Risk prediction models for hepatocellular carcinoma in different populations

Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.

Risk factors for acute kidney injury following coronary artery bypass graft surgery in a Chinese population and development of a prediction model

BACKGROUND Acute kidney injury(AKI)after coronary artery bypass graft(CABG)surgery is associated with significant morbidity and mortality.This retrospective study aimed to establish a risk score for postoperative AKI in a Chinese population.METHODS A total of 1138 patients undergoing CABG were collected from September 2018 to May 2020 and divided into a derivation and validation cohort.AKI was defined according to the Kidney Disease Improving Global Outcomes(KDIGO)criteria.Multivariable logistic regression analysis was used to determine the independent predictors of AKI,and the predictive ability of the model was determined using a receiver operating characteristic(ROC)curve.RESULTS The incidence of cardiac surgery–associated acute kidney injury(CSA-AKI)was 24.17%,and 0.53%of AKI patients required dialysis(AKI-D).Among the derivation cohort,multivariable logistic regression showed that age≥70 years,body mass index(BMI)≥25 kg/m2,estimated glomerular filtration rate(eGFR)≤60 mL/min per 1.73 m2,ejection fraction(EF)≤45%,use of statins,red blood cell transfusion,use of adrenaline,intra-aortic balloon pump(IABP)implantation,postoperative low cardiac output syndrome(LCOS)and reoperation for bleeding were independent predictors.The predictive model was scored from 0 to32 points with three risk categories.The AKI frequencies were as follows:0-8 points(15.9%),9-17 points(36.5%)and≥18 points(90.4%).The area under of the ROC curve was 0.730(95%CI:0.691-0.768)in the derivation cohort.The predictive index had good discrimination in the validation cohort,with an area under the curve of 0.735(95%CI:0.655-0.815).The model was well calibrated according to the Hosmer-Lemeshow test(P=0.372).CONCLUSION The performance of the prediction model was valid and accurate in predicting KDIGO-AKI after CABG surgery in Chinese patients,and could improve the early prognosis and clinical interventions.

Research on Population Prediction of Guizhou Province

In accordance with population development of Guizhou Province in 1977-2007,this paper adopts natural growth method,model prediction method and gray system GM (1,1) model prediction method to predict population of Guizhou Province in 2020. On the basis of overall consideration of many factors of population development and future development trend of Guizhou Province,it analyzes advantages and disadvantages of three prediction methods,and obtains the prediction value of total population of Guizhou Province in 2020.

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayesian predictive probability density function is obtained when the underlying pop-ulation distribution is exponentiated and subjective prior is used. The corresponding predictive survival function is then obtained and used in constructing 100(1 – ?)% predictive interval, using one- and two- sample schemes when the size of the future sample is fixed and random. In the random case, the size of the future sample is assumed to follow the truncated Poisson distribution with parameter λ. Special attention is paid to the exponentiated Burr type XII population, from which the data are drawn. Two illustrative examples are given, one of which uses simulated data and the other uses data that represent the breaking strength of 64 single carbon fibers of length 10, found in Lawless [40].

Thought and Method on Establishing the Prediction Model of TCM Dominant Population with Advanced Digestive Tract Malignant Tumors based on TCM Tumor Registration Platform

Advanced digestive tract malignant tumors,represented by advanced colorectal cancer,advanced gastric cancer and advanced esophageal cancer,have insidious onsets and high mortality.Western medicine based on targeted therapy greatly can improves the benefit and efficacy for patients through population stratification,but its population is limited.Traditional Chinese medicine(TCM)has a long history in treatment of tumors,which is an important part of comprehensive treatment of tumors.Clinical observation has shown that different patients could get different efficacy from TCM treatment.Based on real world registration studies,patients with advanced colorectal cancer,advanced gastric cancer or advanced esophageal cancer who had received TCM treatment were observed and followed,and a TCM dominant population that achieved significant efficacy was screened out to carry out multivariate regression analysis,further explore key factors that affect survival in advanced digestive tract malignant tumors,and establish a prediction model of TCM dominant population.It will provide reference for the follow-up TCM treatment,and provide reference for development of individualized treatment plans,making the TCM treatment for advanced digestive tract malignant tumors more targeted,and helping to improve the benefit rate in TCM.

