ObjectiveTo investigate the association of the single nucleotide polymorphism （SNP） of the insulin-like growth factor binding protein-3 （IGFBP3） gene at rs10225396 （A>G） locus with the susceptibility to hepatocellular carcinoma， as well as its potential molecular regulatory mechanisms， and to provide novel genetic biomarkers and a theoretical basis for early screening and precise targeted therapy for hepatocellular carcinoma. MethodsA total of 192 patients with hepatocellular carcinoma who were admitted to The Affiliated Hospital of Yanbian University and Yanbian Cancer Hospital from January 2009 to August 2016 were enrolled as experimental group， and 190 healthy individuals who underwent physical examination in Physical Examination Center of Yanbian Hospital during the same period of time were enrolled as control group. Peripheral blood samples were collected from all subjects， and after DNA was extracted from whole blood， the DNA samples meeting quality standards were sent to Beijing Genomics Institute Research Center Co.， Ltd.， for Mass ARRAY mass spectrometry， while genotyping was completed. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. A binary Logistic regression model was used to analyze the association of the SNP of the IGFBP3 gene at rs10225396 locus with the susceptibility to hepatocellular carcinoma， and odds ratio （OR） and 95% confidence interval （CI） were calculated to assess the risk of developing hepatocellular carcinoma in individuals carrying different genotypes. ResultsThere were two alleles （G and A） at the rs10225396 locus of the IGFBP3 gene， yielding the three genotypes of AA， AG， and GG， and its genotype distribution was consistent with the Hardy-Weinberg equilibrium （χ²=0.072， P=0.789）. The stratified genetic analysis showed that carriers of the IGFBP3 rs10225396 AA genotype had a significant increased risk of hepatocellular carcinoma among individuals of age <63 years， male sex， smoking history， drinking history， and the Chinese Korean population （all P<0.001）. The binary Logistic regression analysis showed that after adjustment for related risk factors in the codominant model， the population with genotype AG and GG had a significant reduction in the risk of hepatocellular carcinoma compared with the population with genotype AA （P<0.001）， and in the dominant model， the population with genotype AG+GG had a significant reduction in the risk of hepatocellular carcinoma compared with the population with genotype AA （P<0.001）. ConclusionThrough a genotyping analysis of hepatocellular carcinoma patients and healthy individuals in Yanbian Korean Autonomous Prefecture of Jilin Province， China， this study shows that genotype AA at rs10225396 locus of the IGFBP3 gene is significantly associated with the susceptibility to hepatocellular carcinoma， and this genotype can significantly increase the risk of developing hepatocellular carcinoma with the presence of specific risk factors， which provides a potential genetic marker for early screening and precise treatment of hepatocellular carcinoma.

ObjectiveTo investigate the effects of paeoniflorin （PF） on neuroinflammation and cognitive impairment in the rat model of Alzheimer's disease （AD） and to clarify whether its mechanism is associated with regulation of the Janus kinase 2 （JAK2）/signal transducer and activator of transcription 3 （STAT3） signaling pathway. MethodsGlycosides related to AD-associated neuroinflammation were screened through bibliometric analysis， and the potential targets of PF were predicted through network pharmacology and molecular docking. A rat model of AD was established by intraperitoneal injection of D-galactose （100 mg·kg^-1·d^-1）. Rats were assigned into a control group， a model group， a PF （200 mg·kg^-1） group， and a donepezil （0.5 mg·kg^-1·d^-1） group， and drugs were administered by gavage for 21 days. The learning and memory abilities were evaluated by the Morris water maze （MWM）. Hematoxylin-eosin （HE） staining and Nissl staining were performed to observe hippocampal neuronal morphology. The mRNA levels of inflammatory factors were determined by Real-time quantitative polymerase chain reaction （Real-time PCR）， and the protein levels of molecules in the JAK2/STAT3 signaling pathway were determined by Western blot. ResultsCompared with the control group， the model group showed prolonged escape latency and decreased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， accompanied by increased expression of interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， as well as elevated ratios of phosphorylated （p）-JAK2/JAK2 and p-STAT3/STAT3 （P<0.01）. Compared with the model group， the PF group and donepezil group exhibited shortened escape latency and increased platform crossings and time spent in the target quadrant （P<0.05， P<0.01）， decreased expression levels of IL-1β and TNF-α， upregulated mRNA levels of interleukin-4 （IL-4） and interleukin-10 （IL-10）， and reduced ratios of p-JAK2/JAK2 and p-STAT3/STAT3 （P<0.05， P<0.01）. ConclusionPaeoniflorin can alleviate cognitive impairment and neuroinflammation in the rat model of AD by inhibiting the JAK2/STAT3 signaling pathway.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

