RÉSUMÉ
Objective:To investigate the clinical characteristics and offer diagnostic and therapeutic approaches for adult-onset idiopathic hypogonadotropic hypogonadism(AIHH).Methods:Clinical, laboratory, and imaging data, as well as follow-up information, of three male patients diagnosed with AIHH at the Department of Endocrinology and Metabolism of Nanfang Hospital, Southern Medical University, were systematically reviewed and analyzed.Results:All three patients were male, with a median age of 39 years(range, 22 to 40). Two patients reported symptoms of enlarged breasts and reduced sexual function, while one case solely reported a decline in sexual function. Physical examination showed that the median length of the penis was 6 cm(range, 5 to 6 cm), and the bilateral testicular volume was 7.96 mL(4.70-8.82 mL). Basal hormone levels at the time of initial visit to our hospital as follows: the median testosterone level was 0.32 ng/mL(0.24-2.96 ng/mL), median follicle stimulating hormone(FSH) level was 0.56 mIU/mL(0.1-0.75 mIU/mL), and the median luteinizing hormone(LH) level was 0.69 mIU/mL(0.1-1.03 mIU/mL). The levels of other hormones secreted by the anterior pituitary gland were normal. Hypothalamic-pituitary magnetic resonance imaging(MRI) showed that 1 patient had a pituitary microadenoma. Three patients were treated with pulsatile GnRH or gonadotropins, one of which had hypothalamic-pituitary-gonadal(HPG) axis function reversal after GnRH pulse pump therapy and lasted for 1 year, but then still had irreversible reduction.Conclusion:AIHH is marked by adult-onset disease and idiopathic hypogonadism. Enhancing fertility remains a critical requirement for these patients. Pulsatile GnRH treatment or gonadotropin therapy, as viable treatments, exhibit therapeutic effects, albeit with occasional fluctuations. Therefore, the emphasis lies in the timely consideration of fertility preservation.
RÉSUMÉ
Objective:To construct a diabetic foot classification prediction model based on radiomics features of fundus photographs.Methods:A total of 2 035 fundus photographs of patients with type 2 diabetes diagnosed at Nanfang Hospital between December 2011 and December 2018 were retrospectively collected [282 photographs from patients with diabetic foot(DF), and 1 753 from patients with diabetes mellitus(DM)]. All fundus photographs were randomly divided into a training set(1 424 photos) and a test set(611 photos) using a computer generated random number at 7∶3. After pre-processing the fundus photographs, a total of 4 128 texture features based on the gray matrix were extracted by the Radiomic toolkit, and 11 339 other features were extracted using the ToolboxDESC toolkit. The LASSO algorithm was used to select the 30 features most relevant to DF, and then the Bootstrap + 0.632 self-sampling method was used to further select the 7 best combinations. Logistic regression analysis was used to obtain the regression coefficients and establish the final diabetic foot classification prediction model. ROC curve was drawn, and AUC, sensitivity, specificity, and accuracy of the training and test sets were calculated to verify its prediction performance. Results:We screened 7 fundus radiomics markers for diabetic foot patients, and based on this established a DF/DM classification prediction model. The AUC, sensitivity, specificity, and accuracy of the model were 0.958 6, 0.984 0, 0.920 0, and 0.928 0 in the training set, and 0.927 1, 0.988 9, 0.881 0, and 0.896 9 in the test set, respectively.Conclusion:In this study, seven DF fundus markers were screened using radiomics technology. Based on this, a highly accurate and easy-to-use DF/DM classification model was constructed. This technology has the potential to increase the efficiency of DF screening programs.