Objective:To explore the potential categories and influencing factors of medication compliance in patients with cardiometabolic multimorbidity, and provide a reference for formulating targeted intervention measures.Methods:A cross-sectional study design was adopted. From March to October 2024, the patients with cardiometabolic multimorbidity in the First Hospital Affiliated with Hunan Normal University (Hunan Provincial People′s Hospital) were selected by convenience sampling method as research objects. Data were collected using a general information questionnaire, Medication Adherence Rating Scale (MARS), Perceived Social Support Scale (PSSS), and Medication Literacy Questionnaire. The latent class analysis was used to explore the characteristics and classifications of medication compliance in cardiometabolic multimorbidity, and unordered multivariate Logistic regression was used to analyze the influencing factors of different latent classes.Results:A total of 421 subjects were included, consisting of 291 males and 130 females, aged (64.28±9.74) years old. The overall medication adherence score was 6.00 (5.00, 8.00) points, which could be divided into four categories: overall good adherence group (24.47%, 103/421), subjective perception-poor adherence group (15.91%, 67/421), forgetfulness-poor adherence group (37.53%, 158/421), and overall poor adherence group (22.09%, 93/421). The results showed that when taking the overall good adherence group as a reference, the inability to obtain pharmaceutical information from social media, medication literacy scores, social support scores were the influencing factors for the subjective perception-poor adherence group ( OR=4.210, 0.516, 0.733, all P<0.05). Occupational characteristics (employees in public institutions or government-affiliated institutions), age, social support scores were the influencing factors for the forgetfulness-poor adherence group( OR=0.173, 1.155, 0.781, all P<0.05). Occupational characteristics (employees in public institutions or government-affiliated institutions), failure to receive medication guidance from medical staff, medication literacy scores and social support scores were the influencing factors for the overall poor adherence group( OR values were 0.136-5.275, all P<0.05). When taking the overall poor adherence group as a reference, failure to receive medication guidance from medical staff and medication literacy scores were the influencing factors for the subjective perception-poor adherence group ( OR=0.310, 1.752, both P<0.05). Failure to receive medication guidance from medical staff, age, medication literacy scores and social support scores were the influencing factors for the forgetfulness-poor adherence group ( OR values were 0.315-2.554, all P<0.05). Conclusions:There is significant heterogeneity in medication adherence among patients with cardiometabolic multimorbidity. Healthcare professionals should consider individual characteristics in clinical practice and provide targeted, precise interventions to improve adherence in different patient categories.

Objective Pharmacovigilance,as an essential component of post-marketing drug regulation,plays a crucial role in ensuring the safety of public medication use.In recent years,Artificial intelligence(AI)technology,with its powerful data processing and analysis capabilities,has shown tremendous potential in the field of Pharmacovigilance.This article introduced and analyzed the application background of AI technology in Pharmacovigilance,its exploration and current status both internationally and within China,highlighted its application in the country,as well as the associated problems and challenges from the perspective of post-marketing regulation.It aims to provide reference for post-marketing regulation in China.Currently,China's exploration and application of AI in pharmacovigilance have just begun,and efforts should be intensified and the pace accelerated.Regulatory agencies should actively develop and implement regulatory standards or relevant guidelines for AI technologies throughout the product lifecycle,strengthen personnel training,talent cultivation,and introduction,actively promote close cooperation among regulatory agencies,pharmaceutical companies,research institutes,and universities,and jointly promote the innovative application of AI technology in the field of drug regulation.

Objective In view of the potential threat of adverse drug reaction(ADR)to patients'health and the limitations of traditional monitoring methods,this study investigates the construction method of an ADR knowledge graph based on a knowledge-driven technology path to promote the intelligent upgrading of pharmacovigilance field.Methods In this study,an adverse drug reaction knowledge base was established,and the data within the knowledge base were preprocessed.The BERT-CRF model was utilized to identify and extract the relevant entities,relationships and attribute information,and knowledge alignment was carried out by combining the rule method and statistical method.The knowledge fusion data was stored in the Neo4j graph database to construct the knowledge graph.Results Based on the established knowledge base,this study explored the method of constructing knowledge graph of adverse drug reaction by knowledge-driven technology,laying the groundwork for data mining and the development of intelligent auxiliary system tools based on knowledge graph.Conclusions The study on the construction method of an ADR knowledge graph based on a knowledge-driven technology path provides a new perspective and technical path for the intelligent upgrading of pharmacovigilance field.It holds important theoretical and practical significance for improving drug safety,protecting patients'health,and promoting the healthy development of pharmaceutical industry.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

