Objective: This study was done to evaluate the emotional and behavioral status of precocious puberty patients and analyze the effect of gonadotropin-releasing hormone agonist (GnRHa) treatment. Methods: Sixty-six female precocious puberty patients were enrolled prospectively for the study at Kangdong Sacred Heart Hospital of Hallym University Medical Center from September 2011 to December 2012 and self-administered questionnaire was completed during the GnRHa treatment initiation period and after 12 months from the first injection. The patients were evaluated using the Korean version of Child Behavior Checklist (K-CBCL) and Children’s Depression Inventory (CDI). Results: A total of 30.3% (n = 20) of the patients scored within the clinical range for one or more scales of K-CBCL at the initiation of GnRHa treatment, but only 10.6% (seven patients) were within the clinical range after 1 year of treatment. Average CDI scores of the patients decreased from baseline 6.5 ± 6.0 to 4.9 ± 4.7 after GnRHa therapy. Conclusions: This study shows that both K-CBCL and CDI scores improved from baseline score ranges after 1 year of GnRHa treatment in female central precocious puberty patients while significant psychological problems of clinical range amongst them were not noted.

Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.

Newly developed genetic techniques can reveal mosaicism in individuals diagnosed with monosomy X. Noninvasive prenatal diagnosis using maternal blood can detect most fetuses with X chromosome abnormalities. Low-dose and ultralow-dose estrogen replacement therapy can achieve a more physiological endocrine milieu. However, many complicated and controversial issues with such treatment remain. Therefore, lifetime observation, long-term studies of health problems, and optimal therapeutic plans are needed for women with Turner syndrome. In this review, we discuss several diagnostic trials using recently developed genetic techniques and studies of physiological hormone replacement treatment over the last 5 years.

Hypoparathyroidism, sensorineural deafness, and renal dysgenesis syndrome is an autosomal dominant disease caused by mutations in the GATA3 gene on chromosome 10p15. We identified a patient diagnosed with hypoparathyroidism who also had a family history of hypoparathyroidism and sensorineural deafness, present in the father. The patient was subsequently diagnosed and found to be a heterozygote for an insertion mutation c.255_256ins4 (GTGC) in exon 2 of GATA3. His father was also confirmed to have the same mutation in GATA3.

Recent evidence indicates that urinary gonadotropins may be an alternative method for detecting pubertal disorders. The aim of this study was to evaluate the associations of first morning voided (FMV) and random urinary gonadotropins with the pubertal response to a gonadotropin-releasing hormone (GnRH) stimulation test to determine whether random urinary gonadotropins can be used as an alternative method for evaluating central precocious puberty (CPP). In total, 100 girls aged 6.0–8.9 years were enrolled. The subjects were divided into two groups according to their pubertal response to the GnRH stimulation test: a positive group (n = 68) and a negative group (n = 32). Random urinary luteinizing hormone (LH), follicle-stimulating hormone (FSH), and the LH:FSH ratio were significantly positively correlated with FMV urinary LH (r = 0.411, p < 0.001), FMV urinary FSH (r = 0.494, p < 0.001), and the FMV urinary LH:FSH ratio (r = 0.519, p < 0.001). The optimal cutoff values from receiver operating characteristic (ROC) curve analyses were determined to be 0.20 IU/L for random urinary LH (area under the curve (AUC) of 0.812, p < 0.001), 3.03 IU/L for random urinary FSH (AUC of 0.670, p = 0.004) and 0.08 for the random urinary LH:FSH ratio (AUC of 0.784, p < 0.001). No differences were observed between FMV and random urinary LH (p = 0.827), between FMV and random urinary FSH (p = 0.650), or between the FMV and random urinary LH:FSH ratio (p = 0.688) in ROC curve analyses with DeLong’s test. Based on our findings, random urinary gonadotropins may be applicable in clinical practice as a useful initial test for girls with CPP.