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CDKN1A regulation on chondrogenic differentiation of human chondrocytes in osteoarthritis through single-cell and bulk sequencing analysis

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HELIYON 2024年第5期10卷 e27466页

作者： Fang, Chao Zhu, Shanbang Zhong, Rui Yu, Gang Lu, Shuai Liu, Zhilin Gao, Jingyu Yan, Chengyuan Wang, Yingming Feng, Xinzhe First Affiliated Hosp USTC Dept Orthoped Hefei 230001 Peoples R China Nanjing Univ Affiliated Jinling Hosp Dept Orthoped Med Sch 305 Zhongshandonglu Rd Nanjing 210002 Peoples R China Naval Med Univ Changhai Hosp Dept Joint Bone Dis Surg Shanghai 200433 Peoples R China

Objective: Chondrocyte death is the hallmark of cartilage degeneration during osteoarthritis (OA). However, the specific pathogenesis of cell death in OA chondrocytes has not been elucidated. This study aims to validate the role of CDKN1A, a key programmed cell death (PCD)-related gene, in chondrogenic differentiation using a combination of single-cell and bulk sequencing approaches. Design: OA-related RNA-seq data (GSE114007, GSE55235, GSE152805) were downloaded from Gene Expression Omnibus database. PCD-related genes were obtained from GeneCards database. RNA-seq was performed to annotate the cell types in OA and control samples. Differentially expressed genes (DEGs) among those cell types (scRNA-DEGs) were screened. A nomogram of OA was constructed based on the featured genes, and potential drugs targeting the featured genes were predicted. The presence of key genes was confirmed using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot (WB), and immunohistochemistry (IHC). Micromass culture and Alcian blue staining were used to determine the effect of CDKN1A on chondrogenesis. Results: Six cell types, namely HomC, HTC, RepC, preFC, FC, and RegC, were annotated in scRNAseq data. Five featured genes (JUN, CDKN1A, HMGB2, DDIT3, and DDIT4) were screened by multiple biological information analysis methods. TAXOTERE had the highest ability to dock with DDIT3. Functional analysis indicated that CDKN1A was enriched in processes related to collagen catabolism and acts as a positive regulator of autophagy. Additionally, CDKN1A was found to be associated with several KEGG pathways, including those involved in acute myeloid leukemia and autoimmune thyroid disease. CDKN1A was confirmed down-regulated in the joint tissues of OA mouse model and OA model cell. Inhibiting the expression of CDKN1A can significantly suppress the differentiation of OA chondrocytes. Conclusion: Our findings highlight the critical role of CDKN1A in promoting cartilage formation i

关键词： Programmed cell death related genes Osteoarthritis (OA) Principal component analysis (PCA) T-distributed stochastic neighbor embedding (t-SNE) Weighted gene co-expression network analysis (WGCNA) Least absolute shrinkage and selection operator (LASSO) boruta algorithm

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Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018

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CARDIOVASCULAR DIABETOLOGY 2024年第1期23卷 243页

作者： Duan, Mingxuan Zhao, Xi Li, Shaolin Miao, Guangrui Bai, Linpeng Zhang, Qingyang Yang, Wenxuan Zhao, Xiaoyan Zhengzhou Univ Cardiovasc Ctr Affiliated Hosp 1 Dept CardiolHenan Key Lab Hereditary Cardiovasc D Zhengzhou Henan Peoples R China

BackgroundThe prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the boruta algorithm. The aim was to find the best replacement index of IR. MethodsIn this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001-2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. ResultsOver a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate "U-shape". Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950-0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011-1.026 for all-cause mortality and HR 1.028, 95% CI 1.014-1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predo

关键词： Metabolic score for insulin resistance Triglyceride-glucose index Mortality boruta algorithm Insulin resistance NHANES

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Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning

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CARDIOVASCULAR DIABETOLOGY 2024年第1期23卷 163页

