Glutamine supplementation alleviated aortic atherosclerosis in mice model and in vitro.

This study aimed to clarify the role of glutamine in atherosclerosis and its participating mechanism. Forty C57BL/6J mice were divided into wild control (wild Con), ApoE- /- control (ApoE- /- Con), glutamine + ApoE- /- control (Glut + ApoE- /- Con), ApoE- /- high fat diet (ApoE- /- HFD), and glutamine + ApoE- /- HFD (Glut + ApoE- /- HFD) groups. The degree of atherosclerosis, western blotting, and multiomics were detected at 18 weeks. An in vitro study was also performed. Glutamine treatment significantly decreased the degree of aortic atherosclerosis (p = 0.03). O-GlcNAcylation (O-GlcNAc), IL-1ß, IL-1α, and pyruvate kinase M2 (PKM2) in the ApoE- /- HFD group were significantly higher than those in the ApoE- /- Con group (p < 0.05). These differences were attenuated by glutamine treatment (p < 0.05), and aggravated by O-GlcNA transferase (OGT) overexpression in the in vitro study (p < 0.05). Multiomics showed that the ApoE- /- HFD group had higher levels of oxidative stress regulatory molecules (guanine deaminase [GUAD], xanthine dehydrogenase [XDH]), proinflammatory regulatory molecules (myristic acid and myristoleic acid), and stress granules regulatory molecules (caprin-1 and deoxyribose-phosphate aldolase [DERA]) (p < 0.05). These differences were attenuated by glutamine treatment (p < 0.05). We conclude that glutamine supplementation might alleviate atherosclerosis through downregulation of O-GlcNAc, glycolysis, oxidative stress, and proinflammatory pathway.

Independent and joint associations between urinary polycyclic aromatic hydrocarbon metabolites and cognitive function in older adults in the United States.

Background: Polycyclic aromatic hydrocarbons (PAHs), as organic pollutants widely present in daily environments, have been shown by existing epidemiological studies to be significantly associated with deficits in learning and memory functions in children and adults. However, the association between exposure to PAHs and cognitive function in older adults remains unclear. Additionally, existing related studies have only assessed the association between individual PAH exposures and cognitive assessments, overlooking the risks posed by mixed exposures. This study aims to use three statistical models to investigate the individual and overall effects of mixed PAH exposures on the cognition of older adults in the United States. Methods: The study cohort was obtained from the NHANES database, which included individuals aged 60 and older from 2011 to 2014. Weighted generalized linear models (GLM), weighted quantile sum (WQS) models, and Bayesian kernel machine regression (BKMR) models were utilized to evaluate the connections between urinary PAH metabolites and the standardized Z-scores of four cognitive tests: Immediate Recall Test (IRT), Delayed Recall Test (DRT), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Results: Our analysis involved 899 individuals aged 60 and above. In the fully adjusted GLM, 2-hydroxynaphthalene (2-OHNa), 3-hydroxyfluorene (3-OHFlu), and 2-hydroxyfluorene (2-OHFlu) demonstrated negative associations with DSST Z-scores. In the WQS model, six urinary PAH metabolites were negatively linked to AFT Z-scores (ß (95% confidence intervals [CI]): -0.120 (-0.208, -0.033), p = 0.007) and DSST Z-scores (ß (95% CI): -0.182 (-0.262, -0.103), p < 0.001). In both assessments, 2-OHNa exerted the greatest influence among the urinary PAH metabolites. In the BKMR model, there was an overall negative correlation between urinary PAH metabolites and AFT and DSST Z-scores when the concentration was within the 25th to 75th percentile, where 2-OHNa dominated the main effect of the mixture. The WQS and BKMR models were adjusted for all covariates. Conclusion: Increased concentrations of urinary PAH metabolites are associated with cognitive decline in older adults, mainly on language ability, executive function, sustained attention, working memory, and information processing speed, with 2-OHNa playing a major effect.

Lipidomic analyses reveal potential biomarkers for predicting death and heart failure after acute myocardial infarction.

Background Acute myocardial infarction (AMI) and postmyocardial infarction heart failure (pMIHF) have high mortality rates worldwide. This study aimed to explore lipidomic profiles and identify potential biomarkers for the prediction of death and heart failure (HF) after AMI. Methods All serum samples were collected at Xuanwu Hospital, Capital Medical University, and their clinical characteristics and lipidomic profiles were analyzed in different groups. LC-MS/MS was used for lipidomic analyses, and underlying biomarkers were screened by receiver operating characteristic (ROC) curve analysis. Results Lipidomic analyses of the survival and nonsurvival groups revealed that the decrease of the content of SM (d18:1/22:0), PE (P-20:1/18:0), PC (18:2), LPE (18:2), PE (P-20:0/18:0), LPC (18:0) and PC (20:0/20:3) while increase of the content of PG (18:1/18:1) could increase the risk of death after AMI. In parallel, the lipidomic analysis of the HF and non-HF groups revealed that the decrease of the content of PC (20:3/20:4), LPC (20:3), LPC (18:0), LPC (18:2), LPC (20:0), LPC (18:3), LPE (16:1) and PC (18:2/20:3) could increase the risk of HF after AMI. Conclusion Several lipids could be potential biomarkers for the prediction of death and HF after AMI.

Identification of molecular markers for predicting the severity of heart failure after AMI: An Olink precision proteomic study.

