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Summary: Background: Liver cirrhosis (LC) is the highest risk factor for hepatocellular carcinoma (HCC) development worldwide. The efficacy of the guideline-recommended surveillance methods for patients with LC remains unpromising. Methods: A total of 4367 LCs not previously known to have HCC and 510 HCCs from 16 hospitals across 11 provinces of China were recruited in this multi-center, large-scale, cross-sectional study. Participants were divided into Stage Ⅰ cohort (510 HCCs and 2074 LCs) and Stage Ⅱ cohort (2293 LCs) according to their enrollment time and underwent Tri-phasic CT/enhanced MRI, US, AFP, and cell-free DNA (cfDNA). A screening model called PreCar Score was established based on five features of cfDNA using Stage Ⅰ cohort. Surveillance performance of PreCar Score alone or in combination with US/AFP was evaluated in Stage Ⅱ cohort. Findings: PreCar Score showed a significantly higher sensitivity for the detection of early/very early HCC (Barcelona stage A/0) in contrast to US (sensitivity of 51.32% [95% CI: 39.66%–62.84%] at 95.53% [95% CI: 94.62%–96.38%] specificity for PreCar Score; sensitivity of 23.68% [95% CI: 14.99%–35.07%] at 99.37% [95% CI: 98.91%–99.64%] specificity for US) (P

Abstract Background Hepatocellular carcinoma (HCC) generally arises from a background of liver cirrhosis (LC). Patients with cirrhosis and suspected HCC are recommended to undergo serum biomarker tests and imaging diagnostic evaluation. However, the performance of routine diagnostic methods in detecting early HCC remains unpromising. Methods Here, we conducted a large-scale, multicenter study of 1675 participants including 490 healthy controls, 577 LC patients, and 608 HCC patients from nine clinical centers across nine provinces of China, profiled gene mutation signatures of cell-free DNA (cfDNA) using Circulating Single-Molecule Amplification and Resequencing Technology (cSMART) through detecting 931 mutation sites across 21 genes. Results An integrated diagnostic model called “Combined method” was developed by combining three mutation sites and three serum biomarkers. Combined method outperformed AFP in the diagnosis of HCC, especially early HCC, with sensitivities of 81.25% for all stages and 66.67% for early HCC, respectively. Importantly, the integrated model exhibited high accuracy in differentiating AFP-negative, AFP-L3-negative, and PIVKA-II-negative HCCs from LCs.

Objectives Non-alcoholic fatty liver disease (NAFLD) is becoming the leading cause of chronic liver disease worldwide. However, treatment of NAFLD is potentially influenced by psychological conditions. Using the simplified version of the University of Rhode Island Change Assessment (URICA-SV) scale, this study aimed to evaluate the stage of psychological change as a prerequisite to refining implementation strategies for psychological change.Design A multicentre cross-sectional survey.Setting Ninety hospitals in China.Participants 5181 patients with NAFLD were included in this study.Outcome measures All patients completed the URICA-SV questionnaire and were assigned to one of the three stages of change (precontemplation, contemplation or action) according to their readiness scores. A stepwise multivariate logistic regression analysis was used to identify independent factors associated with the stage of psychological change.Results A total of 4832 (93.3%) patients were included in the precontemplation stage and only 349 (6.7%) considered making a change or preparing to make one. There were significant differences in gender (Cohen’s d=0.039, p=0.005), age (Cohen’s d=−0.327, p

Abstract Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM‐related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM‐related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non‐redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG‐mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM‐related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

