Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates havi...Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.展开更多
Background:Hepatocellular carcinoma(HCC)has a poor long-term prognosis.The competition of circular RNAs(circRNAs)with endogenous RNA is a novel tool for predicting HCC prognosis.Based on the alterations of circRNA reg...Background:Hepatocellular carcinoma(HCC)has a poor long-term prognosis.The competition of circular RNAs(circRNAs)with endogenous RNA is a novel tool for predicting HCC prognosis.Based on the alterations of circRNA regulatory networks,the analysis of gene modules related to HCC is feasible.Methods:Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC.Gene function,pathway,and protein interaction analyses were performed for the differentially expressed genes(DEGs)in this regulatory network.In the proteinprotein interaction network,hub genes were identified and subjected to regression analysis,producing an optimized four-gene signature for prognostic risk stratification in HCC patients.Anti-HCC drugs were excavated by assessing the DEGs between the low-and high-risk groups.A circRNA-microRNA-hub gene subnetwork was constructed,in which three hallmark genes,KIF4A,CCNA2,and PBK,were subjected to functional enrichment analysis.Results:A four-gene signature(KIF4A,CCNA2,PBK,and ZWINT)that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas(TCGA)cohort and International Cancer Genome Consortium(ICGC)cohort was developed.CDK inhibitors,PI3K inhibitors,HDAC inhibitors,and EGFR inhibitors were predicted as four potential mechanisms of drug action(MOA)in high-risk HCC patients.Subsequent analysis has revealed that PBK,CCNA2,and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion,regulating microsatellite instability(MSI),and exerting an impact on HCC progression.Conclusions:The present study highlights the role of the circRNA-related regulatory network,identifies a four-gene prognostic signature and biomarkers,and further identifies novel therapy for HCC.展开更多
目的基于随机森林模型探讨中青年乳腺癌患者未成年子女养育忧虑现状及其影响因素,为临床干预提供依据。方法采用便利抽样法,选择2023年4月至12月在本市某三级甲等综合医院乳腺外科接受诊疗的275例乳腺癌患者为研究对象。采用自行设计的...目的基于随机森林模型探讨中青年乳腺癌患者未成年子女养育忧虑现状及其影响因素,为临床干预提供依据。方法采用便利抽样法,选择2023年4月至12月在本市某三级甲等综合医院乳腺外科接受诊疗的275例乳腺癌患者为研究对象。采用自行设计的一般资料问卷、中文版养育忧虑问卷(parenting concerns questionnaire,PCQ)、领悟社会支持量表(perceived social support scale,PSSS)、癌症复发担忧量表(concern about recurrence scale,CARS)、简易疾病感知量表(brief illness perception questionnaire,BIPQ)进行调查。基于随机森林模型和最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)对变量进行重要性排序和变量筛选,将筛选后的变量纳入多元线性回归分析。结果260例患者完成研究。中青年乳腺癌患者养育忧虑得分为(51.1±6.4)分。将随机森林及LASSO回归确定的变量,纳入多元线性回归分析结果显示(并按影响因素重次要排序),疾病感知越高、领悟社会支持越低、癌症复发担忧越大、肿瘤分期Ⅳ期、离异/丧偶、未成年子女个数越多的中青年乳腺癌患者养育的忧虑越严重(均P<0.05),解释总变异的57.0%。结论中青年乳腺癌患者养育忧虑处于中等偏高水平,受多种因素影响,医护人员应针对性制订措施给予干预,以便降低患者养育忧虑水平。展开更多
<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for th...<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for the prevention and treatment of PI in the elderly inpatients with kidney disease. <strong>Methods</strong>: Retrospective collection method is adopted to collect 158 clinical cases of the elderly inpatients with kidney disease aged ≥ 60 in the Nephrology Department, the First Affiliated Hospital of Jinan University from January 2017 to December 2019, and then least absolute shrinkage and selection Operator (LASSO) regression analysis is used to analyze 17 possible influence factors;finally Logistic regression model is established to analyze and screen influence factors of risk. <strong>Results</strong>: 1) Among 158 elderly inpatients with medium and high risk of PI, the incidence of PI is 20.25%;the most common stage of injury is stage I (42.5%);sacrococcygeal (60%) is the high-risk site of pressure injury. 