OBJECTIVE To investigate ameliorative effects and mechanisms of two probiotics(Bacillus subtilis,Lactobacillus acidophilus)combined with Aurantii Fructus Immaturus(AFI)on functional dyspepsia(FD)in rats.METHODS Rats were randomly divided into blank group,model group,positive control group(domperidone group,2.7 mg/kg),AFI group(9 g/kg),L.acidophilus group(5×107 cfu/kg),B.subtilis group(5×107 cfu/kg),L.acidophilus+AFI group(L.acidophilus 5×107 cfu/kg+AFI 9 g/kg),and B.subtilis+AFI group(B.subtilis 5×107 cfu/kg+AFI 9 g/kg),with 8 rats in each group.Except for the blank group,FD model was established by tail-clamping stimulation+hunger and satiety disorder+swimming exhaustion in other groups.After modeling,each group was given the corresponding drug/probiotic suspensions/physiological saline intragastrically,once a day,for 14 consecutive days.After the last medication,gastric emptying rate and the rate of propulsion of the small intestine in rats were measured;the levels of brain-gut peptide-related indicators[gastrin(GAS),substance P(SP),vasoactive intestinal peptide(VIP),somatostatin(SS)and cholecystokinin(CCK)]in the serum of rats were measured.The pathological morphology of the gastric antrum tissue and duodenal tissue was observed.Cecal contents from the rats were collected for gut microbiota sequencing analysis.The protein expression levels of tyrosine kinase receptor c-Kit and stem cell factor(SCF)in the gastric antrum tissue,as well as Toll-like receptor 4(TLR4),myeloid differentiation factor 88(MyD88),and nuclear factor κB(NF-κB)in the duodenal tissue of the rats were detected.RESULTS Compared with the blank group,model group showed significantly lower gastric emptying rate,small intestinal propulsion rate,serum levels of GAS and SP,relative abundance of Firmicutes,Ace,Chao and Sobs indexes of the gut microbiota,and protein levels of SCF and c-Kit in gastric antrum(P＜0.05),while serum levels of VIP,SS and CCK,relative abundance of Bacteroidetes,as well as protein expressions of TLR4,MyD88,and NF-κB,were significantly higher(P＜0.05).The histological structure of the gastric antrum tissue appeared basically normal;however,abnormalities were observed in the duodenal structure,with a significant infiltration of inflammatory cells visible.Compared with the model group,all treatment groups significantly modulated most of the above indexes(P＜0.05).The histological structure of the gastric antrum tissue was normal.Except for the B.subtilis group and the B.subtilis+AFI group,the pathological states of the duodenum in the remaining rats gradually recovered.Compared with each single drug group,most of above indexes in rats from each combination group showed further improvement(P＜0.05).CONCLUSIONS The combination of AFI with two probiotics can improve gastrointestinal motility in FD rats,and the effect is superior to that of using the drugs alone.The specific underlying mechanisms may be related to the activation of the SCF/c-Kit signaling pathway and the inhibition of the TLR4/MyD88/NF-κB signaling pathway.