OBJECTIVE To promote the application of drones in emergency rescue and related fields， expand “low-altitude+ medical” rescue services， and advance the standardization of “low-altitude+medical” distribution services. METHODS The Consensus on Low-altitude Transport and Delivery Services for Emergency Medicines via Drones （2025 Edition） （hereinafter referred to as the Consensus） was jointly initiated by the Division of Therapeutic Drug Monitoring， Chinese Pharmacological Society and the Expert Committee on Precision Medication of the Guangdong Pharmaceutical Association. Guangzhou Red Cross Hospital served as the leading unit， organizing 53 multidisciplinary experts nationwide to participate in drafting and reviewing. A nominal group technique was employed to discuss and finalize the consensus outline， resulting in a preliminary draft. Delphi method was employed， and 11 external review experts were invited to conduct the evaluation. After the experts’ opinions were analyzed and integrated， the Consensus was finalized. RESULTS & CONCLUSIONS The finalized Consensus includes its purpose， principles， and applicable scenarios， basic requirements， and operational procedures for low-altitude transport and delivery of emergency medications； distribution requirements and precautions for controlled substances， fragile medications， and temperature-sensitive medications； and recommendations for emergency medications supplies suitable for the low-altitude transportation and distribution. The release of this Consensus is expected to provide guidance and support for the standardization of “low-altitude+medical” distribution services and the application of low-altitude economy in the healthcare sector.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

Objective To investigate the relationship between the single nucleotide polymorphism of PPARG rs2290449 and the efficacy of combination therapy in sepsis patients with MODS.Methods 382 cases of sepsis with MODS were selected and divided into the effective group and the ineffective group,and a case-control study was conducted.PCR-RFLP and Sequenom MassARRAY were used to detect the genotype and allele frequencies of the loci.Unconditional Logistic regression was used to calculate odds ratio(OR)and 95％confidence interval(CI)to evaluate the efficacy of combination therapy for sepsis with MODS in patients with different genotypes.Re-sults There were G and A alleles in PPARG rs2290449 locus,GG、GA and AA genotype.Non-conditional Logis-tic regression analysis showed that compared with the GG genotype group,the GA genotype group had a significant correlation with the efficacy of combination therapy for sepsis with MODS(OR=0.449,95％CI=0.280-0.722,P=0.001).In the dominant model,there was a significant correlation between the GA+AA genotype and the effi-cacy of combination therapy compared with the GG genotype(OR=2.104,95％CI=1.332-3.321,P=0.001).After adjusting for confounding factors,unconditional Logistic regression analysis showed that compared with the GG genotype,the GA+AA genotype was significantly correlated with the efficacy of combination therapy in sepsis pa-tients with MODS(OR=2.307,95％CI=1.438-3.701,P=0.001).Stratified analysis showed that compared with the population carrying GG genotype,the population carrying GA+AA genotype was significantly correlated with the efficacy of combination therapy in sepsis with MODS in the stratified analysis of age,gender and ethnicity.Conclusion PPARG rs2290449 single nucleotide polymorphism is significantly correlated with the efficacy of com-bination therapy in sepsis patients with MODS in Yanbian area.

OBJECTIVE To improve the diagnosis and treatment level of critically ill infectious diseases， standardize the clinical application of nanopore sequencing and promote the sound development of the technology. METHODS Division of Therapeutic Drug Monitoring of Chinese Pharmacological Society and Expert Committee of Precision Medicine for Clinical Treatment of Guangdong Pharmaceutical Association initiated and organized multidisciplinary experts to discuss and determine the consensus writing outline by using the nominal group method， forming a preliminary consensus draft； expert consultation was performed by using Delphi method， and then experts’ opinions were analyzed and revised to form consensus. RESULTS & CONCLUSIONS Consensuses of Experts on the Application of Nanopore Sequencing Technology in the Detection of Pathogenic Microorganisms covers targeted sequencing， metagenomic sequencing and whole genome sequencing， and is standardized in terms of sample collection and storage， detection process， bioinformatics analysis and report interpretation； the recommendations are provided for the key issues.

Biological age (BA) is a marker to accurately assess aging, facilitating the prediction of age-related diseases and promoting healthy aging. In recent years, first- and second-generation organ-system-specific BA has been developed using chronological age (CA) or aging-related outcomes (mortality) as training phenotypes and data from questionnaires, physical examinations, clinical biochemistry, imaging, and multi-omics to investigate the specificity of organ systems aging. Here, we review the methodologies for constructing BA, current efforts to assess organ system-specific BA, and related genome-wide association studies (GWAS). Previous studies predominantly used the first-generation BA method, using CA as training phenotypes. Organ-system-specific BA can accurately predict the disease risk of corresponding organ systems. We propose the development of organ system-specific BA through second-generation BA models and conducting GWAS and Mendelian randomization studies to explore organ system-specific aging processes, which will provide a theoretical foundation for the clinical application of organ system-specific BA.