2) LASSO regression analysis shows that history of present respiratory infection/respiratory failure (<em>β </em>= 1.2714. <em>P</em> < 0.05) and hospitalization time (<em>β</em> = 0.4177. <em>P </em>< 0.05) are independent factors influencing PI risk in the elderly inpatients with kidney disease. <strong>Concl</strong><strong>usio</strong><strong>n</strong>: The elderly patients with kidney disease and PI risk are the high incidence population of hospital acquired PI;for the elderly inpatients with kidney disease and having respiratory infection history or respiratory failure, prolonged hospitalization will significantly increase the risk of PI. Therefore, targeted preventive and control measures should be taken to reduce the incidence of PI.展开更多
This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） w...This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.展开更多
BACKGROUND Gastric cancer(GC)is one of the most frequently diagnosed gastrointestinal cancers throughout the world.Novel prognostic biomarkers are required to predict the prognosis of GC.AIM To identify a multi-long n...BACKGROUND Gastric cancer(GC)is one of the most frequently diagnosed gastrointestinal cancers throughout the world.Novel prognostic biomarkers are required to predict the prognosis of GC.AIM To identify a multi-long noncoding RNA(lncRNA)prognostic model for GC.METHODS Transcriptome data and clinical data were downloaded from The Cancer Genome Atlas.COX and least absolute shrinkage and selection operator regression analyses were performed to screen for prognosis associated lncRNAs.Receiver operating characteristic curve and Kaplan-Meier survival analyses were applied to evaluate the effectiveness of the model.RESULTS The prediction model was established based on the expression of AC007991.4,AC079385.3,and AL109615.2 Based on the model,GC patients were divided into“high risk”and“low risk”groups to compare the differences in survival.The model was re-evaluated with the clinical data of our center.CONCLUSION The 3-lncRNA combination model is an independent prognostic factor for GC.展开更多
Purpose: To explore the fever-related risk factors of inpatients in Rehabilitation Department, and to provide reference for patients with high risk of fever to take corresponding nursing measures. Methods: The study w...Purpose: To explore the fever-related risk factors of inpatients in Rehabilitation Department, and to provide reference for patients with high risk of fever to take corresponding nursing measures. Methods: The study was conducted on the Rehabilitation Department of The First Affiliated Hospital of Jinan University from July 2019 to December 2019. The fever group included 51 patients and the non-fever group included 49 patients without fever. The two groups of clinical data, comorbidities, related laboratory values, possible risk factors of fever were analyzed by case regression analysis, and the relevant risk factors were screened out by LASSO (least absolute shrinkage and selection operator) regression analysis. Results: According to the results of Lasso regression analysis, pressure sore or skin infection, history of hypertension, current history of respiratory tract infection, feeding patterns were the higher risk factors of fever in inpatients in Rehabilitation Department, while the first course of disease, main diagnosis, history of respiratory tract infection within half a year, kidney damage and hospitalization days were lower risk factors. Conclusion: This study is helpful to early identify the fever risk of inpatients in Rehabilitation Department, and provide reference basis for high-risk fever patients to take positive and effective nursing measures.展开更多
