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Background and Aims: Coronary heart disease (CHD), hypertension (HTN), depression (Dep), and Type 2 diabetes mellitus (T2DM) are often comorbid, resulting in an exacerbated patient condition and worsened prognosis. A lack of systematic metabolomic studies on comorbidities of CHD remains. Therefore, comprehensive metabolomic‐based evaluation of comorbidities of CHD is necessary. Methods and Results: In the current study, 169 healthy subjects, 149 CHD subjects, 107 CHD + HTN subjects, 126 CHD + Dep subjects, and 58 CHD + T2DM subjects were recruited. Gas chromatography–mass spectrometry was used for metabolite determination, and multivariate statistical analysis was conducted to identify metabolites that are differentially expressed with the comorbidities of CHD. There were 9, 16, 14, and 10 metabolites identified in the healthy and CHD group, the CHD and CHD + HTN group, the CHD and CHD + Dep group, and the CHD and CHD + T2DM group, respectively. Six metabolic pathways were affected, involving starch and sucrose metabolism; fructose and mannose metabolism; citrate cycle; alanine, aspartate, and glutamate metabolism; fatty acid biosynthesis; and glycolysis. Conclusion: Our study has systematically elucidated the metabolic changes underlying the comorbidities of CHD, thereby providing insight into the mechanisms associated with these alterations. [ABSTRACT FROM AUTHOR]

Early allograft dysfunction (EAD) is a frequent phenomenon, leading to increased graft loss and higher mortality after liver transplantation (LT). Despite significant efforts for early diagnosis of EAD, there is no existing approach that can predict EAD on the first post‐operative day. The aim is to define a metabolite‐based biomarker on the first day after LT complicated with EAD. Ten patients diagnosed with EAD and 26 non‐EAD are recruited for the study. A HPLC‐MS/MS is used to determine 14 amino acids and 15 bile acids serum concentration. Comparative analyses are conducted between EAD and non‐EAD groups. Arginine is identified as the most significant metabolite distinguishing the EAD and non‐EAD groups, and therefore, is identified as a potential biomarker of EAD. The optimal cut‐off value for arginine is 52.09 µmol L−1, with an AUROC of 0.804 (95% confidence interval: 0.638–0.917, p < 0.001), yielding a sensitivity of 100%, specificity of 53.8%, and Youden index of 0.54, NPVof 100%, and PPV of 45.45%. In summary, the study indicated that targeted metabolomics analysis would be a promising strategy for discovering novel biomarkers to predict EAD. The identified arginine may be helpful in developing an objective diagnostic method for EAD. [ABSTRACT FROM AUTHOR]

To investigate the combined impacts of anthropogenic and biogenic emissions on the formation of secondary organic aerosols (SOA), SOA molecular tracers, their corresponding volatile organic compound precursors, and other air pollutants were measured online during the winter and summer seasons of 2022 in an industrial city, Zibo, China. The results indicate that the average concentrations of SOA tracers were 16.1 ± 9.8 ng m−3 in winter and 99.4 ± 57.2 ng m−3 in summer. During winter, anthropogenic SOA (ASOA, the sum of SOA derived from naphthalene and mono‐aromatic volatile organic compounds) dominated, whereas isoprene SOA (SOAI) prevailed in summer. Correlation analysis between SO42− and both SOAI and high‐order monoterpene SOA tracers (SOAM‐H) (R = 0.46–0.72, p < 0.001) revealed that higher aerosol acidity facilitated the formation of SOAI and SOAM‐H, with SO2 emissions playing a significant role in leading to higher acidity. Most biogenic SOA (BSOA) tracers exhibited a significant positive correlation with NO3−, particularly in winter, implying the remarkable influence of NOx emissions on BSOA formation. The levels of BSOA tracers increased with NH3, indicating that NH3 can enhance the formation of BSOA. In summer, SOA formation correlated with Ox (Ox = O3 + NO2), indicating the substantial impact of atmospheric oxidizing capacity on SOA formation. During winter, aerosol liquid water content (ALWC) correlated well with SOAI tracers (i.e., 3‐hydroxyglutaric acid (3‐HGA) and 3‐hydroxy‐4,4‐dimethylglutaric acid (3‐HDMGA)), and 2,3‐dihydroxy‐4‐oxopentanoic acid (DHOPA) (R > 0.5, p < 0.001), indicating the important contribution of aqueous‐phase formation of SOA. These findings underscore the significant role of anthropogenic pollutant emissions in the formation of ASOA and BSOA in urban environments. Plain Language Summary: Air pollution and climate change are influenced by secondary organic aerosol (SOA), which are formed through a series of chemical reactions that could involve both anthropogenic and natural emissions. However, there is limited research on the relationship between the precursors of SOA and the resulting SOA at the molecular level. To address this gap, we investigated the influence of anthropogenic emissions on the formation of SOA in a typical industrial city (Zibo, China) based on online measurements of SOA molecular tracers and corresponding volatile organic compound precursors, and other air pollutants, at a high‐time resolution during the winter and summer of 2022. Our findings demonstrate the important role of human‐generated pollutants in the formation of anthropogenic SOA and biogenic SOA. These results enhance our understanding of SOA formation in industrial cities and provide valuable insights for the formulation of SOA management and control strategies in such areas. Key Points: Anthropogenic secondary organic tracers were the predominant contributors during winter, whereas isoprene secondary organic aerosols (SOA) tracers were predominant during summerAnthropogenic pollutants have an important impact on the formation of SOAHigh correlation was observed between SOA tracers and Ox in summer and aerosol liquid water content in winter [ABSTRACT FROM AUTHOR]