作者： Yan, Fengjuan Chen, Xiehui Quan, Xiaoqing Wang, Lili Wei, Xinyi Zhu, Jialiang Shenzhen Longhua Dist Cent Hosp Dept Geriatr Shenzhen Guangdong Peoples R China Shandong Univ Tradit Chinese Med Dept Cardiol Affiliated Hosp Jinan Shandong Peoples R China Third Hosp Jinan Dept Cardiol Jinan Shandong Peoples R China Jinan Univ Dept Nephrol Affiliated Hosp 1 Guangzhou Guangdong Peoples R China

Background Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. Methods Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan-Meier curves. The predictive ability of the SHR was assessed using the boruta algorithm, and a prediction model was established using machine learning algorithms. Results Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). Conclusion The SHR exhibited a U-

关键词： Sepsis Stress hyperglycemia ratio Critical illness boruta algorithm Machine learning

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Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study

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REPRODUCTIVE SCIENCES 2024年第5期31卷 1391-1400页

作者： Callbo, Paliz Nordlof Junus, Katja Gabrysch, Katja Bergman, Lina Poromaa, Inger Sundstrom Lager, Susanne Wikstrom, Anna-Karin Uppsala Univ Dept Womens & Childrens Hlth Akad Sjukhuset SE-75185 Uppsala Sweden Uppsala Clin Res Ctr Uppsala Sweden Univ Gothenburg Inst Clin Sci Sahlgrenska Acad Dept Obstet & Gynecol Gothenburg Sweden Stellenbosch Univ Dept Obstet & Gynecol Cape Town South Africa

Prediction of women at high risk of preeclampsia is important for prevention and increased surveillance of the disease. Current prediction models need improvement, particularly with regard to late-onset preeclampsia. Preeclampsia shares pathophysiological entities with cardiovascular disease;thus, cardiovascular biomarkers may contribute to improving prediction models. In this nested case-control study, we explored the predictive importance of mid-pregnancy cardiovascular biomarkers for subsequent preeclampsia. We included healthy women with singleton pregnancies who had donated blood in mid-pregnancy (similar to 18 weeks' gestation). Cases were women with subsequent preeclampsia (n = 296, 10% of whom had early-onset preeclampsia [< 34 weeks]). Controls were women who had healthy pregnancies (n = 333). We collected data on maternal, pregnancy, and infant characteristics from medical records. We used the Olink cardiovascular II panel immunoassay to measure 92 biomarkers in the mid-pregnancy plasma samples. The boruta algorithm was used to determine the predictive importance of the investigated biomarkers and first-trimester pregnancy characteristics for the development of preeclampsia. The following biomarkers had confirmed associations with early-onset preeclampsia (in descending order of importance): placental growth factor (PlGF), matrix metalloproteinase (MMP-12), lectin-like oxidized LDL receptor 1, carcinoembryonic antigen-related cell adhesion molecule 8, serine protease 27, pro-interleukin-16, and poly (ADP-ribose) polymerase 1. The biomarkers that were associated with late-onset preeclampsia were BNP, MMP-12, alpha-L-iduronidase (IDUA), PlGF, low-affinity immunoglobulin gamma Fc region receptor II-b, and T cell surface glycoprotein. Our results suggest that MMP-12 is a promising novel preeclampsia biomarker. Moreover, BNP and IDUA may be of value in enhancing prediction of late-onset preeclampsia.

关键词： Preeclampsia Matrix metalloproteinase-12 Natriuretic peptide B Alpha-L-iduronidase boruta algorithm Predictive biomarkers

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Machine learning-based predictive model for post-stroke dementia

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 334页

作者： Wei, Zemin Li, Mengqi Zhang, Chenghui Miao, Jinli Wang, Wenmin Fan, Hong Shaoxing Peoples Hosp Dept Geriatr Shaoxing Zhejiang Peoples R China Shaoxing Univ Sch Med Shaoxing Zhejiang Peoples R China Tsinghua Univ Yangtze River Delta Biol Med Res & Dev Ctr Zhejian Yangtze Delta Reg Inst Hangzhou 314006 Zhejiang Peoples R China

BackgroundPost-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting ***9 acceptable features were screened out by the Spearman correlation analysis and boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer *** total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral *** findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.