Objectives:This study aims to investigate the correlation between urinary crystals and the components of urinary calculi in patients with urinary calculi，as well as the accuracy of urine crystals in predicting stone components.Methods:A retrospective analysis was performed on 280 patients with positive urine crystal and urinary calculi from January 2022 to December 2024. There were a total of 280 patients consisting of 185 males and 95 females，aged from 23 to 80 years，with an average age of（49.1 ± 12.3）years. Among them，there were 243 cases of renal stones or both renal and ureteral stones，25 cases of ureteral stones，and 12 cases of bladder stones. In all cases，10 ml of morning urine was collected preoperatively and sent for examination within one hour. After centrifuging at 400 g for 1 minute，the urine sediment was examined under a microscope. All positive crystals were categorized into calcium oxalate，uric acid，calcium phosphate，magnesium ammonium phosphate，and cystine based on the morphology of the crystals. Calculi were collected after endoscopic surgery，calculi composition was analyzed using infrared spectroscopy，and the main component（the first predominant component）was recorded. Statistical analysis was conducted using a 5×5 contingency table to analyze the correlation and contingency coefficient，and the positive predictive values of the urinary crystals for predicting calculi components were calculated. Results:Among the 280 patients，calcium oxalate crystals were found in 241 cases，uric acid in 25 cases，calcium phosphate in 7 cases，magnesium ammonium phosphate in 5 cases，and cystine in 2 cases. The main components of 280 calculi were calcium oxalate in 232 cases，uric acid in 21 cases，calcium phosphate in 24 cases，magnesium ammonium phosphate in 1 case，and cystine in 2 cases. There was a statistically significant correlation between urinary crystals and stone components（ χ2 = 152.46， P < 0.01），and the contingency coefficient between crystals and calculi components was 0.809. The overall positive expected value of urine crystals was 87.5%（245/280），among which the positive expected value of calcium oxalate crystals was 91.7%（221/241），uric acid crystals was 72.0%（18/25），calcium phosphate was 42.9%（3/7），magnesium ammonium phosphate was 20.0%（1/5），and cystine was 100.0%（2/2）. Conclusions:The urinary crystals of patients with calculi are significantly related to the main components of the calculi. Using urinary crystals to predict the components of the calculi has a relatively high accuracy.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Objectives:This study aims to investigate the correlation between urinary crystals and the components of urinary calculi in patients with urinary calculi，as well as the accuracy of urine crystals in predicting stone components.Methods:A retrospective analysis was performed on 280 patients with positive urine crystal and urinary calculi from January 2022 to December 2024. There were a total of 280 patients consisting of 185 males and 95 females，aged from 23 to 80 years，with an average age of（49.1 ± 12.3）years. Among them，there were 243 cases of renal stones or both renal and ureteral stones，25 cases of ureteral stones，and 12 cases of bladder stones. In all cases，10 ml of morning urine was collected preoperatively and sent for examination within one hour. After centrifuging at 400 g for 1 minute，the urine sediment was examined under a microscope. All positive crystals were categorized into calcium oxalate，uric acid，calcium phosphate，magnesium ammonium phosphate，and cystine based on the morphology of the crystals. Calculi were collected after endoscopic surgery，calculi composition was analyzed using infrared spectroscopy，and the main component（the first predominant component）was recorded. Statistical analysis was conducted using a 5×5 contingency table to analyze the correlation and contingency coefficient，and the positive predictive values of the urinary crystals for predicting calculi components were calculated. Results:Among the 280 patients，calcium oxalate crystals were found in 241 cases，uric acid in 25 cases，calcium phosphate in 7 cases，magnesium ammonium phosphate in 5 cases，and cystine in 2 cases. The main components of 280 calculi were calcium oxalate in 232 cases，uric acid in 21 cases，calcium phosphate in 24 cases，magnesium ammonium phosphate in 1 case，and cystine in 2 cases. There was a statistically significant correlation between urinary crystals and stone components（ χ2 = 152.46， P < 0.01），and the contingency coefficient between crystals and calculi components was 0.809. The overall positive expected value of urine crystals was 87.5%（245/280），among which the positive expected value of calcium oxalate crystals was 91.7%（221/241），uric acid crystals was 72.0%（18/25），calcium phosphate was 42.9%（3/7），magnesium ammonium phosphate was 20.0%（1/5），and cystine was 100.0%（2/2）. Conclusions:The urinary crystals of patients with calculi are significantly related to the main components of the calculi. Using urinary crystals to predict the components of the calculi has a relatively high accuracy.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1 250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAXGv).The videos were divided into development set(245 A4C,155 PLAX,225 PSAXGV),semi-automated training set(98 A4C,62 PLAX,90 PSAXGV)and test set(147 A4C,93 PLAX,135 PSAXGV)at the ratio of 5:2:3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33％,97.26％,97.26％and 97.26％to 99.73％,99.65％,99.77％and 99.71％,respectively,while the overall accuracy for assessing A4C,PLAX and PSAXGV TTE as standard views in test set improved from 89.12％,83.87％and 90.37％to 93.20％,90.32％and 93.33％,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.