Triple-negative breast cancer(TNBC)poses a significant challenge due to the lack of reliable prognostic gene signatures and an understanding of its immune behavior.Methods:We analyzed clinical information and mRNA exp...Triple-negative breast cancer(TNBC)poses a significant challenge due to the lack of reliable prognostic gene signatures and an understanding of its immune behavior.Methods:We analyzed clinical information and mRNA expression data from 162 TNBC patients in TCGA-BRCA and 320 patients in METABRIC-BRCA.Utilizing weighted gene coexpression network analysis,we pinpointed 34 TNBC immune genes linked to survival.The least absolute shrinkage and selection operator Cox regression method identified key TNBC immune candidates for prognosis prediction.We calculated chemotherapy sensitivity scores using the“pRRophetic”package in R software and assessed immunotherapy response using the Tumor Immune Dysfunction and Exclusion algorithm.Results:In this study,34 survival-related TNBC immune gene expression profiles were identified.A least absolute shrinkage and selection operator-Cox regression model was used and 15 candidates were prioritized,with a concomitant establishment of a robust risk immune classifier.The high-risk TNBC immune groups showed increased sensitivity to therapeutic agents like RO-3306,Tamoxifen,Sunitinib,JNK Inhibitor VIII,XMD11-85h,BX-912,and Tivozanib.An analysis of the Search Tool for Interaction of Chemicals database revealed the associations between the high-risk group and signaling pathways,such as those involving Rap1,Ras,and PI3K-Akt.The low-risk group showed a higher immunotherapy response rate,as observed through the tumor immune dysfunction and exclusion analysis in the TCGA-TNBC and METABRIC-TNBC cohorts.Conclusion:This study provides insights into the immune complexities of TNBC,paving the way for novel diagnostic approaches and precision treatment methods that exploit its immunological intricacies,thus offering hope for improved management and outcomes of this challenging disease.展开更多
BACKGROUND Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease.However,previous single-cell sequencing studies on gastric cancer(GC)hav...BACKGROUND Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease.However,previous single-cell sequencing studies on gastric cancer(GC)have largely focused on immune cells and stromal cells,and further elucidation is required regarding the alterations that occur in gastric epithelial cells during the development of GC.AIM To create a GC prediction model based on single-cell and bulk RNA sequencing(bulk RNA-seq)data.METHODS In this study,we conducted a comprehensive analysis by integrating three singlecell RNA sequencing(scRNA-seq)datasets and ten bulk RNA-seq datasets.Our analysis mainly focused on determining cell proportions and identifying differentially expressed genes(DEGs).Specifically,we performed differential expression analysis among epithelial cells in GC tissues and normal gastric tissues(NAGs)and utilized both single-cell and bulk RNA-seq data to establish a prediction model for GC.We further validated the accuracy of the GC prediction model in bulk RNA-seq data.We also used Kaplan–Meier plots to verify the correlation between genes in the prediction model and the prognosis of GC.RESULTS By analyzing scRNA-seq data from a total of 70707 cells from GC tissue,NAG,and chronic gastric tissue,10 cell types were identified,and DEGs in GC and normal epithelial cells were screened.After determining the DEGs in GC and normal gastric samples identified by bulk RNA-seq data,a GC predictive classifier was constructed using the Least absolute shrinkage and selection operator(LASSO)and random forest methods.The LASSO classifier showed good performance in both validation and model verification using The Cancer Genome Atlas and Genotype-Tissue Expression(GTEx)datasets[area under the curve(AUC)_min=0.988,AUC_1se=0.994],and the random forest model also achieved good results with the validation set(AUC=0.92).Genes TIMP1,PLOD3,CKS2,TYMP,TNFRSF10B,CPNE1,GDF15,BCAP31,and CLDN7 were identified to have high importance values in multiple GC predictive models,and KM-PLOTTER analysis showed their relevance to GC prognosis,suggesting their potential for use in GC diagnosis and treatment.CONCLUSION A predictive classifier was established based on the analysis of RNA-seq data,and the genes in it are expected to serve as auxiliary markers in the clinical diagnosis of GC.展开更多
Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurr...Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurrence and development.Necroptosis is a newly established way for cell programmed death,undertaking essential roles in anti-tumor.Therefore,identifying necroptosis-related l ong non-coding RNAs and based on them to evaluate the signatures of l ung adenocarcinoma is essential for patients’survival prediction and therapy.Methods:We collected data from the public database and performed the least absolute shrinkage to construct a 13-lncRNAs prognostic model.Based on the Consensus Clustering,ESTIMATE,CIRERSORT,and weighted gene co-expression network analysis to identify the immune signatures.Results:This study identified a 13-lncRNAs prognostic model.The model’s prediction accuracy was evaluated by receiver operating characteristic and independent-prognosis analysis;besides,a Gene Expression Omnibus dataset was applied for external validation.Furthermore,we analyzed the immune features of subgroups in multiple dimensions.A consensus clustering analysis based on the 41 genes was implemented to separate lung adenocarcinoma patients into two subgroups.We compared the features of subgroups in multiple dimensions,including survival,immune microenvironment,immune cells infiltration and gene co-expression network analysis.Conclusion:W e established a prognosis necroptosis-related risk model to predict lung adenocarcinoma patients’prognosis and systematically understood the correlation between immune and necroptosis.This study can applicate in clinical to predict the prognosis of lung adenocarcinoma patients and provide new insight into lung adenocarcinoma immune therapy.展开更多
目的分析宁夏回族自治区儿童青少年近视流行现状、影响因素及不同学段间的差异。方法采用分层整群随机抽样的方法,于2019年9月至12月,在宁夏回族自治区银川市、吴忠市、石嘴山市、固原市和中卫市,随机抽取8所小学、6所初中、6所高中、4...目的分析宁夏回族自治区儿童青少年近视流行现状、影响因素及不同学段间的差异。方法采用分层整群随机抽样的方法,于2019年9月至12月,在宁夏回族自治区银川市、吴忠市、石嘴山市、固原市和中卫市,随机抽取8所小学、6所初中、6所高中、4所大学的学生为研究对象,小学每个年级抽取5个班级,初中至大学每个年级抽取4个班级,以抽取班级的全体学生作为研究对象,共抽取学生14211人,对其进行问卷调查、体格检查和视力测量。不同学段儿童近视的影响因素采用最小绝对收缩和选择算子(LASSO)联合Logistic回归进行分析,选择贝叶斯信息准则(Bayesian information criterion,BIC)最小的模型为最优模型。结果宁夏回族自治区儿童青少年近视检出率为70.3%,女生高于男生,城市高于乡镇,差异均有统计学意义(均为P<0.001);按学段分层后,随着年级的增加,近视检出率随之升高,小学最低,大学最高,不同学段近视检出率差异有统计学意义(P<0.001)。近视影响因素的LASSO-Logistic回归分析表明,城乡、性别、年龄、目前是否配戴眼镜、每日课间操节数、是否积极参加体力活动和过去6个月是否保持规律活动是小学生近视的影响因素(均为P<0.05);性别、目前是否配戴眼镜是初中生和高中生近视的影响因素(均为P<0.05);目前是否配戴眼镜是大学生近视的影响因素(P<0.05)。结论宁夏回族自治区儿童青少年近视检出率高,不同学段儿童青少年近视影响因素差异明显。配戴眼镜是控制近视的保护因素。应根据儿童青少年所处学段开展有针对性的视力相关知识的健康教育,增强其健康保健意识,提高儿童青少年视力。展开更多
目的构建跟距联合畸形(talocalcaneal coalition)的X线影像组学模型,并检验其对跟距联合畸形的筛查诊断能力。方法回顾性分析2019年1月至2023年3月吉林大学中日联谊医院放射线科200例行踝关节或足部X线检查的患者临床放射资料(跟距联合...目的构建跟距联合畸形(talocalcaneal coalition)的X线影像组学模型,并检验其对跟距联合畸形的筛查诊断能力。方法回顾性分析2019年1月至2023年3月吉林大学中日联谊医院放射线科200例行踝关节或足部X线检查的患者临床放射资料(跟距联合阳性及阴性各100例),手动勾画跟距联合畸形所在影像学区域,基于Python-pyradiomics库初步提取影像组学特征,通过曼-惠特尼U检验及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)算法实现数据降维和特征筛选,用支持向量机(support vector machine,SVM)对筛选得到的影像组学特征分类建模,最终以受试者工作特征(receiver operating characteristic,ROC)曲线的曲线下面积(area under the curve,AUC)、精确度、召回率、敏感度、特异度及F1分数评价模型的诊断效能。结果从X线图像中初步提取到105个组学特征,经曼-惠特尼U检验及LASSO算法筛选出7个强相关性特征,最终以SVM分类器所构建模型的测试集AUC值为0.93,精确度、召回率、敏感度、特异度和F1分数分别为88%、85%、93%、92%、88%,对跟距联合畸形有良好的筛查诊断能力。结论基于X线的影像组学模型可作为筛查诊断跟距联合畸形的一种准确高效的无创性工具,帮助临床医师诊断跟距联合畸形。展开更多
文摘Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.
