OBJECTIVE To evaluate the influence of pharmaceutical quality control on the efficiency and outcomes of standardized medication therapy management （MTM） services for patients with coronary heart disease by using Economic， Clinical and Humanistic Outcomes （ECHO） model. METHODS This study collected case data of coronary heart disease patients who received MTM services during January-March 2023 （pre-quality control implementation group， n＝96） and June-August 2023 （post-quality control implementation group， n＝164）. Using propensity score matching analysis， 80 patients were selected from each group. The study subsequently compared the economic， clinical， and humanistic outcome indicators of pharmaceutical services between the two matched groups. RESULTS There were no statistically significant differences in baseline data between the two groups after matching （P＞0.05）. Compared with pre-quality control implementation group， the daily treatment cost （16.26 yuan vs. 24.40 yuan， P＜0.001）， cost-effectiveness ratio [23.12 yuan/quality-adjusted life year （QALY） vs. 32.32 yuan/QALY， P＜0.001]， and the incidence of general adverse drug reactions （2.50% vs. 10.00%， P＝0.049） of post-quality control implementation group were decreased significantly； the utility value of the EuroQol Five-Dimensional Questionnaire （0.74± 0.06 vs. 0.71±0.07， P＝0.003）， the reduction in the number of medication related problems （1.0 vs. 0.5， P＜0.001）， the medication adherence score （[ 6.32±0.48） points vs. （6.10±0.37） points， P＝0.001]， and the satisfaction score （[ 92.56±1.52） points vs. （91.95±1.56） points， P＝0.013] all showed significant improvements. Neither group experienced serious adverse drug reactions. There was no statistically significant difference in the incidence of new adverse reactions between the two groups （1.25% vs. 3.75%， P＝0.310）. CONCLUSIONS Pharmaceutical quality control can improve the quality of pharmaceutical care， and the ECHO model can quantitatively evaluate the effect of MTM services， making pharmaceutical care better priced and more adaptable to social needs， thus being worthy of promotion.

ObjectiveTo investigate the possible mechanism of action of Xibining Formula （膝痹宁） for cartilage damage in knee osteoarthritis （KOA） through the cyclic guanosine-adenosine monophosphate synthase （cGAS）- stimulator of interferon genes （STING） signaling pathway. MethodsFifty C57BL/6J mice were randomly divided into five groups （10 per group）， sham operation group， KOA model group， low-dose Xibining Formula group， high-dose Xibining Formula group， and high-dose Xibining Formula + agonist group. The KOA models were constructed using the destabilization of the medial meniscus （DMM） method in all groups but the sham surgery group. Two weeks after surgery， the low- and high-dose Xibining Formula groups were administered Xibining Formula at doses of 3.58 g/（kg·d） and 14.32 g/（kg·d） respectively via gavage. The high-dose Xibining Formula + agonist group received 14.32 g/（kg·d） of Xibining Formula via gavage followed by an intraperitoneal injection of Vadimezan （DMXAA） at 25 mg/kg. The sham surgery group and the KOA model group mice were given an equivalent volume of normal saline at 5 ml/（kg·d） via gavage， once daily for four consecutive weeks. Serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） were measured by ELISA； pathological changes in cartilage tissue were observed using hematoxylin-eosin （HE） staining and Safranin O-Fast Green staining. Pathological changes were scored according to the Mankin scoring system； the levels of cartilage tissue matrix regulation-related indicators such as matrix metalloproteinase 3 （MMP3）， matrix metalloproteinase 13 （MMP13）， a disintegrin and metalloproteinase with thrombospondin motifs 5 （ADAMTS）， type-Ⅱ collagen （CⅡ） and aggregated proteoglycan （Aggrecan）， and also cGAS-STING pathway-related protein and mRNA expression levels were detected by Western blot and qPCR methods. ResultsCompared with the sham surgery group， the KOA model group showed severe cartilage edge destruction， significantly increased Mankin scores， significantly decreased protein and mRNA expression levels of COLⅡ and Aggrecan， and significantly increased protein and mRNA expression levels of cGAS， STING， MMP3， MMP13， and ADAMTS5 （P＜0.01）. Compared with the control group， serum level of IL-6， IL-1β， TNF-α in all the intervented groups decreased （P＜0.01）， while compared with high-dose Xibining Formula group， level of IL-6， IL-1β， and TNF-α in low-dose Xibining Formula group and high-dose Xibining Formula + agonist group increased （P＜0.01）. Compared with the KOA model group， all the intervention groups exhibited alleviated cartilage pathological changes， signi-ficantly reduced Mankin scores， significantly increased protein and mRNA expression levels of COLⅡ and Aggrecan， and significantly decreased protein and mRNA expression levels of cGAS， STING， MMP3， MMP13， and ADAMTS5 （P＜0.01）. Compared with high-dose Xibining Formula group， high-dose Xibining Formula + agonist group showed cartilage edge destruction， significantly increased Mankin scores， significantly decreased protein and mRNA expression levels of COLⅡ and Aggrecan， and increased protein and mRNA expression levels of cGAS， STING， MMP3， MMP13， and ADAMTS5 （P＜0.01）. ConclusionXibining Formula may improve KOA cartilage damage by inhibiting the cGAS-STING signaling pathway， decreasing matrix degradation-related proteins， and elevating matrix composition-related proteins.

