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Pseudohypoparathyroidism (PHP) comprises a cluster of heterogeneous diseases characterized by hypocalcemia and hyperphosphatemia due to parathyroid hormone (PTH) resistance. PHP type 1B (PHP1B) is caused by heterozygous maternal deletions within GNAS or STX16. STX16 exon 2–6 deletion is commonly observed in autosomal dominant (AD)-PHP1B, while sporadic PHP1B commonly results from methylation abnormalities of maternal differentially methylated regions and remains unclear at the molecular level. A 39-year-old male patient with PHP1B, who had his first seizure at 15 years of age, presented to our hospital. The methylation-specific multiplex ligation-dependent probe amplification results showed a half-reduced copy number of STX16 exon 5–7 and loss of methylation at GNAS exon A/B. His mother also had a half-reduced copy number of STX16 exon 5–7 but with normal methylation of GNAS. His father has a normal copy number of STX16 and normal methylation of GNAS. For the recognition and early diagnosis of this kind of disease, here we report the clinical symptoms, auxiliary examinations, genetic testing characteristics, and treatment of the patient. [ABSTRACT FROM AUTHOR]

Background: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement., Methods: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria., Results: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI., Conclusion: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI., (© The Author(s) 2024. Published by Oxford University Press on behalf of the ERA.)