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

ECG is not a reliable predictor of sudden cardiac death in the general population

Objectives: To determine the predictive value of the ECG for sudden death in the general population. Design: In the Copenhagen City Heart Study, a randomly selected population sample in Copenhagen, Denmarkhas been followed prospectively since 1976. From this population sample, we analyzed ECGs of individuals who had suffered sudden cardiac death (SCD) before the age of 50 years and compared them with ECGs of a randomly selected control individuals from the same population sample. Specific ECG signs that could point toward a condition associated with a risk of SCD were noted. Results: From a total of 18,974 individuals in the cohort, 207 had died at an age younger than 50 years. Among these, 24 persons with SCD were identified. The most prevalent ECG abnormality was QRS fragmentation. We found no ECGs with long or short QTc, Brugada sign or WPW. The prevalence of signs of left ventricular hyper-trophy, early repolarization, or fragmentation was not different from the prevalence of these signs in the control group. Conclusion: In the Copenhagen City Heart Study, the ECG failed to predict SCD in persons who died before the age of 50 years.

The impact of genomic relatedness between populations on the genomic estimated breeding values

In genomic selection, prediction accuracy is highly driven by the size of animals in the reference population(RP).Combining related populations from different countries and regions or using a related population with large size of RP has been considered to be viable strategies in cattle breeding. The genetic relationship between related populations is important for improving the genomic predictive ability. In this study, we used 122 French bulls as test individuals. The genomic estimated breeding values(GEBVs) evaluated using French RP, America RP and Chinese RP were compared.The results showed that the GEBVs were in higher concordance using French RP and American RP compared with using Chinese population. The persistence analysis, kinship analysis and the principal component analysis(PCA) were performed for 270 French bulls, 270 American bulls and 270 Chinese bulls to interpret the results. All the analyses illustrated that the genetic relationship between French bulls and American bulls was closer compared with Chinese bulls. Another reason could be the size of RP in China was smaller than the other two RPs. In conclusion, using RP of a related population to predict GEBVs of the animals in a target population is feasible when these two populations have a close genetic relationship and the related population is large.

The Population Carrying Capacity of Water Resources in Yulin City

Assessing the water resource carrying capacity is beneficial for measuring the scale of industry and population agglomeration,and also avoiding the contradiction between increasing people and decreasing available water resource,due to the expansion of industry and city size.Based on the prediction model of optimum population development size,by using hydrological data,also with the demographic data from 1956 to 2010,this article analyzes and predicts the urban moderate scale under the limit of the water resource in the future of Yulin City by GIS. The main conclusions are as follows. There is growing tendency of water resources overloading. According to the result of model simulation,by2015,the overload rate of population size will be 1. 04. By 2020,the overload rate of population size will grow up to 1. 08. The oversized population mainly comes from cities and towns. The overload rate for cities and towns in 2015 and 2020 is 1. 89 and 1. 73,respectively. With the expansion of cities and industries,suburban areas could have a great potential for carrying population,because lots of suburban people may move to cities and towns according to prediction. In view of the above-mentioned facts,the population size should be controlled in a reasonable range.

