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 provide reference for basic analysis of the pharmacodynamic substance in Stahlianthus involucratus. METHODS Overall 24 SD male rats were randomly divided into blank group （purified water）， and administration group （ethanol extract of S. involucratus， 15.75 g/kg， calculated by crude drug）， with 12 rats in each group. They were given drug liquid/purified water intragastrically， twice a day， every 6-8 h， for consecutive 3 days. After medication， the blood， urine and fecal samples were collected from two groups of rats. UPLC-Q-Exactive-MS technology was used to identify the chemical constituents in the ethanol extract of S. involucratus， and metabolites in the blood， urine and fecal of rats after intragastrical administration of the ethanol extract of S. involucratus. Multivariate statistical analysis was employed to screen various serum metabolites. Metabolic pathways were analyzed by MetaboAnalyst 5.0 platform. RESULTS A total of 38 chemical constituents were identified from the ethanol extract of S. involucratus， including fourteen prototype components and three metabolites identified from 5 urine samples， nine prototype components identified from fecal samples， and ten prototype components and one metabolite identified from serum samples. A total of 71 differential metabolites were screened from two groups of rat serum samples， of which 44 differential metabolites， such as ferulic acid， glycyrrhizin， were up-regulated and 27 differential metabolites， such as arachidonic acid， phenylacetylglutamine， were down-regulated. The 71 differential metabolites were mainly enriched in 11 metabolic pathways， including phenylalanine metabolism， linoleic acid metabolism， arachidonic acid metabolism， and tryptophan metabolism. CONCLUSIONS Ferulic acid， liquiritigenin， isofraxidin and formononetin may be the material basis that directly exert pharmacological effects of S. involucratus. S. involucratus may exert anti-inflammatory effects by affecting metabolic pathways， including arachidonic acid metabolism and tryptophan metabolism.

OBJECTIVE To analyze the metabolites of Zhideke granules and speculate its metabolic pathway in rats in vivo. METHODS Male SD rats were randomly divided into blank group and administration group （Zhideke granules， 9.45 g/kg）； they were given ultrapure water or relevant medicine， twice a day， every 6-8 h， for 3 consecutive days. Serum， urine and feces samples of rats were collected， and their metabolites were identified by UPLC-Q-Exactive-MS technique after intragastric administration of Zhideke granules； their metabolic pathways were speculated. RESULTS After intragastric administration of Zhideke granules， 16 prototype components （i.g. irisflorentin， baicalin， chlorogenic acid） and 11 metabolites （i.g. hydration products of kaempferol or luteolin， methylation products of chlorogenic acid， and hydroxylation products of baicalin） were identified in serum， urine and feces of rats. Among them， 8 prototype components and 4 metabolites were identified in serum samples； 10 prototype components and 7 metabolites were identified in urine samples； 8 prototype components and 5 metabolites were identified in the fecal samples. CONCLUSIONS The metabolites of Zhideke granules in rats mainly include baicalin， irisflorentin，chlorogenic acid， and the main metabolic pathways included methylation， hydroxylation， glucuronidation.