Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

Objective To explore the correlation between gallbladder stones and small intestinal bacterial overgrowth(SIBO).Methods A retrospective analysis was conducted on the clinical data of 393 patients who attended the Department of Gastroenterology of the Sixth Medical Center of Chinese PLA General Hospital from January 2021 to September 2023.They were divided into gallbladder stones group(n=190)and control group(n=203)based on the presence of gallbladder stones.Their general clinical data,laboratory test results,and abdominal symptoms were compared.Multivariate logistic regression was used to analyze the risk factors for gallbladder stones.Additionally,the total population was divided into SIBO-positive group(n=239)and SIBO-negative group(n=154),and their clinical characteristics were analyzed by logistic regression to explore the risk factors for SIBO.Results Univariate analysis revealed that gallbladder stones group had a higher rate of age,body mass index(BMI),fasting plasma glucose(FPG),glutaminase levels,prevalence of hypertension,diabetes,coronary heart disease,non-alcoholic fatty liver disease,gallbladder polyps,and SIBO,as well as a higher prevalence of CH4-positive and H2-positive in SIBO group than control group(P＜0.05).In terms of abdominal symptoms,the incidence of bad breath(48.4%vs.35.5%),dyspepsia(38.4%vs.28.6%),abdominal pain(30.5%vs.14.8%),bloating(42.1%vs.28.6%),diarrhea(20.5%vs.7.4%),and more exhaustion(46.8%vs.34.5%)were significantly higher in gallbladder stones group than those in control group(P＜0.05).Multivariate logistic regression analysis showed that independent positive determinants for incident gallbladder stones were age,BMI,FPG,total bilirubin(TBIL),coronary heart disease,gallbladder polyps,and SIBO.Univariate analysis revealed that age,prevalence of gallbladder stones,proportion of single stones,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)were significantly higher in SIBO-positive group than those in SIBO-negative group(P＜0.05).Multivariate logistic regression analysis showed that the risk factors for SIBO were age,coronary heart disease,and gallbladder stones,while the protective factor for SIBO was high-density lipoprotein cholesterol(HDL-C).Conclusion There is a significant correlation between gallbladder stones and small SIBO;interventions on related factors of gallbladder stones and small SIBO may help reduce their incidence.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To analyze the correlation between irritable bowel syndrome(IBS)as well as its subtypes and gallbladder stone.Methods Collected the clinical data of 556 patients who were treated in Department of Gastroenterology of the Sixth Medical Center of Chinese PLA General Hospital from January 2019 to March 2023.The patients were divided into IBS group(n=161)and non-IBS group(n=395).The subjects were investigated by questionnaire,physical examination and blood examination,and the data of gender,age,height,weight,blood pressure and blood biochemical indexes were obtained and compared between two groups.The relation between gallbladder stone and IBS were evaluated by logistic regression analysis.Results There were 90 cases of gallbladder stone in the total population,accounting for 16.2%,including 37 cases of gallbladder stone in IBS group(23.0%)and 53 cases in non-IBS group(13.4%).The prevalence rate of gallbladder stone in IBS group was significantly higher than that in non-IBS group(P<0.05).There were 6 cases of gallbladder muddy stones(3.7%)in IBS group and 3 cases(0.8%)in non-IBS group.And the prevalence rate of gallbladder muddy stones in IBS group was also significantly higher than that in non-IBS group(P<0.05).Logistic regression analysis showed that the age,BMI,total bile acids(TBA),total cholesterol(TC)and combined IBS were independently related to the occurrence of gallbladder stone(P<0.05).In the 161 IBS patients,there were 114 cases of diarrhea-predominant IBS(IBS-D group),including 26 cases(22.8%)of gallbladder stone in IBS-diarrhea(IBS-D,n=114)group and 47 cases of constipation-predominant IBS(IBS-C group),including 11 cases(23.4%)of gallbladder stone.And there were 53 cases(13.4%)of gallbladder stone in the non-IBS group(n=395).Further analysis showed that the prevalence rate of gallbladder stone in IBS-D group was significantly higher than that in non-IBS group(P<0.05).There was no significant difference in gallbladder stone prevalence rate between IBS-C group and non-IBS group(P>0.05).Conclusions There is a correlation between IBS and gallbladder stones.In addition,among the two subtypes of IBS,IBS-D patients may have an increased risk of gallbladder stone compared with non-IBS patients.

COVID-19 has been prevalent for three years. The virulence of SARS-CoV-2 is weaken as it mutates continuously. However, elderly patients, especially those with underlying diseases, are still at high risk of developing severe infections. With the continuous study of the molecular structure and pathogenic mechanism of SARS-CoV-2, antiviral drugs for COVID-19 have been successively marketed, and these anti-SARS-CoV-2 drugs can effectively reduce the severe rate and mortality of elderly patients. This article reviews the mechanism, clinical medication regimens, drug interactions and adverse reactions of five small molecule antiviral drugs currently approved for marketing in China, so as to provide advice for the clinical rational use of anti-SARS-CoV-2 in the elderly.

