Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To evaluate potentially inappropriate medication (PIM) in hospitalized elderly patients with bacterial pneumonia, and explore its influencing factors.Methods:It was a single-center cross-sectional study. The study focused on elderly patients with bacterial pneumonia who were admitted to Xuanwu Hospital, Capital Medical University from January 2018 to November 2022. Patients′ gender, age, weight, length of hospital stay, diagnosis at admission, physical examination, diagnosis at discharge, comorbidities, medications, and laboratory test results were extracted from hospital information system and electronic medical records. Medication use of patients included in the analysis during their hospitalization were evaluated according to the classification of PIMs in the 5 lists of the Beer′s criteria of American Geriatrics Society. Based on whether PIM occurred, the patients were divided into with PIM group and without PIM group. The clinical features between the 2 groups were compared and the influencing factors of PIM were analyzed using multivariable logistic regression.Results:A total of 2 720 patients were included, in which 1 734 (63.75%) were male. The median age was 78 (70, 85) years and their ages ranged from 65 to 103 years. The number of drugs used per patient was 14 (10, 18) kinds, ranging from 1 to 57 kinds. The length of hospital stay was 12 (9, 17) days, ranging from 1 to 162 days. Charlson comorbidity index (CCI) was 6 (5, 8) points. Among the 2 720 patients, 1 894 (69.63%) experienced PIM, with a total of 6 166 cases of PIM. The top 3 drugs ranked by the number of PIM occurrence were antiplatelet agents (1 357 cases), benzodiazapine receptor agonists (956 cases), and antipsychotics (884 cases). The comparison of clinical characteristics between the 2 groups showed that differences in age, CCI, length of hospital stay, and number of medications between with PIM and without PIM patients were statistically significant (all P<0.001). Multivariable logistic regression results showed that CCI, length of hospital stay, and number of medications were independent influencing factors for PIM. The risk increased by 8% and 1% with one point increase in CCI and one day extension in length of hospital stay [odds ratio ( OR)=1.08, 95% confidence interval ( CI): 1.04-1.13, P<0.001; OR=1.01, 95% CI: 1.00-1.03, P=0.03]. PIM risk of patients with more than 15 concurrent medications had a 22.16 times higher PIM risk than those with less than 5 concurrent medications ( OR=22.16, 95% CI: 14.15-34.72, P<0.001). Conclusions:Hospitalized eldery patients with bacterial pneumonia who have more severe comorbidities, longer hospital stay, and multiple concomitant medications are at a higher risk of PIM occurrence. Rational medication use among these patients should be paid attention to in clinical practice.

