Background And Objective: With a worldwide trend to earlier age of onset of puberty, the prevalence of early-onset puberty (EP) among girls has increased. The impact of EP on the pattern of linear growth and bone maturation is unclear. Accordingly, the objective of our study was to describe this pattern for girls with EP in Shenzhen, China.
Methods: A total of 498 untreated girls diagnosed with EP at Shenzhen Children's Hospital, China, between January 2016 and December 2021. A total of 1,307 anthropometric measurements and 1,307 left-hand radiographs were available for analysis. Artificial intelligence (AI) was used to determine bone age (BA). Participants were classified into groups according to chronological age (CA) and BA. The pattern of linear growth (height) and progression of bone maturation was described between groups using the Lambda-Mu-Sigma (LMS) method. Published height-for-CA and height-for-BA norm references for a healthy Chinese population were used for age-appropriate comparisons.
Results: The mean CA of appearance of first pubertal signs (breast buds) was 8.1 ± 0.5 years. Compared to norm-referenced data, girls with EP were significantly taller at a CA of 7-10 years. This was followed by a slowing in linear growth after a CA of 10 years, with 71 girls with EP having already achieved their target adult height. From 7 to 10 years of BA, the linear growth was slower in the EP group compared to norm-reference values. This was followed by a period of catch-up growth at 11.2 years of BA, with growth curves approaching norm-referenced values. The BA progressed rapidly from 7 to 8 years of age in about half of the girls with EP (median ΔBA/ΔCA >1.9), slowing, thereafter, until the period of catch-up growth at 11.2 years of BA.
Conclusions: BA provides a more reliable reference than CA to assess growth parameters among girls with EP. Our limited data set does indicate that EP does not negatively impact final adult height. Therefore, the growth curves from our study are relevant, providing a reference for pediatricians in this clinical population and, thus, preventing over-treatment for EP.
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http://dx.doi.org/10.3389/fped.2023.1056035 | DOI Listing |
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Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education, College of Chemistry, Xiangtan University, Xiangtan, 411105, People's Republic of China.
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December 2024
School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
A novel proposal is introduced with an unlabeled electrochemical immunosensor for the detection of tumor broad-spectrum biomarker vascular endothelial growth factor (VEGF165) Copper-based metal organic frameworks (Cu MOFs)-carbon nanotubes (MWCNTs) were employed as its substrates, functionalized with methylene blue (MB) for signal enhancement. Cu-MOFs-MWCNTs nanocomposites were synthesized successfully via a solvothermal method and were then deposited on the surface of a glassy carbon electrode (GCE), with the addition of methylene blue to amplify the signal. Due to the expansive specific surface area provided by the carbon nanotubes and the amino groups facilitated by the metal-organic framework nanomaterials, the anti-VEGF165 monoclonal antibody was immobilized on the electrochemical immunosensor through covalent bonding, which could bind specifically to VEGF165, thereby causing a detectable change in the current.
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Environmental Health Science and Research Bureau, Health Canada, Vancouver, BC, Canada, Ottawa, ON, Canada.
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December 2024
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India.
Dairy product requirement and the demand-supply gap of milk in Ethiopia have been increasing at an alarming rate due to various factors such as shortage of animal's feeds, feed staffs, feed costs, and poor genetic merits of the local breeds of the country. This problem can be lessened by selecting best breed and modern animal breeding facilities, which require technologies like big data analysis and machine learning. In this study, a prediction model that can predict age at first calving of weaned calves based on their pre-weaning and weaning parameters, including dam's parity number, season of calving, birth weight, pre-weaning health status, pre-weaning average daily weight gain (ADG), weaning age and weaning weight is developed.
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