ObjectiveTo observe the effect of muscle stimulating instrument in enforcing muscle stone of children with cerebral palsy after selective posterior rhizotomy(SPR).Methods106 patients were divided into two groups,treatment group(n=51) and control group(n=55).Two groups received routine rehabilitation therapy,otherwise,treatmwnt group were treated with the muscle stimulating instrument after SPR.Pretreatment and 30d,60d,180d after treatment,muscle stone of two groups was measured and compared.ResultsImprovement of treatment group in muscle stone was better than that of control group(P<0.01).ConclusionMuscle stimulating instrument is effective to improve muscle tone of children with cerebral palsy after SPR.

ObjectiveTo observe the effect of selective posterior rhizotomy (SPR) on children with spastic cerebral palsy.Methods517 spastic cerebral palsy cases were operated on by SPR, and a following up was performed for 24 months. After operation, curative effect of SPR was examined and evaluated.Results298 cases had excellent effect (57.6%); 187 cases had good effect (36.2%).Conclusion SPR is very effective for children with spastic cerebral palsy.

Objective:To clone swine leukocyte antigen,class II,DO alpha (SLA-DOA) gene from Banna mini-pig inbred line (BMI) and detect its mRNA expression level in 19 important tissues.Methods:The complete eDNA sequence of SLA-DOA gene was cloned by RT-PCR method from BMI and the mRNA expression pattern in BMI important tissues was examined by semi-quantative RT-PCR method.Nucleotide and protein sequences of SIA-DOA were used to carry out bioinformatics analysis and construct the phylogenetic tree.Results:The eDNA length of BMI SLA-DOA was 1 079 bp,which encoded a protein of 250 amino acids with molecular weight (Mw) 27.81 kD,and isoelectric point (pI) 6.48.Genome structure analysis showed it localized to Sus scrofa chromosomes 7 and consisted of four exons and three introns.Semi-quantitative expression analysis showed that SLA-DOA gene expressed highly in the lymph nodes and stomach;weakly in the heart,skin and duodenum and none in other 14 tissues.Functional bioinformatics analysis indicated that SLA-DOA protein contained one signal poptide,one transmembrane region,three conserved domains,four O-GlcNAc glycosylation sites,18 potential phosphorylation sites and to be located in the cytoplasm with 94.1％ certainty.Phylogenetic analysis demonstrated that BMI (pig) had the closest relationship with cattle.Conclusion:This study have successfully cloned the SLA-DOA gene of Banna mini-pig inbred line,performed bioinformatics analysis and tissue expression profile analysis.It will provide a basis for the studies of BMI xenotransplantation.

Objective:To construct an early prediction model for post cardiopulmonary resuscitation-acute kidney injury (PCPR-AKI) by machine learning and provide a basis for early identification of acute kidney injury (AKI) high-risk patients and accurate treatment.Methods:It was a single-center retrospective study. The clinical data of patients admitted to Tianjin Medical University General Hospital after cardiopulmonary resuscitation following cardiac arrest from January 1, 2016 to October 31, 2023 were collected. The end-point event of the study was defined as AKI occurring within 48 hours after cardiopulmonary resuscitation. The patients were divided into AKI group and non-AKI group according to the AKI diagnostic criteria, and the differences of baseline clinical data between the two groups were compared. The patients who met the inclusion criteria were randomly (using the train_test_split function, set the random seeds to 1, 2, and 3) divided into training and validation sets at a ratio of 7∶3. Random forest (RF), support vector machine, decision tree, extreme gradient boosting and light gradient boosting machine algorithm were used to develop the early prediction model of PCPR-AKI. The receiver-operating characteristic curve and decision curve analysis were used to evaluate the performance and clinical practicality of the predictive models, and the importance of variables in the optimal model was screened and ranked.Results:A total of 547 patients were enrolled, with age of 66 (59, 70) years old and 282 males (51.6%). There were 238 patients (43.5%) having incidence of AKI within 48 hours after cardiopulmonary resuscitation. In the AKI group, 182 patients (76.5%) were in stage 1, 47 patients (19.7%) were in stage 2, and 9 patients (3.8%) were in stage 3. There were statistically significant differences in the age, time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, proportion of initial defibrillation rhythm, proportion of electric defibrillation, proportion of mechanical ventilation, adrenaline dosage, sodium bicarbonate dosage, proportion of coronary heart disease, proportion of hypertension, proportion of diabetes, serum creatinine, blood urea nitrogen, blood lactic acid, blood potassium, brain natriuretic peptide, troponin, D-dimer, neuron specific enolase, and 24 hours urine volume after cardiopulmonary resuscitation between AKI group and non-AKI group (all P<0.05). Among the five machine learning algorithms, RF model achieved the best performance and clinical practicality, with area under the curve of 0.875, sensitivity of 0.863, specificity of 0.956, and accuracy rate of 90.7%. In the variable importance ranking of RF model, the top 10 variables were as follows: time to reach resuscitation of spontaneous circulation, time from cardiac arrest to starting cardiopulmonary resuscitation, initial defibrillable rhythm, serum creatinine, mechanical ventilation, blood lactate acid, adrenaline dosage, brain natriuretic peptide, D-dimer and age. Conclusions:An early predictive model for PCPR-AKI is successfully constructed based on machine learning. RF model has the best predictive performance. According to the importance of the variables, it can provide clinical strategies for early identification and precise intervention for PCPR-AKI.