Objective:To evaluate potentially inappropriate medication (PIM) in hospitalized elderly patients with bacterial pneumonia, and explore its influencing factors.Methods:It was a single-center cross-sectional study. The study focused on elderly patients with bacterial pneumonia who were admitted to Xuanwu Hospital, Capital Medical University from January 2018 to November 2022. Patients′ gender, age, weight, length of hospital stay, diagnosis at admission, physical examination, diagnosis at discharge, comorbidities, medications, and laboratory test results were extracted from hospital information system and electronic medical records. Medication use of patients included in the analysis during their hospitalization were evaluated according to the classification of PIMs in the 5 lists of the Beer′s criteria of American Geriatrics Society. Based on whether PIM occurred, the patients were divided into with PIM group and without PIM group. The clinical features between the 2 groups were compared and the influencing factors of PIM were analyzed using multivariable logistic regression.Results:A total of 2 720 patients were included, in which 1 734 (63.75%) were male. The median age was 78 (70, 85) years and their ages ranged from 65 to 103 years. The number of drugs used per patient was 14 (10, 18) kinds, ranging from 1 to 57 kinds. The length of hospital stay was 12 (9, 17) days, ranging from 1 to 162 days. Charlson comorbidity index (CCI) was 6 (5, 8) points. Among the 2 720 patients, 1 894 (69.63%) experienced PIM, with a total of 6 166 cases of PIM. The top 3 drugs ranked by the number of PIM occurrence were antiplatelet agents (1 357 cases), benzodiazapine receptor agonists (956 cases), and antipsychotics (884 cases). The comparison of clinical characteristics between the 2 groups showed that differences in age, CCI, length of hospital stay, and number of medications between with PIM and without PIM patients were statistically significant (all P<0.001). Multivariable logistic regression results showed that CCI, length of hospital stay, and number of medications were independent influencing factors for PIM. The risk increased by 8% and 1% with one point increase in CCI and one day extension in length of hospital stay [odds ratio ( OR)=1.08, 95% confidence interval ( CI): 1.04-1.13, P<0.001; OR=1.01, 95% CI: 1.00-1.03, P=0.03]. PIM risk of patients with more than 15 concurrent medications had a 22.16 times higher PIM risk than those with less than 5 concurrent medications ( OR=22.16, 95% CI: 14.15-34.72, P<0.001). Conclusions:Hospitalized eldery patients with bacterial pneumonia who have more severe comorbidities, longer hospital stay, and multiple concomitant medications are at a higher risk of PIM occurrence. Rational medication use among these patients should be paid attention to in clinical practice.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To investigate the knowledge,attitude and practice(KAP)of medical staff and parents of children on the risk of using traditional Chinese patent medicine,analyze the similarities and differences between the two groups of people in their knowledge of traditional Chinese patent medicine,medication attitudes and medication behaviors,and analyze the influencing factors of traditional Chinese patent medicine medication risk from the perspective of"doctor-patient",so as to better guide clinical work.Methods From July to November 2023,the medical staff of Children's Hospital Affiliated to the Capital Institute of Pediatrics(hereinafter referred to as"our hospital")and other hospitals,as well as the parents of children who had visited our hospital and taken traditional Chinese patent medicine were taken as the subjects of the survey.The questionnaires were distributed and collected by Questionnarie Stars to analyze the KAP scores of medical staff and parents of children on the risk of children using traditional Chinese patent medicine,and the factors influencing the scores of KAP of taking traditional Chinese patent medicine were analyzed using Logistic regression analysis.And Spearman correlation analysis was used to explore the correlation among knowledge,attitude and practice.Results A total of 339 valid questionnaires(the effective recovery rate of 98.83%)were collected from the medical staff version of the questionnaire.The medical staff with excellent knowledge,attitude and practice scores accounted for 16.22%,7.08%and 83.19%,respectively.A total of 336 valid questionnaires(the effective recovery rate of 98.82%)were collected from the parents'version.The parents of the children with excellent knowledge,attitude and practice scores accounted for 25.87%,3.57%and 30.65%,respectively.Logistic regression analysis showed that different hospital locations and educational levels were important influencing factors for the KAP of medical personnel,while the age,education level,work status,occupation,and monthly income of parents were important influencing factors for their KAP(P<0.05).The Spearman correlation analysis results showed a significant positive correlation among medication knowledge,attitude,and behavior(P<0.01).Conclusion Medical staff and parents of children need to further improve their knowledge and attitude towards the use of traditional Chinese patent medicine.Parents need to pay special attention to standardizing drug use practice,so as to reduce the risk of children using traditional Chinese patent medicine.Physicians and pharmacists can carry out appropriate traditional Chinese patent medicine knowledge popularization and science popularization for parents of children combined with the results of this study.

