OBJECTIVE To investigate the regulatory effects of tofacitinib combined with methotrexate （MTX） on gut microbiota and the clinical efficacy of this regimen in patients with rheumatoid arthritis （RA）. METHODS A retrospective analysis was conducted on the clinical data of 182 patients with RA admitted to Xingtai People’s Hospital from January 2022 to June 2025. The patients were divided into a control group （ n ＝88， treated with MTX monotherapy） and an observation group （ n ＝94， treated with tofacitinib combined w ith MTX） based on their treatment regimen. Gut microbiota abundance， inflammatory and immunological indicators [C-reactive protein （CRP）， erythrocyte sedimentation rate （ESR）， rheumatoid factor （RF）， and anti-cyclic citrullinated peptide antibody （anti-CCP） ] ， clinical efficacy indicators [American College of Rheumatology 20% response rate （ACR20）， Disease Activity Score in 28 Joints （DAS28）， and Health Assessment Questionnaire （HAQ） score ] ， and adverse reactions during treatment were compared between the two groups before and after 12 weeks of treatment. RESULTS After treatment， the abundance of Lactobacillus and Bifidobacterium were significantly increased in both groups compared with before treatment， whereas the abundances of Enterococcus and Enterobacter ， as well as the levels of CRP， ESR， RF， anti-CCP， DAS28 score， and HAQ score， were significantly decreased （ P ＜0.05）. The degree of improvement in the observation group was significantly greater than that in the control group （ P ＜0.05）. The ACR20 response rate in the observation group was significantly higher than that in the control group （81.91% vs. 56.82%， P ＜0.05）. There was no statistically significant difference in the incidence of adverse reactions between the two groups （ P ＞0.05）， and the main adverse reactions were gastrointestinal reactions and abnormal liver function. CONCLUSIONS Tofacitinib combined with MTX can effectively improve gut microbiota balance in patients with RA by increasing the abundance of probiotics and reducing the abundance of opportunistic pathogenic bacteria， thereby improving immune and inflammatory status. In addition， this combination regimen can enhance clinical efficacy， reduce disease activity， and improve functional status， with a favorable safety profile.

With the deepening of the modernization of traditional Chinese medicine(TCM),the analysis of mineralogical features of mineralogical TCM,as an important part of TCM,has received more and more attention.At present,the research of mineral-based traditional Chinese medicine is faced with problems such as confusion of medicinal resources,unclear material basis,and low exclusivity of quality standards,while common analytical techniques have certain limitations in quantitative analysis,sensitivity and comprehensive characterization,which restrict the modernization research of mineral-based medicines and its clinical applications.The advanced mineral identification and characterization system integrates energy spectrum and spectral analysis techniques,combined with efficient data processing algorithms,which can rapidly and accurately analyze mineral components qualitatively and quantitatively,but its application in mineral medicine research is still in its infancy.This article reviews the common analytical techniques for mineral medicine and the potential application of advanced mineral identification and characterization system in the identification of mineral Chinese medicine matrix and quality control.The advanced mineral identification and characterization system can not only accurately distinguish the mineralogical characteristics of mineral medicines,such as the mineral composition,elemental state and embedded characteristics,and provide data support for the research of mineral medicine resources,but also provide scientific basis for the establishment of systematic quality standards,the analysis of the preparation mechanism,the revelation of its potential medicinal effect of the material basis and the control of the risk of heavy metals.

With the deepening of the modernization of traditional Chinese medicine(TCM),the analysis of mineralogical features of mineralogical TCM,as an important part of TCM,has received more and more attention.At present,the research of mineral-based traditional Chinese medicine is faced with problems such as confusion of medicinal resources,unclear material basis,and low exclusivity of quality standards,while common analytical techniques have certain limitations in quantitative analysis,sensitivity and comprehensive characterization,which restrict the modernization research of mineral-based medicines and its clinical applications.The advanced mineral identification and characterization system integrates energy spectrum and spectral analysis techniques,combined with efficient data processing algorithms,which can rapidly and accurately analyze mineral components qualitatively and quantitatively,but its application in mineral medicine research is still in its infancy.This article reviews the common analytical techniques for mineral medicine and the potential application of advanced mineral identification and characterization system in the identification of mineral Chinese medicine matrix and quality control.The advanced mineral identification and characterization system can not only accurately distinguish the mineralogical characteristics of mineral medicines,such as the mineral composition,elemental state and embedded characteristics,and provide data support for the research of mineral medicine resources,but also provide scientific basis for the establishment of systematic quality standards,the analysis of the preparation mechanism,the revelation of its potential medicinal effect of the material basis and the control of the risk of heavy metals.