BackgroundThis study aimed to establish and validate an accurate prognostic model, based on demographic and clinical parameters, for predicting the cancer-specific survival (CSS) of patients with poorly differentiated thyroid carcinoma (PDTC).Materials and methodsPatients diagnosed with PDTC between 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Randomly split the data into training and validation sets. Kaplan–Meier analysis with the log-rank test was performed to compare the survival distribution among cases. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors, which were subsequently utilized to construct a nomogram for predicting the 5- and 10-year cancer-specific survival of patients with PDTC. The discriminative ability and calibration of the nomogram model were assessed using the concordance index and calibration plots, respectively. In addition, we performed a decision curve analysis to assess the clinical value of the nomogram. Simultaneously, we compared the predictive performance of the nomogram model against that of the American Joint Committee on Cancer (AJCC) T-, N-, M-stage.ResultsA total of 970 eligible patients were randomly assigned to either a training cohort (n = 679) or a validation cohort (n = 291). The Kaplan–Meier analysis revealed that there were no significant differences in cumulative survival based on the race, radiation, and marital status of patients. The stepwise Cox regression model showed that the model was optimal when the following five variables were included: age, tumor size, T-, N-, and M-stage. A nomogram was developed as a graphical representation of the model and exhibited good calibration and discriminative ability in the study. Compared to the T-, N-, and M-stage, the C-index of nomogram (training group: 0.807, validation group: 0.802), the areas under the receiver operating characteristic curve of the training set (5-year AUC: 0.843, 10-year AUC:0.834) and the validation set (5-year AUC:0.878, 10-year AUC:0.811), and the calibration plots of this model all exhibited better performance. At last, compared with T-, N-, and M-stage, the decision curve analysis indicated that the nomogram had excellent clinical net benefit.ConclusionsThe nomogram developed by us can accurately predict the CSS of PDTC patients. It can help clinicians determine appropriate treatment strategies for poorly differentiated thyroid carcinoma patients.

Abstract. Background:. The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm. Methods:. The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models. Results:. Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients. Conclusions:. The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

Abstract Background Health Related Quality of Life (HRQL) is a multi-dimensional construct that can comprehensively evaluate the patient’s health status, including physical, emotional, mental and social well-being. In this study, we aimed to evaluate the impact of non-alcoholic fatty liver disease (NAFLD) on HRQL in a Chinese population. Methods In this national multicenter cross-sectional survey, patients with NAFLD were enrolled. Chronic Liver Disease Questionnaire (CLDQ)-NAFLD was used to qualify HRQL. Univariate and multivariate analysis were used to identify independent risk factors of HRQL. Results A total of 5181 patients with NAFLD from 90 centers were enrolled in this study (mean age, 43.8 ± 13.3 years; male, 65.8%). The overall CLDQ score was 5.66 ± 0.89. Multivariate logistic regression analysis showed that body mass index (BMI: HR, 1.642; 95% CI, 1.330–2.026), alanine transaminase (ALT: HR, 1.006; 95% CI, 1.001–1.011), triglyceride (HR, 1.184; 95% CI, 1.074–1.305), disease severity (HR, 3.203; 95% CI, 1.418–7.232) and cardiovascular disease (HR, 4.305; 95% CI, 2.074–8.939) were independent risk factors for overall CLDQ score. In the logistic analyses of individual domain, BMI and triglyceride were independent risk factors of all domains. ALT, disease severity, diabetes, depression and cardiovascular disease were influencing factors for the CLDQ score of several domains. Conclusions This national multicenter cross-sectional survey in China indicated that the HRQL in patients with NAFLD was impaired. HRQL was found to be significantly associated with sociodemographic and clinical factors. Attention should be paid to the optimally managing care of patients with NAFLD to improve their HRQL.

PurposeAnaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) have similar histological findings and are currently treated using the same approaches; however, the characteristics and prognosis of these cancers are poorly researched. The objective of this study was to determine the differences in characteristics between ATC and PSCCTh and establish prognostic models.Patients and MethodsAll variables of patients with ATC and PSCCTh, diagnosed from 2004–2015, were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) database. Percentage differences for categorical data were compared using the Chi-square test. Kaplan-Meier curves, log-rank test, and Cox-regression for survival analysis, and C-index value was used to evaluate the performance of the prognostic models.ResultsAfter application of the inclusion and exclusion criteria, a total of 1164 ATC and 124 PSCCTh patients, diagnosed from 2004 to 2015, were included in the study. There were no differences in sex, ethnicity, age, marital status, or percentage of proximal metastases between the two cancers; however, radiotherapy, chemotherapy, incidence of surgical treatment, and presence of multiple primary tumors were higher in patients with ATC than those with PSCCTh. Further cancer-specific survival (CSS) of patients with PSCCTh was better than that of patients with ATC. Prognostic factors were not identical for the two cancers. Multivariate Cox model analysis indicated that age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis status are independent prognostic factors for CSS in patients with ATC, while for patients with PSCCTh, the corresponding factors are age, radiotherapy, multiple primary tumors, and surgery. The C-index values of the two models were both > 0.8, indicating that the models exhibited good discriminative ability.ConclusionPrognostic factors influencing CSS were not identical in patients with ATC and PSCCTh. These findings indicate that different clinical treatment and management plans are required for patients with these two types of thyroid cancer.

Studies on codon property would deepen our understanding of the origin of primitive life and enlighten biotechnical application. Here, we proposed a quantitative measurement of codon-amino acid association and found that seven out of 13 physicochemical properties have stronger associations with the nucleotide identity at the second codon position, indicating that protein structure and function may associate more closely with it than the other two sites. When extending the effect of codon-amino acid association to protein level, it was found that the correlation between the second codon position (measured by the relative frequencies of nucleobase T and A at this codon site) and hydrophobicity (by the form of GRAVY value) became stronger with 96% genomes having R > 0.90 and p

The ring-like In(Ga)As quantum dot pairs (QDPs) is prepared on GaAs ring-ring-disk (R-R-D) nanostructures template by droplet epitaxy (DE) method. The surface morphology of GaAs samples are characterized with Scanning Tunneling Microscope (STM), and studying local controlled growth mechanism of quantum dots (QDs) on GaAs R-R-D nanostructures. STM images show that In(Ga)As QDPs self-assembled a kind of complicated structure with one quantum dot (QD) pair in the center of nanostructures, other QDPs appearing both double rings and disk areas of original GaAs R-R-D nanostructures distributed like a exquisite ring. These In(Ga)As QDPs are uniformly arranged along the [1 $\bar{1}$ 0] direction of GaAs(001) surface. Combining Stranski-Krastanov (S-K) growth mode and the tensile effect of strain field in the [1 $\bar{1}$ 0] direction on sample surface, the nucleation position and distribution shape of ring-like In(Ga)As QDPs is locally controllable. It is found that high uniformly QDPs can be locally controlled by the original GaAs R-R-D nanostructures templates. These results can provide certain guiding significance for the controllable growth of QD by DE method.

Composition bias from Chargaff’s second parity rule (PR2) has long been found in sequenced genomes, and is believed to relate strongly with the replication process in microbial genomes. However, some disagreement on the underlying reason for strand composition bias remains. We performed an integrative analysis of various genomic features that might influence composition bias using a large-scale dataset of 1111 genomes. Our results indicate (1) the bias was stronger in obligate intracellular bacteria than in other free-living species (p-value = 0.0305); (2) Fusobacteria and Firmicutes had the highest average bias among the 24 microbial phyla analyzed; (3) the strength of selected codon usage bias and generation times were not observably related to strand composition bias (p-value = 0.3247); (4) significant negative relationships were found between GC content, genome size, rearrangement frequency, Clusters of Orthologous Groups (COG) functional subcategories A, C, I, Q, and composition bias (p-values < 1.0 × 10−8); (5) gene density and COG functional subcategories D, F, J, L, and V were positively related with composition bias (p-value < 2.2 × 10−16); and (6) gene density made the most important contribution to composition bias, indicating transcriptional bias was associated strongly with strand composition bias. Therefore, strand composition bias was found to be influenced by multiple factors with varying weights.