Objective To construct a patient decision aid(PtDA)for exercise training in pa-tients with chronic obstructive pulmonary disease(COPD)and explore its impacts on decision-making quality of patients' exercise regimens.Methods The development of the PtDA for exercise training in COPD patients was accomplished through literature analysis,the Delphi method,and user surveys,followed by an intervention study.A total of 59 inpatients with COPD were included as study sub-jects.The control group received routine care along with general exercise training guidance,while the intervention group received routine care combined with shared decision-making for exercise training based on the PtDA.The decision conflict and decision preparedness levels of patients in both groups were compared before the intervention and on the day of discharge.The exercise self-efficacy of pa-tients was measured before intervention,on the day of discharge,and 1 month and 3 months after dis-charge.Results The intervention group had significantly lower scores for decision conflict and signif-icantly higher scores for decision preparedness and exercise self-efficacy compared with the control group(P＜0.05).Conclusion The PtDA for exercise training can improve decision conflict and de-cision preparedness in COPD patients,enhance their exercise self-efficacy levels,and provide references for healthcare professionals in improving exercise adherence.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

Purpose To explore the application value of clinical-ultrasound parameter model in secondary hyperparathyroidism(SHPT).Materials and Methods A total of 86 patients(134 lesions)with renal insufficiency who underwent maintenance hemodialysis in the First Affiliated Hospital of Shihezi University from October 2020 to August 2022 were included and divided into group 1 according to the level of parathyroid hormone(iPTH)(iPTH＜300 pg/ml),group 2(iPTH 300-800 pg/ml)and group 3(iPTH≥800 pg/ml),all patients underwent gray-scale parathyroid ultrasound and acoustic palpation tissue quantitative imaging examinations.The characteristics of glandular gray-scale ultrasound and virtual touch tissue imaging quantification parameters between different groups,combined with relevant clinical indicators,established a clinical-ultrasound parameter model,used multiple linear regression to analyze the correlation between the model and iPTH,explored the independent risk factors of iPTH,and evaluated this model to evaluate SHPT the value of.Results There were significant differences in dialysis age,phosphorus,alkaline phosphatase,serum creatinine,corrected calcium and phosphorus product,lesion size,number,echo,shear wave velocity(SWV)max,SWVcen,and SWVmean among the three groups(F/x2/H=6.396-53.524,all P＜0.05).Dialysis age,phosphorus,alkaline phosphatase,and SWVratio were independent influencing factors of iPTH level(β=0.514,0.422,0.226,-0.368,all P＜0.005).The area under the curve,sensitivity,specificity and accuracy of the model for diagnosing SHPT and predicting surgical treatment with iPTH levels of 300 pg/ml and 800 pg/ml were 0.967,95.00％,100.00％,97.73％and 0.824,77.42％,71.43％and 90.00％,respectively.Conclusion Dialysis age,phosphorus,alkaline phosphatase and SWVratio are independent influencing factors of iPTH level,and the clinical-ultrasound parameter model is of great value in accurately assessing the severity of SHPT.

Objective To investigate the impact of a peer support model on the mental health of caregivers and the perceived social support and psychiatric symptoms of the mental disorder patients under their care. Methods Patients with mental disorders undergoing long-term community-based rehabilitation and their primary caregivers were recruited for this study. A total of 44 pairs of eligible patients and caregivers were selected based on a 1∶1 matching ratio. Systematic peer support activities were conducted exclusively for the caregivers. The General Health Questionnaire (GHQ) and the Symptom Checklist-90 (SCL-90) were administered before and after the intervention to assess the mental health status of caregivers. The Perceived Social Support Scale (PSSS) and the Brief Psychiatric Rating Scale (BPRS) were employed to evaluate the patients' perceived social support and psychiatric conditions before and after the intervention. Results A total of 44 valid questionnaires from caregivers and 42 from patients were collected. The GHQ score and the total scores, the number of positive item, positive total scores, and positive mean scores of and SCL-90 of caregivers were significantly lower after the intervention compared to pre-intervention (P < 0.05 or P < 0.001). For patients, the family support score, extracurricular support score, and total score of the PSSS were significantly higher post-intervention compared to pre-intervention (P < 0.05 or P < 0.001). Compared to pre-intervention, patients'scores on anxiety and depression, lack of energy, thought disturbance, activation, hostility and suspicion, and the total score on the BPRS were significantly lower after the caregiver peer support intervention (P < 0.001). Conclusion The peer support model contributes to improving the mental health of caregivers for patients with mental disorders and enhancing the patients'psychiatric conditions.