基金This study was supported by grants from the High-level Preresearch Program of Zhejiang Shuren University 2019(KXJ121860)Zhejiang Provincial Natural Science Foundation of China(LQ20H190004)。
文摘Background:Hepatocellular carcinoma(HCC)has a poor long-term prognosis.The competition of circular RNAs(circRNAs)with endogenous RNA is a novel tool for predicting HCC prognosis.Based on the alterations of circRNA regulatory networks,the analysis of gene modules related to HCC is feasible.Methods:Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC.Gene function,pathway,and protein interaction analyses were performed for the differentially expressed genes(DEGs)in this regulatory network.In the proteinprotein interaction network,hub genes were identified and subjected to regression analysis,producing an optimized four-gene signature for prognostic risk stratification in HCC patients.Anti-HCC drugs were excavated by assessing the DEGs between the low-and high-risk groups.A circRNA-microRNA-hub gene subnetwork was constructed,in which three hallmark genes,KIF4A,CCNA2,and PBK,were subjected to functional enrichment analysis.Results:A four-gene signature(KIF4A,CCNA2,PBK,and ZWINT)that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas(TCGA)cohort and International Cancer Genome Consortium(ICGC)cohort was developed.CDK inhibitors,PI3K inhibitors,HDAC inhibitors,and EGFR inhibitors were predicted as four potential mechanisms of drug action(MOA)in high-risk HCC patients.Subsequent analysis has revealed that PBK,CCNA2,and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion,regulating microsatellite instability(MSI),and exerting an impact on HCC progression.Conclusions:The present study highlights the role of the circRNA-related regulatory network,identifies a four-gene prognostic signature and biomarkers,and further identifies novel therapy for HCC.
文摘目的基于随机森林模型探讨中青年乳腺癌患者未成年子女养育忧虑现状及其影响因素,为临床干预提供依据。方法采用便利抽样法,选择2023年4月至12月在本市某三级甲等综合医院乳腺外科接受诊疗的275例乳腺癌患者为研究对象。采用自行设计的一般资料问卷、中文版养育忧虑问卷(parenting concerns questionnaire,PCQ)、领悟社会支持量表(perceived social support scale,PSSS)、癌症复发担忧量表(concern about recurrence scale,CARS)、简易疾病感知量表(brief illness perception questionnaire,BIPQ)进行调查。基于随机森林模型和最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)对变量进行重要性排序和变量筛选,将筛选后的变量纳入多元线性回归分析。结果260例患者完成研究。中青年乳腺癌患者养育忧虑得分为(51.1±6.4)分。将随机森林及LASSO回归确定的变量,纳入多元线性回归分析结果显示(并按影响因素重次要排序),疾病感知越高、领悟社会支持越低、癌症复发担忧越大、肿瘤分期Ⅳ期、离异/丧偶、未成年子女个数越多的中青年乳腺癌患者养育的忧虑越严重(均P<0.05),解释总变异的57.0%。结论中青年乳腺癌患者养育忧虑处于中等偏高水平,受多种因素影响,医护人员应针对性制订措施给予干预,以便降低患者养育忧虑水平。
文摘<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for the prevention and treatment of PI in the elderly inpatients with kidney disease. <strong>Methods</strong>: Retrospective collection method is adopted to collect 158 clinical cases of the elderly inpatients with kidney disease aged ≥ 60 in the Nephrology Department, the First Affiliated Hospital of Jinan University from January 2017 to December 2019, and then least absolute shrinkage and selection Operator (LASSO) regression analysis is used to analyze 17 possible influence factors;finally Logistic regression model is established to analyze and screen influence factors of risk. <strong>Results</strong>: 1) Among 158 elderly inpatients with medium and high risk of PI, the incidence of PI is 20.25%;the most common stage of injury is stage I (42.5%);sacrococcygeal (60%) is the high-risk site of pressure injury. 