关键词： Stroke Post-stroke dementia boruta algorithm Machine learning Prediction model

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Determinants of stunting among under-five children: Evidence from Cambodian Demographic and Health Survey 2021-2022

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CHILD CARE HEALTH AND DEVELOPMENT 2024年第4期50卷 e13291页

作者： Sumon, Imran Hossain Akter, Suchana Sujon, Md. Sazzad Hossan Alam, Muhammad Khairul Yasmin, Sabina Yeasmin, Sabina Kabir, Mohammad Alamgir Hossain, Md. Moyazzem Jahangirnagar Univ Dept Stat & Data Sci Savar 1342 Dhaka Bangladesh

BackgroundEpidemiological and nutritional modifications are causing an increase in stunting in many low- and middle-income countries (LMIC), which will eventually result in juvenile diseases and mortality. Therefore, this study aimed to identify the influential factors contributing to stunting among under-five children in *** secondary dataset consisting of 3268 under-five children was extracted from the latest Cambodian Demographic and Health Survey (CDHS)-2021/2022 dataset. The Chi-square test and boruta algorithm were used for covariate selection, and logistic regression approaches were used to determine the influence of demographic, socioeconomic and other factors on the presence of *** revealed that about 21% of under-five children were stunted, and the prevalence of stunting was higher in rural areas than in urban areas. The prevalence of child stunting was lower in families with highly educated parents. A child whose father had a secondary education had 0.71 times lower (adjusted odds ratio [AOR]: 0.71, 95% CI: 0.520-0.969) chance of stunting than a child whose father had no education. Findings revealed that Ratnak Kiri, Mondul Kiri, Stung Treng, Pursat and Kampot had a greater prevalence of stunting than other places, ranging from 27.11% to 35.70%, whereas Banteay Meanchey, Phnom Penh and Kandal had the lowest rates, ranging from 12.80% to 16.00%. Results of the boruta algorithm and logistic regression suggested that under-five stunting is significantly influenced by factors such as the child's age, size at birth, mother's age at first birth, mother's body mass index (BMI), father's educational status, cooking fuel, and wealth *** is necessary to take initiatives for reducing the prevalence of stunted children prioritising the identified factors that ultimately help to reduce the burden of child health. The authors believed that the findings of this study will be helpful for policymakers in designing the app

关键词： boruta algorithm logistic regression stunting under-five children

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Disparities in residential PM2.5 and disease burden across urban and peri-urban: A 2018-2019 multicenter on-site survey in China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 383卷 125396页

作者： Fan, Lin Zhang, Wenying Zhang, Xiaotong Du, Hang Zhang, Weiyi Li, Li Han, Xu Wang, Chao Wang, Wenhao Wang, Xianliang Chinese Ctr Dis Control & Prevent Natl Inst Environm Hlth China CDC Key Lab Environm & Populat Hlth Beijing 100021 Peoples R China Nanjing Med Univ Sch Publ Hlth Nanjing 211166 Peoples R China Capital Normal Univ High Sch Beijing 100048 Peoples R China