BACKGROUND: Acute myocardial infarction (AMI) still has a high incidence of varying degrees of heart failure (HF). The aim of this study is to identify new molecular markers for predicting the severity of HF after AMI. METHODS: We analyzed demographic indicators, past medical history, clinical indicators, major adverse cardiac events (MACEs) and molecular markers in patients with different Killip classifications after AMI. Olink proteomics was used to explore new molecular markers for predicting different severity of HF after AMI. RESULTS: Neutrophil count was the independent risk factors for in-hospital MACEs. Nineteen differentially expressed proteins (DEPs) increased significantly with increasing Killip classification. Five DEPs were also found to have an AUC (95 % CI) value greater than 0.8: GDF-15, NT-pro BNP, TNF-R2, TNF-R1 and TFF3. CONCLUSIONS: Neutrophil count, GDF-15, TNF-R2, TNF-R1 and TFF3 were closely related to the Killip classification of HF after AMI, which suggests that the inflammatory response plays an important role in the severity of HF after AMI and that regulating inflammation might become a new target for controlling HF.

Bird Object Detection: Dataset Construction, Model Performance Evaluation, and Model Lightweighting.

The application of object detection technology has a positive auxiliary role in advancing the intelligence of bird recognition and enhancing the convenience of bird field surveys. However, challenges arise due to the absence of dedicated bird datasets and evaluation benchmarks. To address this, we have not only constructed the largest known bird object detection dataset, but also compared the performances of eight mainstream detection models on bird object detection tasks and proposed feasible approaches for model lightweighting in bird object detection. Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of 73.7% compared to one-stage models. In addition, compared to one-stage object detection models, two-stage object detection models have a stronger robustness to variations in foreground image scaling and background interference in bird images. On bird counting tasks, the accuracy ranged between 60.8% to 77.2% for up to five birds in an image, but this decreased sharply beyond that count, suggesting limitations of object detection models in multi-bird counting tasks. Finally, we proposed an adaptive localization distillation method for one-stage lightweight object detection models that are suitable for offline deployment, which improved the performance of the relevant models. Overall, our work furnishes an enriched dataset and practice guidelines for selecting suitable bird detection models.

Effects of negative life events on depression in middle school students: The chain-mediating roles of rumination and perceived social support.

Background: Negative life events in middle school students have a significant impact on depression. However, the mechanism of this association is not fully understood. This study used rumination and perceived social support as mediating variables to explore the influence of negative life events on depression. Materials and methods: Due to the COVID-19 pandemic and social distancing, a convenient sampling method was adopted to collect information about middle school students in Shandong Province by means of online questionnaire. Adolescent Self-Rating Life Events Check List, Ruminative Responses Scale, Perceived Social Support Scale and Children's Depression Inventory were used. Descriptive statistics and correlation analysis were conducted for four variables of middle school students, including life events, depression, rumination thinking and perceived social support, and the chain mediated effect was tested by using process plug-in. All statistically analysis was conducted by SPSS 23.0. Results: 493 middle school students (16.7000 ± 0.9500 years) including 343 female students (69.6000%) from Shandong Province recruited. Results showed that the total effect between life events and depression was significant (effect = 0.2535, 95%CI: 0.2146, 0.2924). The total indirect effect was significant (effect = 0.1700, 95%CI: 0.1349, 0.2072). The indirect effect was significant (effect = 0.0988, 95%CI: 0.0741, 0.1252) with rumination as the mediating variable. The indirect effect of pathway with perceived social support as the mediating variable was significant (effect = 0.0476, 95%CI: 0.0295, 0.0674). The indirect effect of pathway with rumination and perceived social support as mediating variables was also significant (effect = 0.0236, 95%CI: 0.0147, 0.0339). Conclusion: This study indicated that ruminant thinking and perceived social support had a significant chain mediating effect on adolescents' life events and depression. Life events can not only directly affect depressive emotions, but also indirectly affect depressive emotions by affecting ruminant thinking and perceived social support. The results of this study not only provide new directions for the relationship between life events and depression, but also provide possible approaches for future prevention and intervention of depression in middle school students.

Magnetic supramolecular polymer: Ultrahigh and highly selective Pb(II) capture from aqueous solution and battery wastewater.

For the practical capture of heavy metal ions from wastewater, fabricating environmental friendly adsorbents with high stability and super adsorption capacity are pursuing issue. In this work, we develop magnetic supramolecular polymer composites (M-SMP) by using a simple two-step hydrothermal method. Systematical characterizations of morphological, chemical and magnetic properties were conducted to confirm the formation of M-SMP composites. The resulting M-SMP composites were applied to remove Pb(II) from aqueous solution and from real battery wastewater, and easy separation was achieved using a permanent magnet. By investigating the effects of various parameters, we optimized their operating condition for Pb(II) adsorption by the M-SMP. The uptake of Pb(II) onto M-SMP fitted well the pseudo-second-order and Langmuir isotherm models, and favourable thermodynamics showed a spontaneous endothermic process. The SMP endowed M-SMP with ultrahigh adsorption capacity for Pb(II) (946.9 mg g-1 at pH = 4.0, T = 298 K), remarkable selectivity, satisfactory stability and desirable recyclability. In Pb-contaminated lead-acid battery industrial wastewater, the concentration of Pb(II) declined from 18.070 mg L-1 to 0.091 mg L-1, which meets the current emission standard for the battery industry. These merits, combined with simple synthesis and convenient separation, make M-SMP an outstanding scavenger for the elimination of industrial Pb(II) wastewater.