Objective:To utilize a deep learning approach based on speech to distinguish between depressive and manic mood states in patients with bipolar disorder (BD).Methods:Sixty-one BD patients who visited the outpatient department of psychiatry at Peking University Sixth Hospital were recruited to participate in the study from June 2018 to March 2022. Quick Inventory of Depressive Symptomatology, Mood Disorder Questionnaire and Young Mania Rating Scale were used to determine patients′ mood states. The voices of the patients were recorded, including 190 samples during the patient′s remission, depressive, and manic mood period respectively. A total of 136 features were extracted from the voice samples, including Mel-frequency cepstral coefficients and zero-crossing rates using the speech analysis library in Python. A LIGHT-SERNET-based network was then used to train a model for emotion classification. Accuracy is used to evaluate the performance of the model, using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic curve (ROC) to evaluate the predictive results of model for three mood states. Kruskal-Wallis H tests or χ 2 tests were conducted to compare the differences among the demographic information of three groups. Results:There were statistically significant differences among the three groups in age ( H=25.83, P<0.001), years of education ( H=25.25, P<0.001) and marital status (χ 2=23.81, P<0.001). There is no significant difference in gender (χ 2=4.63, P=0.099). The accuracy of the model in detecting the three emotional states was 0.84. The sensitivity and specificity in detecting remission were 0.88 and 0.93, respectively, and the positive predictive value and negative predictive value were 0.87 and 0.94, respectively. The sensitivity and specificity in detecting depressive episodes were 0.82 and 0.92, respectively, and the positive predictive value and negative predictive value were 0.84 and 0.92, respectively. The sensitivity and specificity in detecting manic episodes were 0.82 and 0.91, respectively, and the positive predictive value and negative predictive value were 0.83 and 0.91, respectively. The areas of the receiver operation characteristic curve for the three mood states were similar and all exceeded 0.90. Conclusion:The LIGHT-SERNET-based deep learning model shows good discrimination ability between depressive and manic mood states based on speech analysis.

Objective:To utilize a deep learning approach based on speech to distinguish between depressive and manic mood states in patients with bipolar disorder (BD).Methods:Sixty-one BD patients who visited the outpatient department of psychiatry at Peking University Sixth Hospital were recruited to participate in the study from June 2018 to March 2022. Quick Inventory of Depressive Symptomatology, Mood Disorder Questionnaire and Young Mania Rating Scale were used to determine patients′ mood states. The voices of the patients were recorded, including 190 samples during the patient′s remission, depressive, and manic mood period respectively. A total of 136 features were extracted from the voice samples, including Mel-frequency cepstral coefficients and zero-crossing rates using the speech analysis library in Python. A LIGHT-SERNET-based network was then used to train a model for emotion classification. Accuracy is used to evaluate the performance of the model, using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic curve (ROC) to evaluate the predictive results of model for three mood states. Kruskal-Wallis H tests or χ 2 tests were conducted to compare the differences among the demographic information of three groups. Results:There were statistically significant differences among the three groups in age ( H=25.83, P<0.001), years of education ( H=25.25, P<0.001) and marital status (χ 2=23.81, P<0.001). There is no significant difference in gender (χ 2=4.63, P=0.099). The accuracy of the model in detecting the three emotional states was 0.84. The sensitivity and specificity in detecting remission were 0.88 and 0.93, respectively, and the positive predictive value and negative predictive value were 0.87 and 0.94, respectively. The sensitivity and specificity in detecting depressive episodes were 0.82 and 0.92, respectively, and the positive predictive value and negative predictive value were 0.84 and 0.92, respectively. The sensitivity and specificity in detecting manic episodes were 0.82 and 0.91, respectively, and the positive predictive value and negative predictive value were 0.83 and 0.91, respectively. The areas of the receiver operation characteristic curve for the three mood states were similar and all exceeded 0.90. Conclusion:The LIGHT-SERNET-based deep learning model shows good discrimination ability between depressive and manic mood states based on speech analysis.

Objective:To analyze the prognosis of glucose metabolism and its impacting factors at 6-12 weeks postpartum in patients with abnormal blood glucose during pregnancy.Methods:In this cross-sectional study, a total of 192 patients with abnormal blood glucose during pregnancy enrolled and delivered in the maternity clinic of Daxing Teaching Hospital of Capital Medical University from December 1, 2019 to December 31, 2020 were collected. The 75 g oral glucose tolerance test (OGTT) was applied for diabetes screening at 6-12 weeks after delivery. According to the results of postpartum blood glucose, the patients were divided into two groups: postpartum normal blood glucose group (148 cases) and abnormal blood glucose group (44 cases). Hypothesis testing was used to compare the clinical data before, during and after the pregnancy between the two groups. Multi-factor logistic regression was performed to analyze the influencing factors of postpartum abnormal blood glucose in patients with abnormal blood glucose during pregnancy.Results:Among the 192 patients with abnormal blood glucose during pregnancy, the incidence of postpartum abnormal blood glucose was 22.92% (44/192), including 6 cases of diabetes mellitus (DM) (13.64%), 38 cases of impaired glucose tolerance (IGT) (86.36%). Neck circumference, waist circumference, multiparous women and insulin use during pregnancy in postpartum abnormal blood glucose group were all significantly higher than those in postpartum normal blood glucose group [34.25(33.00, 36.00) vs 33.55 (32.00, 35.00) cm, 87.00 (82.00, 93.00) vs 84.00 (78.00, 90.00) cm, 54.55% vs 37.16%, 18.18% vs 6.76%] (all P<0.05). Neck circumference ( OR=1.315, 95% CI: 1.026-1.685), multiparous women ( OR=2.261, 95% CI: 1.057-4.836), insulin use during pregnancy ( OR=3.767, 95% CI: 1.236-11.478) were positively correlated with the occurrence of postpartum abnormal blood glucose (all P<0.05). Conclusions:The incidence of postpartum abnormal blood glucose is high at 6-12 weeks postpartum in patients with abnormal blood glucose during pregnancy. Neck circumference, waist circumference, parity and insulin use during pregnancy are important impacting factors of postpartum abnormal blood glucose.

