Objective To study the clinical effect of massage therapy on infants and young children with asthmatic disease.Methods 100 infants and young children with asthmatic disease were selected.The children and parents who agreed to cooperate with massage therapy were enrolled as treatment group.The children and parents who were unwilling to cooperate with massage therapy,but willing to cooperate with home aerosol therapy were enrolled as control group,50 cases in each group.The two groups of children took comprehensive treatment measures,the treatment group was given massage therapy,the control group was given home aerosol therapy.The therapeutic effects of the two groups were compared and analyzed.Results The respite time,recurrent frequency of wheezing within 1 year,respiratory infection occurred within 1 year,number of re-hospitalization within 1 year of the control group were (5.6 ± 1.36) d,(2.35 ± 1.13) times,(2.96 ± 1.22) times,(0.85 ± 0.58) times,which of the treatment group were (5.82 ± 1.44) d,(2.31 ± 1.09) times,(2.89 ± 1.19) times,(0.86 ± 0.51) times,the differences were not statistically significant between the two groups (t=0.573,0.417,0.523,0.872,all P ＞0.05).The peak time before treatment,1 month after treatment and 6 months after treatment,and the peak volume ratio before treatment,1 month after treatment and 6 months after treatment between the two groups had no statistically significant differences(all P ＞ 0.05).Conclusion Massage therapy can achieve the same effect with the home aerosol therapy,the method is simple,easy,without any adverse reactions,it is worthy of clinical promotion.

Objective:To construct preeclampsia (PE) prediction models using information from the hospital electronic medical information and clinical laboratory data through logistic regression (LR) and machine learning algorithms, and to compare their predictive performance.Methods:The study was conducted based on the information from Rouji Pregnancy Test Database and the perinatal data of women who visited the Third Affiliated Hospital of Guangzhou Medical University from January 1, 2012, to December 31, 2019. Drawing upon clinical treatment guidelines and related literature, 28 clinical indicators from 2 736 pregnant women at 24 to 28 weeks of gestation were selected after a thorough integration and used for the construction of the PE prediction model dataset. Patients diagnosed with PE comprised the PE group ( n=245), while another 255 cases from the rest who did not have PE were selected, with undersampling method, as the control group. The Random Forest algorithm (RF), eXtreme Gradient Boosting (XGB) algorithm, and LR model were each employed to develop predictive models for PE. Following the construction of the models, external validation of PE prediction accuracy was carried out using data acquired from an independent prospective cohort study on PE that was conducted from June 2019 to December 2022, in which 38 PE cases and 80 controls were chosen. The performance of predictive models were evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic. Results:Indicators included in the construction of the three predictive models suggested that uric acid, creatinine, maternal age, early pregnancy body mass index, urea, triglycerides, red blood cell count, eosinophil count, total cholesterol, neutrophil count, urine protein, alanine aminotransferase, and urine occult blood were influential in PE prediction models. The AUCs for RF, XGB, and LR models in the training and test sets were 0.851 (95% CI:0.730-0.891), 0.955 (95% CI:0.865-0.987), 0.884 (95% CI:0.767-0.923) vs. 0.845 (95% CI:0.723-0.868), 0.907 (95% CI:0.791-0.919), 0.851 (95% CI:0.755-0.893), respectively. In the test set, the accuracy, sensitivity, and specificity for RF, XGB, and LR models were 0.803, 0.607, 0.958, 0.864, 0.790, 0.927, and 0.832, 0.661, 0.971, respectively. In the external validation of the RF, XGB and LR predictive models, the accuracy were 0.822, 0.814, and 0.763; the sensitivity were 0.737, 0.789, and 0.605, and the specificity were 0.863, 0.825, and 0.838, respectively. Among them, XGB model showed the highest Youden's index (0.614). Conclusion:Compared to traditional methods of model construction, machine learning algorithms can establish more effective PE prediction models using real clinical data.

Objective:To study the effectiveness of noninvasive embryo chromosome screening (NICS) based on blastocyst culture medium and cystic fluid in preimplantation genetic detection (PGT) of embryos in different age groups.Methods:A retrospective analysis of 62 couples who underwent PGT assisted pregnancy in Shenzhen Hospital of Peking University from January 2019 to June 2021. A total of 310 blastocysts were biopsied. At the same time, D3-6 blastocyst culture medium and blastocyst cavity fluid were collected for NICS. According to the age of the patients, they were divided into three groups: <35 years old group, 35≤age<40 years old group and ≥40 years old group. The results of NICS were compared with those of embryonic trophoblast (TE) biopsy in each group, and the consistency, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Then the consistency, sensitivity, specificity, positive predictive value and negative predictive value of NICS and TE among the three age groups were statistically analyzed.Results:There was no statistically significant difference in the consistency rate of NICS and TE among the three age groups ( P>0.05), but there was an upward trend in the elderly group (35≤age<40 years and ≥40 years). There was no statistically significant difference in specificity, sensitivity and PPV among the three age groups ( P>0.05). There was significant difference in NPV between the ≥40 years group and the other two groups ( P<0.05). Conclusions:There was no statistical difference in the effectiveness of NICS among different age groups, However, there was an increasing trend in people ≥35 years of age.