2) LASSO regression analysis shows that history of present respiratory infection/respiratory failure (<em>β </em>= 1.2714. <em>P</em> < 0.05) and hospitalization time (<em>β</em> = 0.4177. <em>P </em>< 0.05) are independent factors influencing PI risk in the elderly inpatients with kidney disease. <strong>Concl</strong><strong>usio</strong><strong>n</strong>: The elderly patients with kidney disease and PI risk are the high incidence population of hospital acquired PI;for the elderly inpatients with kidney disease and having respiratory infection history or respiratory failure, prolonged hospitalization will significantly increase the risk of PI. Therefore, targeted preventive and control measures should be taken to reduce the incidence of PI.
基金Project(21465016)supported by the National Natural Foundation of China
文摘This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.
基金Supported by Liaoning S&T Project,No.20180550971 and No.20180550999Shenyang Young and Middle-Aged Scientific&Technological Innovation Talents Support Plan,No.2018416017.
文摘BACKGROUND Gastric cancer(GC)is one of the most frequently diagnosed gastrointestinal cancers throughout the world.Novel prognostic biomarkers are required to predict the prognosis of GC.AIM To identify a multi-long noncoding RNA(lncRNA)prognostic model for GC.METHODS Transcriptome data and clinical data were downloaded from The Cancer Genome Atlas.COX and least absolute shrinkage and selection operator regression analyses were performed to screen for prognosis associated lncRNAs.Receiver operating characteristic curve and Kaplan-Meier survival analyses were applied to evaluate the effectiveness of the model.RESULTS The prediction model was established based on the expression of AC007991.4,AC079385.3,and AL109615.2 Based on the model,GC patients were divided into“high risk”and“low risk”groups to compare the differences in survival.The model was re-evaluated with the clinical data of our center.CONCLUSION The 3-lncRNA combination model is an independent prognostic factor for GC.
文摘Purpose: To explore the fever-related risk factors of inpatients in Rehabilitation Department, and to provide reference for patients with high risk of fever to take corresponding nursing measures. Methods: The study was conducted on the Rehabilitation Department of The First Affiliated Hospital of Jinan University from July 2019 to December 2019. The fever group included 51 patients and the non-fever group included 49 patients without fever. The two groups of clinical data, comorbidities, related laboratory values, possible risk factors of fever were analyzed by case regression analysis, and the relevant risk factors were screened out by LASSO (least absolute shrinkage and selection operator) regression analysis. Results: According to the results of Lasso regression analysis, pressure sore or skin infection, history of hypertension, current history of respiratory tract infection, feeding patterns were the higher risk factors of fever in inpatients in Rehabilitation Department, while the first course of disease, main diagnosis, history of respiratory tract infection within half a year, kidney damage and hospitalization days were lower risk factors. Conclusion: This study is helpful to early identify the fever risk of inpatients in Rehabilitation Department, and provide reference basis for high-risk fever patients to take positive and effective nursing measures.
