A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province, China.

The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), acute physiology and chronic health evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931-0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.

Development of Models to Predict Postoperative Complications for Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Surgical treatment remains the best option for patients with hepatocellular carcinoma (HCC) caused by chronic hepatitis B virus (HBV) infection. However, there is no optimal tool based on readily accessible clinical parameters to predict postoperative complications. Herein, our study aimed to develop models that permitted risk of severe complications to be assessed before and after liver resection based on conventional variables. METHODS: A total of 1,047 patients treated by hepatectomy for HCC with HBV infection at three different centers were recruited retrospectively between July 1, 2014, and July 1, 2018. All surgical complications were recorded and scored by the Comprehensive Complication Index (CCI). A CCI ≥26.2 was used as a threshold to define patients with severe complications. We built two models for the CCI, one using preoperative and one using preoperative and postoperative data. Besides, CCI and other potentially relevant factors were evaluated for their ability to predict early recurrence and metastasis. All the findings were internally validated in the Hangzhou cohort and then externally validated in the Lanzhou and Urumqi cohorts. RESULTS: Multivariable analysis identified National Nosocomial Infections Surveillance (NNIS) index, tumor number, gamma-glutamyltransferase (GGT), total cholesterol (TC), potassium, and thrombin time as the key preoperative parameters related to perioperative complications. The nomogram based on the preoperative model [preoperative CCI After Surgery for Liver tumor (CCIASL-pre)] showed good discriminatory performance internally and externally. A more accurate model [postoperative CCI After Surgery for Liver tumor (CCIASL-post)] was established, combined with the other four postoperative predictors including leukocyte count, basophil count, erythrocyte count, and total bilirubin level. No significant association was observed between CCI and long-term complications. CONCLUSION: Based on the widely available clinical data, statistical models were established to predict the complications after hepatectomy in patients with HBV infection. All the findings were extensively validated and shown to be applicable nationwide. Such models could be used as guidelines for surveillance follow-up and the design of post-resection adjuvant therapy.

Polyphenol-Enriched Extracts from Trapa acornis Husks Inhibit Her2-Positive SK-BR-3 Breast Cancer Cell Proliferation and In Vivo Tumor Angiogenesis.

The study aimed to investigate the antitumor effects of Trapa acornis husks (TAH) extract on SK-BR-3 cells of Her2-positive breast cancer. The bioactive compounds of TAH extracts were analyzed qualitatively and quantitatively by Ultra-Performance Liquid Chromatography/Mass Spectrometry (UPLC-MS)/high-performance liquid chromatographic system (HPLC). The effects of TAH extracts on cell proliferation, cell cycle, and apoptosis of SK-BR-3 cells were determined by CCK-8 and flow cytometry. Besides, the In Vivo antitumor effect of TAH extracts was detected. UPLC-MS/HPLC showed that the main bioactive compounds of TAH were gallic acid and galloylglucose derivatives. TAH extracts significantly inhibited the proliferation of SK-BR-3 cells in a dose- and time-dependent manner (P < 0.01). With the increase of TAH extracts concentration, cells in G2/M stage were increased and cell apoptosis was significantly increased. Immunohistochemical analysis showed that TAH extracts can significantly reduce the positive expression rate of Ki67 and Factor VIII index in tumor tissues. The mRNA expression levels of VEGF, MMP2, MMP9, and uPA were reduced after TAH extracts intervention (P < 0.01). TAH extracts also decreased the protein expression of p-Her2, p-ERK1/2, VEGF, MMP2, MMP9, and uPA (P < 0.01). In conclusion, polyphenol-enriched extracts from TAH might inhibit breast cancer cell proliferation and In Vivo tumor angiogenesis.

Corrigendum: Identification and Validation of Novel Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma.

[This corrects the article DOI: 10.3389/fonc.2020.541479.].

Identification and Validation of Novel Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

The longitudinal trend of hypertension prevalence in Chinese adults from 1959 to 2018: a systematic review and meta-analysis.

BACKGROUND: Hypertension is one of the most prevalent non-communicable diseases (NCDs). However, unbalanced regional development and different research designs lead to greater heterogeneity of hypertension data in China, and lack of a summary of long-term variation trends. The aim was to estimate the pooled prevalence of hypertension and to describe the secular trend in hypertension. METHODS: Literatures, related to the prevalence of hypertension among Chinese adults, were searched through both English and Chinese databases. The pooled prevalence was estimated with random effects. Subgroup analysis and meta-regression was conducted to address heterogeneity. Continuous fractional polynomial regression model and compound model were used to estimate the trend of hypertension prevalence with time. RESULTS: A total of 18 studies were included and the whole population was 9, 191, 121. The pooled prevalence of hypertension among Chinese adults was 24.3% (95% CI: 18.8-29.8%), increasing from the west to the east. Hypertension was more common in male than in female (27.8% vs. 25.1%) and in urban population than in rural population (27.0% vs. 26.0%). The annual increase of prevalence was about 0.29% nonlinearly before 2004 and maintained approximately 2.45% per year between 2004 and 2010. After a significant decline in 2011, there was a slight incline. CONCLUSIONS: The prevalence of hypertension in Chinese adults has been increasing, indicating that more efforts should be strengthened for hypertension management in China.

Novel gene signatures for prognosis prediction in ovarian cancer.

Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi-gene signature based on the LASSO Cox regression model in the TCGA database and time-dependent ROC curves showed good predictive accuracy for 1-, 3- and 5-year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi-gene signature stratified patients into high-risk and low-risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi-gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug-gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.

Prevalence of hypertension, diabetes, and dyslipidemia, and their additive effects on myocardial infarction and stroke: a cross-sectional study in Nanjing, China.

BACKGROUND: This study aimed to investigate the prevalence and risk factors for hypertension, diabetes, and dyslipidemia, and to evaluate their additive effects on myocardial infarction (MI) and stroke in Nanjing in East China. METHODS: A multistage, stratified random cluster sampling method was used to select representative participants. All eligible participants completed questionnaires, physical measurements, and blood tests. Multivariable and univariable logistic regression analyses were used to identify associated risk factors and evaluate additive effects on cardiovascular events, respectively. RESULTS: Hypertension was the most prevalent chronic disease among 11,036 participants enrolled (18.5%), followed by dyslipidemia (8.3%) and diabetes (6.0%). The prevalence of hypertension was higher in men than in women while no sex-related difference was observed in the prevalence of diabetes and dyslipidemia. Older age and higher body mass index were risk factors for all three diseases. Sex, central obesity, smoking, number of family members, salt intake, and family history of hypertension were associated with hypertension; central obesity, smoking, alcohol assumption, and family history of diabetes correlated with diabetes; and female sex, higher education, and alcohol assumption were risk factors for dyslipidemia. Hypertension complicated with dyslipidemia conferred more risk of MI and stroke than independent effects. Diabetes also contributed to risk based on hypertension or dyslipidemia. CONCLUSIONS: The burden of hypertension and diabetes has stopped increasing. However, total cholesterol (TC) concentration in the population has not been well controlled. A more comprehensive approach to managing dyslipidemia, hypertension, and diabetes needs to be developed, especially for individuals with multiple cardiovascular risk factors.