Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.
Objective To explore the knowledge distribution, knowledge clustering, and the trend in development of wound therapy, by revealing the same keywords with multiple statistical method and social network analysis. Methods We searched the CNKI under the term " wound” , " therapy” , and " wound therapy” in February 2016. After the core keywords had been identified by Bicomb and Endnote X6 software in each stage, the co-occurrence matrix was built. Transformation, dimensionality reduction and clustering of the co-occurrence matrix were finished by SPSS 22.0 software, leading the strategic plot to be built. The visualized network images were drawn using Ucinet 6.0 software. Results The visualized domain knowledge-mapping was successfully built, and it directly reflected the structure of knowledge-mapping of the discipline, as well as key clusters. Boost development had been identified in this research. The subject developed own core research areas and clusters, but there was still lack of fitting characteristics. The newly wound therapeutic techniques had limited correlation with other clusters, while provided limited contributions to forward this subject. However, enriched core keywords had been demonstrated, and formed clear domain parts of this subject. Conclusions The analysis demonstrates that wound therapy has developed well, and hot research points follow the direction of medication treatment. The network of wound therapeutic subject has become mature and completed within a short period. Comprehensive therapy and long term follow-up results according to evidence-based nursing have become the domain field. Moreover, the newly therapeutic techniques should be paid more attention to shift the development of this subject. And the interactive research within this subject and among other regions should be enhanced.
ObjectiveTo explore the key modules and Hub genes related to the development of breast cancer from the level of gene network, and to verify whether these Hub genes have breast cancer specificity.MethodsThe key modules for the development of breast cancer were screened by weighted gene co-expression network analysis (WGCNA). The gene annotation database Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrich the function of the modules, exploring the Hub genes having the highest correlation between the development of breast cancer. Simultaneously, we analyzed the relationship between Hub genes and tumor collection unit.ResultsWGCNA defined 10 co-expression modules, of which the blue module was the key module related to the development of breast cancer and other malignant tumors. The genes included in this module were significantly enriched in pathways such as the Cell Cycle pathway (KEGG ID: cfa04110), the Viral Oncogenic pathway (KEGG ID: hsa05203), Cancer pathway (KEGG ID: hsa05200), and Systemic Lupus Erythematosus (KEGG ID: hsa05322). The top eight Hub genes were finally extracted from the blue module including NUSAP1, FOXM1, KIF20A, BIRC5, TOP2A, RRM2, CEP55, and ASPM. Among them, NUSAP1, KIF20A, TOP2A, CEP55 and ASPM were also closely related to the occurrence and development of tumor collection unit.ConclusionWGCNA can screen for key modules and Hub genes which are biologically relevant to the clinical features of our interest, and the Hub genes have no breast cancer specificity participating in breast cancer development .
ObjectiveTo explore the expression of genes related to hepatocellular carcinoma (HCC) stem cells and their prognostic correlation by using weighted gene co-expression network analysis (WGCNA).MethodsFirstly, the transcriptome sequencing (RNA-seq) and clinical data of HCC were downloaded from the public database the Cancer Genome Atlas (TCGA), and the mRNA expression-based stiffness index (mRNAsi) table of cancer stem cells was downloaded and sorted out to analyze the relationship between mRNAsi and pathological grade and prognosis of HCC. The mRNAsi of HCC was downloaded and the prognostic value of mRNAsi was discussed. Then we used WGCNA to screen the key modules related to liver cancer stem cells (LSCS). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. The online database STRING was used to construct hub genes coding proteins interaction (PPI) network and screen key genes. Finally, the key genes were analyzed for expression differences and expression correlations. The online database Kaplan-Meier plotter was used for survival analysis and verified.ResultsmRNAsi was significantly upregulated in cancer tissues (P<0.001), and increased with the increase of pathological grade of HCC (P=0.001). The mortality rate of the higher mRNAsi group was higher than that of the lower mRNAsi group (P=0.006). GO analysis found that hub genes were mainly involved in biological processes, such as mitosis and DNA replication, and KEGG showed that hub genes were enriched in cell cycle, DNA mismatch repair, oocyte meiosis, and other signaling pathways. We screened 10 key genes (included CCNB1, CDC20, CDCA8, NDC80, KIF20A, TTK, CDC45, KIF15, MCM2, and NCAPG) related to mRNAsi of HCC based on WGCNA. The key genes were highly expressed in the tumor samples compared to the normal samples. In addition, there was a strong interaction between proteins of these key genes (P<0.05), a strong co-expression relationship at the transcriptional level, and all related to prognosis of HCC.ConclusionsmRNAsi plays an important role in the occurrence and development of HCC. Ten key genes related to LSCS were screened, which may act as therapeutic targets for inhibiting the stem cell characteristics of HCC.