Occurrence Regularity and Prediction Model of Underground Pest Adults in Hangzhou District of China

To improve forecasting and sustained control level of underground pests, trapping quantity of underground pests （black cutworm,mole cricket and scar-ab） by lamps and their field dynamics in Hangzhou district from 2005 to 2011 were investigated in the paper. The results showed that different pests had obvious differences in population dynamic. The black cutworm （Agrotis ypsilon） had several damage peaks （late May, late June and late July） and the moth amount in early period was relatively high. The mole cricket （ Gryllotalpa africana） had two damage peaks （late May to early July, early September to mid and late October）. The scarab （Anomala corpulenta） had one damage peak （late May to late June）. There were periodic changes in total quantity of underground pests among years, and the peak period appeared in the year of 2005, 2007 to 2009 and 2011, respectively. On this basis, temperature, humidity, rainfall and light were used as forecas- ting factors, using the method of stepwise regression, 19 factors with significant correlation were screened out and prediction models for occurrence quantity and oc- currence period of the three pests were established. By using accuracy degree judge model for verification, the score values of prediction model for occurrence quan-tity and occurrence period of the three underground pests were more than 58 and 70, which indicated that the historical coincident rate and prediction accuracy of estabhshed prediction models were good.

Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions

This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly?type II censored sample. We consider the one sample prediction and two sample prediction techniques. Bayesian prediction intervals for the median of future sample of generalized order statistics having odd and even sizes are obtained. Our results are specialized to ordinary order statistics and ordinary upper record values. A mixture of two Gompertz components model is given as an application. Numerical computations are given to illustrate the procedures.

Evidence of increasing risk of schistosomiasis among school-age children in municipality of Calatrava,Province of Negros Occidental,Philippines

Objective:To explore risk of school-age children being infected with schistosomiasis in selected villages in the municipality of Calatrava,province of Negros Occidental,Philippines.Methods:As part of the monitoring and evaluation of the helminth control program in the province of Negros Occidental,parasitological monitoring,through the use microscopy of stool samples processed using Kato-Katz technique,was conducted to describe the baseline and follow-up parasitological status of school-age children in 2010 and 2012.respectively.Seven villages from the municipality of Calatrava were selected as study sites.Results:During baseline assessment,only one case of schistosomiasis was reported from the village of Marcelo.During follow-up assessment,32 cases(6.9%) of schistosomiasis were reported and the prevalence of moderate-heavy intensity infection was 13% in six villages.Among the seven villages included in the follow-up,Minapasuk had the highest prevalence at 14.6%.while San Isidro reported no case of schistosomiasis.Conclusions:Non-endemic villages,which have reported positive cases in school-age children,may need to be assessed for possible cndemicity for schistosomiasis.Transmission of the disease may need to be determined in these villages through active parasitological and malacological surveillance.Other nonendemic villages adjacent to or share river networks with endemic villages in Calatrava may need to be explored for possible introduction of the disease,especially after typhoons and Hooding.Establishing endemicity for schistosomiasis in these villages will help infected and at risk individuals to receive yearly treatment to reduce morbidities caused by this disease.

EA-YPredictor:基于Y-STR数据的家系特异性单倍群归属判别分析软件

目的Y染色体为男性所特有,其遗传标记蕴含着丰富的生物地理信息,故可溯源家系,在嫌疑人排查和追踪中发挥作用。Y-STR突变率较高,而Y-SNP突变率极低,几乎不会发生回复突变,所以后代男性群体携带祖先特有的Y-SNP。本研究期望通过现在我国Y库建设中通用的17个Y-STR的单倍型数据预测Y-SNP单倍群细支。方法基于前期观察,选取千人基因组计划III期中的513例东亚人群(中国及周边区域)作为基础数据集,在Java平台和Microsoft Excel软件框架下,以遗传距离计算和Y染色体进化树构建手段相联合研发Y-STR数据的家系特异性单倍群归属判别分析软件:EA-YPredictor。结果本研究揭示了15个单倍群大支下的核心单倍型。通过随机选取70个公开数据库样本,EA-YPredictor软件预测准确性达到92.8%(95%置信区间:[84.1%,97.6%])。结论在Y-SNP复合扩增检测尚无定论的情况下,本软件可基于二代测序样本对Y-STR数据库样本进行单倍群细支的准确预测,能适用于辅助家系单倍群判断。随着测序技术的不断换代和优化,更多高通量的Y-STR和Y-SNP数据补充将会使本软件进一步优化。此外,本软件对于Y数据库中Y-SNP遗传标记的筛查建库有一定指向作用。

Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease:An extended study

Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.We aimed to explore machine learning tools in predicting NAFLD.Methods:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models.Nine evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the models.The selected clinical and biochemical factors were ranked according to the importance in prediction ability.Results:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,respectively.Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD.Among these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built models.Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.Conclusions:The XGBoost model has the best overall prediction ability for diagnosing NAFLD.The novel machine learning tools provide considerable beneficial potential in NAFLD screening.

Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores:evidence from the China Kadoorie Biobank

Background:Several studies have reported that polygenic risk scores(PRSs)can enhance risk prediction of coronary artery disease(CAD)in European populations.However,research on this topic is far from sufficient in non-European countries,including China.We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population.Methods:Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training(n=28,490)and testing sets(n=72,150).Ten previously developed PRSs were evaluated,and new ones were developed using clumping and thresholding or LDpred method.The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set.Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms.Prediction of the 10-year first CAD events was assessed using hazard ratios(HRs)and measures of model discrimination,calibration,and net reclassification improvement(NRI).Hard CAD(nonfatal I21-I23 and fatal I20-I25)and soft CAD(all fatal or nonfatal I20-I25)were analyzed separately.Results:In the testing set,1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years.The HR per standard deviation of the optimal PRS was 1.26(95%CI:1.19-1.33)for hard CAD.Based on a traditional CAD risk prediction model containing only non-laboratory-based information,the addition of PRS for hard CAD increased Harrell’s C index by 0.001(-0.001 to 0.003)in women and 0.003(0.001 to 0.005)in men.Among the different high-risk thresholds ranging from 1%to 10%,the highest categorical NRI was 3.2%(95%CI:0.4-6.0%)at a high-risk threshold of 10.0%in women.The association of the PRS with soft CAD was much weaker than with hard CAD,leading to minimal or no improvement in the soft CAD model.Conclusions:In this Chinese population sample,the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD.Therefore,this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.

Prediction of pork supply via the calculation of pig population based on population prediction model

Based on the perspective of pig population system prediction,and accorded to principle of pig months transfer,this paper refers to the modeling principle and method of discrete population quantity prediction model.Then the prediction model of pork supply is derived and established:Firstly,the recursive model of pig population system and estimation model of pork supply was established.Then this study estimated the sum of monthly mortality and culling rate of breeding sows.Furthermore,the method for new left gilts in each month and estimation of breeding sows at each month of age was established.Last,this research established the estimation method model of the initial state of pig population.On this basis,an example calculation is made to predict the monthly pork supply in Heilongjiang Province from January 2016 to March 2018 in the future.The results showed that the prediction model of pork supply based on the prediction of pig population system is an effective perspective to study the forecast of pork supply.In the prediction stage,the prediction accuracy of the number of slaughtered fattened hogs was 96.36%and 97.54%,and the prediction accuracy of pork supply was 98.08%and 93.82%.This study not only lay a theoretical foundation for further study on the balance between pork supply and demand,but also helps to guide pork producers and governments at all levels to make relevant production decisions and plans.

中国城镇失能老年人口规模及养老服务需求预测

以全国第六次和第七次人口普查数据为基准数据,结合CLHLS微观数据库,对2050年以前城镇失能老年人口养老服务需求进行了预测。结果显示:随着年龄的增加,中国城镇老年人的失能率不断提高,其中在65岁以上、80岁以上及100岁以上的老年人群体中,其平均失能率分别为28.98%、42.12%和76.04%;未来城镇重度失能老年人口规模将不断扩大,2050年以前其所占比例会超过城镇总失能老年人口的25%,而且男性重度失能人口规模始终低于女性重度失能人口规模;未来城镇重度失能老年人养老服务人员需求数量处于上升状态,预计2050年以前其每年需求的平均规模会超过500万人;随着城镇化的推进,未来城镇失能人口将高于农村失能人口。因此,应尽快建立覆盖城镇居民的长期照护机制;针对不同的城镇失能人群构建差异化的长期护理模式;加快建设针对城镇失能老年人的专业照护人员队伍;完善失能老年人养老服务规制。

