Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

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.

Analysis of 21 Cases of Risperidone-induced Leucocytopenia ADR Reports in Our Hospital / 中国药房

OBJECTIVE:To analyze the features of risperidone-induced leucocytopenia ADR.METHODS:Twenty-one ADR cases of risperidone-induced leucocytopenia reported by our hospital were collected and analyzed during 2004-2015.The characteris tics of risperidone-induced leucocytopenia were discussed.RESULTS:Among 21 patients,there were 10 male and 11 female.The age was from 15 to 72 years old.Nine cases of patients were 31-40 years old (42.9％).Most of the original disease was schizophre nia.Incubation period of leucocytopenia caused by risperidone was (28.6 ± 21.4) d.Patients had no discomfort complain when leucocytopenia occurred.The lowest white blood cells reported was(3.1 ± 0.5)× 109 L-1.The leucocytopenia were improved after reduc tion,drug withdrawal and symptomatic treatment.CONCLUSIONS:Patients usually have no body discomfort complain when risperidone-induced leucocytopenia appears.Doctors should moniter ADR regularly,identify it earlier and treat carefully.