High Plasma Fibrinogen Level Elevates the Risk of Cardiac Complications Following Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Cardiac complications are related to poor prognosis after spontaneous intracerebral hemorrhage (ICH). This study aims to predict the cardiac complications arising from small intracranial hematoma at ultraearly stage. METHODS: The data of this work were derived from the Risk Stratification and Minimally Invasive Surgery in Acute ICH Patients study (ClinicalTrials.gov Identifier: NCT03862729). This work included patients with ICH but without brain herniation, as confirmed by a brain computed tomography scan within 48 hours of symptom onset. Every Patient's information recorded at the emergent department, including clinical, laboratory, electrocardiogram, and medical records, was derived from the electronic data capture. Cardiac complications were defined as the occurrence of myocardial damage, arrhythmias, and ischemic electrocardiogram changes during hospitalization. Variables associated with cardiac complications were filtrated by univariate and multivariate regression analyses. Independent risk factors were used to form the early predictive model. The restricted cubic splines were employed to investigate the nonlinear associations in a more sophisticated and scholarly manner. RESULTS: A total of 587 ICH patients were enrolled in this work, including 72 patients who suffered from cardiac complications after ICH. Out of the 78 variables, 24 were found to be statistically significant in the univariate logistic regression analysis. These significant variables were then subjected to multivariate logistic regression analysis and utilized for constructing risk models. Multivariate logistic regression analysis showed high plasma fibrinogen (FIB) level [odds ratio (OR) per standard deviation (SD) 1.327, 95% confidence intervals (CI) 1.037-1.697; P = 0. 024)] and older age (OR per SD 1.777, 95% CI 1.344-2.349; P ＜0.001) were associated with a higher incidence of cardiac complications after ICH. High admission pulse rate (OR 0.620, 95% CI 0.451-0.853; P = 0. 003) was considered a protective factor for cardiac complications after ICH. In the restricted cubic spline regression model, FIB and cardiac complications following ICH were positively correlated and almost linearly (P for nonlinearity = 0.073). The reference point for FIB in predicting cardiac complications after ICH was 2.64 g/L. CONCLUSIONS: Emergent factors, including plasma FIB level, age, and pulse rate, might be independently associated with cardiac complications after ICH, which warrants attention in the context of treatment.

Development and validation of a diagnostic model for AFP-negative hepatocellular carcinoma.

PURPOSE: AFP appears to be negative in about 30% of overall hepatocellular carcinoma (HCC). Our study aimed to develop a nomogram model to diagnose AFP-negative HCC (AFPN-HCC). PATIENTS AND METHODS: The training set included 294 AFPN-HCC patients, 159 healthy objects, 63 patients with chronic hepatitis B(CHB), and 64 patients with liver cirrhosis (LC). And the validation set enrolled 137 healthy controls objects, 47 CHB patients and 45 patients with LC. LASSO, univariate, and multivariable logistic regression analysis were performed to construct the model and then transformed into a visualized nomogram. The receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were further used for validation. RESULTS: Four variables including age, PIVKA-II, platelet (PLT) counts, and prothrombin time (PT) were selected to establish the nomogram. The area under the curve (AUC) of the ROC to distinguish AFPN-HCC patients was 0.937(95% CI 0.892-0.938) in training set and 0.942(95% CI 0.921-0.963) in validation set. We also found that the model had high diagnostic value for small-size HCC (tumor size < 5 cm) (AUC = 0.886) and HBV surface antigen-positive AFPN-HCC (AUC = 0.883). CONCLUSIONS: Our model was effective for discrimination of AFPN-HCC from patients with benign liver diseases and healthy controls, and might be helpful for the diagnosis for AFPN-HCC.

Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage.

Background: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth. Methods: We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT. Results: A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686-0.991), "heterogeneous" in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228-28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047-1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %. Conclusion: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

Time Trend of Upper Gastrointestinal Cancer Incidence in China from 1990 to 2019 and Analysis Using an Age-Period-Cohort Model.

The aim of this study was to investigate the upper gastrointestinal cancer incidence trend in China from 1990 to 2019 with Joinpoint software and to evaluate the age effect, cohort effect, and period effect using the age-period-cohort model, with the data obtained from the Global Burden of Disease, Injuries, and Risk Factors Study. The crude incidence rate (CR) of upper gastrointestinal cancer in China increased from 41.48/100,000 in 1990 to 62.64/100,000 in 2019, and the average annual percent change (AAPC) was 1.42 (p < 0.05). The age-standardized incidence rate (ASIR) decreased from 50.77/100,000 to 37.42/100,000, and the AAPC was -1.12 (p < 0.05). The net drift was -0.83 (p < 0.05), and the local drifts in the 35-79 age groups of males and all age groups of females were less than 0 (p < 0.05). The age effect showed that the upper gastrointestinal cancer onset risk gradually increased with age, the period effect was fundamentally manifested as a downward trend in onset risk after 2000, and the cohort effect indicated the decreased onset risk of the overall birth cohort after 1926. The ASIR of upper gastrointestinal cancer in China from 1990 to 2019 showed a downward trend, and the onset risk indicated the age, period, and cohort effects.

Early Predictors of the Increase in Perihematomal Edema Volume After Intracerebral Hemorrhage: A Retrospective Analysis From the Risa-MIS-ICH Study.

