BACKGROUND ACTA2 gene is a specific gene that encodes actin α2. Multisystem smooth muscle dysfunction syndrome (MSMDS) is a multisystem disease characterized by aortic and cerebrovascular lesions caused by ACTA2 gene mutations. There have been many reports of cardiac, pulmonary and cerebrovascular lesions caused by MSMDS; however, few studies have focused on seizures caused by MSMDS. CASE SUMMARY Our patient was a girl aged 7 years and 8 mo with recurrent cough, asthma and seizures for 7 years. She was diagnosed with severe pneumonia, congenital heart disease, cardiac insufficiency, and malnutrition in the local hospital. Cardiac ultrasonography revealed congenital heart disease, patent ductus arteriosus (with a diameter of 0.68 cm), left coronary arteriectasis, patent oval foramen (0.12 cm), tricuspid and pulmonary regurgitation, and pulmonary hypertension. Cerebral magnetic resonance imaging and magnetic resonance angiography indicated stiffness in the brain vessels, together with multiple aberrant signaling shadows in bilateral paraventricular regions. A heterozygous mutation ( c.536 G>A) was identified in the ACTA2 gene, resulting in generation of p.R179H. Finally, the girl was diagnosed with MSMDS combined with epilepsy. The patient had 4 episodes of seizures before treatment, and no onset of seizure was reported after oral administration of sodium valproate for 1 year. CONCLUSION MSMDS has a variety of clinical manifestations and unique cranial imaging features. Cerebrovascular injury and white matter injury may lead to seizures. Gene detection can confirm the diagnosis and prevent missed diagnosis or misdiagnosis.
Background: Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes. Methods:We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein-protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells. Results:We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE. Conclusion:We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.
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