Objective To evaluate the clinical effectiveness of Smecta versus Xilei Powder in treatment of oral ulcer in children.Methods CBM, CNKI, VIP and WanFang Data were searched for the randomized controlled trials (RCTs) of Smecta versus Xilei Powder in treatment of oral ulcer in children from the date of their establishment to June 31, 2010. The bibliographies of the included studies were searched, too. Two reviewers evaluated the quality of the included RCTs and extracted data critically and independently, and then the extracted data were analyzed by using RevMan 5.1 software. Results Twenty-two RCTs involving 1 489 patients meeting the inclusion criteria. The results of meta-analyses showed that compared with Xilei Powder, Smecta could increase the total effective rate by 1.38 times (RR=1.38, 95%CI 1.31 to 1.45, Plt;0.000 01) and decrease the average course of treatment (MD= –1.54, 95%CI –1.77 to –1.31, Plt;0.000 01), with significant differences. Only 3 RCTs mentioned adverse events, but no adverse events were reported. Conclusion The current evidence shows that Smecta is effective and safe in treatment of oral ulcer in children. Due to the limitations of the included RCTs, the quality of outcomes are moderate based on GRADE, which should be recommended by clinicians as “Weak Recommendation”. More large-sample and high-quality RCTs are needed to confirm the reliability of this study.
Objective To explore the application effect of " dual-channel teaching” of the rain classroom platform in airway obstruction teaching. Methods A total of 228 nursing undergraduate students in Grade 2015 were selected as the research subjects by the method of cluster random sampling. Class B (n=115) was randomly selected as the control group and Class A (n=113) was selected as the experimental group. Class B adopted the traditional practical teaching mode, and Class A was integrated with the rain classroom platform on this basis. After the end of the course, the learning situation of the experimental group students were understood through the rain classroom background data, the teaching effects of the two groups were evaluated by the students’ theoretical scores and applied case test scores, and the experimental group student’s evaluation of the rain class was understood by questionnaire survey. Results There were 105 nursing students (92.92%) completing pre-school preparation tasks, 103 (91.15%) participating in the class answering, and 113 (100.00%) completing the after-school exercises. The theoretical scores and applied case assessment scores of the experimental group students were 79.44±6.25 and 83.24±3.64, respectively, and those of the control group students were 68.50±7.96 and 70.59±5.51, respectively, the differences between the two groups were statistically significant (P<0.05). In the experimental group, 92 nursing students (81.42%) liked the rain classroom platform teaching. Conclusion The rain classroom platform teaching can bring about the ecological transformation of the open speech in classroom, and comprehensively improve the overall quality and comprehensive ability of students, which is worthy of promotion and application in teaching.
Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.
Objective To investigate the prognostic value of troponin I ( cTNI) , brain natriuretic peptide ( BNP) and D-dimer in acute pulmonary embolism ( APE) .Methods The plasma levels of cTNI, BNP, and D-dimer were measured in 98 consecutive patients with APE at the time of admission. The relationship between these parameters and mortality were evaluated. Results APE was diagnosed in 98 consecutive patients during January 2009 to December 2010, in which 49 were males and 49 were females. 14 ( 14. 3% ) patients died at the end of follow-up. The patients with positive cTNI tests had more rapid heart rates, higher rate of syncope, cardiogenic shock and mortality than the patients with normal serumcTNI. However the age and blood pressure were lower in the patients with abnormal serum cTNI ( P lt; 0. 05) . A receiver-operating characteristic curve analysis identified BNP≥226. 5 ng/L was the best cut-off value ( AUC 0. 829, 95% CI 0. 715-0. 942) with the negative predictive value of 97. 1% for death. The mortality of the patients whose serum D-dimer level ranging from 500 to 2499 ng/mL, 2500 to 4999 ng/mL, and ≥5000 ng/mL was 7. 8% , 12% , and 41. 2% , respectively ( P = 0. 009) . Upon multivariate analysis, cardiogenic shock ( OR=2. 931, 95% CI 0. 828-12. 521, P =0.000) , cTNI≥0. 3 ng/mL ( OR=1. 441, 95% CI 0. 712-4. 098, P = 0. 0043) , BNP gt; 226. 5 ng/L ( OR = 1. 750, 95% CI 0. 690-6. 452, P = 0. 011) and D-dimer≥5000 ng/mL( OR = 1. 275, 95% CI 0. 762-2. 801, P = 0. 034) were independent predictors of death. Conclusions Combined monitoring of cTNI, BNP or D-dimer levels is helpful for prognosis prediction and treatment decision for APE patients.
Objective To understand the changing patterns and characteristics of the number of patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) before, during, and in the post-epidemic period of the COVID-19 pandemic and the Association between acute respiratory infections and hospitalization of patients with AECOPD. Methods A retrospective analysis was conducted to count the patients hospitalized for AECOPD in the Department of Respiratory Medicine of the Third Affiliated Hospital of Chongqing Medical University from July 2017 to June 2024. The pattern of change in the number of AECOPD hospitalizations and the associations with patients with respiratory tract infections in outpatient emergency departments were analyzed. Results During the COVID-19 epidemic, the number of hospitalizations of patients with AECOPD did not increase compared with the pre-epidemic period. Instead, it significantly decreased, especially in the winter and spring peaks (P<0.05). The only exception was a peak AECOPD hospitalization in the summer of 2022. COPD inpatient mortality and non-medical discharge rates tended to increase during the epidemic compared with the pre-epidemic period. Analysis of the curve of change in the number of patients with respiratory infections in our outpatient emergency departments during the same period revealed a downward trend in the number of patients with respiratory infections during the epidemic and an explosive increase in the number of patients with respiratory infections in the post epidemic period, whose average monthly number was more than twice as high as that during the epidemic. Correlation analysis of the number of patients with respiratory infections between AECOPD hospitalizations and outpatient emergency departments showed that there was a good correlation between the two in the pre-epidemic and post-epidemic periods, and the correlation between the two in the post-epidemic period was more significant in particular (r=0.84-0.91, P<0.001).In contrast, there was no significant correlation in 2021 and 2022 during the epidemic (r=0.24 and 0.50, P>0.05 ). The most common respiratory infection pathogens among AECOPD hospitalized patients during the post-epidemic period were influenza virus, COVID-19 virus, and human rhinovirus, respectively. Conclusions The pandemic period of COVID-19 infection did not show an increase in the number of AECOPD hospitalizations but rather a trend towards fewer hospitalizations. Respiratory infections were strongly associated with the number of AECOPD hospitalizations in the pre- and post-pandemic periods, while the correlation between the two was poorer during the pandemic period. Influenza virus was the most important respiratory infection pathogen for AECOPD during the post-epidemic period.
