AIM To identify the chemical components of Longmu Qingxin Mixture by UPLC-Q-TOF-MS and study its material basis for the treatment of attention deficit hyperactivity disorder.METHODS The sample was detected by mass spectrometry in positive and negative ion mode on a Waters CORTECS? UPLC? T3 chromatographic column.The data were analyzed with Peakview 1.2 software and matched with the Natural Products HR-MS/MS Spectral Library 1.0 database,and the components were identified in combination with literature reports.The material basis of Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder was analysed according to the identified components.RESULTS Forty chemical components were identified,including 11 flavonoids,6 monoterpene glycosides,4 triterpene saponins,3 phenolic acids,6 alkaloids etc.,which mainly derived from Radix Astragali,Radix Paeoniae Alba,Radix Scutellariae,licorice root,Ramulus Uncariae cum,etc.,baicalein,formononetin,astragaloside Ⅳ and rhynchophylline may be the material basis for the therapeutic effect of Longmu Qingxin Mixture.CONCLUSION UPLC-Q-TOF-MS can quickly identify the chemical components of Longmu Qingxin Mixture.Flavonoids,triterpene saponins and alkaloids may be the material basis for Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder,which can provide the basis for its material basis research,quality standard establishment and pharmacological study of the dismantled formula.

Objective To observe the value of augmented reality(AR)navigation system for assisting CT-guided puncture of pulmonary nodules in dog models.Methods Five healthy dogs were selected,and 4 target lung rings were implanted in each dog to build pulmonary nodule models.Deferring to crossover design,CT-guided punctures were performed with or without AR navigation 2 and 4 weeks after successful modeling,respectively,while punctures with AR navigation were regarded as AR group and the others as conventional group,respectively.The time duration of puncturing,the times of CT scanning,of needle adjustment,and the deviation distance between needle pinpoint to the center of pulmonary nodule shown on three-dimensional reconstruction were compared between groups.Results The duration time of puncture in AR group and conventional group was(13.62±5.11)min and(20.16±4.76)min,respectively.In AR group,the times of CT scanning,of needle adjustment,and the deviation distance was 2.40±0.50,2.75±0.44 and(2.94±1.92)mm,respectively,while in conventional group was 3.10±0.64,3.70±0.57 and(4.90±3.38)mm,respectively.The introduction of AR navigation was helpful to shortening the duration of puncture,reducing times of CT scanning and needle adjustment,also decreasing positioning error of needle pinpoint(all P＜0.05).In contrast,the variance of puncture sequences and dogs had no obvious effect on the results(both P＞0.05).Conclusion AR navigation system could improve accuracy and efficiency in CT-guided puncture of pulmonary nodules in dog models.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective To establish an indirect enzyme-linked immunosorbent assay(ELISA)method for testing the concentration of a monoclonal antibody target tumor necrosis factor-α(TNF-α)in animal serum.Methods The critical parameters of the method including coating concentration of human TNF-α,source,concentration and stability of HRP-labeled goat anti-human immunoglobulin G(IgG)were investigated.The specificity,accuracy,precision,linearity and Limited of Determination of the method were investigated.Results The critical parameters of the method were confirmed as below:TNF-α was coated at 400 ng·mL-1;HRP labeled goat anti-human IgG antibody was diluted at 1:3.0 ×105;the diluted horseradish peroxidase labeled goat anti-human IgG antibody is well stored at 4 ℃ for 3 days.Meanwhile the method was confirmed to have good specificity,the recovery rate ranged from 84.00％to 106.82％,the coefficient of variation of different antibody concentration levels were no more than 10％;the method had a good linearity and the standard curve was y=(-8.37×103-2.37 × 106)/[1+(x/29.80)106]+2.37 × 106(R2=0.999);the limit of quantification was 1 ng·mL-1,all of which met the requirements.Conclusion A accurate and robust ELISA method was developed to test the concentration of anti-TNF-α monoclonal antibody in serum.

The breakage pattern of unit particles during the production of oral solid dosage forms (OSD) is closely related to the quality of intermediate or final products. To accurately characterize the particles and study the evolution law of particle breakage, the Bonding model of the discrete element method (DEM) was used to investigate the breakage patterns of model parameters, particle shape and process conditions (loading mode and loading rate) on the dynamic breakage, force-time curve, breakage rate, maximum breakage size ratio and fracture strength of particles. The results showed that the particle breakage force was positively correlated with normal strength and bonded disk scale, negatively correlated with normal stiffness per unit area and tangential stiffness per unit area, and weakly correlated with tangential strength. The particle breakage rate was negatively correlated with the aspect ratio of the particles, and the maximum breakage size ratio was positively correlated with the aspect ratio of the particles; among the three loading modes, the breakage rate of compression breakage model was the largest, the breakage rate of shear breakage model was the second largest, and the breakage rate of wear breakage model was the smallest; the maximum breakage size ratio was positively correlated with the loading rate, the loading mode and the loading rate had no mutual influence on particle breakage rate, but had mutual influence on the maximum breakage size ratio. The research results will provide a theoretical basis for the shift of OSD from batch manufacturing to advanced manufacturing.