Objective:A nationwide multi-center and large sample survey was conducted to compare the validity of the Mini International Neuropsychiatric Interview (Hypo-) Manic Episode with Mixed Features-DSM-5 Module (MINI-M) questionnaire and the Clinically Useful Depression Outcome Scale Supplemented with Questions for the DSM-5 Mixed Features Specifier (CUDOS-M) depression subscale in identifying mixed features in patients experiencing (hypo-) manic episodes.Methods:Using a convenience sampling method, 366 patients with bipolar disorder experiencing acute (hypo-) manic episodes who met the inclusion and exclusion criteria were recruited. The diagnosis of "with mixed features" was based on the DSM-5 criteria for mixed features. The predictive validity of the MINI-M questionnaire and the CUDOS-M depression subscale to screen mixed features was analyzed using the receiver operating characteristic (ROC) curve. Additionally, the difference in area under the ROC curve (AUC) between the two instruments was compared.Results:The AUC for the MINI-M questionnaire and the CUDOS-M depression subscale in screening mixed features were 0.79 (95 %CI=0.75-0.84) and 0.81 (95 %CI=0.77-0.86), respectively. There was no statistically significant difference in AUC between the two measurements ( Z=-1.19, P>0.05). Among patients with acute (hypo-) manic episodes, 45.9% (168/366) presented with mixed features according to the DSM-5 criteria, while the corresponding figures were 43.7% (160/366) using the MINI-M questionnaire (total score≥3) and 42.1% (154/366) using the CUDOS-M depression subscale (total score≥20). Screening results were comparable among the three measures. Conclusion:Mixed features are common among patients experiencing acute (hypo-) manic episodes. The MINI-M questionnaire and the CUDOS-M depression subscale demonstrate equivalent validity in identifying mixed features.

Objective:A nationwide multi-center and large sample survey was conducted to compare the validity of the Mini International Neuropsychiatric Interview (Hypo-) Manic Episode with Mixed Features-DSM-5 Module (MINI-M) questionnaire and the Clinically Useful Depression Outcome Scale Supplemented with Questions for the DSM-5 Mixed Features Specifier (CUDOS-M) depression subscale in identifying mixed features in patients experiencing (hypo-) manic episodes.Methods:Using a convenience sampling method, 366 patients with bipolar disorder experiencing acute (hypo-) manic episodes who met the inclusion and exclusion criteria were recruited. The diagnosis of "with mixed features" was based on the DSM-5 criteria for mixed features. The predictive validity of the MINI-M questionnaire and the CUDOS-M depression subscale to screen mixed features was analyzed using the receiver operating characteristic (ROC) curve. Additionally, the difference in area under the ROC curve (AUC) between the two instruments was compared.Results:The AUC for the MINI-M questionnaire and the CUDOS-M depression subscale in screening mixed features were 0.79 (95 %CI=0.75-0.84) and 0.81 (95 %CI=0.77-0.86), respectively. There was no statistically significant difference in AUC between the two measurements ( Z=-1.19, P>0.05). Among patients with acute (hypo-) manic episodes, 45.9% (168/366) presented with mixed features according to the DSM-5 criteria, while the corresponding figures were 43.7% (160/366) using the MINI-M questionnaire (total score≥3) and 42.1% (154/366) using the CUDOS-M depression subscale (total score≥20). Screening results were comparable among the three measures. Conclusion:Mixed features are common among patients experiencing acute (hypo-) manic episodes. The MINI-M questionnaire and the CUDOS-M depression subscale demonstrate equivalent validity in identifying mixed features.

