With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

Kidney transplantation is the most effective treatment for end-stage renal failure, and antibody-mediated rejection remains the leading cause of late allograft loss. Macrophages, as central effectors of innate immunity, play a crucial role in the initiation, progression and tissue damage of antibody-mediated rejection. This article reviews the spatiotemporal dynamic evolution of macrophage polarization status in different stages of antibody-mediated rejection, the fine regulation of key signaling pathways for macrophage polarization, macrophage related molecules and the application prospects of targeted macrophage therapy. In depth analysis of the research progress of macrophages in antibody-mediated rejection, aiming to provide important theoretical basis for the development of precision diagnostic tools based on macrophages and novel immune intervention targets for antibody mediated rejection, ultimately promoting the improvement of long-term prognosis in kidney transplantation.

Objective To explore the risk factors of painful diabetic neuropathy (PDN) in patients with type 2 diabetes mellitus (T2DM). Methods A total of 269 patients with type 2 diabetes mellitus (T2DM) who were treated in the Department of Endocrinology at Zhangjiagang Hospital Affiliated to Soochow University from January 2020 to December 2024 were selected. The patients were divided into two groups: T2DM without PDN (n=190) and T2DM with PDN (n=79). The general characteristics and biochemical indicators of the two groups of patients were compared. Multivariate logistic regression was used to analyze the associated factors with PDN in T2DM. The receiver operating characteristic (ROC) curve was used to evaluate fasting C-peptide (FC-P), body mass index (BMI), and disease duration to predict the risk of PDN. Results Compared with the T2DM group without concurrent PDN, the T2DM group with concurrent PDN had a longer disease course, lower BMI, higher HDL-C, lower FC-P, and a higher proportion of diabetic retinopathy. The differences between the two groups were statistically significant (P<0.05). The results of multivariate logistic regression analysis showed that BMI, duration, and FC-P were associated factors of PDN. Conclusion BMI, duration and FC-P are associated factors of painful neuropathy complicated with type 2 diabetes.

Objective: To analyze the epidemiological characteristics of hand, foot and mouth disease (HFMD) among children aged 0-9 years in Guangzhou, so as to provide reference basis for further exploring scientifically effective prevention and control measures. Methods: Reported cases of HFMD in children aged 0-9 years in Guangzhou City from 2013-2025 were selected from China disease prevention and control information system, and the incidence in different years was analyzed descriptively; the trends of the composition ratio and incidence in various age groups were analyzed by using the Joinpoint regression model, spatial autocorrelation analyses were performed at the district level, and the seasonal autoregressive integrated moving average model (SARIMA) was applied to predict annual incidence rate for 2026. Results: From 2013 to 2025, the average annual reported incidence rate of HFMD among children aged 0-9 years in Guangzhou was 2 513.98/100 000, showing a fluctuating trend. Peaks of incidence occurred from May to July and from September to October. The male to female ratio was 1.49∶1. The average annual incidence rate was 4 743.99/100 000 in the 0-1 years age group, 3 298.19/100 000 in the 2-5 years age group, and 544.22/ 100 000 in the 6-9 years age group. Joinpoint regression analysis showed a decreasing trend in the proportion of the 0-1 years age group (APC= -6.02 %, t =-3.77) and an increasing trend in the proportion of the 6-9 years age group (APC=16.39%, t =5.67) (both P < 0.05 ). No significant global spatial clustering was observed at the district level; however, local scattered clusters were identified in certain areas. Using the SARIMA(2,0,0)(2,0,0) 12 model, the predicted annual incidence rate of HFMD among children aged 0-9 years in Guangzhou in 2026 was 2 680.56/100 000. Conclusions From 2013 to 2025, the annual incidence rate of HFMD among children aged 0-9 years in Guangzhou exhibited a fluctuating trend; the highest incidence rate is observed in the 0-1 years age group, while the proportion of cases in the 6-9 years age group increase year by year;and local spatial clusters of abnormal distribution are identified. It is essential to enhance risk monitoring and health education in key populations and areas to mitigate the risk of infection in children.

