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Background: Central precocious puberty (CPP) is a precocious puberty due to premature activation of the hypothalamic–pituitary‐gonadal axis (HPG). MKRN3 defects are well‐known causes of CPP, while DLK1 mutations were recently identified in a few patients with CPP. Methods: The study was approved by the Institutional Review and the scientific committee of the hospital. The clinical data were collected. Whole‐exome sequencing (WES) was performed to detect causative variants. Key words 'DLK1', 'MKRN3', and "central precocious puberty" were used for literature search in PubMed, Google Scholar, HGMD, and OMIM databases. Results: The patient, a male, whose puberty began before age nine, had significant metabolic abnormalities including overweight, hyperlipidemia, and hyperuricemia. WES detected a recurrent frame‐shift mutation, NM_003836.5:c.479delC(p.P160fs*50) in DLK1 in the patient and his father. Conclusion: The familial DLK1‐CPP was identified in China for the first time, which supported that short stature is predicted in patients with CPP without GnRHa treatment. Therefore, we recommend that children with DLK1‐CPP should be treated as early as possible to improve adult height. The patient in this study had persistent hyperuricemia, further suggests that this antiadipogenic factor represents a link between reproduction and metabolism. [ABSTRACT FROM AUTHOR]

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings., Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists., Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024., Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation., Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE)., Results: The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001)., Conclusions and Relevance: In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

Injectable bone biomaterials like bone cement should be designed and fabricated with certain biological criteria, which include: 1) recruitment and polarization of the macrophages from M1 (pro-inflammatory) to M2 (anti-inflammatory) phenotype, 2) enhance vascularization, and 3) activate osteogenic differentiation of bone marrow-derived stem cells to promote bone healing. So far, no injectable biomaterials could spontaneously regulate the entire bone healing process that involves inflammation, angiogenesis, and osteogenesis. Therefore, in this study, we designed bone cement comprised of strontium and copper-incorporated borosilicate glass (Sr/Cu-BSG) in the liquid phase of chitosan to modulate bone healing. In vitro studies showed that the controlled release of Sr and Cu ions up-regulated anti-inflammatory genes(IL-1Ra and TGF-β1) while down-regulating pro-inflammatory genes(IL-1β and IL-6) in macrophages at 3 days. Sr and Cu ions also increased the expressions of angiogenic genes (VEGF and bFGF) in HUVECs at 5 days and osteogenic genes (Runx-2, OCN, and OPN) in hBMSCs at 7, 14, and 21 days. 5Sr3Cu-BSG bone cement exhibited the best anti-inflammatory, angiogenic, and osteogenic properties among the bone cement groups with different Sr and Cu ratios. Short-term and long-term implantation of Sr/Cu-BSGs in femoral condylar bone defects of rats and rabbits confirmed the in vitro results, where the degradation rate of Sr/Cu-BSG matched the bone healing rate. Similar to in vitro , the 5Sr3Cu-BSG group also showed the highest bone formation in vivo. Excellent physical and chemical properties, along with its bone repairing ability, make the Sr/Cu-BSG bone cement a good candidate biomaterial for treating bone defects., Competing Interests: The authors declared that they have no conflicts of interest to this work., (© 2022 The Authors.)