文摘Triple-negative breast cancer(TNBC)poses a significant challenge due to the lack of reliable prognostic gene signatures and an understanding of its immune behavior.Methods:We analyzed clinical information and mRNA expression data from 162 TNBC patients in TCGA-BRCA and 320 patients in METABRIC-BRCA.Utilizing weighted gene coexpression network analysis,we pinpointed 34 TNBC immune genes linked to survival.The least absolute shrinkage and selection operator Cox regression method identified key TNBC immune candidates for prognosis prediction.We calculated chemotherapy sensitivity scores using the“pRRophetic”package in R software and assessed immunotherapy response using the Tumor Immune Dysfunction and Exclusion algorithm.Results:In this study,34 survival-related TNBC immune gene expression profiles were identified.A least absolute shrinkage and selection operator-Cox regression model was used and 15 candidates were prioritized,with a concomitant establishment of a robust risk immune classifier.The high-risk TNBC immune groups showed increased sensitivity to therapeutic agents like RO-3306,Tamoxifen,Sunitinib,JNK Inhibitor VIII,XMD11-85h,BX-912,and Tivozanib.An analysis of the Search Tool for Interaction of Chemicals database revealed the associations between the high-risk group and signaling pathways,such as those involving Rap1,Ras,and PI3K-Akt.The low-risk group showed a higher immunotherapy response rate,as observed through the tumor immune dysfunction and exclusion analysis in the TCGA-TNBC and METABRIC-TNBC cohorts.Conclusion:This study provides insights into the immune complexities of TNBC,paving the way for novel diagnostic approaches and precision treatment methods that exploit its immunological intricacies,thus offering hope for improved management and outcomes of this challenging disease.
文摘BACKGROUND Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease.However,previous single-cell sequencing studies on gastric cancer(GC)have largely focused on immune cells and stromal cells,and further elucidation is required regarding the alterations that occur in gastric epithelial cells during the development of GC.AIM To create a GC prediction model based on single-cell and bulk RNA sequencing(bulk RNA-seq)data.METHODS In this study,we conducted a comprehensive analysis by integrating three singlecell RNA sequencing(scRNA-seq)datasets and ten bulk RNA-seq datasets.Our analysis mainly focused on determining cell proportions and identifying differentially expressed genes(DEGs).Specifically,we performed differential expression analysis among epithelial cells in GC tissues and normal gastric tissues(NAGs)and utilized both single-cell and bulk RNA-seq data to establish a prediction model for GC.We further validated the accuracy of the GC prediction model in bulk RNA-seq data.We also used Kaplan–Meier plots to verify the correlation between genes in the prediction model and the prognosis of GC.RESULTS By analyzing scRNA-seq data from a total of 70707 cells from GC tissue,NAG,and chronic gastric tissue,10 cell types were identified,and DEGs in GC and normal epithelial cells were screened.After determining the DEGs in GC and normal gastric samples identified by bulk RNA-seq data,a GC predictive classifier was constructed using the Least absolute shrinkage and selection operator(LASSO)and random forest methods.The LASSO classifier showed good performance in both validation and model verification using The Cancer Genome Atlas and Genotype-Tissue Expression(GTEx)datasets[area under the curve(AUC)_min=0.988,AUC_1se=0.994],and the random forest model also achieved good results with the validation set(AUC=0.92).Genes TIMP1,PLOD3,CKS2,TYMP,TNFRSF10B,CPNE1,GDF15,BCAP31,and CLDN7 were identified to have high importance values in multiple GC predictive models,and KM-PLOTTER analysis showed their relevance to GC prognosis,suggesting their potential for use in GC diagnosis and treatment.CONCLUSION A predictive classifier was established based on the analysis of RNA-seq data,and the genes in it are expected to serve as auxiliary markers in the clinical diagnosis of GC.
