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Objective: To analyze the anemia status of infants aged 6-11 months in Beijing, Shanxi, Jiangxi and Zhejiang of China, and to explore the association between timing of introducing complementary foods and hemoglobin level, anemia of infants aged 6-11 months.
Methods: Data was from National Nutrition and Health Systematic Survey for 0-18 Years Old Children in China. A total of 1404 infants aged 6-11 months from Beijing, Shanxi, Jiangxi and Zhejiang were enrolled in this study. Demographic characteristics, socioeconomic status, birth status and complementary feeding information were collected through questionnaire survey. HemoCue Hb201+ hemoglobin analyzer was used to measure hemoglobin value. The exposure variables in this study were timing of introducing complementary foods(≤5 months, 6 months and ≥7 months), and the outcome variables were hemoglobin level and anemia rate. The association between timing of introducing complementary foods and hemoglobin level was analyzed by using multivariate linear regression model, and the association between timing of introducing complementary foods and anemia rate was analyzed by using multivariate Logistic regression model.
Results: The hemoglobin levels of infants aged 6-11 months were(114.8±11.0)g/L, (115.5±10.5)g/L in urban areas and(114.1±11.5) g/L in rural areas. The anemia rate was 28.2%, 24.0% in urban areas and 32.9% in rural areas. The hemoglobin levels of infants introducing complementary foods at ≤5 months, 6 months and ≥7 months were(114.0±11.1), (115.2±10.9) and(114.5±10.7) g/L, respectively. After adjusting for potential confounding factors, there was no significant difference in hemoglobin level between the ≤5 months group and 6 months group(F=2.37, P=0.124), and no significant difference between the ≥ 7 months group and the 6 months group(F=0.09, P=0.770). The anemia rate of infants introducing complementary foods at ≤5 months, 6 months and ≥7 months were 32.3%, 27.9% and 22.7%, respectively. After adjusting for potential confounding factors, there was no significant difference in anemia rate between the ≤5 months group and 6 months group(OR=1.26(95%CI 0.86-1.83)), and no significant difference between the ≥7 months group and the 6 months group(OR=0.65(95%CI 0.35-1.20)).
Conclusion: Anemia remains a serious problem for infants aged 6-11 months in Beijing, Shanxi, Jiangxi and Zhejiang. Timing of introducing complementary foods may not be related with hemoglobin level and anemia rate of infants aged 6-11 months.
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http://dx.doi.org/10.19813/j.cnki.weishengyanjiu.2021.06.004 | DOI Listing |
IEEE Trans Cybern
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Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective.
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L'Oréal Research and Innovation, Aulnay sous Bois, France.
As hyperpigmentation can worsen with exposure to ultraviolet (UV) and visible light (VL), sunscreens with well-balanced UVB/UVA protection and VL-blocking pigments are recommended. Assessing efficiency against VL-induced pigmentation is then mandatory. Recently, an in vivo pigmentation assessment allowing a VL protection factor (pVL-PF) determination, and an in vitro predictive method based on transmittance measures were introduced.
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September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
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Deciphering the three-dimensional structure of proteins remains a grand challenge in biology and medicine, as it holds the key to understanding their biological functions and facilitating drug discovery. In this paper, we introduce DECIPHER (Deep Encoding of Cellular Interactions and Protein HiErarchical Representation), a novel deep graph learning framework for protein structure prediction. By representing proteins as graphs, where residues and atoms serve as nodes and their interactions form edges, we capture the intricate spatial relationships within these complex biomolecules.
View Article and Find Full Text PDFJ Phys Chem Lett
September 2025
School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH, Switzerland.
Protein folding remains a formidable challenge despite significant advances, particularly in sequence-to-structure prediction. Accurately capturing thermodynamics and intermediates via simulations demands overcoming time scale limitations, making effective collective variable (CV) design for enhanced sampling crucial. Here, we introduce a strategy to automatically construct complementary, bioinspired CVs.
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