ObjectiveTo study the pharmacological substances and mechanisms through which sesquiterpene ZH-13 from Aquilariae Lignum Resinatum improves neuroinflammation. MethodsBV-2 microglial cells were stimulated with lipopolysaccharide （LPS） to induce neuroinflammation. The cells were divided into the normal group， the model group， and the ZH-13 low- and high-dose treatment groups （10， 20 μmol·L^-1）. The model group was treated with 1 μmol·L^-1 LPS. Cell viability was assessed using the cell proliferation and activity assay （CCK-8 kit）. Nitric oxide （NO） release in the cell supernatant was measured using a nitric oxide kit （Griess method）. The mRNA expression levels of interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， inducible nitric oxide synthase （iNOS）， and interleukin-6 （IL-6） were detected by real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. The phosphorylation of mitogen-activated protein kinase （MAPK） pathway proteins was assessed by Western blot. ResultsCompared with the model group， ZH-13 dose-dependently reduced NO release from BV-2 cells under LPS stimulation （P<0.05， P<0.01）. In the 20 μmol·L^-1 ZH-13 treatment group， the mRNA expression levels of IL-1β， TNF-α， iNOS， and IL-6 were significantly reduced compared to the model group （P<0.05， P<0.01）. In both the low- and high-dose ZH-13 groups， the expression of the inflammatory factor TNF-α and the phosphorylation of c-Jun N-terminal kinase （JNK） in the upstream MAPK pathway were significantly reduced （P<0.05）. After stimulation with the JNK agonist anisomycin （Ani）， both low- and high-dose ZH-13 treatment groups showed reduced phosphorylation of JNK proteins compared to the Ani-treated group （P<0.01）. ConclusionThe sesquiterpene compound ZH-13 from Aquilariae Lignum Resinatum significantly ameliorates LPS-induced neuroinflammatory responses in BV-2 cells by inhibiting excessive JNK phosphorylation and reducing TNF-α expression. These findings elucidate the pharmacological substances and mechanisms underlying the sedative and calming effects of Aquilariae Lignum Resinatum.

Clostridioides difficile infection(CDI)is the leading cause of hospital-acquired diarrhea and has be-come a major challenge in the global public health field.In recent years,it has been found that nutrients and gut mi-crobes play key roles in regulating growth,metabolic activity,and virulence expression of Clostridioides difficile.However,current research focuses on the independent effects of a single nutrient or gut microbe,systematic under-standing on the interactions between them is still lacking.This paper reviews the specific effect of different nutrients on Clostridioides difficile,explores how other gut microbes inhibit the growth of Clostridioides difficile by com-peting or metabolizing nutrients.In addition,this paper also discusses the application of emerging technologies in CDI research and their potentiality in clinical intervention strategies.Future research needs to integrate multi-omics data and artificial intelligence analysis,deeply analyze the complex interactive network of nutrient-microbe-host,and provide new ideas for precise prevention and treatment of CDI.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, which affects maternal and infantile glucose metabolism both during pregnancy and in the long-term postpartum and is closely related to several perinatal complications. In recent years, many studies devote to exploring early predictive biomarkers of GDM, and a variety of early prediction models of GDM have been established using clinical indicators, biochemical markers, metabolic/proteomic markers, gene polymorphisms, etc. This review summaries and evaluates the published GDM prediction models in terms of research types, modeling methods, indicators, model prediction performance, and validation, attempting to inform the early prevention of GDM.

Objective:To assess the influencing factors of glycemic control among type 2 diabetes mellitus (T2DM) patients, in particular the correlations between peripheral blood eosinophils and metabolic indicators such as glucose, lipids, and uric acid (UA) in these patients.Methods:T2DM patients who were regularly followed up in the Intensive Diabetes Mellitus Clinic of Peking Union Medical College Hospital from January 2016 to September 2024 were prospectively selected as the research subjects. The clinical data of these patients at their first visit and at the 3rd, 6th, 9th, and 12th months of follow-up were collected. The potential correlations of peripheral blood eosinophils with blood glucose control, lipid metabolism, UA metabolism, and inflammation as well as their influencing factors were analyzed.Results:A total of 161 T2DM patients were included. The glycated hemoglobin A1c (HbA1c) compliance rate was significantly higher during the follow-up visits (3, 6, 9, and 12 months) than baseline (all P<0.05) but showed no significant correlation with the count or percentage of peripheral blood eosinophils. Spearman correlation analysis showed that the percentage of peripheral blood eosinophils was negatively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) ( P=0.049) and high-density lipoprotein cholesterol (HDL-C) ( P=0.002) and positively with serum triglyceride (TG) ( P=0.034) and UA levels ( P<0.001). A further linear regression model analysis of these four variables and the percentage of eosinophils revealed that the percentage of peripheral blood eosinophils was only linearly correlated with serum UA level ( P<0.001). Conclusion:The percentage of peripheral blood eosinophils in T2DM patients is independently correlated with serum UA level, and monitoring serum UA level is important in T2DM management. Early identification and intervention of hyperuricemia can help provide more comprehensive and precise medical interventions for T2DM patients.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, which affects maternal and infantile glucose metabolism both during pregnancy and in the long-term postpartum and is closely related to several perinatal complications. In recent years, many studies devote to exploring early predictive biomarkers of GDM, and a variety of early prediction models of GDM have been established using clinical indicators, biochemical markers, metabolic/proteomic markers, gene polymorphisms, etc. This review summaries and evaluates the published GDM prediction models in terms of research types, modeling methods, indicators, model prediction performance, and validation, attempting to inform the early prevention of GDM.