Objective To analyze risk factors of nosocomial infections in cancer patients after undergoing radiotherapy. Methods A total of 1 000 tumor patients receiving radiotherapy were chosen as the study objects. The related factors of nosocomial infections were analyzed. Results Out of 1 000 tumor patients, there were 68 cases with nosocomial infections, accounting for 6. 8％, and the infections were mainly located in respiratory tract (29. 41％), and lungs (26. 47％). Age≥65, diabetes history, hospital stay≥30 d, Ⅲ to Ⅳ stages of TNM classification, simultaneous radiotherpy and chemotherapy, and invasive operation (PICC) were found to be risk factors of nosocomial infection (P ＜ 0. 05 or P ＜ 0. 01). Conclusion Risk factors of nosocomial infections are varied. Hence the prevention and control measures should be taken correspondingly to decrease the hospital infection rate.

Objective To analyze risk factors of nosocomial infections in cancer patients after undergoing radiotherapy. Methods A total of 1 000 tumor patients receiving radiotherapy were chosen as the study objects. The related factors of nosocomial infections were analyzed. Results Out of 1 000 tumor patients, there were 68 cases with nosocomial infections, accounting for 6. 8％, and the infections were mainly located in respiratory tract (29. 41％), and lungs (26. 47％). Age≥65, diabetes history, hospital stay≥30 d, Ⅲ to Ⅳ stages of TNM classification, simultaneous radiotherpy and chemotherapy, and invasive operation (PICC) were found to be risk factors of nosocomial infection (P ＜ 0. 05 or P ＜ 0. 01). Conclusion Risk factors of nosocomial infections are varied. Hence the prevention and control measures should be taken correspondingly to decrease the hospital infection rate.

Objective To study the relationship between serum uric acid and insulin resistance in type 2 diabetic patients Methods A total of 728 middle-aged and elderly type 2 diabetic patients were recruited and the anthropometric,clinical and biochemical parameters and fasting serum C-peptide were measured and retrospectively studied.All patients were divided into groups based on the levels of uric acid and on metabolic syndrome diagnosis or not.Results Among a total of 728 patients,the proportion of hyperuricemia was 26.9％(196/728),and metabolic syndrome(MS)was 64.6％.The levels of body mass index(BMI),waist circumference(WC),systolic pressure (SBP),diastolic pressure (DBP),triglyceride (TG),fasting C-peptide,HOMA(C-Peptide) were significantly higher in hyperuricemia group than in normal uric acid group,while the level of HDL was lower in hyperuricemia group than in normal uric acid group.The proportion of MS was statistically higher in hyperuricemia group (81.6％)than in normal uric acid group (58.3％,P ＜ 0.05).Groups Ⅰ,Ⅱ,Ⅲ,Ⅳ-the four quartiles of serum uric acid from low-to high-level were correlated with MS component number of 2.1±1.0,3.1±1.1,3.3±1.2,3.7±0.8,separately,and with HOMA(CP)of 3.7± 0.8,3.7 ± 0.8,4.0 ± 0.6,4.1 ± 0.9,separately (all P ＜ 0.05).When all patients were divided into groups based on with or without MS,the level of uric acid in MS group was (377.3 ± 43.5)μumol/L,and the proportion of hyperuricemia was 34.0％ (160/470),and HOMA(CP)was 3.90±0.72,all were significantly higher than those of non-MS group[(318.2±47.9)μmol/L,14.0％ (36/258),3.64±0.67].The levels of serum uric acid in groups with 1-5 components of MS were(285.0±62.8)μmol/L,(335.7±62.7)μmol/L,(367.1±45.0) μmol/L,(377.9±40.2) μmol/L,(389.8±46.6)μmol/L,and HOMA(CP) was 3.6±0.6,3.7±0.7,3.9±0.8,3.9±0.7,4.3±0.7 separately(P＜0.05).Serum uric acid was positively correlated with age,WC,BMI,TG,SBP,fasting C-peptide,and HOMA (CP),while negatively correlated with glycosylated hemoglobin(HbA1C),high density lipoprotein cholesterol (HDL-C).Binary logistic regression showed that the OR value of MS was 1.033 with the increase of serum uric acid,and 95％CI:1.026-1.041,P＜0.05.Conclusions Serum uric acid is correlated with insulin resistance in middle-aged and elderly hospitalized patients with type 2 diabetes.Hyperuricemia might be a new risk factor for MS.