Clinical report and genetic analysis of a Chinese patient with developmental and epileptic encephalopathy associated with novel biallelic variants in the ST3GAL3 gene.

BACKGROUND: Defects in the Golgi enzyme beta-galactoside-alpha-2,3-sialyltransferase-III (ST3Gal-III) caused by biallelic ST3GAL3 gene variants are associated with human neurodevelopmental disorders. Although ST3GAL3 gene variants have been linked to developmental and/or epileptic encephalopathy 15 (DEE15), their presence has only been reported in nine patients; however, the real frequency may be masked by insufficient screening. METHODS: Phenotypic information was collected from a male patient with severe psychomotor developmental delay and epileptic seizures, and genetic testing was done using whole exome sequencing. A molecular dynamics simulation analysis was performed to assess the potential impacts of the identified ST3GAL3 variants on the ST3Gal-III protein function, and a literature review was conducted to compare this case with previously described cases and assess disease manifestation and genetic characteristics. RESULTS: The patient inherited compound heterozygous ST3GAL3 gene variants, NM_006279.5:c.809G>A (p.Arg270Gln) and c.921dupG (p.Thr308fs*8). Neither variant had been previously reported in the general population. The p.Arg270Gln variant disrupted a hydrogen bond in the simulated ST3Gal-III protein structure. Among 25 patients with ST3GAL3 gene defects, eight ST3GAL3 gene variants were identified, and five variants had DEE signs. CONCLUSION: Patients with DEE15 may have novel ST3GAL3 gene variants, and this study may be the first clinical report of their occurrence in a Chinese patient. These variants should be considered when evaluating patients presenting with unexplained early-onset epileptic encephalopathy, severe developmental delay, and/or intellectual disability.

Knowledge of osteoporosis prevention among people with endocrine disorders: A cross-sectional study.

People with endocrine disorders are at an increased risk of osteoporosis, yet their knowledge of osteoporosis prevention is rarely studied. This study aimed to assess the knowledge related to osteoporosis prevention and its associated factors among people with endocrine disorders in China. A cross-sectional study was conducted in a Chinese hospital's Department of Metabolism and Endocrinology. A total of 562 people with endocrine disorders completed the Chinese version of the Osteoporosis Prevention and Awareness Tool to assess their knowledge of osteoporosis prevention. Results showed that participants had a mean knowledge of 59.36 ± 23.90 out of 100, with only 52.1% scoring above 60 points. Being female, having higher education, with comorbidities, with a recent osteoporosis diagnosis, and having received health education related to osteoporosis prevention were associated with higher knowledge of osteoporosis prevention. Our study indicates that more efforts are needed to improve the knowledge related to osteoporosis prevention among people with endocrine disorders. This may be realized by strengthening and expanding diverse education, focusing on males and those with lower education and without comorbidities.

A decision tree model of cerebral palsy based on risk factors.

OBJECTIVE: A risk prediction model of cerebral palsy (CP) was established by a decision tree model to predict the individual risk of CP. METHODS: A hospital-based case-control study was conducted with 109 cases of CP and 327 controls without CP. The cases and the controls were obtained from Hunan Children's Hospital. A questionnaire was administered to collect the variables relevant to CP by face to face interviews. Chi-square test was used to identify the factors associated with CP, and a decision tree model was used to construct the prediction model. RESULTS: Univariate analysis showed that there were significant differences between cases group and controls group on maternal age, weight gain during pregnancy, medical treatment during pregnancy, preterm birth, low birth weight and birth asphyxia (all p-values <.05). Three factors, including preterm birth, birth asphyxia, and maternal age >35 years old, entered the decision tree model. The area under the receiver operating characteristic curve (AUC) was 0.722 (95%CI: 0.659-0.784, p < .001). CONCLUSION: The decision tree prediction model can be used for predicting the individual risk of CP. Further large-scale, population-based cerebral palsy studies are needed to improve the model.