AIM: To explore the dynamic expression of high mobility group box 1(HMGB1)in scar tissues after glaucoma drainage valve implantation, and to further reveal the role and possible mechanism of HMGB1 in scarring after glaucoma surgery.METHODS: A total of 60 New Zealand white rabbits were randomly divided into control group(n=20), model group(n=20, silicone implantation under conjunctival sac)and model with drug administration group(n=20, silicone implantation under conjunctival sac combined with 5-fluorouracil injection). The conjunctival tissues were collected at 4 and 8 wk after surgery. HE staining and Masson staining were used to detect the proliferation and distribution of fibroblasts and collagen fibers in conjunctival tissues. Immunohistochemistry was utilized to detect the distribution and changes of HMGB1, transforming growth factor(TGF)-β1, Smad3 and α-smooth muscle actin(SMA)in conjunctival tissues. RT-PCR and Western blot were adopted to detect the mRNA and protein expression of HMGB1, TGF-β1, Smad3 and α-SMA in conjunctival tissues.RESULTS: HE staining and Masson staining showed that the proliferation of inflammatory cells, fibroblasts and collagen fibers in the model group was significantly higher than that in the control group at both 4 and 8 wk. Meanwhile, the proliferation of fibroblasts and collagen fibers in the model with drug administration group was significantly lower than that in the model group. Immunohistochemical staining showed that the expression of HMGB1, TGF-β1, Smad3 and α-SMA protein was observed in the conjunctival tissues of the model group both 4 and 8 wk, with brown and significantly deeper staining of the model group at 8 wk. Meanwhile, the positive staining in the model with drug administration group at both 4 and 8 wk was significantly lower than that in the model group. There was positive correlations between the number of fibroblasts stained with HE and the expression of HMGB1 in the conjunctival tissue of the model group at both 4 and 8 wk(r=0.602, 0.703, all P<0.05). RT-PCR and Western blot revealed that the mRNA and protein expression levels of HMGB1, TGF-β1, Smad3 and α-SMA in the model group were significantly higher than those in the control group at both 4 and 8 wk(all P<0.05). Meanwhile, the mRNA and protein expression levels of HMGB1, TGF-β1, Smad3 and α-SMA in the model with drug administration group were significantly lower than those in the model group(all P<0.05). There was positive correlations between mRNA expressions of HMGB1 and TGF-β1, Smad3 in the model group and the model with drug administration group(all P<0.05).CONCLUSION: The expression of HMGB1 increased at a time-dependent manner after glaucoma valve implantation. HMGB1 acts an indispensable role in the initiation and progression of scar formation after glaucoma surgery, which may be involved in the regulation of TGF-β/Smad signaling pathway.

ObjectiveTo investigate the efficacy and safety of cinobufagin tablets combined with thalidomide/dexamethasone （TD） regimen in the treatment of newly diagnosed multiple myeloma （NDMM） with phlegm and stasis obstruction. MethodsThe clinical data of 50 patients with NDMM of phlegm and stasis obstruction who were hospitalized at the Jiangsu Province Hospital of Chinese Medicine from June 1st， 2015 to July 31th， 2019 were retrospectively analyzed， and they were divided into a control group （bortezomib/dexamethasone-containing regimen， 27 cases） and an observation group （cinobufagin tablets combined with TD regimen， 23 cases）. The clinical efficacy and safety were compared between the two groups after two or three courses of treatment. The primary outcomes were clinical remission rate including overall response rate and deep remission rate， one-year and two-year overall survival rate， and adverse effects. The secondary outcomes were the proportion of plasma cells in bone marrow， hemoglobin， β2-microglobulin， lactate dehydrogenase， serum creatinine， blood urea nitrogen， bone pain score， and KPS functional status score （KPS score） before and after treatment. ResultsIn terms of clinical efficacy， there was no statistically significant difference （P＞0.05） in the overall response rate ［the observation group 69.57%（16/23） vs the control group 70.37% （19/27）］ and deep remission rate ［the observation group 56.52% （13/23） vs the control group 55.56% （15/27）］ between groups after the treatment. The one-year overall survival rates of the observation group and the control group were 90.9% and 92.4%， and the two-year overall survival rates were 81.8% and 80.9% respectively， with no statistically significant differences between groups （P＞0.05）. During the treatment， no renal function injury occurred in both groups. The incidence of peripheral nerve injury in the observation group was 8.70%， which was lower than 48.15% in the control group （P＜0.01）. After the treatment， the proportion of myeloma plasma cells， β2-microglobulin， serum creatinine level， and bone pain score decreased， while the hemoglobin level and KPS score increased in both groups （P＜0.05 or P＜0.01）. Compared between groups after treatment， the bone pain score of the observation group was lower than that of the control group， while the KPS score was higher than that of the control group （P＜0.05）. ConclusionThe clinical efficacy of cinobufagin tablets combined with TD in the treatment of NDMM is equivalent to bortezomib/dexamethasone-containing regimen， but the former is more helpful in relieving the pain and improving the quality of life， and has better safety.