Objective:This study investigates the difference in the detection rate and symptomatology between ICD-10 and DSM-5 diagnostic criteria for bipolar disorder with mixed states.Methods:Based on the Phase Ⅰ (2012) and Phase Ⅱ (2021) databases of National Bipolar Mania Pathway Survey (BIPAS), patients with bipolar disorder were included. General demographic data, clinical characteristics, symptomatic phenotypes, and mixed characteristics were retrieved. The detection rates and symptomatic performances of patients with or without mixed states in Phase Ⅰ and Ⅱ were compared using the chi-square test.Results:For patients with mixed states, the detection rate during Phase Ⅱ (2021) using DSM-5 (18.79%, 199/1 059) criteria was significantly higher than that during Phase Ⅰ (2012) using ICD-10 (6.78%, 199/2 934; χ 2=125.05, P<0.001). Whether using ICD-10 or DSM-5 criteria, patients with mixed states had a significantly higher frequency of multiple symptomatic manifestations. Conclusion:The DSM-5 diagnostic criteria generate a high detection rate for bipolar disorder with mixed states. The clinical phenotypes of bipolar disorder with mixed states vary significantly using different diagnostic tools.

Objective:This study investigates the difference in the detection rate and symptomatology between ICD-10 and DSM-5 diagnostic criteria for bipolar disorder with mixed states.Methods:Based on the Phase Ⅰ (2012) and Phase Ⅱ (2021) databases of National Bipolar Mania Pathway Survey (BIPAS), patients with bipolar disorder were included. General demographic data, clinical characteristics, symptomatic phenotypes, and mixed characteristics were retrieved. The detection rates and symptomatic performances of patients with or without mixed states in Phase Ⅰ and Ⅱ were compared using the chi-square test.Results:For patients with mixed states, the detection rate during Phase Ⅱ (2021) using DSM-5 (18.79%, 199/1 059) criteria was significantly higher than that during Phase Ⅰ (2012) using ICD-10 (6.78%, 199/2 934; χ 2=125.05, P<0.001). Whether using ICD-10 or DSM-5 criteria, patients with mixed states had a significantly higher frequency of multiple symptomatic manifestations. Conclusion:The DSM-5 diagnostic criteria generate a high detection rate for bipolar disorder with mixed states. The clinical phenotypes of bipolar disorder with mixed states vary significantly using different diagnostic tools.

Objective:To compare the effect of different β values on the semi-quantitative accuracy and image quality of 68Ga-prostate specific membrane antigen (PSMA) PET/CT imaging after partial volume effect correction (PVC). Methods:In the model experiment, image reconstruction was carried out based on block sequential regularized expectation maximization algorithm (BSREMA) with the range of β values from 100 to 1 000. Recovery coefficient (RC), contrast recovery (CR) and background variability (BV) were measured to evaluate semi-quantitative accuracy and image quality. In the clinical study, image data of 21 prostate cancer patients (age 45-78 years) who underwent 68Ga-PSMA PET/CT examination in the First Hospital of Shanxi Medical University from March 2019 to February 2020 were retrospectively collected. A total of 29 abdominal imaging positive lymph nodes were divided into the small lymph node group (diameter <10 mm; n=12) and the large lymph node group (10 mm≤diameter≤30 mm; n=17). SUV parameters including SUV max, SUV mean and peak of SUV (SUV peak) and the influence of different β values on the SUV parameters were evaluated. The signal to noise ratio (SNR) and subjective scores were used to evaluate image quality. Independent-sample t test, Kappa test and Pearson correlation analysis were used to analyze data. Results:The model experiment showed that CR, RC and BV decreased with the increase of β values. The image quality, image clarity, lesion significance, and total image scores given by nuclear medicine physicians showed strong consistency ( Kappa values: 0.65-0.87, P values: 0.026-0.043). The small lymph node group had the highest score (13 and 14) with β value of 600, while the large lymph node group had the highest score (13 and 14) with β value of 700. SNR of the two groups increased steadily within β values from 100 to 600 ( t values: 2.49-8.99, P values: 0.023-0.038). When the β value was higher than 600, SNR of the small lymph node group reached a plateau ( t values: 1.28-2.00, P values: 0.072-0.098), while the SNR of the large lymph node group continued to increase ( t values: 2.98-4.63, P values: 0.012-0.029). Before PVC, there were significant negative correlations between SUV parameters and β values ( r values: from -0.94 to -0.64, P values: 0.039-0.046). After PVC, it was found that SUV mean and SUV max still had significant negative correlation with β values ( r values: from -0.78 to -0.68, P values: 0.035-0.042), while the SUV peak showed no significant correlation with β values ( r values: -0.22, -0.28, P values: 0.069, 0.126). Conclusions:Based on subjective scores and semi-quantitative indicators, 68Ga-PSMA PET/CT is superior to select β values of 600 and 700 for image reconstruction based on BSREMA. The SUV peak of small lesions is stable after PVC and the clinical value should be explored in further.

