Association of cumulative resting heart rate exposure with rapid renal function decline:a prospective cohort study with 27,564 older adults

OBJECTIVE To evaluate the prospective association between cumulative resting heart rate(cumRHR)and rapid renal function decline(RRFD)in a cohort of individuals aged 60 and older.METHODS In the Tianjin Chronic Kidney Disease Cohort Study,the individuals who underwent three consecutive physical examinations between 2014 and 2017,with estimated glomerular filtration rate(eGFR)greater than 60 mL/min per 1.73 m2 and aged 60 years or older were enrolled.A total of 27,564 patients were prospectively followed up from January 1,2017 to December 31,2020.The 3-year cumRHR was calculated.The primary outcome was RRFD,defined as an annualized decline in eGFR of 5 mL/min per 1.73 m2 or greater.Logistic and restricted spline regression models and subgroup analysis were used to investigate the association of cumRHR with RRFD after adjusting for all confounders.RESULTS During a median follow-up of 3.2 years,a total of 4,347(15.77%)subjects developed RRFD.In fully-adjusted models,compared with the lowest quartile of cumRHR,the odds ratio(OR)for the highest was 1.44(1.28–1.61),P<0.001.Furthermore,each 1-standard deviation(27.97 beats/min per year)increment in cumRHR was associated with a 17%(P<0.001)increased risk of RRFD,with a linear positive correlation(P for non-linear=0.803).Participants with a 3-year cumRHR≥207(beats/min)*year(equivalent to≥69 beats/min per year in 3 years)were found to be at a higher risk of RRFD.CONCLUSIONS The cumRHR is significantly associated with a higher risk of RRFD among older adults.These results might provide an effective goal for managing and delaying the decline of renal function in the older adults.

基于机器学习算法的前列腺癌风险预测和在线计算研究

目的 基于临床常用指标,采用机器学习方法 构建前列腺癌风险预测模型,为前列腺癌的早期诊疗提供科学依据,评价人工智能技术在医疗健康数据平台下的应用价值。方法 对国家临床医学科学数据中心提供的前列腺肿瘤预警数据集预处理后,使用平滑剪切绝对偏差(smoothly clipped absolute deviation,SCAD)算法筛选特征指标。采用随机森林(Radom forest,RF)、支持向量机(support vector machine,SVM)、反向传播(back propagation,BP)神经网络、卷积神经网络(convolutional neural network,CNN) 4种模型预测前列腺癌发生风险,其中神经网络模型使用经SMOTE增强后数据拟合。不同模型的预测能力采用受试者操作特性(ROC)曲线下面积(areaunderthe curve,AUC)进行比较。在确定最优模型后,使用Shiny开发前列腺癌风险预测在线平台。结果 在预测变量中,除活检标本碎组织体积、血游离前列腺特异抗原(fPSA)外,无机磷、甘油三酯、游离钙等临床常用指标与前列腺癌也密切相关。在4种模型中,RF预测效果最好(准确率:96.80%;AUC:0.975,95%CI:0.964-0.986),其次为BP神经网络(准确率:85.36%;AUC:0.892,95%CI:0.849-0.934),SVM (准确率:82.67%;AUC:0.824,95%CI:0.805-0.844)与BP神经网络预测效果相近,CNN预测能力最低(准确率:72.37%;AUC:0.724,95%CI:0.670-0.779)。基于RF及预测指标成功开发了一种前列腺癌风险预测在线平台结论 本研究揭示了医疗信息化平台下传统机器学习方法 和基础神经网络模型在疾病风险预测中的应用价值,为疑似前列腺癌并接受穿刺活检人群的前列腺癌预测提出了新思路。此外,开发在线预测系统有助于增强人工智能预测技术的实用性,使医疗应用更为便捷。

Endothelial PDGF-BB/PDGFR-βsignaling promotes osteoarthritis by enhancing angiogenesis-dependent abnormal subchondral bone formation

The mechanisms that coordinate the shift from joint homeostasis to osteoarthritis(OA)remain unknown.No pharmacological intervention can currently prevent the progression of osteoarthritis.Accumulating evidence has shown that subchondral bone deterioration is a primary trigger for overlying cartilage degeneration.We previously found that H-type vessels modulate aberrant subchondral bone formation during the pathogenesis of OA.However,the mechanism responsible for the elevation of H-type vessels in OA is still unclear.Here,we found that PDGFR-βexpression,predominantly in the CD31^(hi)Emcn^(hi) endothelium,was substantially elevated in subchondral bones from OA patients and rodent OA models.A mouse model of OA with deletion of PDGFR-βin endothelial cells(ECs)exhibited fewer H-type vessels,ameliorated subchondral bone deterioration and alleviated overlying cartilage degeneration.Endothelial PDGFR-βpromotes angiogenesis through the formation of the PDGFR-β/talin1/FAK complex.Notably,endothelium-specific inhibition of PDGFR-βby local injection of AAV9 in subchondral bone effectively attenuated the pathogenesis of OA compared with that of the vehicle-treated controls.Based on the results from this study,targeting PDGFR-βis a novel and promising approach for the prevention or early treatment of OA.