ObjectiveTo find the hub genes related to bone metastasis of breast cancer by weighted geneco-expression network analysis (WGCNA) method, and provide theoretical support for the development of new targeted therapeutic drugs.MethodsThe basic clinical features of 286 breast cancer patients and the gene expression information of tumor specimens were downloaded from the GSE2034 dataset from the Gene Expression Omnibus. R software was used to analyze the gene microarray. The WGCNA package embedded in the R software was used for various analysis in weighted correlation network analysis. Cox proportional hazard regression was performed by using SPSS software.ResultsThe top one quarter genes with the greatest variance variability were selected by WGCNA, and a total of 5 000 genes were used for further enrichment analysis. Finally, 15 gene co-expression modules were constructed, and the magenta module (r=0.94, P<0.001) was significantly positively correlated with bone metastasis of breast cancer. It was further found that six hub genes highly associated with bone metastasis in the magenta module were: Ral GTPase-activating protein subunitalpha-1 (RALGAPA1), B-cell antigen receptor complex-associated protein alpha chain (CD79A), immunoglobulin kappa chain C region (IGKC), arrestin beta 2 (ARRB2), differentially expressed in FDCP 6 homolog (DEF6), and immunoglobulin lambda variable 2 (IGLV2).ConclusionWe found that RALGAPA1, CD79A, IGKC, ARRB2. DEF6, and IGLV2 may play an important role in bone metastasis of breast cancer.
Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant (P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.
Objective To understand the research status of social network analysis methods in the medicine and health field, help medical scientific research managers quickly understand the publication situation and research hotspots of the methods, and provide references for them to use social network analysis methods to enter deeper research. Methods PubMed, Web of Science, Springer Link, ScienceDirect, China National Knowledge Infrastructure, Wanfang and VIP databases were searched for related literature on social network analysis methods in the medical and health field from the establishment of databases to April 2022. Bibliometric analysis was used to analyze the included articles. Results A total of 432 articles were included, with 424 in Chinese and 8 in English. The included articles were published between 1993 and 2020, involving 154 journals and 913 key words. The number of documents increased rapidly at first, and then entered a stable stage. The hot research directions were the spread and prevention of diseases and the power of social support networks. Conclusions Although the number of applications of social network analysis methods in the medical and health field has increased year by year and the application flexibility has increased, the application depth is still lacking. Scientific researchers should dig deep into the research direction, combine theory with practice, and focus on innovation.
ObjectiveTo provide a scoping review of the healthcare provider patient-sharing network. MethodsPubMed, EMbase, Scopus, ProQuest, Web of Science Core Collection, ScienceDirect, SAGE, Wiley Online Library, Google Scholar, CNKI and WanFang Data databases were electronically searched to collect studies on patient-sharing network of healthcare providers from inception to July 31, 2021. Two reviewers independently screened literature, extracted data and then Arksey and O 'Malley's scoping review method was used to analyze the study. ResultsA total of 110 studies were included. In which, 70.0% were published in 2016 and later, 78.2% were carried out in the United States, 96.4% used secondary data, and 45.5% adopted social network analysis methods such as exponential random graph model. In terms of network characteristics, 43.6% of the studies adopted the theoretical framework of social network theory, and the network node type was mainly 1-mode, accounting for 87.3%. When constructing the physician patient-sharing networks, 64.5% of the studies had a threshold of 1 patient. We also synthesized existing studies on patient-sharing networks of healthcare providers in the light of factors of networks and related outcomes. ConclusionThe studies of healthcare provider patient-sharing network have potentials to improve clinical practice and health policies. Further studies should consider adopting longitudinal design to validate evidence of study, expanding the scope of study subjects except physicians and enriching the evidence of the relationship between network and health-related outcomes.
Objective To explore the potential mechanism of the occurrence and development of lupus nephritis (LN) and identify key biomarkers and immune-related pathways associated with the progression of LN. Methods We downloaded a dataset from the Gene Expression Omnibus database. By analyzing the differential expression of genes and performing weighted gene co-expression network analysis (WGCNA), as well as Gene Ontology enrichment, Disease Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, we explored the biological functions of differentially expressed genes in LN. Using three machine learning models, namely LASSO regression, support vector machine, and random forest, we identified the hub genes in LN, and constructed a line diagram diagnosis model based on the hub genes. The diagnostic accuracies of the hub genes were evaluated using the receiver operating characteristic curve, and the relationship between known marker gene sets and hub gene expression was analyzed using single sample gene set enrichment analysis. Results We identified a total of 2297 differentially expressed genes. WGCNA generated 7 co-expression modules, among which the cyan module had the highest correlation with LN. We obtained 347 target genes by combining differential genes. Using the three machine learning methods, LASSO regression, support vector machine, and random forest, we identified three hub genes (CLC, ADGRE4P, and CISD2) that could serve as potential biomarkers for LN. The area under the receiver operating characteristic curve (AUC) analysis showed that these three hub genes had significant diagnostic value (AUCCLC=0.718, AUCADGRE4P=0.813, AUCCISD2=0.718). According to single sample gene set enrichment analysis, the hub genes were mainly associated with apoptosis, glycolysis, metabolism, hypoxia, and tumor necrosis factor-α-nuclear factor-κB-related pathways. Conclusions By combining WGCNA and machine learning techniques, three hub genes (CLC, ADGRE4P, and CISD2) that may be involved in the occurrence and development of LN are identified. These genes have the potential to aid in the early clinical diagnosis of LN and provide insight into the mechanisms underlying LN progression.
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.