Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package "DESeq2" and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

A method for Kashin-Beck disease auxiliary diagnosis based on the features in regions of the potential lesion

BACKGROUND

Kashin-Beck disease (KBD) is a severe arthropathy that causes deformity. Patients with advanced stages of KBD often show symptoms, such as short stature. Early-stage diagnosis and treatment can effectively prevent the disease from worsening. Diagnosis of early-stage patients is usually made by X-ray examination. However, the time-consuming image recognition and the lack of professional doctors may delay the patient's condition. Therefore, a method that can efficiently complete the auxiliary diagnosis is necessary.

PURPOSE

This study presents a KBD auxiliary diagnosis method based on radiographs, which uses deep learning to locate potential lesion regions and extract features for accurate diagnosis.

METHODS

This work presents a method that relies on hand radiographs to locate eight regions of the potential lesion (RoPL) and finally make the KBD auxiliary diagnosis. The localization of RoPL is achieved through a two-step model, with the first step predicting a rough location and a deep convolutional neural network (DCNN) with attention mechanism used to generate precise center coordinates based on the previous step's results. Based on the localization result, regional features are extracted, which provides information about the joints and textures of RoPL from a finer granularity. Another DCNN is utilized to obtain general features from hand radiographs, which provide morphological and structural information about the entire hand bone These features offer a concatenated feature for categorization to raise accuracy. A doctor-like approach is adopted to diagnose based on regional features to enhance performance, and a final decision is made using a vote that considers diagnostic outcomes from all aspects. The dataset used in our study was collected by our research team in KBD-endemic areas of Tibet since 2017, containing 373 diseased and 764 normal images.

RESULT

Our model guarantees that over 95% of the predicted coordinates are within five pixels of the real coordinates according to Euclidean distance. The accuracy of the diagnostic network achieved 91.3%, with precision and recall achieving 83% and 87%, respectively. Compared to the approach without exact localization of the illness region on the same test set, our method achieved a roughly 6% increase in accuracy and nearly 30% increase in recall rate.

CONCLUSIONS

Based on hand radiographs, this study suggests a novel method for KBD diagnosis. The high-precision localization network guarantees precise extraction of lesion-prone regional features, and the multi-scale features and innovative classification method further enhance model performance compared to related approaches.

Determination of individual factors associated with hallux valgus using SVM-RFE

INTRODUCTION

This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE).

METHODS

A total of 864 participants aged ≥ 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet ≥ 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE.

RESULTS

The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people.

CONCLUSION

Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE.

MiR-214-3p may alleviate T-2 toxin-induced chondrocyte apoptosis and matrix degradation by regulating NF-κB signaling pathway in vitro

T-2 toxin is part of the most toxic fungal secondary metabolites contaminating different kinds of grains. Previous studies have demonstrated that T-2 toxin can influence the survival of chondrocytes and extracellular matrix (ECM) composition. MiR-214-3p is essential for the homeostasis of chondrocytes and ECM. However, the molecular machinery underlying T-2 toxin-induced chondrocyte apoptosis and ECM degradation remain to be elucidated. The present study aimed to investigate the mechanism of miR-214-3p's involvement in T-2 toxin-induced chondrocyte apoptosis and ECM degradation. Meanwhile, the role of the NF-κB signaling pathway was scrutinized. C28/I2 chondrocytes were treated with 8 ng/ml of T-2 toxin for 24 h, after the pretreatment of miR-214-3p interfering RNAs for 6 h. Gene and protein levels involved in chondrocyte apoptosis and ECM degradation were assessed through RT-PCR and Western blotting. The apoptosis rate of chondrocyte was measured by flow cytometry. Results and data indicated that miR-214-3p was decreased in a dose-dependent manner at different concentrations of T-2 toxin. The enhancement of miR-214-3p could alleviate chondrocyte apoptosis and ECM degradation due to T-2 toxin exposure. The upregulation of miR-214-3p was associated with the decreased expression of apoptosis-promoting genes such as Bax and Cleaved-caspase3/caspase3 as well as the increased expression of anti-apoptotic genes such as Bcl2 and Survivin. Furthermore, miR-214-3p stimulated the relative protein expression of collagen Ⅱ but inhibited the expression of MMP13. Overexpressing miR-214-3p could suppress the relative protein expression of IKKβ and phospho-p65/p65, thus blocking the activation of the NF-κB signaling pathway. The study suggested that the miR-214-3p attenuates T-2 toxin-induced chondrocyte apoptosis and ECM degradation through a potential NF-κB signaling pathway.

