate chronological age based on this information.Tooth eruption stages were measured on a diagnostic model from 488 patients in 5 – 13 year old children. Based on the information on eruption stage, eruption sequence in maxilla was first permanent molar, central incisor, lateral incisor, first premolar, canine, second premolar and second permanent molar. Eruption sequence in mandible was first permanent molar, central incisor, lateral incisor, canine, first premolar, second premolar and second permanent molar. There were significant differences between males and females in the eruption stage of canine, first and second premolar, and second molar at several ages. The chronological age of male and female was estimated by the coefficient of determination of 0.816, 0.826 respectively.
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Niño , Femenino , Humanos , Masculino , Diente Premolar , Incisivo , Mandíbula , Maxilar , Diente Molar , Erupción Dental , DienteRESUMEN
Objective To use the liquid protein combined with MALDI-TOF-MS for screening the serum differential peptides markers in lung adenocarcinoma patients and to establish the lung adenocarcinoma diag-nosed prediction model for founding the potential markers for the diagnosis of lung adenocarcinoma.Methods 37 patients with lung adenocarcinoma and 33 healthy subjects and benign lung disease which were made up in control group were collected,in the two groups the age and the sex were matched.The two groups were ran-domly divided into training group(30 cases of lung adenocarcinoma,26 cases of control)and test group(7 ca-ses of lung adenocarcinoma,7 cases of control)according to 3:1.T he differential diagnosis of lung adenocarci-noma and control group was performed by liquid chip-time-of-flight mass spectrometry and software ClinPro-Tools 3.0 to establish a prediction model of lung adenocarcinoma.The diagnostic model was validated by using serum samples from the test group to assess the diagnostic efficacy of the model.Results Nine peptide peaks with significant differences(P<0.05)were obtained by ClinProTools 3.0 software analysis.The up-regulated peaks in lung adenocarcinoma(m/z)were 8 976.5,4 469.05,4 966.78,8 925.5,4 531.05,and the down-reg-ulated m/z were 3 304.44,8 594.76,3 266.82,3 195.52.According to the genetic algorithm(GA),the lung ad-enocarcinoma diagnosis and prediction model was established.The overall recognition ability of the model was 94.49%.The model was evaluated by the test group.The results showed that the sensitivity of the model was 100.0% and the specificity was 85.7%.Conclusion Among lung adenocarcinoma patients,serum benign lung disease and healthy,there are differences in the serum peptide.T he use of differential peptide peaks to estab-lish lung adenocarcinoma diagnostic prediction model for the early diagnosis of lung adenocarcinoma provides a new method.
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Objective A BP neural network model for diagnosing type 2 diabetic nephropathy based on laboratory tests was developed and evaluated. Methods Patients with type 2 diabetic nephropathy from 5 hospitals of Chongqing,Guizhou and Sichuan Provinces from January 2016 to December 2016 were collected in the study. Totally 89 parameters were analyzed by univariate analysis to identify significant variables by SPSS 19. 0 and MATLAB 2014a. The diagnostic performance of the two methods were compared. Results A total of 477 patients with type 2 diabetic nephropathy and 449 patients of control group were included. Univariate analysis showed that 42 variables had significant difference. Logistic regression analysis showed that 12 variables were included in the optimal regression equation. This BP neural network had 42 input layer nodes,15 hidden layer nodes and 1 output layer nodes. The Youden index of logistic regression analysis and BP neural network(training set and test set) were 0.76,0.89 and 0.83. The accurately diagnosed were 88.12%,94.24%,and 91.34%,the AUC were 0.95,0.98,and 0.96. Conclusion A BP neural network model was developed,which has important accessory diagnostic value for diagnosis of type 2 diabetic nephropathy. But all these conclusions need further validation in clinic.