Purpose: This study aimed to evaluate the contrast-enhanced ultrasound with Sonazoid (Sonazoid-CEUS) features of hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). Methods: In this retrospective study, patients who underwent surgical resection and were histopathologically diagnosed with NAFLD or cirrhosis-related HCC were included. All patients received Sonazoid-CEUS examinations within 1 week prior to hepatic surgery. The enhancement patterns of HCC lesions were evaluated and compared between the two groups according to the current World Federation for Ultrasound in Medicine and Biology guidelines. Multivariate logistic regression analysis was used to assess the correlations between Sonazoid-CEUS enhancement patterns and clinicopathologic characteristics. Results: From March 2022 to April 2023, a total of 151 patients with HCC were included, comprising 72 with NAFLD-related HCC and 79 with hepatitis B virus (HBV) cirrhosis–related HCC. On Sonazoid-CEUS, more than half of the NAFLD-related HCCs exhibited relatively early and mild washout within 60 seconds (54.2%, 39/72), whereas most HBV cirrhosis–related HCCs displayed washout between 60 and 120 seconds (46.8%, 37/79) or after 120 seconds (39.2%, 31/79) (P<0.001). In the patients with NAFLD-related HCC, multivariate analysis revealed that international normalized ratio (odds ratio [OR], 0.002; 95% confidence interval [CI], 0.000 to 0.899; P=0.046) and poor tumor differentiation (OR, 21.930; 95% CI, 1.960 to 245.319; P=0.012) were significantly associated with washout occurring within 60 seconds. Conclusion Characteristic Sonazoid-CEUS features are useful for diagnosing HCC in patients with NAFLD.

Purpose: This study aimed to evaluate the contrast-enhanced ultrasound with Sonazoid (Sonazoid-CEUS) features of hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). Methods: In this retrospective study, patients who underwent surgical resection and were histopathologically diagnosed with NAFLD or cirrhosis-related HCC were included. All patients received Sonazoid-CEUS examinations within 1 week prior to hepatic surgery. The enhancement patterns of HCC lesions were evaluated and compared between the two groups according to the current World Federation for Ultrasound in Medicine and Biology guidelines. Multivariate logistic regression analysis was used to assess the correlations between Sonazoid-CEUS enhancement patterns and clinicopathologic characteristics. Results: From March 2022 to April 2023, a total of 151 patients with HCC were included, comprising 72 with NAFLD-related HCC and 79 with hepatitis B virus (HBV) cirrhosis–related HCC. On Sonazoid-CEUS, more than half of the NAFLD-related HCCs exhibited relatively early and mild washout within 60 seconds (54.2%, 39/72), whereas most HBV cirrhosis–related HCCs displayed washout between 60 and 120 seconds (46.8%, 37/79) or after 120 seconds (39.2%, 31/79) (P<0.001). In the patients with NAFLD-related HCC, multivariate analysis revealed that international normalized ratio (odds ratio [OR], 0.002; 95% confidence interval [CI], 0.000 to 0.899; P=0.046) and poor tumor differentiation (OR, 21.930; 95% CI, 1.960 to 245.319; P=0.012) were significantly associated with washout occurring within 60 seconds. Conclusion Characteristic Sonazoid-CEUS features are useful for diagnosing HCC in patients with NAFLD.

