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3 results on '"Chenglong Ai"'

1. Genetic link between depression and musculoskeletal disorders: insights from Mendelian randomization analysis

Author

Yanpeng Wang, Yinzhen Zhang, Changwei Zhao, Hao Yang, Chenglong Ai, Wenhai Zhao, and Ji Xu

Subjects
depression, cervical spondylosis, musculoskeletal diseases, Mendelian randomization, GWAS, Medicine (General), R5-920
Abstract

BackgroundThe association between depression and musculoskeletal diseases has long been a subject of contentious debate. However, the causal relationship between the two remains uncertain. This study employs a two-sample Mendelian randomization (MR) analysis to investigate the causality between depression and six musculoskeletal diseases.MethodsIn this study, we performed MR analysis to systematically explore the causal relationship between depression and six musculoskeletal disorders. Single nucleotide polymorphisms (SNPs) that are linked to depression were employed as instrumental variables. To ensure robust and reliable conclusions, multiple analytical approaches were utilized, including inverse variance weighting(IVW), weighted median, and MR-Egger regression. Additionally, sensitivity analysis methods such as the MR-Egger intercept test, Cochran’s Q test, leave-one-out analysis, and funnel plot were employed.ResultsOur MR analysis revealed a significant association between depression and cervical spondylosis (depression: OR 1.003, 95% CI 1.002–1.005, P = 8.32E-05; major depressive disorder: OR 1.003, 95% CI 1.001–1.005, P = 0.0052). Furthermore, a strong correlation was noted between major depressive disorder (MDD) and knee osteoarthritis (KOA) (OR 1.299, 95% CI 1.154–1.463, P = 1.50E−5). Sensitivity analysis confirmed the robustness of these findings. Our independent validation study also corroborated these results.ConclusionThe MR analysis conducted in this study provides evidence supporting a genetic link between depression and cervical spondylosis, as well as KOA. Targeted interventions to manage depression in susceptible populations may contribute to lowering the risk of cervical spondylosis and KOA in these cohorts.

Published
2024
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2. Influence of brands on a discrimination model for adulterated milk based on asynchronous two-dimensional correlation spectroscopy slice spectra

Author

Haiyun, Wu, Renjie, Yang, Yong, Wei, Guimei, Dong, Hao, Jin, Yanan, Zeng, and Chenglong, Ai

Subjects
Milk, Spectroscopy, Near-Infrared, Animals, Discriminant Analysis, Food Contamination, Least-Squares Analysis, Instrumentation, Spectroscopy, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Abstract

To improve the robustness of near infrared (NIR) identification models for the milk adulteration, a novel approach was explored based on asynchronous two-dimensional correlation spectroscopy (2D-COS) slice spectra obtained at characteristic wavebands for pure milk and adulterant combined with an N-way partial least squares discriminant analysis (NPLS-DA). NIR diffuse reflectance spectra from four different brands, Guangming (GM), Mengniu (MN), Sanyuan (SY), and Wandashan (WDS), were collected in range of 11,000 to 4000 cm

Published
2022

3. Analysis of chalk in rice by two-dimensional correlation spectroscopy

Author

Wu Haiyun, Yang Renjie, Chenglong Ai, Jin Hao, and Wei Yong

Subjects
010405 organic chemistry, Starch, Organic Chemistry, Analytical chemistry, Infrared spectroscopy, 010402 general chemistry, 01 natural sciences, 0104 chemical sciences, Analytical Chemistry, Inorganic Chemistry, chemistry.chemical_compound, Key factors, chemistry, Attenuated total reflection, Peak intensity, Nir spectra, Two-dimensional nuclear magnetic resonance spectroscopy, Spectroscopy
Abstract

To extract the characteristic information of chalk in rice, the chalky milled rice were analyzed systematically by mid-infrared (IR), near-infrared (NIR) spectra analysis and two-dimensional (2D) correlation spectroscopy. 24 non-chalky and 24 chalky rice samples for four types milled rice were prepared in laboratory. NIR diffusing reflection and IR attenuated total reflection (ATR) spectra of all samples were collected at room temperature. The synchronous 2D NIR-NIR correlation spectra was calculated to show the differences between chalky and non-chalky rice more remarkably. The 2D IR-NIR correlation spectra was used to confirm the near-infrared characteristic peak. According to the IR spectra, a lot of starch was found in rice samples. Besides, in the NIR spectra, peaks at around 4332 and 4732 cm−1 could be considered as the key factors to discriminate chalky rice kernels. And the higher peak intensity ratio of 5168/5624 cm−1 was found compared to the non-chalky sample. In addition, significant differences emerged in the 2D NIR-NIR correlation by the intensity and number of the auto-peaks and cross-peaks in synchronous spectra. There was three auto-peaks near 4484 cm−1, 4752 cm−1and 4958 cm−1 in chalky rice sample. While only one auto-peak near 4958 cm−1 in non-chalky rice sample. Besides, chalky rice sample has one cross-peak between 4484 and 4958 cm−1, while it was absent in non-chalky rice. The technique for chalky rice kernels detection of milled rice kernels was rapid and non-destructive, which could be used to provide technical support for rice quality improvement breeding.

Published
2020

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