OBJECTIVE To form an expert consensus on the clinical application of parenteral direct thrombin inhibitors （DTIs） in patients during the perioperative period. METHODS Led by Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital （the Affiliated Hospital of UESTC）， a multidisciplinary working group was established. Through literature review and the Delphi method， clinical questions related to the rational perioperative use of parenteral DTIs were identified. A structured design was adopted using the “Population-Intervention-Comparison-Outcome” framework； systematic searches were conducted in CNKI， Medline， Embase and other databases. Relevant evidence from randomized controlled trials and cohort studies was included and synthesized. Evidence quality was assessed using the Grades of Recommendations Assessment，Development and Evaluation （GRADE） approach， and recommendations were formulated through multiple rounds of Delphi surveys and expert consensus meetings. RESULTS &CONCLUSIONS Seven recommendations （each with an expert consensus rate exceeding 90%） on the use of parenteral DTIs in perioperative patients were developed. These recommendations specify drug selection， dosing ranges， key monitoring points， and safety management strategies for parenteral DTIs in various scenarios， including the perioperative period of ventricular assist device implantation， the perioperative period of cardiac surgery， perioperative patients with lower-extremity atherosclerotic disease， the perioperative period of percutaneous coronary intervention in patients with acute coronary syndrome， the perioperative period of carotid artery stenting in patients with carotid stenosis， the perioperative period of patients with right heart thrombosis， and patients who develop related thrombosis and dysfunction after a central venous catheter insertion. In addition， warning and management pathways for perioperative bleeding and thrombotic events were proposed. This expert consensus， which is formulated based on the best available evidence， provides evidence-based guidance for standardized and individualized use of parenteral DTIs in perioperative period.

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.

Chimeric antigen receptor (CAR) cell therapy offers promising new avenues for the treatment of hepatocellular carcinoma. However, several challenges hinder its full potential. Firstly, the high heterogeneity of hepatocellular carcinoma results in a lack of ideal targets, complica-ting the ability of CAR cells to specifically recognize and effectively eliminate tumor cells. Secondly, the immunosuppressive microenvironment of hepatocellular carcinoma, characterized by regulatory T cells and myeloid-derived suppressor cells, diminishes the efficacy of CAR cell therapy, further affecting treatment efficacy. Additionally, safety concerns such as cytokine release syndrome and neurotoxicity remain significant obstacles to clinical application. Finally, the high cost and complex manufacturing processes involved in CAR cell therapy present major barriers to its widespread use. Future research should focus on optimizing target selection, particularly by identifying hepato-cellular carcinoma specific molecular markers; improving CAR cells resilience in immunosuppre-ssive environments; enhancing safety protocols; and streamlining production methods to reduce costs. Addressing these critical issues will facilitate the broader application of CAR cell therapy in hepatocellular carcinoma and other solid tumors, paving the way for a paradigm shift in cancer treatment. Based on relevant literature and combined it with clinical practice, the authors explore the prospects and challenges of CAR cell therapy for the treatment of hepatocellular carcinoma, aiming to provide new ideas for its clinical application.

In recent years, the global prevalence of obesity has shown a persistent upward trajectory, including significant increases within China. The compromised functionality of adipose tissue in obese individuals leads to chronic inflammation, dysregulated adipokine secretion, visceral fat accumulation, and insulin resistance, all of which are closely linked to the onset of major chronic diseases such as type 2 diabetes and atherosclerotic cardiovascular disease. The implementation of diversified intervention strategies aimed at weight reduction and adipose tissue functional improvement represents an effective paradigm for preventing and managing obesity-related metabolic disorders. Krill oil, an efficacious active ingredient derived from Antarctic krill, is abundant in bioactive compounds such as omega-3 fatty acids and astaxanthin, and exhibits health-promoting properties including anti-inflammatory, antioxidative, lipid metabolism enhancement, and insulin sensitivity improvement effects. This study systematically reviews the impact of krill oil and its active components on adipose tissue function, with the aim of providing more effective lifestyle intervention strategies for the prevention and management of obesity and metabolic diseases.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Objectives:To analyze the risk factors of atrial fibrillation in patients with non-obstructive hypertrophic cardiomyopathy(NOHCM)and explore the relationship between left ventricular/atrial volume and atrial fibrillation.Methods:Consecutive NOHCM patients admitted at Fuwai Hospital from January 2023 to January 2024 with complete clinical data,satisfactory echocardiography imaging data were included in this analysis,patients were divided into atrial fibrillation group(n=28)and non-atrial fibrillation group(n=57).Left-sided volumetric and functional parameters were measured by one-beat real-time full-volume three-dimensional echocardiography,including left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),left ventricular ejection fraction(LVEF),left atrial volume(LAV).The left ventricular end-diastolic volume(LVEDVi)index,left ventricular end-systolic volume index(LVESVi)and left atrial volume index(LAVi)were calculated.Other echocardiographic parameters including interventricular septum(IVSmax)thickness,left ventricular posterior wall thickness(LVPW),and left atrial diameter(LAD)were routinely measured.Mitral valve forward flow spectrum,tissue Doppler,tricuspid regurgitation velocity,and left atrial size were used to evaluate the left ventricular diastolic function of patients,and diastolic dysfunction was classified into grade I,II,and III.Multivariate logistic regression was used to analyze the influencing factors of atrial fibrillation in patients with non-obstructive hypertrophic cardiomyopathy Results:Compared with the non-atrial fibrillation group,LVEDV([143.8±26.7]ml vs.[117.1±21.9]ml)and LVEDVi([79.4±11.9]ml/m2 vs.[64.2±10.6]ml/m2)were smaller in atrial fibrillation group(both P<0.001).Compared with the non-atrial fibrillation group,LAD([40.2±4.7]mm vs.[48.6±4.8]mm)and LAVi([37.3±8.9]ml/m2 vs.[64.4±17.1]ml/m2)were lager in atrial fibrillation group(both P<0.001).Compared with the non-atrial fibrillation group,the proportion of NYHA functional classification≥Ⅲ was higher(15.8%vs.50.0%,P<0.001),LVEF was lower([61.5±5.5]%vs.[57.6±5.0]%,P=0.002),and proportion of severe diastolic dysfunction was higher in atrial fibrillation group(P<0.001).Logistic regression analysis showed that the factors associated with atrial fibrillation in NOHCM patients were LVEDVi(OR=0.744,95%CI:0.575-0.962,P=0.024)and LAVi(OR=1.602,95%CI:1.032-2.486,P=0.036).Conclusions:LVEDVi and LAVi are related factors for atrial fibrillation in patients with non-obstructive hypertrophic cardiomyopathy.LVEDVi is negatively,while LAVi is positively associated with the occurrence of atrial fibrillation in NOHCM patients.

Objective Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Objective Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.