Purpose: This study aimed to evaluate the contrast-enhanced ultrasound with Sonazoid (Sonazoid-CEUS) features of hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). Methods: In this retrospective study, patients who underwent surgical resection and were histopathologically diagnosed with NAFLD or cirrhosis-related HCC were included. All patients received Sonazoid-CEUS examinations within 1 week prior to hepatic surgery. The enhancement patterns of HCC lesions were evaluated and compared between the two groups according to the current World Federation for Ultrasound in Medicine and Biology guidelines. Multivariate logistic regression analysis was used to assess the correlations between Sonazoid-CEUS enhancement patterns and clinicopathologic characteristics. Results: From March 2022 to April 2023, a total of 151 patients with HCC were included, comprising 72 with NAFLD-related HCC and 79 with hepatitis B virus (HBV) cirrhosis–related HCC. On Sonazoid-CEUS, more than half of the NAFLD-related HCCs exhibited relatively early and mild washout within 60 seconds (54.2%, 39/72), whereas most HBV cirrhosis–related HCCs displayed washout between 60 and 120 seconds (46.8%, 37/79) or after 120 seconds (39.2%, 31/79) (P<0.001). In the patients with NAFLD-related HCC, multivariate analysis revealed that international normalized ratio (odds ratio [OR], 0.002; 95% confidence interval [CI], 0.000 to 0.899; P=0.046) and poor tumor differentiation (OR, 21.930; 95% CI, 1.960 to 245.319; P=0.012) were significantly associated with washout occurring within 60 seconds. Conclusion Characteristic Sonazoid-CEUS features are useful for diagnosing HCC in patients with NAFLD.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To explore the feasibility and safety of ultrasound-guided percutaneous microwave ablation for unresectable pancreatic cancer.Methods Totally 84 patients who underwent ultrasound-guided percutaneous microwave ablation for unresectable pancreatic cancer were enrolled,and the technical success rate,complete ablation rate,complication rate,pain relief rate and survival time,etc.were observed.Results The median age of 84 cases was 61.5 years.Totally 86 tumors,including 44.19％(38/86)at the head/neck and 55.81％(48/86)at the body/tail of pancreas were detected,and a total of 85 ablation sessions were performed with the median ablation energy applied per tumor of 9.90(1.08,21.60)kJ and the complete ablation rate of 42.86％(36/84).The technical success rate was 100％(85/85).Thirty-nine complication events occurred in 25 cases,no ablation-related death.Among 34 patients underwent ablation mainly for pain symptoms,the pain score decreased from(6.22±1.12)points before treatment to(1.94±1.64)points after treatment(P＜0.001).During 6.8(3.3,12.9)months' follow-up,the mean survival time was(8.5±6.7)months,and all 47 patients died due to tumor progression.Conclusion Ultrasound-guided percutaneous microwave ablation was safe and feasible for unresectable pancreatic cancer.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

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.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

Objective To analyze the correlation between lesion volume,lesion mass,and maximum lesion diameter in the assessment of advanced hepatocarcinoma with lung metastasis,and to evaluate the application value of total volume response and total mass response of lung metastatic lesions in efficacy assessment.Methods A retrospective analysis was conducted on the CT imaging data of 20 patients clinically confirmed with hepatocarcinoma and lung metastases,followed by subsequent follow-up to monitor their survival outcomes.Volume measurement software was used to measure the volume of lesions before and after treatment.We recored lesion diameter,volume measurements and CT values,calculated the mass of the lesions.The correlation between lesion volume,mass and diameter was analyzed,as well as the correlation between the change rates of volume,mass and lesion diameter.Additionally,the total volume and total mass of all lesions were calculated.The correlation between the change rates of total volume/total mass and the change rate of pulmonary lesion diameter under the RECIST 1.1 criteria,as well as the correlation with changes in patients'tumor markers,were analyzed.Furthermore,the overall volume response and overall mass response of lesions were evaluated based on changes in total volume and total mass,and their consistencies with the RECIST 1.1 criteria for efficacy evaluation were analyzed.Finally,univariate Cox regression analysis was performed to explore the association between these variables and patient survival outcomes.Results There was strong correlation between lesion volume,mass and tumor diameter(r=0.771,0.775),between the rate of change in mass and the rate of change in lesion diameter(r=0.846),and between the rates of change in total volume/total mass and the rate of change in pulmonary lesion diameter under the RECIST 1.1 criteria(r=0.800,0.896).The correlation between the rates of change in total volume/total mass and patients'tumor markers was not statistically significant.There was moderate correlation between the rate of change in volume and the rate of change in lesion diameter(r=0.692).The evaluation results of total volume response and total mass response for pulmonary lesions in advanced hepatocarcinoma with lung metastasis were generally consistent with the RECIST 1.1 criteria(Kappa=0.486,0.426).Univariate Cox regression analysis revealed that total lesion volume(P=0.047)and total lesion mass(P=0.049)were independent prognostic factors for survival outcomes.Conclusion Lesion volume,mass,and diameter,as well as their respective change rates,were found to be interrelated.Furthermore,total lesion volume and total lesion mass were identified as independent prognostic factors for survival outcomes.The total volume response and total mass response are promising evaluation methods in evaluating the efficacy of lung metastasis of hepatocarcinoma,which are different from the RECIST 1.1 evaluation criteria.