Screening of key genes in childhood asthma based on bioinformatics analysis.

Background: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on transcriptome sequencing results and explore potential diagnostic markers. Methods: The transcriptome sequencing results (GSE188424) of pediatric asthmatic plasma samples were downloaded from the Gene Expression Omnibus database, including 43 controlled pediatric asthma serum samples and 46 uncontrolled pediatric asthma samples. R software (AT&T Bell Laboratories) was used to construct the weighted gene co-expression network and screen the hub genes. The penalty model was established by least absolute shrinkage and selection operator (LASSO) regression analysis to further screen the genes in the hub genes. The receiver operating characteristic curve (ROC) was used to confirm the diagnostic value of key genes. Results: A total of 171 differentially expressed genes were screened from the controlled and uncontrolled samples. Chemokine (C-X-C motif) ligand 12 (CXCL12), matrix metallopeptidase 9 (MMP9), and wingless-type MMTV integration site family member 2 (WNT2) were the key genes, which were upregulated in the uncontrolled samples. The areas under the ROC curve of CXCL12, MMP9, and WNT2 were 0.895, 0.936, and 0.928, respectively. Conclusions: The key genes CXCL12, MMP9, and WNT2 in pediatric asthma were identified by a bioinformatics analysis and machine-learning algorithm, which may be potential diagnostic biomarkers.

Investigation of Characteristic Motion Patterns of the Knee Joint During a Weightbearing Flexion.

This study aimed to develop and validate a novel flexion axis concept by calculating the points on femoral condyles that could maintain constant heights during knee flexion. Twenty-two knees of 22 healthy subjects were investigated when performing a weightbearing single leg lunge. The knee positions were captured using a validated dual fluoroscopic image system. The points on sagittal planes of the femoral condyles that had minimal changes in heights from the tibial plane along the flexion path were calculated. It was found that the points do formulate a medial-lateral flexion axis that was defined as the iso-height axis (IHA). The six degrees of freedom (6DOF) kinematics data calculated using the IHA were compared with those calculated using the conventional transepicondylar axis and geometrical center axis. The IHA measured minimal changes in proximal-distal translations and varus-valgus rotations along the flexion path, indicating that the IHA may have interesting clinical implications. Therefore, identifying the IHA could provide an alternative physiological reference for improvement of contemporary knee surgeries, such as ligament reconstruction and knee replacement surgeries that are aimed to reproduce normal knee kinematics and medial/lateral soft tissue tensions during knee flexion.

Screening of key pathogenic genes of type 1 diabetes in children.

Background: The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. Methods: The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. Results: A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Conclusions: Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.