Perforation optimization of layer-penetration fracturing for commingling gas production in coal measure strata

Optimization of fracturing perforation is of great importance to the commingling gas production in coal measure strata.In this paper,a 3 D lattice algorithm hydraulic fracturing simulator was employed to study the effects of perforation position and length on hydraulic fracture propagation in coal measures of the Lin-Xing block,China.Based on field data,three lithologic combinations are simulated:1)a thick section of coal seam sandwiched by sandstones;2)a thin coal seam layer overlay by gas-bearing tight sandstone;3)two coal seams separated by a thin layer of sandstone.Our simulation shows that perforation position and length in multi-layer reservoirs play a major role in hydraulic fracture propagation.Achieving maximum stimulated volume requires consideration of lithologic sequence,coal seam thickness,stress states,and rock properties.To improve the combined gas production in coal measure strata,it is possible to simultaneously stimulate multiple coal seams or adjacent gas-bearing sandstones.In these cases,perforation location and length also significantly impact fracture propagation,and therefore should be carefully designed.Our simulation results using 3 D lattice algorithm are qualitatively consistent with laboratory physical simulation.3 D lattice models can be used to effectively simulate the fracture propagation through layers in coal measure strata.The numerical results provide guidance for perforation optimization in the hydraulic fracturing of coal measure strata.

Lactobacillus levels and prognosis of patients with acute myocardial infarction

OBJECTIVE To explore the relationship between Lactobacillus and prognosis of acute myocardial infarction(AMI)patients treated by percutaneous coronary intervention(PCI)and its correlation with clinical parameters.METHODS Consecutive patients with AMI in the coronary care unit of Tianjin Chest Hospital in China who received emergency PCI between July 2017 and December 2018 were enrolled.Subjects’fecal 16 S r DNA gene sequencing data were analyzed and subjects were categorized into low,medium and high level groups according to stool Lactobacillus measurements.The primary endpoints were major adverse cardiac events.Cox regression analysis was used to analyze the relationship between Lactobacillus and prognosis.Spearman correlation analysis and trend tests were used to assess the relationship between Lactobacillus and the clinical indicators.RESULTS The data of 254 patients were included in the analysis.Mean age was 65.90±11.56 years,and 152 patients(59.84%)were male.Follow-up time was 652(548.25-753.00)days.Multivariate Cox regression analysis showed a significantly lower risk of major adverse cardiac events in patients with Lactobacillus>7.1 copies/g[adjusted hazard ratio(HR)=0.216,95%CI:0.094-0.493,P<0.001]compared to patients with Lactobacillus≤3.6 copies/g.Statistically significant differences were shown in ST-segment elevation myocardial infarction(STEMI)(HR=0.217,95%CI:0.085-0.551,P=0.001).Lactobacillus was a protective factor for male smokers aged over 60 years whose brain natriuretic peptide was over 1,000 pg/m L.Spearman correlation analysis showed that Lactobacillus correlated negatively with white blood cells,neutrophils,high-sensitivity C-reactive protein,Troponin T,creatine kinase,creatine kinase-MB and brain natriuretic peptide(downward trend),and correlated positively with left ventricular ejection fraction(upward trend).CONCLUSIONS This study is the first to reveal the correlation between Lactobacillus and inflammation and myocardial damage after STEMI.STEMI patients,especially male smokers aged over 60 years with severe impairment of cardiac function,have better outcomes with high levels of Lactobacillus,suggesting new therapeutic strategies for improving the prognosis and quality of life of AMI patients.

Economic Evaluation of the Comprehensive AIDS Prevention and Control Program—Tianjin Municipality,China,2011-2022

What is already known about this topic?Acquired immunodeficiency syndrome(AIDS)represents a significant public health challenge globally,not only inflicting harm on the health of individuals but also placing a considerable economic strain on society.What is added by this report?This study represents the inaugural report on the potential reduction in economic burden attributable to human immunodeficiency virus(HIV)prevention strategies in Tianjin.Between 2011 and 2022,it is estimated that effective measures could prevent 2,965 new HIV infections and avert 658 deaths,resulting in an economic benefit of approximately 14.437 billion Chinese Yuan.What are the implications for public health practice?The findings of this study offer valuable evidence to inform the development of localized HIV prevention and control strategies,as well as to guide public health policymaking.

Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China:a multicenter observational study

Background:Blood glucose control is closely related to type 2 diabetes mellitus(T2DM)prognosis.This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network(EN)with a machine-learning algorithm to predict glycemic control.Methods:Basic information,biochemical indices,and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017.An EN regression was used to address variable collinearity.Then,three common machine learning algorithms(random forest[RF],support vector machine[SVM],and back propagation artificial neural network[BP-ANN])were used to simulate and predict blood glucose status.Additionally,a stepwise logistic regression was performed to compare the machine learning models.Results:The well-controlled blood glucose rate was 45.82%in North China.The multivariable analysis found that hypertension history,atherosclerotic cardiovascular disease history,exercise,and total cholesterol were protective factors in glycosylated hemoglobin(HbAlc)control,while central adiposity,family history,T2DM duration,complications,insulin dose,blood pressure,and hypertension were risk factors for elevated HbAlc.Before the dimensional reduction in the EN,the areas under the curve of RF,SVM,and BP were 0.73,0.61,and 0.70,respectively,while these figures increased to 0.75,0.72,and 0.72,respectively,after dimensional reduction.Moreover,the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models(the sensitivity and accuracy of logistic were 0.52 and 0.56;RF:0.79,0.70;SVM:0.84,0.73;BP-ANN:0.78,0.73,respectively).Conclusions:More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications.The EN and machine learning algorithms are alternative choices,in addition to the traditional logistic model,for building predictive models of blood glucose control in patients with T2DM.