The Prevalence of Kashin-Beck Disease in China: a Systematic Review and Meta-analysis

Kashin-Beck disease (KBD) is a serious degenerative chronic joint disease. However, there are few quantitative syntheses of KBD prevalence studies. In this study, an initial systematic review and meta-analysis were performed to study the prevalence of KBD in China. Five databases (PubMed, Web of Science, Chinese National Knowledge Infrastructure (CNKI), WanFang Data, and the China Science-Technology Journal Database (VIP)) were searched by performing an overall search method to identify studies of KBD prevalence in China that were published from the inception of the database to May 30, 2022. The risk of bias was assessed with the standardized risk of bias tool. Heterogeneity was assessed with the I² statistic. A random-effect meta-analysis was performed to study the prevalence of KBD through an analysis of published studies. A total of 34 studies involving 24,820 patients with KBD were included in this meta-analysis. These studies were geographically divided into 3 endemic areas. The pooled overall prevalence rate for KBD was 0.06% (95% CI, 0.04-0.08%). The pooled prevalence estimates were 0.05% (95% CI, 0.01-0.12%) for northeast endemic areas, 0.06% (95% CI, 0.03-0.09%) for northwest endemic areas, and 0.04% (95% CI, 0-0.14%) for southwest endemic areas. There was a negative correlation between KBD prevalence and the publication year. No potential risk of publication bias was found by Begg's test and Egger's test. The publication year and quality score were significantly associated with the detected heterogeneity. Our study indicates that the occurrence and development of KBD have been effectively controlled in recent decades. More effective strategies are needed to prevent and treat KBD.

Identification and validation of differentially expressed chromatin regulators for diagnosis of aortic dissection using integrated bioinformatics analysis and machine-learning algorithms

Background: Aortic dissection (AD) is a life-threatening disease. Chromatin regulators (CRs) are indispensable epigenetic regulators. We aimed to identify differentially expressed chromatin regulators (DECRs) for AD diagnosis.

Methods: We downloaded the GSE52093 and GSE190635 datasets from the Gene Expression Omnibus database. Following the merging and processing of datasets, bioinformatics analysis was applied to select candidate DECRs for AD diagnosis: CRs exertion; DECR identification using the “Limma” package; analyses of enrichment of function and signaling pathways; construction of protein–protein interaction (PPI) networks; application of machine-learning algorithms; evaluation of receiver operating characteristic (ROC) curves. GSE98770 served as the validation dataset to filter DECRs. Moreover, we collected peripheral-blood samples to further validate expression of DECRs by real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Finally, a nomogram was built for clinical use.

Results: A total of 841 CRs were extracted from the merged dataset. Analyses of functional enrichment of 23 DECRs identified using Limma showed that DECRs were enriched mainly in epigenetic-regulation processes. From the PPI network, 17 DECRs were selected as node DECRs. After machine-learning calculations, eight DECRs were chosen from the intersection of 13 DECRs identified using support vector machine recursive feature elimination (SVM-RFE) and the top-10 DECRs selected using random forest. DECR expression between the control group and AD group were considerably different. Moreover, the area under the ROC curve (AUC) of each DECR was >0.75, and four DECRs (tumor protein 53 (TP53), chromobox protein homolog 7 (CBX7), Janus kinase 2 (JAK2) and cyclin-dependent kinase 5 (CDK5)) were selected as candidate biomarkers after validation using the external dataset and clinical samples. Furthermore, a nomogram with robust diagnostic value was established (AUC = 0.960).

Conclusion: TP53, CBX7, JAK2, and CDK5 might serve as diagnostic DECRs for AD diagnosis. These DECRs were enriched predominantly in regulating epigenetic processes.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Objective

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.

Methods

Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.

Results

129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.

Conclusions

In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.