Objective Pharmacovigilance,as an essential component of post-marketing drug regulation,plays a crucial role in ensuring the safety of public medication use.In recent years,Artificial intelligence(AI)technology,with its powerful data processing and analysis capabilities,has shown tremendous potential in the field of Pharmacovigilance.This article introduced and analyzed the application background of AI technology in Pharmacovigilance,its exploration and current status both internationally and within China,highlighted its application in the country,as well as the associated problems and challenges from the perspective of post-marketing regulation.It aims to provide reference for post-marketing regulation in China.Currently,China's exploration and application of AI in pharmacovigilance have just begun,and efforts should be intensified and the pace accelerated.Regulatory agencies should actively develop and implement regulatory standards or relevant guidelines for AI technologies throughout the product lifecycle,strengthen personnel training,talent cultivation,and introduction,actively promote close cooperation among regulatory agencies,pharmaceutical companies,research institutes,and universities,and jointly promote the innovative application of AI technology in the field of drug regulation.

Objective In view of the potential threat of adverse drug reaction(ADR)to patients'health and the limitations of traditional monitoring methods,this study investigates the construction method of an ADR knowledge graph based on a knowledge-driven technology path to promote the intelligent upgrading of pharmacovigilance field.Methods In this study,an adverse drug reaction knowledge base was established,and the data within the knowledge base were preprocessed.The BERT-CRF model was utilized to identify and extract the relevant entities,relationships and attribute information,and knowledge alignment was carried out by combining the rule method and statistical method.The knowledge fusion data was stored in the Neo4j graph database to construct the knowledge graph.Results Based on the established knowledge base,this study explored the method of constructing knowledge graph of adverse drug reaction by knowledge-driven technology,laying the groundwork for data mining and the development of intelligent auxiliary system tools based on knowledge graph.Conclusions The study on the construction method of an ADR knowledge graph based on a knowledge-driven technology path provides a new perspective and technical path for the intelligent upgrading of pharmacovigilance field.It holds important theoretical and practical significance for improving drug safety,protecting patients'health,and promoting the healthy development of pharmaceutical industry.

Medical ultrasound technology is widely used for diagnosis and therapy in clinical practice.Ultrasound probes,which are directly contact with patients,pose a potential risk of pathogen transmission.This expert consen-sus was developed by a multidisciplinary team based on international guidelines,standards in China,and the results of a national survey,aiming to reduce the risk of healthcare-associated infection through standardizing reprocessing of medical ultrasound probes,and formulating consensus recommendations with the Delphi method.The consensus clarifies the reprocessing principles for three types of ultrasound probes of different infection risks:external-use ul-trasound probes,interventional percutaneous ultrasound probes,and internal-use ultrasound probes,puts forward systematic suggestions on the reprocessing standards and disinfection levels of ultrasound probe isolation covers and coupling agents,the reprocessing procedures and methods of ultrasound probes,as well as architectural layout and management of reprocessing,so as to provide a scientific prevention and control framework for ensuring ultrasound diagnosis and therapy safety.

