Objective: To study using isotope-labeled relative and absolute quantitative proteomics methodologies to screen for salivary biological markers as a simple, non-invasive tool for identifying hepatitis B-related HCC at an early stage. Methods: Saliva samples were collected to extract salivary proteins. Isotope-labeled relative and absolute quantitative proteomics were used to analyze the differentially expressed proteins between the hepatocellular carcinoma (HCC) and non-HCC groups. Western blotting, immunohistochemistry, and enzyme-linked immunosorbent assays were used to verify differential proteins and identify markers in liver cancer tissues and saliva. Statistical analysis was used to analyze the diagnostic efficiency of salivary biomarkers. Results: 152 differentially expressed salivary proteins were screened out between the HCC and non-HCC groups. Western blot, immunohistochemistry, and enzyme-linked immunosorbent assays validated that the expressions of α-1-acid glycoprotein 1 (ORM1) and alpha-fetoprotein (AFP) were significantly increased in HCC (P < 0.05). There was a significant correlation between salivary AFP and serum AFP (P < 0.05). HCC was diagnosed when salivary α-1-acid glycoprotein 1 combined with AFP. The area under the receiver operating characteristic curve was 0.8726 (95% confidence interval: 0.8104 ~ 0.9347), the sensitivity was 78.3%, and the specificity was 88%. Conclusion: Salivary AFP and α-1-acid glycoprotein 1 can serve as potential biomarkers for hepatitis B-related hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/pathology* ; alpha-Fetoproteins/metabolism* ; Biomarkers ; Hepatitis B ; ROC Curve ; Glycoproteins ; Biomarkers, Tumor

Objective: To investigate the clinical value of plasma scaffold protein SEC16A level and related models in the diagnosis of hepatitis B virus-related liver cirrhosis (HBV-LC) and hepatocellular carcinoma (HBV-HCC). Methods: Patients with HBV-LC and HBV-HCC and a healthy control group diagnosed by clinical, laboratory examination, imaging, and liver histopathology at the Third Hospital of Hebei Medical University between June 2017 and October 2021 were selected. Plasma SEC16A level was detected using an enzyme-linked immunosorbent assay (ELISA). Serum alpha-fetoprotein (AFP) was detected using an electrochemiluminescence instrument. SPSS 26.0 and MedCalc 15.0 statistical software were used to analyze the relationship between plasma SEC16A levels and the occurrence and development of liver cirrhosis and liver cancer. A sequential logistic regression model was used to analyze relevant factors. SEC16A was established through a joint diagnostic model. Receiver operating characteristic curve was used to evaluate the clinical efficacy of the model for liver cirrhosis and hepatocellular carcinoma diagnosis. Pearson correlation analysis was used to identify the influencing factors of novel diagnostic biomarkers. Results: A total of 60 cases of healthy controls, 60 cases of HBV-LC, and 52 cases of HBV-HCC were included. The average levels of plasma SEC16A were (7.41 ± 1.66) ng/ml, (10.26 ± 1.86) ng/ml, (12.79 ± 1.49) ng /ml, respectively, with P < 0.001. The sensitivity and specificity of SEC16A in the diagnosis of liver cirrhosis and hepatocellular carcinoma were 69.44% and 71.05%, and 89.36% and 88.89%, respectively. SEC16A, age, and AFP were independent risk factors for the occurrence of HBV-LC and HCC. SAA diagnostic cut-off values, sensitivity, and specificity were 26.21 and 31.46, 77.78% and 81.58%, and 87.23% and 97.22%, respectively. The sensitivity and specificity for HBV-HCC early diagnosis were 80.95% and 97.22%, respectively. Pearson correlation analysis showed that AFP level was positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBil), and γ-glutamyltransferase (GGT) with P < 0.01, while the serum SEC16A level was only slightly positively correlated with ALT and AST in the liver cirrhosis group (r = 0.268 and 0.260, respectively, P < 0.05). Conclusion: Plasma SEC16A can be used as a diagnostic marker for hepatitis B-related liver cirrhosis and hepatocellular carcinoma. SEC16A, combined with age and the AFP diagnostic model with SAA, can significantly improve the rate of HBV-LC and HBV-HCC early diagnosis. Additionally, its application is helpful for the diagnosis and differential diagnosis of the progression of HBV-related diseases.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; alpha-Fetoproteins/metabolism* ; Endoplasmic Reticulum/metabolism* ; Golgi Apparatus/metabolism* ; Vesicular Transport Proteins ; Liver Cirrhosis/complications* ; Hepatitis B/complications* ; ROC Curve ; Hepatitis B virus/metabolism* ; Biomarkers, Tumor