COVID-19, a disease caused by SARSCoV-2 infection, has caused a global pandemic in a short time. The key immunopathological features of COVID-19 include lymphopenia, neutropenia, cytokine storm and related Immune damage to parenchymal organs such as lung. Granulocyte colony stimulating factor (G-CSF) can cause excessive inflammatory reaction, which may aggravate the condition of some patients. Therefore, it is controversial whether COVID-19 should be treated with G-CSF, and clinicians need to evaluate the effectiveness and safety of G-CSF according to the specific conditions of each patient.

Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

This article summarized the general situation,impact and evaluation of voice disorders in children.According to the five models of clinical research in the field of speech therapy and audiology,the research on behav-ioral intervention of voice disorders in children was organized as follows:Model 1 involved studies on treatment se-lection and efficacy prediction.Model 2 focused on efficacy exploration and clinical trial preparation.Model 3 encom-passed studies on clinical implementation and efficacy verification.Model 4 involved comparative studies that explore different treatment methods.Model 5 focused on the analysis of social cost.These models could be used as a refer-ence for scientific research and clinical professionals.

Objective To evaluate the effect of preoperative metabolic syndrome on early function of renal allografts in allogeneic kidney transplant recipients.Methods Clinical data of 117 kidney transplant recipients were retrospectively analyzed.According to the renal allograft function,they were divided into the delayed graft function(DGF)group(n=29)and non-DGF group(n=88).Relevant risk factors of DGF in recipients undergoing allogeneic kidney transplantation were assessed by univariate and multivariate regression analyses.The effect of preoperative metabolic syndrome on early function of renal allografts was analyzed.Results Among 117 kidney transplant recipients,47 cases were complicated with preoperative metabolic syndrome,and 29 cases developed postoperative DGF.In the DGF group,83％of the recipients were complicated with preoperative metabolic syndrome,higher than 74％in the non-DGF group(P＜0.05).Univariate analysis showed that the body mass index(BMI)and terminal serum creatinine(Scr)level of the donors,and BMI,blood glucose level,triglyceride level and the proportion of preoperative metabolic syndrome of the recipients in the DGF group were higher than those in the non-DGF group(all P＜0.05).Multivariate logistic regression analysis revealed that high Scr levels of the donors,high hemoglobin levels of the recipients and preoperative metabolic syndrome of the recipients were the independent risk factors for DGF after kidney transplantation(all P＜0.05).Conclusions Preoperative metabolic syndrome is an independent risk factor for DGF in allogeneic kidney transplant recipients.Corresponding measures should be taken to lower the incidence of DGF and other metabolic complications.