文摘Background:Lung adenocarcinoma is one of the most common pathological types of lung malignant tumor with high morbidity and mortality.Long non-coding RNAs are gradually recognized to play crucial roles in tumor occurrence and development.Necroptosis is a newly established way for cell programmed death,undertaking essential roles in anti-tumor.Therefore,identifying necroptosis-related l ong non-coding RNAs and based on them to evaluate the signatures of l ung adenocarcinoma is essential for patients’survival prediction and therapy.Methods:We collected data from the public database and performed the least absolute shrinkage to construct a 13-lncRNAs prognostic model.Based on the Consensus Clustering,ESTIMATE,CIRERSORT,and weighted gene co-expression network analysis to identify the immune signatures.Results:This study identified a 13-lncRNAs prognostic model.The model’s prediction accuracy was evaluated by receiver operating characteristic and independent-prognosis analysis;besides,a Gene Expression Omnibus dataset was applied for external validation.Furthermore,we analyzed the immune features of subgroups in multiple dimensions.A consensus clustering analysis based on the 41 genes was implemented to separate lung adenocarcinoma patients into two subgroups.We compared the features of subgroups in multiple dimensions,including survival,immune microenvironment,immune cells infiltration and gene co-expression network analysis.Conclusion:W e established a prognosis necroptosis-related risk model to predict lung adenocarcinoma patients’prognosis and systematically understood the correlation between immune and necroptosis.This study can applicate in clinical to predict the prognosis of lung adenocarcinoma patients and provide new insight into lung adenocarcinoma immune therapy.
文摘目的分析宁夏回族自治区儿童青少年近视流行现状、影响因素及不同学段间的差异。方法采用分层整群随机抽样的方法,于2019年9月至12月,在宁夏回族自治区银川市、吴忠市、石嘴山市、固原市和中卫市,随机抽取8所小学、6所初中、6所高中、4所大学的学生为研究对象,小学每个年级抽取5个班级,初中至大学每个年级抽取4个班级,以抽取班级的全体学生作为研究对象,共抽取学生14211人,对其进行问卷调查、体格检查和视力测量。不同学段儿童近视的影响因素采用最小绝对收缩和选择算子(LASSO)联合Logistic回归进行分析,选择贝叶斯信息准则(Bayesian information criterion,BIC)最小的模型为最优模型。结果宁夏回族自治区儿童青少年近视检出率为70.3%,女生高于男生,城市高于乡镇,差异均有统计学意义(均为P<0.001);按学段分层后,随着年级的增加,近视检出率随之升高,小学最低,大学最高,不同学段近视检出率差异有统计学意义(P<0.001)。近视影响因素的LASSO-Logistic回归分析表明,城乡、性别、年龄、目前是否配戴眼镜、每日课间操节数、是否积极参加体力活动和过去6个月是否保持规律活动是小学生近视的影响因素(均为P<0.05);性别、目前是否配戴眼镜是初中生和高中生近视的影响因素(均为P<0.05);目前是否配戴眼镜是大学生近视的影响因素(P<0.05)。结论宁夏回族自治区儿童青少年近视检出率高,不同学段儿童青少年近视影响因素差异明显。配戴眼镜是控制近视的保护因素。应根据儿童青少年所处学段开展有针对性的视力相关知识的健康教育,增强其健康保健意识,提高儿童青少年视力。
文摘目的构建跟距联合畸形(talocalcaneal coalition)的X线影像组学模型,并检验其对跟距联合畸形的筛查诊断能力。方法回顾性分析2019年1月至2023年3月吉林大学中日联谊医院放射线科200例行踝关节或足部X线检查的患者临床放射资料(跟距联合阳性及阴性各100例),手动勾画跟距联合畸形所在影像学区域,基于Python-pyradiomics库初步提取影像组学特征,通过曼-惠特尼U检验及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)算法实现数据降维和特征筛选,用支持向量机(support vector machine,SVM)对筛选得到的影像组学特征分类建模,最终以受试者工作特征(receiver operating characteristic,ROC)曲线的曲线下面积(area under the curve,AUC)、精确度、召回率、敏感度、特异度及F1分数评价模型的诊断效能。结果从X线图像中初步提取到105个组学特征,经曼-惠特尼U检验及LASSO算法筛选出7个强相关性特征,最终以SVM分类器所构建模型的测试集AUC值为0.93,精确度、召回率、敏感度、特异度和F1分数分别为88%、85%、93%、92%、88%,对跟距联合畸形有良好的筛查诊断能力。结论基于X线的影像组学模型可作为筛查诊断跟距联合畸形的一种准确高效的无创性工具,帮助临床医师诊断跟距联合畸形。