Objective: To explore carnosine dipeptidase 1 (CNDP1) potential value as a diagnostic and prognostic evaluator of hepatocellular carcinoma (HCC). Methods: A gene chip and GO analysis were used to screen the candidate marker molecule CNDP1 for HCC diagnosis. 125 cases of HCC cancer tissues, 85 cases of paracancerous tissues, 125 cases of liver cirrhosis tissues, 32 cases of relatively normal liver tissue at the extreme end of hepatic hemangioma, 66 cases from serum samples of HCC, and 82 cases of non-HCC were collected. Real-time fluorescent quantitative PCR, immunohistochemistry, western blot, and enzyme-linked immunosorbent assay were used to detect the differences in mRNA and protein expression levels of CNDP1 in HCC tissue and serum. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival were used to analyze and evaluate the value of CNDP1 in the diagnosis and prognosis of HCC patients. Results: The expression level of CNDP1 was significantly reduced in HCC cancer tissues. The levels of CNDP1 were significantly lower in the cancer tissues and serum of HCC patients than those in liver cirrhosis patients and normal controls. ROC curve analysis showed that the area under the curve of serum CNDP1 in the diagnosis of HCC patients was 0.753 2 (95% CI 0.676-0.830 5), and the sensitivity and specificity were 78.79% and 62.5%, respectively. The combined detection of serum CNDP1 and serum alpha-fetoprotein (AFP) significantly improved the diagnostic accuracy (AUC = 0.820 6, 95% CI 0.753 5-0.887 8). The diagnostic sensitivity and specificity of serum CNDP1 for AFP-negative HCC patients were 73.68% and 68.75% (AUC = 0.793 1, 95% CI 0.708 8-0.877 4), respectively. In addition, the level of serum CNDP1 distinguished small liver cancer (tumor diameter < 3 cm) (AUC = 0.757 1, 95% CI 0.637 4-0.876 8). Kaplan-Meier survival analysis showed that CNDP1 was associated with a poor prognosis in HCC patients. Conclusion: CNDP1 may be a potential biomarker for the diagnostic and prognostic evaluation of HCC, and it has certain complementarity with serum AFP.
Humans ; Carcinoma, Hepatocellular/genetics* ; Liver Neoplasms/pathology* ; Prognosis ; Carnosine ; alpha-Fetoproteins/metabolism* ; Biomarkers, Tumor/genetics* ; Liver Cirrhosis/diagnosis* ; ROC Curve