Purpose To analyze the expression of SNAP23 in esophageal squamous cell carcinoma(ESCC)and its influence on invasion of ESCC cells.Methods A total of 41 cases of ESCC and paired normal adjacent tissue(NAT)were collected.The expression and localization of SNAP23 were detected by immunohistochemical EnVision method.The differences of SNAP23 expression levels between ESCC tissues and NAT tissues were compared to analyze the relationship between SNAP23 expression and clinicopathological characteristics.The expression level of SNAP23 in human immortalized esophageal epithelial cells SHEE and ESCC cell lines TE-1,TE-13 and KYSE150 were detected by Western blot.Lentiviral vector transfection technique was used to construct stable transfection cell lines with knock-down and overexpression of SNAP23 gene.The effect of SNAP23 on invasion of ESCC cell line TE-13 was evaluated by cell invasion experiment.Results The expression of SNAP23 in ESCC was significantly higher(53.7％,22/41)than that in the NAT group(19.5％,8/41),the difference was statistically significant(x2=10.303,P＜0.01).The expression level of SNAP23 in ESCC tissues was significantly correlated with maximum tumor diameter(x2=4.193,P＜0.05)and invasion depth(x2=7.264,P＜0.05),but was not correlated with patient gender,age,tumor site,tumor type,pathologic grade,vascular embolus,nerve invasion and lymph node metastasis(P＞0.05).Compared with human immortalized esophageal epithelial cells SHEE,SNAP23 was highly expressed in ESCC cell lines(P＜0.000 1).Compared with the sh-NT group,the invasion of ESCC cell line TE-13 was inhibited when SNAP23 expression was knocked down(P＜0.000 1);compared with the Vector group,the invasion of ESCC cell line TE-13 was enhanced after overexpression of SNAP23(P＜0.000 1).Conclusion SNAP23 is highly expressed in ESCC tis-sue and cell lines,and its expression level in ESCC tissues is positively correlated with tumor maximum diameter and invasion depth;SNAP23 promotes the invasion of ESCC cells.

Heatstroke,especially in high-temperature and high-humidity environments,is a life-threatening acute heat-injury disease that seriously endangers human health.Timely and effective on-site treatment is crucial for patients'survival and prognosis.Early recognition,rapid assessment,and on-site cooling are the core of pre-hospital treatment of heatstroke.Currently,there is a lack of standardized application procedures for pre-hospital emergency care of heatstroke.Therefore,the"Expert Consensus on Pre-hospital Emergency Management of Heatstroke(2024 edition)"was initiated by the Expert Group on Heatstroke Prevention of the People's Liberation Army and developed in collaboration with experts from local pre-hospital emergency care,emergency departments,and intensive care units.This consensus focuses on heatstroke prevention,on-site and ambulance-based treatment,and early emergency room interventions,and puts forward 10 evidence-based recommendations,aiming to provide a reference for scientific and standardized pre-hospital emergency care of heatstroke.

Purpose Discover new prostate cancer-related single nucleotide variants.Methods Tissue wax blocks from 21 prostate cancer patients who underwent radical surgery and had relatively complete clinical data were collected for somatic mutation detection to analyze new mutated genes associated with prostate cancer.The levels of cor-responding proteins in the urine of prostate cancer patients were tested according to the results of the selected genes.Results All 21 prostate cancer patients showed obvious somatic mutations,and the mutation types were dominated by C＞T and G＞A.The number of somatic mutations was 521,of which 27 genes had high mutation proportions(≥2 ca-ses),including ZSWIM6(5/21),FOXA1(4/21),SPTA1(2/21),FAM47C(2/21),FLG2(2/21),PRSS3(2/21),TP53(2/21),FLG(2/21),UBR4(2/21),and the mutations occurring in ZSWIM6 were all deletion muta-tions,and the mutations occurring in FOXA1 were missense mutations,deletion mutations,and deletion insertion mu-tations.The urinary levels of UPF1,SPTA1,and IDH1 proteins of the 10 prostate cancer patients were significantly different than those of the healthy controls.Correlation analysis showed that FOXA1 was positively correlated with UBR4(r=0.669,P=0.001),SPTA1 was positively correlated with FLG2(r=1.000,P＜0.001),FAM47C was positively correlated with PRSS3(r=1.000,P＜0.001),and there was a significant positive correlation between TP53 and FLG(r=1.000,P＜0.001).ZSWIM6 and FOXA1 were not correlated with biochemical recurrence.SP-TA1 mutation affected progression-free survival(PFS)[(66.0±0)months vs(30.0±7.8)months,P=0.008].FAM47C was positively correlated with PFS[(66.0±0)months vs(19.0±0)months,P＜0.001].ZNF676 was correlated with PFS[(66.0±0)months vs(26.0±5.0)months,P=0.008].FLG2 was correlated with PFS[(66.0±0)months vs(30.0±7.8)months,P=0.008].PRSS3 was correlated with PFS[(66.0±0)months vs(19.0±0)months,P＜0.001].Conclusion All 21 prostate cancer patients harbored somatic mutations,including ZSWIM6(5/21)and FOXA1(4/21)mutations.SPTA1,FAM47C,ZNF676,FLG2,and PRSS3 may be associated with prognosis.