Objective: Inflammatory cascade reactions play a crucial role in secondary neuronal injury in acute ischemic stroke (AIS). The aim of this study was to explore the correlations between specific serological indicators, early neurological deterioration (END), disease prognosis, and syndrome factors in AIS based on this injury mechanism. Methods: The data for this study were collected from 135 patients with AIS admitted to the emergency department of Fangshan Hospital, Beijing University of Chinese Medicine, within 24 h of onset between November 2019 and May 2021. Among these, 29 patients had complete data and experienced END. Additionally, 9 non-END patients were matched from the remaining 90 patients with complete data, resulting in a total of 38 patients for statistical analysis. Statistical methods, including logistic regression and receiver operating curves, were used to analyze the correlation between serum levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-10 (IL-10), vascular endothelial growth factor (VEGF), and intercellular adhesion molecule-1 (ICAM-1) within 24 h of END onset, disease prognosis, and syndrome factors. Grouping criteria included END occurrence, presence of syndrome elements on the first and third day post-onset, and prognosis at 90 days post-onset. Results: All 38 cases had onset time of less than 12 h, and there were no significant differences in age, gender, and onset time between the END and non-END groups. The TNF-α serum level within 24 h of onset was not associated with the occurrence of END but was negatively correlated with all-cause mortality at 90 days [0.110; P<0.05). An elevated IL-10 serum level within 24 h of onset showed a strong negative correlation with non-disabling and good functional outcomes at 90 days post-onset (0.110; P<0.05). When the ICAM-1 serum levels exceeded 233 599.500 μg/L and 125 141.500 μg/L, respectively, the susceptibility to endogenous wind and endogenous fire on the third day of onset was 15.364 times and 6.071 times higher, respectively, than that in patients with serum levels below these thresholds. Conclusion As accessible and objective biomarkers, inflammatory serum indicators are closely associated with both disease progression and traditional Chinese medicine (TCM) syndrome elements. They enhance AIS diagnosis, assessment, and treatment and contribute to a deeper understanding of the role of TCM syndrome differentiation in AIS diagnosis and treatment.

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

OBJECTIVES: To analyze the differences in the prognosis of gastric signet ring cell carcinoma (SRCC) among different races using the US Surveillance Epidemiology and End Results (SEER) database and The Cancer Genome Atlas (TCGA) database. METHODS: We analyzed the data of patients with gastric SRCC from the SEER database from 2000 to 2020, and divided the patients into cohorts of whites, blacks, Asians or Pacific Islanders, American Indians/Alaska Natives according to their race. The prognosis and treatment of the cohorts were evaluated using baseline demographic analysis, Kamplan-Meier survival curve, and nomogram analysis. RESULTS: We analyzed the data of a total of 2058 patients, including 8.6% blacks, 72.4% whites, 16.6% Asians or Pacific Islanders, 1.0% American Indians/Alaska Natives, and 1.4% other races. The tumor grade varied among different races, and the prevalence and survival rates of patients differed significantly across races. The differences in the white cohort were the most prominent, and all the differences were statistically significant (P<0.05). Racial differences were also noted in patient management and prognosis. CONCLUSIONS There are racial differences in tumor grades and prognosis of gastric SRCC, and these differences provide evidence for optimizing clinical diagnosis and treatment strategies for this malignancy.
Aged ; Female ; Humans ; Male ; Middle Aged ; Carcinoma, Signet Ring Cell/therapy* ; Databases, Factual ; Prognosis ; Racial Groups ; SEER Program ; Stomach Neoplasms/therapy* ; Survival Rate ; United States/epidemiology* ; White ; Asian American Native Hawaiian and Pacific Islander ; American Indian or Alaska Native ; Black or African American

Long-term exposure to particulate matter has been increasingly implicated in the progression of chronic kidney disease (CKD). However, its impact on IgA nephropathy (IgAN), a leading cause of end-stage renal disease (ESRD), remains unclear. A total of 1768 IgAN patients, confirmed by renal biopsy were included in this cohort study. Long-term exposure to PM_2.5 and PM₁₀ was assessed using high-resolution satellite-based data from the China High Air Pollutants (CHAP) dataset. Cox proportional hazards models were used to estimate the associations between PM_2.5 or PM₁₀ and ESRD risk, adjusting for demographic, clinical, and biochemical covariates. Over a median follow-up of 3.63 years, 209 participants progressed to ESRD. Higher exposure to both PM_2.5 and PM₁₀ was significantly associated with an increased risk, with hazard ratios of 1.62 and 1.36 per 10 µg/m³ increase, respectively. A nonlinear dose-response relationship was observed, with risk increasing markedly beyond threshold levels. Trajectory modeling of prebaseline exposure identified a subgroup with persistently high and fluctuating particulate matter exposure that showed the highest risk. This study provides strong evidence that prolonged exposure to ambient particulate matter contributes to renal disease progression in individuals with IgAN.
Humans ; Glomerulonephritis, IGA/pathology* ; Particulate Matter/adverse effects* ; Male ; Female ; Kidney Failure, Chronic/epidemiology* ; Adult ; China/epidemiology* ; Disease Progression ; Environmental Exposure/adverse effects* ; Middle Aged ; Proportional Hazards Models ; Risk Factors ; Air Pollutants/adverse effects* ; Cohort Studies

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