OBJECTIVE: To investigate the relationship between stress glucose elevation and the risk of 28 d all-cause mortality in intensive care unit (ICU) patients, and to compare the predictive efficacy of different stress glucose elevation indicators. METHODS: ICU patients who met the inclusion and exclusion criteria in the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database were used as the study subjects, and the stress glucose elevation indicators were divided into Q1 (0-25%), Q2 (>25%- 75%), and Q3 (>75%-100%) groups, with whether death occurred in the ICU and the duration of treatment in the ICU as outcome variables, and demographic characteristics, laboratory indicators, and comorbidities as covariates, Cox regression and restricted cubic splines were used to explore the association between stress glucose elevation and the risk of 28 d all-cause death in ICU patients; and subject work characteristics [receiver operating characteristic (ROC) and the area under curve (AUC)] were used to evaluate the predictive efficacy of different stress glucose elevation indicators, The stress hyperglycemia indexes included: stress hyperglycemia ratio (SHR1, SHR2), glucose gap (GG); and the stress hyperglycemia index was further incorporated into the Oxford acute severity of illness score (OASIS) to investigate the predictive efficacy of the improved scores: the AUC was used to assess the score discrimination, and the larger the AUC indicated, the better score discrimination. The Brier score was used to evaluate the calibration of the score, and a smaller Brier score indicated a better calibration of the score. RESULTS: A total of 5 249 ICU patients were included, of whom 7.56% occurred in ICU death. Cox regression analysis after adjusting for confounders showed that the HR (95%CI) for 28 d all-cause mortality in the ICU patients was 1.545 (1.077-2.217), 1.602 (1.142-2.249) and 1.442 (1.001-2.061) for the highest group Q3 compared with the lowest group Q1 for SHR1, SHR2 and GG, respectively, and The risk of death in the ICU patients increased progressively with increasing indicators of stressful blood glucose elevation (P_trend < 0.05). Restricted cubic spline analysis showed a linear relationship between SHR and the 28 d all-cause mortality risk (P>0.05). the AUC of SHR2 and GG was significantly higher than that of SHR1: AUC_SHR2=0.691 (95%CI: 0.661-0.720), AUC_GG=0.685 (95%CI: 0.655-0.714), and AUC_SHR1=0.680 (95%CI: 0.650-0.709), P < 0.05. The inclusion of SHR2 in the OASIS scores significantly improved the discrimination and calibration of the scores: AUC_OASIS=0.820 (95%CI: 0.791-0.848), AUC_OASIS+SHR2=0.832 (95%CI: 0.804-0.859), P < 0.05; Brier score_OASIS=0.071, Brier score_OASIS+SHR2=0.069. CONCLUSION Stressful glucose elevation is strongly associated with 28 d all-cause mortality risk in ICU patients and may inform clinical management and decision making in intensive care patients.
Humans ; Intensive Care Units ; Prognosis ; Retrospective Studies ; Critical Care ; ROC Curve ; Hyperglycemia ; Glucose

OBJECTIVE: To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics. METHODS: We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T₂WI, T₁WI, FLAIR, CE-T₁WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists. RESULTS: The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05). CONCLUSION Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.
Humans ; Magnetic Resonance Imaging ; Chromosome Aberrations ; Area Under Curve ; Glioma/genetics* ; ROC Curve

Humans ; Hospital Mortality ; ROC Curve ; Emergency Service, Hospital ; Heart Failure ; Renal Insufficiency, Chronic ; Retrospective Studies

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.
Humans ; Radiotherapy, Intensity-Modulated ; Immune Tolerance ; Nasopharyngeal Carcinoma ; ROC Curve ; Nasopharyngeal Neoplasms/radiotherapy*

Objective: To explore the influencing factors of abnormal pulmonary function in dust-exposed workers and establish the risk prediction model of abnormal pulmonary function. Methods: In April 2021, a total of 4255 dust exposed workers from 47 enterprises in 2020 were included in the study. logistic regression was used to analyze the influencing factors of abnormal pulmonary function in dust-exposed workers, and the corresponding nomogram prediction model was established. The model was evaluated by ROC curve, Calibrationpolt and decision analysis curve. Results: logistic regression analysis showed that age (OR=1.03, 95%CI=1.02~1.05, P<0.001) , physical examination type (OR=4.52, 95%CI=1.69~12.10, P=0.003) , dust type (Comparison with coal dust, Cement dust, OR=3.45, 95%CI=1.45~8.18, P=0.005, Silica dust (OR=2.25, 95%CI=1.01~5.03, P=0.049) , blood pressure (OR=1.63, 95%CI=1.22~2.18, P=0.001) , creatinine (OR=0.08, 95%CI=0.05~0.12, P<0.001) , daily exposure time (OR=1.06, 95%CI=1.10~1.12, P=0.034) and total dust concentration (OR=1.29, 95%CI=1.08~1.54, P=0.005) were the influencing factors of abnormal pulmonary function. The area under the ROC curve of risk prediction nomogram model was 0.764. The results of decision analysis curve showed that the nomogram model had reference value in the prevention and intervention of abnormal pulmonary function when the threshold probability exceeded 0.05. Conclusion: The accuracy ofthe nomogram model constructed by logistic regression werewell in predicting the risk of abnormal lung function of dust-exposed workers.
Humans ; Dust/analysis* ; Lung ; Nomograms ; Risk Factors ; ROC Curve

