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BACKGROUND: Fibromyalgia syndrome, as a common rheumatic disease, is related to central sensitization and immune abnormalities. However, the specific mechanism has not been elucidated, and there is a lack of specific diagnostic markers. Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE: To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods, such as weighted gene co-expression network analysis (WGCNA), and machine learning. METHODS: Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus (GEO) database. The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine -recursive feature elimination (SVM-RFE) machine learning algorithm were further used to identify hub biomarkers, and draw receiver operating characteristic curve (ROC) to evaluate the accuracy of diagnosing fibromyalgia syndrome. Finally, single sample gene set enrichment analysis (ssGSEA) and gene set enrichment analysis (GSEA) were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION: Eight down-regulated differentially expressed genes (DEGs) were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)| > 0 and P < 0.05. After WGCNA analysis, 497 genes were included in the module (MEdarkviolet) with the highest positive correlation (r=0.22, P=0.04), and 19 genes were included in the module (MEsalmon2) with the highest negative correlation (r=-0.41, P=6×10-5). After intersecting DEGs and the module genes of WGCNA, seven genes were obtained. Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm. After the intersection of the two, three core genes were identified, which were germinal center associated signaling and motility like, integrin beta-8, and carboxypeptidase A3. The areas under the ROC curve of the three core genes were 0.744, 0.739, and 0.734, respectively, indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome. The results of immune infiltration analysis showed that memory B cells, CD56 bright NK cells, and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group (P < 0.05), and were significantly positively correlated with the above three biomarkers (P < 0.05). The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways, mainly related to olfactory transduction pathway, neuroactive ligand- receptor interaction, and infection pathway. The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes, abnormal immune regulation, and multiple pathways imbalance. However, the interactions between these genes and immune cells, as well as their relationships with various pathways need to be further investigated. [ABSTRACT FROM AUTHOR]

Background In recent years, numerous studies have indicated that hyperuricemia (HUA) is a contributing factor to certain diseases. However, whether HUA is a contributing factor to chronic pulmonary heart disease (CPHD) still requires further investigation. Objective To explore the association between HUA and CPHD, aiming to provide a theoretical basis for the management of serum uric acid (SUA) levels in patients with CPHD. Methods A total of1 171 patients with chronic obstructive pulmonary disease (COPD) admitted to the First Affiliated Hospital of Xinjiang Medical University from 2019 to 2023 were included in the study. They were divided into a CPHD group (470 cases) and a COPD group (701 cases) based on whether they had CPHD. General information, laboratory test results, and echocardiographic findings of the patients were collected. LASSO regression was used to select variables, and propensity score matching (PSM) was employed to eliminate the influence of confounding factors. Multivariate Logistic regression analysis was conducted to explore the influencing factors of CPHD in COPD patients. Results The CPHD group had lower proportions of females, Han ethnicity, smokers, drinkers, idiopathic pulmonary fibrosis, chronic bronchitis, bronchial asthma, lymphocyte percentage, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, cardiac output, left ventricular ejection fraction compared to the COPD group. Higher proportions of heart function class 3-4, HUA, pulmonary embolism, congenital heart disease, red blood cell count, neutrophil percentage, SUA, blood urea nitrogen, D-dimer, N-terminal pro-B-type natriuretic peptide, right atrial diameter, right ventricular diameter, left atrial diameter, right ventricular outflow tract diameter, and pulmonary artery diameter were observed in the CPHD group, with statistically significant differences (P381.29 μmol/L) had a 1.421-fold inScrsed risk of having CPHD. Conclusion HUA is a contributing factor to the occurrence and development of CPHD. Actively controlling SUA levels may help prevent the occurrence and development of CPHD.

Objective To construct a diagnostic scoring model for early Parkinson's disease (PD) and to verify its efficacy. Methods Seventy-five patients with early PD and 75 gender-age matched healthy volunteers were selected and randomly divided into the verification group (38 patients with early PD and 37 healthy volunteers) and training group (37 patients with early PD and 38 healthy volunteers）. Medical records of subjects were collected. The least absolute shrinkage and selection operator (LASSO) was used to determine the optimal parameters through 10-fold cross-validation； the relevant diagnostic factors were screened from the relevant data of the training group, and the diagnostic scoring model was constructed according to each factor coefficient. The nomogram was constructed by Logistic regression； the receiver operating characteristic curve (ROC) was drawn, and the diagnostic efficiency and fit degree of the model were evaluated by the area under the curve (AUC) and the calibration curve. Results There was no significant difference in relevant data between the training group and the verification group (P>0. 05）. In the training group, the best parameter λ=0. 052 was determined by LASSO algorithm, and seven indexes of identification ability were screened out. The diagnosis scoring model formula =–1. 048+0. 961× RBDSQ score +0. 079× HAMA-14 score－0. 0002× NSE－0. 011×VEGF－0. 001× uric acid－0. 046× FA+0. 003× DFV. Multivariate Logistic regression analysis confirmed that the seven selected indicators could be used as independent diagnostic factors for early PD patients. In the verification group, the area under the curve of this diagnostic scoring model in diagnosing early PD patients was 0. 91, which was higher than that of seven factors alone； the fitting curve showed that the model had good goodness of fit. Conclusion Based on RBDSQ scoring, HAMA-14 scoring, VEGF, FA, NSE, uric acid and DFV, the diagnostic scoring model of early PD has been constructed, and the model has relatively high diagnostic efficiency. [ABSTRACT FROM AUTHOR]

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Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Objectives: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading., Methods: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T 1 -weighted imaging (T 1 WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy., Results: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve ( P =0.808), indicating high predictive accuracy of the model., Conclusions: The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.

The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L 1 -norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.