Objective:To explore the accuracy of artificial intelligence (AI) system based on deep learning in evaluating bone age of children with abnormal growth and development.Methods:The positive X-ray films of the left wrist of children with abnormal growth and development who were treated at the Affiliated Hospital of Guizhou Medical University from January 2020 to December 2021 were collected retrospectively. A total of 717 children were collected, including 266 males and 451 females, aged 2-18 (11±3) years. Based on Tanner Whitehouse 3 (TW 3)-RUS (radius, ulna, short bone) and TW3-Carpal (carpal bone) method, bone age was measured by 3 senior radiologists, and the mean value was taken as reference standard. The bone ages were independently evaluated by the AI system (Dr.Wise bone age prediction software) and two junior radiologists (physicians 1 and 2). The accuracy within 0.5 year, the accuracy within 1 year, the mean absolute error (MAE) and the root mean square error (RMSE) between the evaluation results and the reference standard were analyzed. Paired sample t-test was used to compare MAE between AI system and junior physicians. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI system, junior physician and reference standard. The Bland-Altman diagram was drawn and the 95% consistency limit was calculated between AI system and reference standard. Results:For TW3-RUS bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 75.3% (540/717), 62.1% (445/717) and 66.2% (475/717), respectively. The accuracy within 1 year was 96.9% (695/717), 86.3% (619/717) and 89.1% (639/717), respectively. MAE was 0.360, 0.565 and 0.496 years, and RMSE was 0.469, 0.634 and 0.572 years, respectively. For TW3-Carpal bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 80.9% (580/717), 65.1% (467/717) and 71.7% (514/717), respectively. The accuracy within 1 year was 96.0% (688/717), 87.3% (626/717) and 90.4% (648/717), respectively. MAE was 0.330, 0.527 and 0.455 years, and RMSE was 0.458, 0.612, 0.538 years, respectively. Based on TW3-RUS and TW3-Carpal bone age, the MAE of AI system were lower than those of physician 1 and physician 2, and the differences were statistically significant ( P all<0.001). The evaluation results of AI, physician 1 and physician 2 were in good agreement with the reference standard (ICC all>0.950). The Bland-Altman analysis showed that the 95% agreement limits of AI system for assessing TW3-RUS and TW3-Carpal bone age were -0.75-1.02 years and-0.86-0.91 years, respectively. Conclusion:The accuracy of AI system in evaluating the bone age of children with abnormal growth and development is close to that of senior doctors, better than that of junior doctors, and in good agreement with senior doctors.

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.
Adult ; Female ; Humans ; Male ; Alcohol Drinking ; Diseases in Twins/genetics* ; Hypertension/genetics* ; Twins, Dizygotic/genetics* ; Twins, Monozygotic/genetics*

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.
Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; China/epidemiology* ; Diseases in Twins/genetics* ; Hyperlipidemias/genetics* ; Metabolic Diseases ; Twins, Dizygotic ; Twins, Monozygotic/genetics*

Objective: To investigate the clinical value of plasma scaffold protein SEC16A level and related models in the diagnosis of hepatitis B virus-related liver cirrhosis (HBV-LC) and hepatocellular carcinoma (HBV-HCC). Methods: Patients with HBV-LC and HBV-HCC and a healthy control group diagnosed by clinical, laboratory examination, imaging, and liver histopathology at the Third Hospital of Hebei Medical University between June 2017 and October 2021 were selected. Plasma SEC16A level was detected using an enzyme-linked immunosorbent assay (ELISA). Serum alpha-fetoprotein (AFP) was detected using an electrochemiluminescence instrument. SPSS 26.0 and MedCalc 15.0 statistical software were used to analyze the relationship between plasma SEC16A levels and the occurrence and development of liver cirrhosis and liver cancer. A sequential logistic regression model was used to analyze relevant factors. SEC16A was established through a joint diagnostic model. Receiver operating characteristic curve was used to evaluate the clinical efficacy of the model for liver cirrhosis and hepatocellular carcinoma diagnosis. Pearson correlation analysis was used to identify the influencing factors of novel diagnostic biomarkers. Results: A total of 60 cases of healthy controls, 60 cases of HBV-LC, and 52 cases of HBV-HCC were included. The average levels of plasma SEC16A were (7.41 ± 1.66) ng/ml, (10.26 ± 1.86) ng/ml, (12.79 ± 1.49) ng /ml, respectively, with P < 0.001. The sensitivity and specificity of SEC16A in the diagnosis of liver cirrhosis and hepatocellular carcinoma were 69.44% and 71.05%, and 89.36% and 88.89%, respectively. SEC16A, age, and AFP were independent risk factors for the occurrence of HBV-LC and HCC. SAA diagnostic cut-off values, sensitivity, and specificity were 26.21 and 31.46, 77.78% and 81.58%, and 87.23% and 97.22%, respectively. The sensitivity and specificity for HBV-HCC early diagnosis were 80.95% and 97.22%, respectively. Pearson correlation analysis showed that AFP level was positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBil), and γ-glutamyltransferase (GGT) with P < 0.01, while the serum SEC16A level was only slightly positively correlated with ALT and AST in the liver cirrhosis group (r = 0.268 and 0.260, respectively, P < 0.05). Conclusion: Plasma SEC16A can be used as a diagnostic marker for hepatitis B-related liver cirrhosis and hepatocellular carcinoma. SEC16A, combined with age and the AFP diagnostic model with SAA, can significantly improve the rate of HBV-LC and HBV-HCC early diagnosis. Additionally, its application is helpful for the diagnosis and differential diagnosis of the progression of HBV-related diseases.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; alpha-Fetoproteins/metabolism* ; Endoplasmic Reticulum/metabolism* ; Golgi Apparatus/metabolism* ; Vesicular Transport Proteins ; Liver Cirrhosis/complications* ; Hepatitis B/complications* ; ROC Curve ; Hepatitis B virus/metabolism* ; Biomarkers, Tumor