Objective: To identify the predisposing factors, clinical characteristics, and risk factors of disease progression to establish a novel predictive survival model and evaluate its application value for hepatitis B virus-related acute-on-chronic liver failure. Methods: 153 cases of HBV-ACLF were selected according to the guidelines for the diagnosis and treatment of liver failure (2018 edition) of the Chinese Medical Association Hepatology Branch. Predisposing factors, the basic liver disease stage, therapeutic drugs, clinical characteristics, and factors affecting survival status were analyzed. Cox proportional hazards regression analysis was used to screen prognostic factors and establish a novel predictive survival model. The receiver operating characteristic curve (ROC) was used to evaluate predictive value with the Model for End-Stage Liver Disease (MELD) and the Chronic Liver Failure Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLF). Results: 80.39% (123/153) based on hepatitis B cirrhosis had developed ACLF. HBV-ACLF's main inducing factors were the discontinuation of nucleos(t)ide analogues (NAs) and the application of hepatotoxic drugs, including Chinese patent medicine/Chinese herbal medicine, non-steroidal anti-inflammatory drugs, anti-tuberculosis drugs, central nervous system drugs, anti-tumor drugs, etc. 34.64% of cases had an unknown inducement. The most common clinical symptoms at onset were progressive jaundice, poor appetite, and fatigue. The short-term mortality rate was significantly higher in patients complicated with hepatic encephalopathy, upper gastrointestinal hemorrhage, hepatorenal syndrome, and infection (P < 0.05). Lactate dehydrogenase, albumin, the international normalized ratio, the neutrophil-to-lymphocyte ratio, hepatic encephalopathy, and upper gastrointestinal bleeding were the independent predictors for the survival status of patients. The LAINeu model was established. The area under the curve for evaluating the survival of HBV-ACLF was 0.886, which was significantly higher than the MELD and CLIF-C ACLF scores (P < 0.05), and the prognosis was worse when the LAINeu score ≥ -3.75. Conclusion: Discontinuation of NAs and the application of hepatotoxic drugs are common predisposing factors for HBV-ACLF. Hepatic decompensation-related complications and infection accelerate the disease's progression. The LAINeu model can predict patient survival conditions more accurately.
Humans ; Hepatitis B virus ; Hepatic Encephalopathy/complications* ; Acute-On-Chronic Liver Failure/diagnosis* ; End Stage Liver Disease/complications* ; Severity of Illness Index ; Risk Factors ; ROC Curve ; Prognosis ; Retrospective Studies

This study investigated whether free prostate-specific antigen (fPSA) performs better than total PSA (tPSA) in predicting prostate volume (PV) in Chinese men with different PSA levels. A total of 5463 men with PSA levels of <10 ng ml^-1 and without prostate cancer diagnosis were included in this study. Patients were classified into four groups: PSA <2.5 ng ml^-1, 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1. Pearson/Spearman's correlation coefficient (r) and receiver operating characteristic (ROC) curves were used to evaluate the ability of tPSA and fPSA to predict PV. The correlation coefficient between tPSA and PV in the PSA <2.5 ng ml^-1 cohort (r = 0.422; P < 0.001) was markedly higher than those of the cohorts with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1 (r = 0.114, 0.167, and 0.264, respectively; all P ≤ 0.001), while fPSA levels did not differ significantly among different PSA groups. Area under ROC curve (AUC) analyses revealed that the performance of fPSA in predicting PV ≥40 ml (AUC: 0.694, 0.714, and 0.727) was better than that of tPSA (AUC = 0.545, 0.561, and 0.611) in men with PSA levels of 2.5-3.9 ng ml^-1, 4.0-9.9 ng ml^-1, and 2.5-9.9 ng ml^-1, respectively, but not at PSA levels of <2.5 ng ml^-1 (AUC: 0.713 vs 0.720). These findings suggest that the relationship between tPSA and PV may vary with PSA level and that fPSA is more powerful at predicting PV only in the ''gray zone'' (PSA levels of 2.5-9.9 ng ml^-1), but its performance was similar to that of tPSA at PSA levels of <2.5 ng ml^-1.
Male ; Humans ; Prostate-Specific Antigen ; Prostate ; East Asian People ; Prostatic Neoplasms/diagnosis* ; ROC Curve