Chen et al. (2022) analyzed the event of rapid nocturnal O 3 enhancement (NOE) observed on 31 July 2021 at the surface level in the North China Plain and proposed transport of substantial stratosphere ozone to the surface by Typhoon In-fa followed by downdraft of shallow convection as the mechanism of the NOE event. The analysis seems to be valid from the viewpoint of atmospheric physics. This comment revisits the NOE phenomenon on the basis of the China National Environmental Monitoring Center (CNEMC) network data used in Chen et al. (2022), together with the CNEMC data from Zibo (ZB) and O 3 , NO x , PAN (peroxyacetic nitric anhydride), and VOC (volatile organic compound) data from the Zibo supersite operated by the China Research Academy of Environmental Sciences (CRAES). We found (a) O x (O 3 + NO 2) levels during the NOE period approaching those of O 3 during 14:00–17:00 LT, (b) levels of PAN and the relationship between O 3 and PAN consistent with dominance of chemical and physical processes within the boundary layer, and (c) estimated photochemical ages of air mass shorter than 1 d and showing no drastic increases during the NOE. We argue that the NOE was not caused by typhoon-induced stratospheric intrusion but originated from fresh photochemical production in the lower troposphere. Our argument is well supported by the analysis of atmospheric transport as well as ground-based remote sensing data. [ABSTRACT FROM AUTHOR]

Chen et al. (2022) analyzed the event of rapid nocturnal O3 enhancement (NOE) observed on 31 July 2021 at surface level in the North China Plain and proposed transport of substantial stratosphere ozone to the surface by Typhoon In-fa followed by downdraft of shallow convection as the mechanism of the NOE event. The analysis seems to be valid in the view-point of atmospheric physics. This comment revisits the NOE phenomenon on the basis of the China National Environmental Monitoring Center (CNEMC) network data ever used in Chen et al. (2022), together with the CNEMC data from Zibo (ZB), and O3, NOx, PAN and VOCs data from the Zibo supersite operated by China Research Academy of Environmental Sciences (CRAES). We found (a) Ox (O3+NO2) levels during the NOE period approaching to those of O3 during 14:00–17:00 LT; (b) the relationship between O3 and PAN (peroxyacetic nitric anhydride) consistent with dominance of chemical and physical processes within the boundary layer, and (c) estimated photochemical ages of air mass being shorter than one day and showing no drastic increases during the NOE. We argue that the NOE was not caused by typhoon-induced stratospheric intrusion but originated from fresh photochemical production in the lower troposphere. [ABSTRACT FROM AUTHOR]

Objective: Diets high in glucose or fat contribute to an increased prevalence of the diseases. Therefore, the objective of the current research was to observe and evaluate the impact of dietary components on different metabolomic profiles in primary tissues of mice., Methods: For 8 weeks, diet with high-glucose or-fat was given to C57BL/6 J mice. The levels of metabolites in the primary tissues of mice were studied using gas chromatography-mass spectrometry (GC-MS) and analyzed using multivariate statistics., Results: By comparing the metabolic profiles between the two diet groups and control group in mice main tissues, our study revealed 32 metabolites in the high-glucose diet (HGD) group and 28 metabolites in the high-fat diet (HFD) group. The most significantly altered metabolites were amino acids (AAs; L-alanine, L-valine, glycine, L-aspartic acid, L-isoleucine, L-leucine, L-threonine, L-glutamic acid, phenylalanine, tyrosine, serine, proline, and lysine), fatty acids (FAs; propanoic acid, 9,12-octadecadienoic acid, pentadecanoic acid, hexanoic acid, and myristic acid), and organic compounds (succinic acid, malic acid, citric acid, L-(+)-lactic acid, myo-inositol, and urea). These metabolites are implicated in many metabolic pathways related to energy, AAs, and lipids metabolism., Conclusion: We systematically analyzed the metabolic changes underlying high-glucose or high-fat diet. The two divergent diets induced patent changes in AA and lipid metabolism in the main tissues, and helped identify metabolic pathways in a mouse model., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xie, Zhang, Guo, Xue, Zhao, Geng, Li, Yang, Gan, Li, Ren and Jiang.)