Objective:To assess the influencing factors of glycemic control among type 2 diabetes mellitus (T2DM) patients, in particular the correlations between peripheral blood eosinophils and metabolic indicators such as glucose, lipids, and uric acid (UA) in these patients.Methods:T2DM patients who were regularly followed up in the Intensive Diabetes Mellitus Clinic of Peking Union Medical College Hospital from January 2016 to September 2024 were prospectively selected as the research subjects. The clinical data of these patients at their first visit and at the 3rd, 6th, 9th, and 12th months of follow-up were collected. The potential correlations of peripheral blood eosinophils with blood glucose control, lipid metabolism, UA metabolism, and inflammation as well as their influencing factors were analyzed.Results:A total of 161 T2DM patients were included. The glycated hemoglobin A1c (HbA1c) compliance rate was significantly higher during the follow-up visits (3, 6, 9, and 12 months) than baseline (all P<0.05) but showed no significant correlation with the count or percentage of peripheral blood eosinophils. Spearman correlation analysis showed that the percentage of peripheral blood eosinophils was negatively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) ( P=0.049) and high-density lipoprotein cholesterol (HDL-C) ( P=0.002) and positively with serum triglyceride (TG) ( P=0.034) and UA levels ( P<0.001). A further linear regression model analysis of these four variables and the percentage of eosinophils revealed that the percentage of peripheral blood eosinophils was only linearly correlated with serum UA level ( P<0.001). Conclusion:The percentage of peripheral blood eosinophils in T2DM patients is independently correlated with serum UA level, and monitoring serum UA level is important in T2DM management. Early identification and intervention of hyperuricemia can help provide more comprehensive and precise medical interventions for T2DM patients.

To analyze the distribution of serotypes and antimicrobial resistance of clinical Salmonella isolates from multiple centers across China.

This paper reviews the published articles of recent years on the application of deep learning methods in automatic localization of acupoint, and summarizes it from 3 key links, i.e. the dataset construction, the neural network model design, and the accuracy evaluation of acupoint localization. The significant progress has been obtained in the field of deep learning for acupoint localization, but the scale of acupoint detection needs to be expanded and the precision, the generalization ability, and the real-time performance of the model be advanced. The future research should focus on the support of standardized datasets, and the integration of 3D modeling and multimodal data fusion, so as to increase the accuracy and strengthen the personalization of acupoint localization.
Deep Learning ; Acupuncture Points ; Humans ; Neural Networks, Computer

In recent years, the global prevalence of diabetes has shown a continuous upward trend, leading to an increasingly heavy disease burden. Poorly controlled diabetes and its complications not only significantly reduce patients' quality of life but may also be life-threatening, while simultaneously posing severe challenges to socioeconomic development. In December 2024, based on the latest evidence-based medical findings, the American Diabetes Association (ADA) released the 2025 American Diabetes Association Standards of Care in Diabetes, which provide comprehensive updates to the integrated management strategies for diabetes, its complications, and comorbidities. This article interprets the key elements of these guidelines, aiming to offer the latest evidence-based references and practical guidance for the clinical diagnosis and treatment of diabetes.

Clostridioides difficile infection(CDI)is the leading cause of hospital-acquired diarrhea and has be-come a major challenge in the global public health field.In recent years,it has been found that nutrients and gut mi-crobes play key roles in regulating growth,metabolic activity,and virulence expression of Clostridioides difficile.However,current research focuses on the independent effects of a single nutrient or gut microbe,systematic under-standing on the interactions between them is still lacking.This paper reviews the specific effect of different nutrients on Clostridioides difficile,explores how other gut microbes inhibit the growth of Clostridioides difficile by com-peting or metabolizing nutrients.In addition,this paper also discusses the application of emerging technologies in CDI research and their potentiality in clinical intervention strategies.Future research needs to integrate multi-omics data and artificial intelligence analysis,deeply analyze the complex interactive network of nutrient-microbe-host,and provide new ideas for precise prevention and treatment of CDI.