Background and Purpose: Perihematomal edema (PHE) is associated with poor functional outcomes after intracerebral hemorrhage (ICH). Early identification of risk factors associated with PHE growth may allow for targeted therapeutic interventions. Methods: We used data contained in the risk stratification and minimally invasive surgery in acute intracerebral hemorrhage (Risa-MIS-ICH) patients: a prospective multicenter cohort study. Patients' clinical, laboratory, and radiological data within 24 h of admission were obtained from their medical records. The absolute increase in PHE volume from baseline to day 3 was defined as iPHE volume. Poor outcome was defined as modified Rankin Scale (mRS) of 4 to 6 at 90 days. Binary logistic regression was used to assess the relationship between iPHE volume and poor outcome. The receiver operating characteristic curve was used to find the best cutoff. Linear regression was used to identify variables associated with iPHE volume (ClinicalTrials.gov Identifier: NCT03862729). Results: One hundred ninety-seven patients were included in this study. iPHE volume was significantly associated with poor outcome [P = 0.003, odds ratio (OR) 1.049, 95% confidence interval (CI) 1.016-1.082] after adjustment for hematoma volume. The best cutoff point of iPHE volume was 7.98 mL with a specificity of 71.4% and a sensitivity of 47.5%. Diabetes mellitus (P = 0.043, ß = 7.66 95% CI 0.26-15.07), black hole sign (P = 0.002, ß = 18.93 95% CI 6.84-31.02), and initial ICH volume (P = 0.018, ß = 0.20 95% CI 0.03-0.37) were significantly associated with iPHE volume. After adjusting for hematoma expansion, the black hole sign could still independently predict the increase of PHE (P < 0.001, ß = 21.62 95% CI 10.10-33.15). Conclusions: An increase of PHE volume >7.98 mL from baseline to day 3 may lead to poor outcome. Patients with diabetes mellitus, black hole sign, and large initial hematoma volume result in more PHE growth, which should garner attention in the treatment.

Association Between Circular RNAs and Intracranial Aneurysm Rupture Under the Synergistic Effect of Individual Environmental Factors.

Introduction: To study the association between specific circular RNAs and rupture of intracranial aneurysm. To explore its clinical diagnostic significance and synergistic effects with individual environmental influencing factors. Methods: Three hundred and forty seven cases and controls were included in this study. Multivariate analysis was used to explore the main individual environmental factors. Intracranial aneurysm rupture related circular RNAs screened based on sequencing was verified in peripheral blood by PCR. ROC curve, logistic regression model and fork analysis were used to study the association, diagnostic values, and synergistic effects of circular RNA with intracranial aneurysms and individual environmental factors. Results: Smoking, hair dyeing, sitting time ≥6 h/day, single animal oil intake and hypertension are the main risk factors for intracranial aneurysm rupture; People with higher education, sleeping time ≥7 h/day, tea drinking, diabetes, higher levels of (hemoglobin, low density lipoprotein, serum calcium, and apolipoprotein-A1) have a low risk of intracranial aneurysm rupture. Hsa_circ_0008433 and hsa_circ_0001946 are closely related to intracranial aneurysm rupture and have certain clinical diagnostic significance (AUC = 0.726; 95% CI: 0.668~0.784). Hsa_circ_0008433 (OR = 0.497, 95% CI: 0.338~0.731), hsa_circ_0001946 (OR = 0.682, 95% CI: 0.509~0.914) were independent epigenetic factors affecting intracranial aneurysm rupture, and have a multiplicative interaction with age (OR = 3.052, 95% CI: 1.006~9.258). Conclusions: Low expressions of hsa_circ_0008433 and hsa_circ_0001946 are risk factors for intracranial aneurysms rupture and have good clinical diagnostic value. There was a multiplicative interaction between epigenetic score and age. The older and the higher the epigenetic score was, the more likely to have intracranial aneurysm rupture.

Association of circular RNAs and environmental risk factors with coronary heart disease.

BACKGROUND: Coronary heart disease (CHD) is a complex disease caused by multi-factors and a major threat to human health. Circular RNAs (circRNAs) have critical roles in various biological processes and diseases. This study explores the independent role of circRNAs and their interaction with environmental factors in CHD. METHODS: A case-control study was conducted from March 2015 to September 2017 in Fuzhou, China. A total of 585 CHD patients and 585 gender- and age-matched healthy controls were enrolled. Questionnaire survey, health examination and molecular biology laboratory testing were conducted. Microarray technology and quantitative real-time polymerase chain reaction (PCR) were used to profile the expression levels of circRNAs. The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to determine the diagnostic cut-offs. Multivariate logistic regression and multiplicative analysis were used to analyse the effects of environmental factors and hsa_circ_0008507, hsa_circ_0001946, hsa_circ_0000284 and hsa_circ_0125589 on CHD. RESULTS: The expression profile of circRNAs showed that 3423 circRNAs were differentially expressed at P < 0.05, but none pass multiple testing correction. qRT-PCR further confirmed the expression levels of hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 in peripheral blood leukocytes in CHD cases were higher than those in non-CHD subjects (All p < 0.05). Hsa_circ_0008507 (OR = 1.29; 95% CI: 1.11-1.50), hsa_circ_0001946 (OR = 1.20; 95% CI: 1.01-1.42) and hsa_circ_0000284 (OR = 2.05; 95% CI: 1.32-3.19) were independent risk factors for CHD after controlling other common environmental risk factors. The AUC for hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 was 0.75, 0.71 and 0.68, respectively. Compared with non-smoking individuals with low hsa_circ_0008507 expression, the smokers with high hsa_circ_0008507 expression showed the highest magnitude of OR in CHD risk. Additionally, a statistically significant multiplicative interaction was found between hsa_circ_0008507 and smoking for CHD. CONCLUSIONS: Hsa_circ_0008507, hsa_circ_0001946 and hsa_circ_0000284 were closely related to the occurrence and development of CHD. The combination of smoking and high hsa_circ_0008507 expression causes the occurrence and development of CHD.