Objective:To evaluate direct and indirect costs for schizophrenia,major depressive disorder(MDD)and bipolar disorder,and to compare their differences of cost composition,and to explore the drivers of the total costs.Methods:A total of 3 175 inpatients with schizophrenia,MDD,and bipolar disorder were recruited.In-patient's self-report total direct of medical costs outpatient and inpatient,out-of-pocket costs,and direct non-medical costs were regarded as direct costs.Productivity loss and other loss caused by damaging properties were defined as indirect costs.The perspectives of this study included individual and societal levels.Multivariate regression analysis was applied for detecting the factors influencing disease costs.Results:The total cost of schizophrenia was higher than those of MDD and bipolar disorder at individual and societal levels.The indirect costs of three mental disorders were higher than the direct costs,and the indirect cost ratio of bipolar disorder was higher than those of schizophre-nia and MDD.Age,gender,working condition and marital status(P＜0.05)were the important drivers of total costs.Conclusion:The economic burden of the three mental disorders is relatively heavy.Schizophrenia has heaviest disease burden,and the productivity loss due to mental disorders is the driving force of the soaring disease cost

Objective To propose a distribution feature extraction algorithm based on wavelet packet coefficients to reconstruct the signal energy sequence for effectively identifying the pathological features of heart sounds,thereby realizing the early screening of heart diseases.Methods The original heart sound signal was decomposed into 10 layers using wavelet packet decomposition algorithm.After obtaining the wavelet packet coefficients of each layer,each coefficient was reconstructed,and the energy of the reconstructed signal was calculated and arranged in the original order to form the energy sequence.The distribution characteristics of the energy sequence of the reconstructed signals at each layer were analyzed,and distribution features were taken as classification features.Support vector machine,K-nearest neighbor,and decision tree were used to classify and recognize normal heart sounds and the heart sound signals of various diseases.Results The combination of the distribution features of the reconstructed signal energy sequence and decision tree classifier had an accuracy of 93.6%for classifying 5 types of heart sounds on the public dataset,and the accuracy was 95.6%for identifying normal heart sounds and hypertrophic cardiomyopathy heart sounds.Conclusion The proposed algorithm can extract the effective pathological information of abnormal heart sounds,providing a reference for clinical cardiac auscultation.

Objective To observe the characteristics of infant biliary atresia(BA)missed diagnosis by ultrasound.Methods Data of 72 BA infants missed by ultrasound(false-negative,group A),72 BA infants accurately diagnosed by ultrasound(true-positive,group B)and 72 non BA infants(controls,group C)were retrospectively analyzed.Clinical and ultrasonic features were compared between each 2 groups.Logistic regression analysis was performed to screen the impact factors for diagnosing and missed-diagnosing of BA.Results There were significant differences of total bilirubin,direct bilirubin,gamma glutamyl transpeptidase(GGT)and glutamic-oxaloacetic transaminase(GOT)between group A and C,also of total bilirubin,direct bilirubin and GGT between group B and C(all P＜0.05).Significant differences of the incidences of small gallbladder,gallbladder wall rigidity,poor gallbladder contraction function and fibrous plaques in the hepatic hilum were found between group A and B,of undetected or cystic changes in the gallbladder,gallbladder wall rigidity,poor gallbladder contraction function,fibrous plaques in the hepatic hilum and hepatic hilum cysts were noticed between group A and C(all P＜0.05),as well as of undetected or cystic changes in the gallbladder,small gallbladder,gallbladder wall rigidity,gallbladder wall thickening,poor gallbladder contraction function and fibrous plaques in the hepatic hilum were detected between group B and C(all P＜0.05).There were significant differences of display rate of common bile duct between each 2 groups(all P＜0.05).The lower incidence of fibrous plaques in the hepatic hilum and poor gallbladder contraction function were an impact factors for missed diagnosis of BA(both P＜0.05),while higher GGT,higher incidence of fibrous plaques in the hepatic hilum and poor gallbladder contraction function were all impact factors for diagnosis of BA in group A(all P＜0.05).Conclusion Infant BA missed diagnosis by ultrasound had lower incidence of fibrous plaques in the hepatic hilum and poor gallbladder contractile function.Combining with clinical data was helpful for reducing ultrasonic missed diagnosis of BA in infants.