Objective To investigate a novel stroke recurrence risk prediction model,which utilized radiomics machine learning methods and specifically combined carotid computed tomography angiography(CT A)with the Essen stroke risk score(ESRS).Methods A total of 136 patients who underwent carotid CT A were analyzed retrospectively.The features of carotid plaque were extrac-ted by machine learning to construct a radiomics feature model,as well as combined with ESRS.Based on clinical outcomes at one-year follow-up,the stroke recurrence risk prediction model was constructed using the logistic regression(LR)machine learning model.To construct an effective and robust model,the dataset was divided into a training set and a validation set in a ratio of 7∶3.The performance of this model was evaluated using area under the curve(AUC)of receiver operating characteristic(ROC)curve,sensi-tivity and specificity.Results The model had strong predictive value.In the training set,AUC,sensitivity and specificity of this model were 0.903,0.796 and 0.761,respectively.In the validation set,AUC,sensitivity and specificity of this model were 0.869,0.667 and 0.850,respectively.Conclusion The stroke recurrence risk prediction model constructed based on radiomics analysis of carotid plaque characteristics in carotid CTA,in combination with the ESRS,can provide reliable predictions for stroke prognosis.

Glyceryl phenylbutyrate(GPB)serves as a long-term management medication for Ornithine transcarbamylase deficiency(OTCD),effectively controlling hyperammonemia,but there is a lack of experience in using this medicine in China.This article retrospectively analyzes the case of a child diagnosed with OTCD at Shanghai Children's Medical Center,Shanghai Jiao Tong University School of Medicine,including a review of related literature.After diagnosis,the patient was treated with GPB,followed by efficacy follow-up and pharmacological monitoring.The 6-year and 6-month-old male patient exhibited poor speech development,disobedience,temper tantrums,and aggressive behavior.Blood ammonia levels peaked at 327 μmol/L;urine organic acid analysis indicated elevated uracil levels;cranial MRI showed extensive abnormal signals in both cerebral hemispheres.Genetic testing revealed de novo mutation in the OTC gene(c.241T＞C,p.S81P).Blood ammonia levels were approximately 43,80,and 56 μmol/L at 1,2,and 3 months after starting GPB treatment,respectively.During treatment,blood ammonia was well-controlled without drug-related adverse effects.The patient showed improvement in developmental delays,obedience,temperament,and absence of aggressive behavior.

Objective Evaluation of the clinical efficacy of f trochanteric flip osteotomy combined with Kocher-Langen-beck approach for high acetabular posterior wall fracture.Methods Between January 2020 and December 2022,20 patients with high acetabular posterior wall fractures were retrospectively analyzed,including 12 males and 8 females,aged 18 to 75 years old.They were divided into two groups according to the different surgical methods.Ten patients were treated with greater trochanteric osteotomy combined with Kocher-Langenbeck approach as the observation group,including 5 males and 5 fe-males,aged from 18 to 75 years old.Ten patients were treated with Kocher-Langenbeck approach alone as the control group,including 7 males and 3 females,aged from 18 to 71 years old.Matta reduction criteria were used to evaluate the reduction quality of the two groups,and Harris score was used to compare the hip function of the two groups at the latest follow-up.The operation time,blood loss and postoperative complications of the two groups were analyzed.Results All patients were followed up for 10 to 24 months.According to the Matta fracture reduction quality evaluation criteria,the observation group achieved anatomical reduction in 6 cases,satisfactory reduction in 3 cases,and unsatisfactory reduction in 1 case,while the control group only achieved anatomical reduction in 3 cases,satisfactory reduction in 3 cases,and unsatisfactory reduction in 4 cases.At the final follow-up,the Harris hip score ranged from 71.4 to 96.6 in the observation group and 65.3 to 94.5 in the control group.According to the results of Harris score.The hip joint function of the observation group was excellent in 6 cases,good in 3 cases,and fair in 1 case.The hip joint function of the control group was excellent in 2 cases,good in 3 cases,fair in 3 cases,and poor in 2 cases.In the observation group,the intraoperative blood loss ranged from 300 to 700 ml,and the operation dura-tion ranged from 120 to 180 min;in the control group,the intraoperative blood loss ranged from 300 to 650 ml,and the opera-tion duration ranged from 100 to 180 min.Complications in the observation group included 1 case of traumatic arthritis and 1 case of heterotopic ossification,while complications in the control group included 3 cases of traumatic arthritis,3 cases of het-erotopic ossification and 1 case of hip abduction weakness.Conclusions Trochanteric flip osteotomy combined with the Kocher-Langenbeck approach significantly improved anatomical fracture reduction rates,enhanced excellent and good hip joint function outcomes,and reduced surgical complication incidence compared to the Kocher-Langenbeck approach alone.Clinical application of this combined approach is promising,although larger studies are needed for further validation.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.