Models In Chinese Population Research Articles

Validation of combined AFP, AFP-L3, and PIVKA II for diagnosis and monitoring of hepatocellular carcinoma in Chinese patients

BackgroundIn this study, we aimed to investigate the performance of GALAD, GALAD-C, and GAAP models in Chinese population in comparison to our newly build statistical model. MethodsIn this study, we built the AALP model based on age, α-fetoprotein (AFP), AFP-L3, and prothrombin induced by vitamin K absence-II (PIVKA II) to differentiate between patients with HCC and patients with CLD. We then compared the serum levels of AFP-L3 and PIVKA II in patients with HCC who were defined as remission or progression and showed the prognostic value of combined biomarkers. ResultsThe AUC value of the AALP model for HCC detection was 0.939 and AALP model exhibited a sensitivity of 81 % and a high specificity of 95 %. AALP model also exhibited good performance in the subgroups of patients with CLD. Furthermore, we demonstrated the consistency between imaging results and serum levels of AFP-L3 and PIVKA II. ConclusionsThe AALP model achieved a good diagnostic performance and a high sensitivity for predicting HCC patients. Our research also showed that AFP-L3 and PIVKA II are complementary to each other but irreplaceable in the clinical detection and monitoring of HCC.

Association between the LINC00673 rs11655237 C> T polymorphisms with cancer risk in the Chinese population: A meta-analysis.

Objectives:The present study aimed to conduct a meta-analysis of previously published studies in order to clarify the association of long noncoding RNA (lncRNAs) LINC00673 rs11655237 C> T polymorphism with cancer risk.Design:Systematic review and meta-analysis.Setting:Electronic databases of PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, and Wanfang Database were used to search relevant studies. Studies published up to October 20, 2019 were included. The included studies were assessed in the following genetic model: allelic model, homozygote model, Heterozygote model, dominant model, recessive model. Data syntheses were conducted using STATA 12.0.Participants:Participants with various types cancers were included.Primary and secondary outcome measures:Odds ratio (ORs) and 95% confidence interval (CIs) were calculated to assess the risk of tumor.Results:Seven articles including 7 case-control studies, 7423 cases and 11,049 controls were adopted for meta-analysis. Our result demonstrated that LINC00673 rs11655237 C> T was related to the cancer among all model including allelic model (T vs C: pooled OR = 1.24, 95% CI = 1.16–1.41, P < .001), homozygous model (TT vs CC: pooled OR=1.54, 95% CI = 1.36–1.76, P < .001), heterozygous model (CT vs CC: pooled OR=1.24, 95% CI = 1.16–1.32, P < .001), dominant model (CT + TT vs CC: pooled OR=1.28, 95% CI = 1.20–1.36, P < .001) and recessive model (TT vs CT+ CC: pooled OR=1.42, 95% CI = 1.25–1.61, P < .001). Subgroup analysis also demonstrated that polymorphisms at this site also increased the risk of neuroblastoma.Conclusions:Our results find that rs11655237 contributed to occurrence of cancer in all models in Chinese population.

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.

Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.

Quantitative assessment of lncRNA HOTAIR polymorphisms and cancer risk in Chinese population: a meta-analysis based on 26,810 subjects.

As a well-known long non-coding RNA, HOTAIR has been demonstrated to be involved in carcinogenesis and progression of various human cancers. Previous studies have investigated the potential association between HOTAIR polymorphisms and cancer risk in Chinese population. However, the results remain conflicting. Therefore, for the first time, we conducted a meta-analysis to derive a more precise estimation of these associations for Chinese. PubMed, Embase, CNKI and Wanfang databases were systematically searched. Odds ratios with 95% confidence intervals were applied to assess the association between rs920778, rs4759314, rs7958904, rs874945 and rs1899663 polymorphisms of HOTAIR and cancer susceptibility. Heterogeneity, sensitivity analysis and publication bias were conducted to measure the robustness of our findings. A total of 21 eligible studies comprising 12,278 cases and 14,532 controls were analyzed. The pooled data showed that rs920778 polymorphism was significantly associated with an increased cancer risk in all five genetic models in Chinese population. As for rs4759314 and rs874945 polymorphisms, similarly increased risks were found in specific genetic models and stratified groups. However, significant decreases in cancer risk were observed for rs7958904 in the total population, as well as in subgroup analyses. In addition, lack of association was detected between rs1899663 polymorphism and cancer susceptibility. In summary, our meta-analysis implicates possible relationship between HOTAIR polymorphisms and cancer risk in Chinese population.

Population Analysis Based on Neural Network of Rational Spline Weight Functions with Cubic Numerator and Quadratic Denominator

With the steady growth of the population in our country, the analysis of population is essential to the distribution of social resources and the improvement of the population quality. In this paper, we use the neural network of rational spline weight functions with cubic numerator and quadratic denominator to establish the model of Chinese population by four indicators, gender ratio, birth rate, elderly dependency ratio and natural increase rate. Finally we come to the conclusion through MATLAB simulation that the training speed is fast and the error is small. This kind of neural network can be applied to population analysis and it could be used in various fields.

Evaluating the performance of models for predicting the BRCA germline mutations in Han Chinese familial breast cancer patients

Established models (Penn, Myraid and BRCApro) are useful of estimating the probability that a person has a BRCA mutation. But the value of these models in Chinese population is unclear. The aim of the study is to evaluate the performance of three models on the assisting in pre-test genetic risk counseling. Three risk assessment models, Penn II, Myriad and BRCApro, were applied to 212 familial breast cancer patients who had undergone BRCA1/2 mutation analysis. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operator characteristic (ROC) curve were calculated for each model. Myriad showed a better ROC curve than BRCApro either for BRCA1 or BRCA1/2 combination mutation prediction, but BRCApro had a higher positive likelihood ratio when using 10% as the probability threshold. The performance of three models improved when they were evaluated in 66 patients from high risk families, presenting increased ROC and positive likelihood ratio. Especially that of BRCApro for BRCA2, the ROC was increased to 0.716 and its positive likelihood was 5.6. Three models had the similar impact on the pre-test probability of BRCA mutation. But at a 10% cutoff point, BRCApro had the best BRCA mutation carrier prediction value. The performance of BRCApro for BRCA2 mutation prediction was improved when it was restricted in patients from high risk families.

中国人口モデルの作成と人口の長期推計 : 中国のエネルギー・経済・環境モデル(CHINA1)に関する研究 第1報

In this paper, a model of Chinese population was developed. With this model, the population of China is predicted according to the total fertility rate. The main results from this study are as follows: (1) the average total fertility rate over the period of 1990-1995 is 1.87; (2) when the TFR is equal to 1.87, the population of China will grow to 1.27 billion, 1.49 billion and 1.46 billion in the years of 2000, 2035 and 2050 respectively; (3) the ratio of dependent population will grow to more than 80% in the year of 2050 if the TFR is equal to 1.0; (4) the TFR should be between 1.5 to 2.0 in China to sustain the society economically and environmentally; (5) equations were developed as the functions of time and TFR, with which the Chinese population can be forecasted easily.