Acute Thyrotoxic Myopathy Combined with Neck Pain: A Case Report.

INTRODUCTION: Acute thyrotoxic myopathy (ATM) is a rare and potentially lethal complication of thyrotoxicosis. The typical clinical symptoms of ATM are characterized by bulbar paralysis. Reports of the successful treatment of ATM are sporadic due to its low incidence. However, no English literature has reported Chinese patients with ATM and neck pain. Here, we report for the first time a Chinese patient with ATM and neck pain who recovered through large doses of systemic glucocorticoids and one intrathyroidal steroid injection. CASE REPORT: A 23-year-old woman visited our hospital with a two-year history of progressive weakness of her bulbar muscles, hoarseness, cough when swallowing, dysphagia, and a one-month history of recurrent painful swelling of the thyroid gland. She was diagnosed with ATM, chronic thyrotoxic myopathy (CTM), and Graves' ophthalmopathy (GO) due to Graves' disease (GD). After she was treated with a combination of low-dose glucocorticoids, antithyroid drugs (ATDs), propranolol, and ultrasound-guided percutaneous intrathyroidal injection of glucocorticoids, her bulbar paralysis, proximal myopathy, and neck pain simultaneously improved without recurrence during follow-up. To our knowledge, this is the first case report of a patient with ATM, CTM, GD, GO and neck pain treated by administering a combination of low-dose glucocorticoids, one intrathyroidal steroid injection and antithyroid agents. CONCLUSIONS: Clinicians should consider ATM and intervene with aggressive glucocorticoid therapy, and this is the key to reversing the progression of ATM when a patient has bulbar paralysis and thyrotoxic symptoms. Our case report references the clinical diagnosis and treatment of such cases.

Ultrasound measurement of vastus lateralis and vastus medialis muscle parameters to identify chronic thyrotoxic myopathy.

Introduction: Chronic thyrotoxic myopathy (CTM) is a common, easily neglected complication of hyperthyroidism. There are currently no standard diagnostic criteria for CTM, and the ultrasonic characteristics of CTM-affected skeletal muscle remain unclear. Herein, we aimed to evaluate hyperthyroid patients for CTM by ultrasound and identify ultrasonic muscle parameter cutoffs for CTM diagnosis. Materials and methods: Each participant underwent ultrasonography. The original (muscle thickness (MT), pennation angle (PA), and cross-sectional area (CSA)) and corrected (MT/height (HT), MT/body mass index (BMI), CSA/HT, and CSA/BMI) parameters of the vastus lateralis and vastus medialis (VM) were evaluated. The diagnostic effectiveness of ultrasound for predicting CTM was determined using receiver operating characteristic (ROC) curve analysis. Our study included 203 participants: 67 CTM patients (18 males, 49 females), 67 non-CTM patients (28 males, 39 females) and 69 healthy controls (20 males, 49 females). Results: The CTM group had lower muscular ultrasonic and anthropometric parameters, higher thyroid hormone and thyroid-stimulating hormone receptor antibody (TRAb) levels, and a longer duration of hyperthyroidism than the non-CTM group (P < 0.05). The VM-PA, VM-CSA, VM-CSA/HT, and VM-CSA/BMI were lower in females than in males (P < 0.05). Free thyroxine (FT4) and TRAb both showed significant negative correlations with VM-MT, VM-MT/HT, VM-CSA, and VM-CSA/HT (P < 0.05). VM-MT/BMI and VM-CSA/HT, respectively, best predicted male and female CTM (AUC = 0.84, 0.85; cutoff ≤ 0.07, < 4.01). Conclusion: Ultrasound measurement of muscular parameters, especially in the VM, is a valid and feasible way of diagnosing and characterizing possible CTM in hyperthyroidism.

Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.

OBJECTIVE: To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. METHODS: A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. RESULTS: The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. CONCLUSION: The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.

A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). METHODS: The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. RESULTS: A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C-index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. CONCLUSION: The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

Identification of Potential Biomarkers and Biological Pathways in Juvenile Dermatomyositis Based on miRNA-mRNA Network.

OBJECTIVE: The aim of this study is to explore the potential pathogenesis of juvenile dermatomyositis by bioinformatics analysis of gene chips, which would screen the hub genes, identify potential biomarkers, and reveal the development mechanism of juvenile dermatomyositis. MATERIAL AND METHODS: We retrieved juvenile dermatomyositis's original expression microarray data of message RNAs (mRNAs) and microRNAs (miRNAs) from NCBI's Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/); through the R package of limma in Bioconductor, we can screen the differentially expressed miRNAs and mRNAs, and then we further analyzed the predicted target genes by the methods such as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and miRNA-mRNA regulatory network construction and protein-protein interaction (PPI) network using Cytoscape 3.6.1. RESULTS: Compared with normal juvenile skin tissues, 6 upregulated microRNAs and 5 downregulated microRNAs were identified from 166 downregulated microRNAs and 58 upregulated microRNAs in juvenile dermatomyositis tissues. The enrichment pathways of differentially expressed microRNAs include cell adhesion molecules (CAMs), autoimmune thyroid disease, Type I diabetes mellitus, antigen and presentation, viral myocardium, graft-versus-host disease, and Kaposi sarcoma-associated herpes virus infection. By screening of microRNA-messenger RNA regulatory network and construction of PPI network map, three target miRNAs were identified, namely, miR-193b, miR-199b-5p, and miR-665. CONCLUSION: We identified mir-193b, mir-199b-5p, and mir-6653 target miRNAs by exploring the miRNA-mRNA regulation network mechanism related to the pathogenesis of juvenile dermatomyositis, which will be of great significance for further study on the pathogenesis and targeted therapy of juvenile dermatomyositis.