Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

Bacillus coagulans can utilize the hydrolyzed carbon source of agricultural waste to produce lactic acid via a homofermentative pathway. However, a significant carbon source metabolic repression effect was observed when the strain metabolized mixed sugars (glucose and xylose), reducing the productivity of lactic acid. In this study, we obtained the fermentation conditions for the simultaneous utilization of the mixed sugars by B. coagulans by changing the ratio of glucose to xylose in the medium. Through transcriptome sequencing, several key genes responsible for xylose utilization were identified. The critical role of xylose isomerase (XylA, EC 5.3.1.5) in the synchronous utilization of glucose/xylose in B. coagulans was investigated via qRT-PCR (quantitative real-time polymerase chain reaction). Subsequently, the heterologous expression and characterization of the XylA-encoding gene (XylA) were conducted. It was determined that the gene encoded a protein composed of 440 amino acid residues. The secondary structure of the encoded protein was predominantly composed of α-helixes and random coils, while the higher structure of the protein was identified as a homotetramer. Then, XylA was cloned and expressed in Escherichia coli BL21(DE3), and the recombinant protein Bc-XlyA was obtained with a molecular weight of approximately 50 kDa. The optimal pH and temperature of Bc-XylA were 8.0 and 60 ℃, respectively, and Mn²⁺, Mg²⁺, and Co²⁺ had positive effects on the activity of Bc-XlyA. The present study provides scientific data on the molecular modification of B. coagulans, offering theoretical support for the efficient utilization of xylose in the strain.
Xylose/metabolism* ; Cloning, Molecular ; Bacillus coagulans/enzymology* ; Aldose-Ketose Isomerases/metabolism* ; Fermentation ; Bacterial Proteins/metabolism* ; Glucose/metabolism*

The research on the cardiovascular toxicity of air pollutants is in urgent need of collaborative innovation across multiple models. This paper systematically reviewed the advantages and limitations of four principal research models of cardiotoxicity, including epidemiological model, mammalian model, zebrafish model, and in vitro model. Epidemiological models have been used to demonstrate a significant correlation between exposure to PM_2.5 and both the incidence and mortality of cardiovascular diseases within populations; however, these models face challenges in establishing causal inferences and interpreting individual mechanisms. Mammalian models have been applied to elucidate the pathogenic mechanisms of PM_2.5 at both the systemic and organ-specific levels, yet they encounter difficulties related to interspecies differences and throughput constraints. Zebrafish models, with their transparent embryos and observable development, offer a distinctive opportunity for high-throughput screening and mechanistic investigation of PM_2.5-induced cardiac developmental toxicity. Nonetheless, their cardiac physiological structure diverges from that of mammals, limiting their capacity to accurately model chronic conditions such as coronary heart disease. In vitro models, particularly human heart organoids and chip technologies, have provided profound insights into the direct toxic mechanisms of PM_2.5, including disruptions in calcium homeostasis, cellular senescence, and electrophysiological irregularities at the cellular and molecular levels. Despite these advancements, the complexity and developmental maturity of these models present challenges to their broader application. This paper proposed that the key to overcoming the bottlenecks of single models lies in the construction of an integrated evaluation system that combines “epidemiological studies, mammalian models, zebrafish models, and in vitro models”. By focusing on three aspects, namely model integration, technological convergence, and policy support, it is intended to collaboratively address issues such as standardization of multi-model data, simulation of complex exposure scenarios and susceptible life stages, and transformation pathways. This will provide innovative methodological support for the analysis of the cardiotoxic mechanisms of air pollutants, the assessment of environmental health impacts, and the formulation of precise prevention and control strategies.

