PurposeWe aim to develop myopia classification models based on machine learning algorithms for each schooling period, and further analyze the similarities and differences in the factors influencing myopia in each school period based on each model.DesignRetrospective cross-sectional study.ParticipantsWe collected visual acuity, behavioral, environmental, and genetic data from 7,472 students in 21 primary and secondary schools (grades 1–12) in Jiamusi, Heilongjiang Province, using visual acuity screening and questionnaires.MethodsMachine learning algorithms were used to construct myopia classification models for students at the whole schooling period, primary school, junior high school, and senior high school period, and to rank the importance of features in each model.ResultsThe main influencing factors for students differ by school section, The optimal machine learning model for the whole schooling period was Random Forest (AUC = 0.752), with the top three influencing factors being age, myopic grade of the mother, and Whether myopia requires glasses. The optimal model for the primary school period was a Random Forest (AUC = 0.710), with the top three influences being the myopic grade of the mother, age, and extracurricular tutorials weekly. The Junior high school period was an Support Vector Machine (SVM; AUC = 0.672), and the top three influencing factors were gender, extracurricular tutorial subjects weekly, and whether can you do the “three ones” when reading and writing. The senior high school period was an XGboost (AUC = 0.722), and the top three influencing factors were the need for spectacles for myopia, average daily time spent outdoors, and the myopic grade of the mother.ConclusionFactors such as genetics and eye use behavior all play an essential role in students’ myopia, but there are differences between school periods, with those in the lower levels focusing on genetics and those in the higher levels focusing on behavior, but both play an essential role in myopia.
Background Cerebral malaria (CM) is a manifestation of malaria caused by plasmodium infection. It has a high mortality rate and severe neurological sequelae, existing a significant research gap and requiring further study at the molecular level. Methods We downloaded the GSE117613 dataset from the Gene Expression Omnibus (GEO) database to determine the differentially expressed genes (DEGs) between the CM group and the control group. Weighted gene coexpression network analysis (WGCNA) was applied to select the module and hub genes most relevant to CM. The common genes of the key module and DEGs were selected to perform further analysis. The least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) were applied to screen and verify the diagnostic markers of CM. Eventually, the hub genes were validated in the external dataset. Gene set enrichment analysis (GSEA) was applied to investigate the possible roles of the hub genes. Results The GO and KEGG results showed that DEGs were enriched in some neutrophil-mediated pathways and associated with some lumen structures. Combining LASSO and the SVM-RFE algorithms, LEF1 and IRAK3 were identified as potential hub genes in CM. Through the GSEA enrichment results, we found that LEF1 and IRAK3 participated in maintaining the integrity of the blood–brain barrier (BBB), which contributed to improving the prognosis of CM. Conclusions This study may help illustrate the pathophysiology of CM at the molecular level. LEF1 and IRAK3 can be used as diagnostic biomarkers, providing new insight into the diagnosis and prognosis prediction in pediatric CM.
BACKGROUND Over the past few years, research into the pathogenesis of colon cancer has progressed rapidly, and cuproptosis is an emerging mode of cellular apoptosis. Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease. AIM To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients. The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers. METHOD Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation. The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis- and immune-related combination model, and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients. A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer. RESULTS Once prognostic characteristics were obtained, the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer: The first was a risk factor, whereas the second was a protective factor. The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant. Within the component expressions, the expressions of HSPA1A, CDKN2A, and UCN3 differed markedly. Transcription analysis primarily reflects the differential activation of related immune cells and pathways. Furthermore, genes linked to immune checkpoint inhibitors were expressed differently between the subgroups, which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy. CONCLUSION The prognosis of the high-risk group evaluated in the combined model was poorer, and cuproptosis was highly correlated with the prognosis of colon cancer. It is possible that we may be able to improve patients’ prognosis by regulating the gene expression to intervene the risk score.
Objectives This investigation aimed to examine the correlation between coffee and caffeine intake with the risk of COPD and lung function based on NHANES 2007–2012.Materials and Methods Exposure variables were established as coffee and caffeine consumption, while the risk of COPD and lung function were considered as the outcome variables. Other covariates were deemed potential confounders. A cross-sectional study was conducted using data from the NHANES to determine a definitive correlation between exposure variables and outcome variables.Results Multivariable regression models revealed positive associations between coffee and caffeine consumption and the risk of COPD and lung function. Subgroup analyses, stratified by sex, DM, hypertension status, and smoking habits, identified potential effect modifiers, as well as infection points from threshold effect examinations.Conclusions The results of this cross-sectional study indicated significant positive associations between coffee and caffeine consumption and the risk of COPD. Additionally, positive associations between exposure variables and FEV1 and FVC were discovered. Among the stratification factors, smoking status exhibited the most potential for modifying effects.
Background Bacterial vaginosis (BV) is one of the most common infections among women of reproductive age and accounts for 15–50% of infections globally. The role played by folate in the pathogenesis and progression of BV is poorly understood. The aim of this study was to investigate the association between serum folate, red blood cell (RBC) folate, and BV in American women. Methods 1,954 participants from the 2001-2004 National Health and Nutrition Examination Survey (NHANES) program were included in this study. Multiple logistic regression was used to analyze the association between serum folate, RBC folate, and BV, and covariates including race, age, education level, and body mass index were used to construct adjusted models. Stratified analysis was used to explore the stability of the above associations in different populations. Results In the present cross-sectional study, we found that serum folate and RBC folate were inversely associated with the risk of BV. In the fully adjusted model, the risk of BV was reduced by 35% (OR=0.65, 95% CI: 0.51~0.83, p=0.0007) in the highest serum folate group and 32% (OR=0.68, 95% CI: 0.53~0.87, p=0.0023) in the highest RBC folate group compared to the lowest group. Conclusions The results of this study indicated that serum folate and RBC folate were inversely associated with the risk of BV folate supplementation may play an important role in the prevention and management of BV.
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