Objective To study the effect of age-related white matter changes (ARWMC) on first symptomatic ischemic stroke events in the oldsters. Methods For the prospective study, a total of 368 eligible oldsters were enrolled in the study from January 2010 to August 2012. The degrees of ARWMC were assessed by ARWMC scale;according to the scores, they were divided into non ARWMC group, mild-moderate ARWMC group and severe ARWMC group. The patients were followed up once every 3 months. The clinical endpoint events and time (first symptomatic ischemic stroke, myocardial infarction and all-cause death) were recorded. Analyses of variance and Chi-square test were used to compare the differences of clinical data among the 3 groups. COX regression was used to assess the risk differences of first symptomatic ischemic stroke in the oldsters of three groups. Results After an average of follow-up for 48.7 months, 50 participants (13.6％) had first symptomatic ischemic stroke;25 (25.8％) were categorized as the severe ARWMC group, 22 (10.9％) were as the mild-medium group, and 3 (4.4％) were as the non ARWMC group. Among the three groups, the differences in age, history of hypertension, systolic blood pressure, incidence of clinical endpoint events and first symptomatic ischemic stroke, and follow-up time of endpoint events were statistically significant (P<0.05); patients from the severe ARWMC group were the oldest, and had the longest history of hypertension, the highest systolic blood pressure, the highest incidence of clinical end events and first symptomatic ischemic stroke, and the shortest follow-up period for clinical end events. COX regression analysis showed that the risk of first symptomatic ischemic stroke in the severe ARWMC group was about 8 times higher than that in the non ARWMC group (hazard ratio=9.012, 95％CI: 2.310-35.154, P=0.002). Conclusion In oldsters, severe ARWMC often accompany hypertension history and poor blood pressure controll, and it is an independent and serious risk factor for long-term first symptomatic ischemic stroke.

OBJECTIVETo analyze the hematological and genetic characteristics of unstable hemoglobin Rush (Hb Rush) and compound heterozygote of Hb Rush and thalassemia.

METHODSPeripheral blood samples and genomic DNA from three patients (including two ethnic Dai and one Han Chinese) with anemia of undetermined origin were collected. Hematological phenotypes of these patients were determined through red blood cell analysis and hemoglobin electrophoresis. Genotypes of alpha- and beta-globin genes, -158 XmnⅠ polymorphic site ofγ promoter region, and haplotypes of 7 polymorphic restriction sites in the beta-globin gene cluster were determined using PCR-based methods and DNA sequencing.

RESULTSAll patients have presented hypochromic microcytic anemia and hemoglobin fraction with significant increased measurement (30.5%-59.2%) in the region of fetal hemoglobin during alkaline medium electrophoresis. DNA analysis suggested that all patients have carried mutations leading to the unstable hemoglobin Rush (HBB codon 101, GAG>CAG, Glu>Gln). Two of them were compound heterozygotes of Hb Rush and thalassemia mutations of -α,CD17 and Hb E, respectively. Hb Rush mutation was associated with various haplotypes of the β-globin gene cluster. No significant association was found between increased abnormal hemoglobin fraction in the region of Hb F and the polymorphism ofγ promoter or large deletion of the beta-globin gene cluster.

CONCLUSIONThis study has confirmed the distribution of Hb Rush among various Chinese populations and is the third report of its kind. Hb Rush can result in increased measurement of hemoglobin fraction in the region of fetal hemoglobin (Hb F) during routine hemoglobin electrophoresis under alkaline condition. Hb Rush heterozygote alone can lead to hypochromic microcytic anemia and thalassemia-like phenotype. Prenatal diagnosis of Hb Rush is necessary for carriers.

Adult ; Base Sequence ; Blood Protein Electrophoresis ; methods ; Female ; Fetal Hemoglobin ; genetics ; metabolism ; Genotype ; Haplotypes ; Hemoglobins, Abnormal ; genetics ; metabolism ; Heterozygote ; Humans ; Infant ; Mutation ; Phenotype ; Polymorphism, Genetic ; Sequence Analysis, DNA ; methods ; Thalassemia ; blood ; diagnosis ; genetics ; Young Adult ; alpha-Globins ; genetics ; metabolism ; beta-Globins ; genetics ; metabolism

OBJECTIVE: To establish a HPLC method for the determination of Paeonol in Yuxianling granules. METHODS: The determination was performed on Phenomenex C18-ODS with mobile phase consisted of methanol-water (60∶40) at a flow rate of 1.0mL?min-1. The detection wavelength was set at 274nm. RESULTS: The linear range of Paeonol was 8.24～41.20?g?mL-1(r=0.999 7). The recovery was 99.50% (RSD=1.12%). CONCLUSION: The established method is simple, rapid, sensitive, accurate, stable and reproducible, and suitable for the determination of the content of paeonol in Yuxianling granules.