Objective:To investigate the distribution characteristics of skeletal muscle mass and strength in the older adults over 65 years old in 18 longevity areas in China.Methods:The subjects were selected from the Healthy Aging and Biomarkers Cohort Study conducted in 18 longevity areas of China. A total of 4 662 older adults over 65 years old from a cross- sectional survey in 2021 were included in the study. The information about their sociodemographic characteristics, lifestyle, nutrient intake and other factors were collected through questionnaire surveys and physical examinations. Grip strength was measured by using professional electronic grip dynamometer. Total skeletal muscle mass (TSM) was measured using bioelectrical impedance analysis, and TSM was adjusted by height squared and BMI to obtain TSM Ht2 and TSM BMI. The proportion of individuals with low muscle mass and strength was determined according to the recommended method by the Asian Working Group for Sarcopenia (AWGS). Descriptive analysis was conducted on the population and regional distribution characteristics of people with different muscle mass and grip strength. A generalized additive model was used to analyze the age-related trends of muscle mass and grip strength. Results:The age of 4 662 study subjects was (82.69±10.54) years, men accounted for 46.85% (2 184 cases) and Han Chinese accounted for 96.27% (4 488 cases). The M( Q1, Q3) of TSM, TSM Ht2 and TSM BMI in men were 23.30 (20.50, 26.20) kg, 9.02 (8.13, 9.89) kg/m 2, and 1.01 (0.90, 1.13) kg·(kg/m 2) -1, respectively, which were all higher than those in women [TSM: 18.20 (15.70, 20.70) kg, TSM Ht2: 8.18 (7.42, 9.07) kg/m 2 and TSM BMI: 0.79 (0.69, 0.90) kg·(kg/m 2) -1], the differences were significant (all P<0.001). The grip strength of men [ M( Q1, Q3): 24.50 (17.80, 30.80) kg] was higher than that of women [ M( Q1, Q3): 15.60 (11.10, 19.90) kg], the difference was significant ( P<0.001). Southern elderly men had lower TSM and TSM Ht2 compared with northern elderly men (all P<0.001), while there was no significant regional difference in TSM BMI ( P>0.05). Southern elderly women had higher TSM Ht2 and TSM BMI compared with northern elderly women (all P<0.001), while there was no significant regional difference in TSM ( P>0.05). Furthermore, according to the method recommended by AWGS, the elderly with low muscle mass and grip strength were characterized by older age, illiteracy, being unmarried/divorced/widowed, poor chewing ability, impaired activity of daily living and living in southern region. Conclusion:There were population and regional differences in muscle mass and grip strength in the older adults over 65 years in 18 longevity areas of China, and these differences showed decreasing trends with age.

Objective To explore the mechanism underlying the protective effect of Lonicerae japonicae flos(LJF)extract against doxorubicin(DOX)-induced liver injury in mice.Methods Network pharmacology methods were used to obtain the intersection genes between LJF targets and disease targets,based on which the protein-protein interaction(PPI)network was constructed using STRING database for screening the core targets using Cytoscape software.DAVID database was used for bioinformatics analysis,and the core components and core targets were verified using molecular docking study.In a mouse model of DOX-induced liver injury,the effect of LJF extract on liver pathologies,serum levels of ALT and AST,and hepatic expressions of HYP,ROS,TNF-α,IL-6,COL-IV and P53 proteins were evaluated using HE and Masson staining,ELISA,and Western blotting.Results We identified 12 core targets from 43 intersection genes involving cancer pathway,IL-17 signaling pathway,and TNF signaling pathways.Molecular docking study suggested that 10 core components of LJF could bind to different core targets.The mice with DOX-induced liver injury showed elevated serum AST and ALT levels with obvious liver injury and fibrosis,increased ROS content,and enhanced expressions of TNF-α,IL-6,HYP,COL-IV and P53 proteins in the liver tissue.All these changes in the mouse models were significantly alleviated by treatment with LJF extract,suggesting obviously lowered levels of oxidative stress,inflammation and fibrosis in the liver tissues.Conclusion LJF extract is capable of alleviating DOX-induced liver injury in mice by downregulating Trp53,TNF and IL-6 to reduce liver oxidative stress,inflammation and fibrosis.