Residential fine particulate matter (PM2.5) poses a substantial health hazard, yet disparities across urban and peri-urban areas remain unclear in China due to the absence of systematic monitoring data. Key knowledge gaps persist regarding the characteristics, influencing factors, and health burdens of residential PM2.5 pollution, particularly in peri-urban regions. Between 2018 and 2019, 746 urban and 339 peri-urban households in 15 typical Chinese cities were randomly selected through the Chinese Indoor Environment and Health Surveillance Project (CIEHS). The relationships between influencing factors and residential PM2.5 were assessed using a general linear model (GLM), and factors importance was identified using the boruta algorithm with 10-fold crossvalidation and 100 replications. Disability-adjusted life-years (DALYs) attributable to the residential PM2.5 were estimated using the population-attributable fraction method. The mean residential PM2.5 concentration was 62.2 mu g/m3, with higher levels in peri-urban (Odds ratio [OR] = 1.0705 95 % Confidence interval [95 % CI]: 1.0615, 1.0795) and during cold seasons (OR = 1.3499 95 %CI: 1.3388, 1.3610). Environmental factors, building conditions, family-related information, and lifestyle behaviors were all associated with residential PM2.5, notably, with the five most influential factors remaining consistent across urban and peri-urban areas. The mean DALY rate was 2160 per 100,000 in urban areas, slightly higher at 2186 per 100,000 in peri-urban areas. According to China's seventh national census, residential PM2.5 exposure accounted for 19.48 million urban and 11.14 million peri-urban DALYs nationally. These findings underscore the urgency for differentiated environmental management and targeted mitigation policies to address the disproportionate health burden in peri-urban areas.

关键词： Residential PM 2.5 Indoor air quality (IAQ) Urban and peri-urban disparities General linear model (GLM) boruta algorithm Disability-adjusted life years (DALYs)

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Prediction and feature selection of low birth weight using machine learning algorithms

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JOURNAL OF HEALTH POPULATION AND NUTRITION 2024年第1期43卷 1-13页

作者： Reza, Tasneem Binte Salma, Nahid Jahangirnagar Univ Dept Stat & Data Sci Dhaka 1342 Bangladesh

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual's health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017-2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML *** pick out the key features, the boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Na & iuml;ve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model's performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC *** shows, boruta algorithm identifies eleven significant features including respondent's age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating boruta algorithm's significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model's respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy =

关键词： Low birth weight boruta algorithm Wrapper method Key features Machine learning algorithms

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Feature Selection for Alzheimer's Gene Expression Data Using Modified Binary Particle Swarm Optimization

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IETE JOURNAL OF RESEARCH 2023年第1期69卷 9-20页

作者： Ramaswamy, Ramya Kandhasamy, Premalatha Palaniswamy, Swathypriyadharsini Bannari Amman Inst Technol Dept Comp Sci & Engn Sathyamangalam India

Alzheimer's Disease (AD) is a neurological disorder that destroys memory and other significant mental functions. One of the most accurate methods to identify the disease-causing genes is to monitor gene expression values in various samples. Selecting significant genes for classification is important in gene expression studies. In this study, the experimental data are taken from the gene expression data of human brain in persons with AD and older control subjects GEO GSE5281 data set. In this work, a new two-step gene selection is applied to filter the noisy and redundant genes, based on the statistical method and heuristic optimization approach. T-statistic (T-test), Signal to Noise Ratio (SNR) and F-test, are used in the first step of the gene selection process. The top ten significant genes selected from the statistical methods are applied to Particle Swarm Optimization (PSO) to obtain the optimal number of features of Alzheimer's disease. To avoid the stagnation issue in PSO, a modified PSO approach is proposed which finds a new particle position by utilizing the Genetic algorithm (GA) crossover and mutation operators. The classifiers, Decision tree, Support Vector Machine (SVM), Linear Model, Random Forest and Neural network, are employed in training and testing data to analyse the performance of GA & PSOs. Modified PSO with t-Test in Random forest and Linear model provides 100% accuracy for the test dataset of GSE5281 with optimum number of genes. The significant genes identified through this research are EGR1, CKMT1B, RPL15, PSMB3, GRK4, COX6A1 and PHIP from the GSE5281 dataset.

关键词： Alzheimer's disease boruta algorithm feature selection gene selection genetic algorithm modi?ed particle swarm optimization random forest linear model

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boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Yuvalatha, S. Nithyapriya, S. Kuzhali, R. Tamizh Savitha, S. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science C K college of Engineering and Technology Chennai India Department of Computer Science nad Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798350305708

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease. © 2023 IEEE.

关键词： Adaboosting boruta algorithm Cardio vascular Hyper parameter

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