ObjectiveTo observe the effects of Yuejuwan in the treatment of psychological and heart diseases （PHD） and explore its mechanism. MethodThirty 6-week-old healthy male SPF AopE^-/- mice and 10 homologous C57BL/6J mice were selected for the experiment. The 30 AopE^-/- mice were divided into a model group， low-dose （7.58 g·kg^-1·d^-1） and high-dose （30.32 g·kg^-1·d^-1） Yuejuwan groups， with 10 mice in each group， and 10 C57BL/6J mice were assigned to the blank control group. Intragastrical administration lasted 12 weeks. During feeding， the PHD model was induced by chronic unpredictable mild stress （CUMS） combined with high-fat diet in mice. After intragastric administration， the behavioral results ［open field test （OFT） and sucrose preference test （SPT）］ of mice in each group， the content of aspartic transaminase （AST）， alanine aminotransferase （ALT）， 5-hydroxytryptamine （5-HT）， noradrenaline （NE）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， total cholesterol （TC）， and triglyceride （TG） in serum of mice detected by the automatic biochemical analyzer， the oil red O staining and HE staining of aorta and liver and Masson staining of myocardial tissues were used for model evaluation. Finally， liver TMT-labeled quantitative proteomics was used to explore the mechanism of action. ResultThe model mice showed obvious manifestations of depression， anxiety， loss of interest， and despair， manifest lipid deposition in the aorta and liver by pathological observation， and increased myocardial fibrosis in myocardial tissues. After intragastric administration of Yuejuwan， the above symptoms and indexes of the PHD model mice were improved. Compared with the blank control group， the model group showed decreased standing times， cumulative time in the central area， total moving distance， moving speed， and sucrose preference at week 12 （P<0.01）. Compared with the model group， the Yuejuwan groups showed decreased indexes mentioned above （P<0.01）. After sample collection， AST， ALT， and TG levels in the model group were higher （P<0.01） and the levels of 5-HT， NE， and HDL-C were lower than those in the blank control group （P<0.01）. The results of liver TMT labeled quantitative proteomics suggested that the PHD model mainly caused the changes in protein expression levels such as ApoE， UGT1A5， and FASN in mice，involving acetyl CoA metabolism，response to bacteria，cellular amino acid catabolism， and other processes，which were related to the abnormal metabolic function of the liver. The efficacy of Yuejuwan against PHD was achieved mainly through the regulation of high mobility group nucleosomal-binding domain 2 （HMGN2）， CALD1， and Mup7 protein expression levels and correcting the biological processes and abnormal pathways related to the pathogenesis of PHD，including muscle contraction，tight junction pathway，myocardial contraction pathway，and focal adhesion pathway. ConclusionCUMS combined with high-fat diet is reasonable in the induction of the PHD model in AopE^-/- mice. Yuejuwan can correct the depression and anxiety conditions of PHD model mice，reduce the aortic plaque， and recover the abnormal blood lipid and liver function levels. Furthermore， Yuejuwan can correct abnormal biological processes and pathways of PHD model mice. The differential proteins screened throughout the experiment and the involved physiological and pathological changes are the focus of the next experiment.

In vitro cell experiment based on serum pharmacology is a common way to study the pharmacodynamic mechanism of Chinese medicine， and screening the optimal intervention concentration of serum containing Chinese medicine is a key step in the whole experiment. Over-diluted serum containing Chinese medicine or over-high concentration leads to false negative or positive results， while the optimal concentration of medicated serum reduces the number of groups in subsequent experiments as well as the operation error. Thus it is of great significance to screen the optimal serum concentration in studying the pharmacodynamic mechanism of Chinese medicine in in vitro cell experiments. However， there has been no unified standards for the screening methods at present. After reviewing the literature in China and abroad in the past 20 years， this paper conducted an analysis from three aspects of intragastric dose of Chinese medicine， blood collection time and serum dilution degree， and then summarized the screening methods for optimal concentration of serum containing drugs， providing guidance for the study of pharmacodynamic mechanism of Chinese medicine. The screening methods mainly included "same intragastric dose， same blood collection time， and different concentrations" "different intragastric doses， same blood collection time， and same concentration" "same intragastric dose， different blood collection time， and same concentration" "different intragastric doses， different blood collection time， and different concentrations" "serum lyophilized powder" "simulation of gradient concentration of serum containing western medicine" and "pure serum intervention". Among them， the former two were the dominant ways in current related research. The above screening methods had their own advantages and disadvantages， and researchers should make a reasonable choice according to the specific requirements and actual situation of the experiments.