基于决策树模型的城市人群稳态分布预测实证——以泉州湾片区为例

基于预测的城市人群时空变化特征对城市空间资源进行配置是城市规划的重要工作,城市人口在不同的空间中快速变迁移动,具有非均衡分布的特点。这种人群非均衡分布的时空变化具有一定的规律性,即稳态分布。本文基于LBS数据以及建成环境的多源异构大数据,选取4大类36小类指标,采用智能决策树模型构建城市人群稳态分布的预测模型。对于泉州的实证发现,预测模型的准确度达84.32%,具有较高的准确性和依据,其内在理论依据是城市人群在空间上的规律性时空活动,能够为未来的城市规划提供决策参考。

基于弱化算子的黑龙江省人口老龄化灰色预测

近年来,黑龙江省人口老龄化率高于全国,老龄人口规模持续上升,对医疗保障体系、劳动力资源结构以及养老服务体系等产生影响,因此,研究黑龙江省人口老龄化的预测问题具有十分重要的现实意义。针对黑龙江省人口老龄化的灰色特性,提出一种结合一阶弱化算子优化的均值GM(1,1)预测模型,对黑龙江省老龄人口发展趋势进行预报。以2006—2021年间的黑龙江省老龄人口原始数据为样本,构建基于一阶弱化算子均值GM(1,1)的黑龙江省人口老龄化预测模型,并与基于均值GM(1,1)的黑龙江省人口老龄化预测模型精度进行对比分析。结果表明:黑龙江省老龄人口的一阶弱化算子均值GM(1,1)预测模型精度高于传统的均值GM(1,1)模型,说明一阶弱化算子的均值GM(1,1)模型能够提高黑龙江省老龄人口预报精度。

老年急性心肌梗死患者经皮冠状动脉介入术后心力衰竭预测模型的构建

目的建立老年急性心肌梗死(AMI)患者经皮冠状动脉介入术(PCI)后并发心力衰竭(HF)的预测模型。方法回顾性纳入2019年1月—2022年12月哈尔滨医科大学附属第一医院收治的326例老年AMI患者,依据术后6个月时HF发生情况将其分为AMI-HF组89例和非AMI-HF组237例。通过电子病历系统收集所有患者的临床资料,采用多因素Logistic回归分析法筛选AMI并发HF的危险因素,并构建预测模型;采用受试者工作特征(ROC)曲线评价预测模型的性能。结果单因素分析显示,与非AMI-HF组比较,AMI-HF组年龄>75岁、糖尿病患者比率以及Gensini评分、心肌肌钙蛋白I(cTnI)、N末端B型钠尿肽前体(NT-proBNP)、血尿酸、超敏C反应蛋白(hs-CRP)水平均较高,左心室射血分数(LVEF)、心肌梗死溶栓试验(TIMI)血流3级比率较低,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,年龄、Gensini评分、NT-proBNP、hs-CRP是老年AMI患者PCI后发生HF的独立危险因素(P<0.05),TIMI分级是其保护因素(P<0.05)。基于筛选的危险因素计算出C-Index后,采用ROC曲线评估C-Index对HF的预测能力,结果显示AUC为0.878(95%CI:0.834~0.921),灵敏度为86.52%,特异度为75.95%,准确率为78.83%。结论年龄、Gensini评分、NT-proBNP、hs-CRP是老年AMI患者PCI后发生HF的独立危险因素,TIMI分级是其保护因素,据此构建的模型可预测HF发生风险,从而辅助临床早期识别HF高危人群,并进行个体化干预。