Objective: To explore the relationship among cognitive flexibility, depression and eating attitude of adolescents and the influencing factors of eating attitude. Methods: A total of 1 231 subjects were assessed using general information questionnaire, Kutcher Adolescent Depression Scale 11 item(KADS-11), Cognitive Flexibility Inventory(CFI), Eating Attitude Test 26(EAT-26). Data were analyzed by independent sample t-test, univariate linear regression, spearman correlation analysis and Logistic regression. Results: There were significant differences in EAT 26 among adolescents between genders and between those with or without depression(P<0.01). Statistically significant differences were observed in CFI, flexible control and KADS 11 among adolescents with or without eating disorders(P<0.01). The scores of EAT 26 was negatively correlated with CFI (r=-0.19, P<0.01) and flexible control(r=-0.23, P<0.01). And there was a significant positive correlation between EAT 26 and KADS 11(r=0.23, P<0.01). Female(OR=2.40, 95%CI=1.87-3.23), depression (OR=1.76, 95%CI=1.35-2.29) and poor flexible control (OR=1.94, 95%CI=1.48-2.54) were risk factors for eating disorders. Conclusion Female, individuals with depressive symptoms or with poor flexible control ability are more likely to have eating disorders which need more attention.

Objective: To explore the prognostic value of human mitochondrial transcription termination factor 3 (hMTERF3) and forkhead box protein 3 (Foxp3) in non-small cell lung cancer (NSCLC). Methods: The clinical data of 88 patients with NSCLC who were admitted to the Third Medical Center of PLA General Hospital from March 2017 to March 2018 were retrospectively analyzed. All patients were diagnosed by pathological puncture. The patients were followed-up by telephone for 12 months, and according to the prognosis, the patients were divided into good prognosis group and poor prognosis group. The pathological tissues were taken from all patients, and the expressions of hMTERF3 and Foxp3 proteins were detected by immunohistochemistry. The expressions of hMTERF3 and Foxp3 in the good prognosis group and the poor prognosis group were compared. Logistic regression model was used to analyze the risk factors of poor prognosis in patients with NSCLC. Results: Of 88 patients, 61 patients (69.3%) had good prognosis and 27 patients (30.7%) had poor prognosis. The positive expression rate of hMTERF3 in the good prognosis group was 57.4% (35/61), which was significantly lower than that in the poor prognosis group (81.5%, 22/27) (χ ²= 4.766, P= 0.029). The positive expression rate of Foxp3 in the good prognosis group was 55.7% (34/61), which was significantly lower than that in the poor prognosis group (85.2%, 23/27) (χ ²= 7.113, P= 0.008). The proportions of patients with medium and high differentiation or stage Ⅰ- Ⅱ in the good prognosis group were 82.0% (50/61) and 68.8% (42/61), respectively, which were significantly higher than those in the poor prognosis group [48.15% (13/27) and 25.93% (7/27)] (both P < 0.05). Logistic regression analysis showed that the poor differentiation, stage Ⅲ-Ⅳ, hMTERF3-positive and Foxp3-positive were the risk factors for poor prognosis in NSCLC patients (all P < 0.05). Conclusions The positive expression rates of hMTERF3 and Foxp3 in patients with good prognosis are lower. The hMTERF3-positive and Foxp3-positive are risk factors for poor prognosis in NSCLC patients.