Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Objective To measure changes in the basal ganglia volume,characteristics of neu-romelanin in substantia nigra-1 and iron deposition using susceptibility-weighted imaging(SWI)in Parkinson's disease(PD)patients with freezing of gait at different stages,and to explore their value in evaluating these stages.Methods A total of 178 PD patients with freezing of gait were enrolled.According to Hoehn-Yahr stage,the patients were divided into stage Ⅰ to Ⅱ group(112 cases)and divided into stage Ⅲ to Ⅴ group(66 cases).Additionally,70 healthy volunteers undergoing routine physical examinations were randomly selected as control group.SWI was used to measure basal gan-glia volumes(globus pallidus,putamen and caudate nucleus),disappearance rate of substantia nigra-1(including bilateral display,unilateral display and bilateral non-display),and iron deposition(phase shift values)among groups.Multivariate logistic regression analysis was conducted to identify imaging factors associated with different stages.Receiver operating characteristic(ROC)curve analy-sis was performed to calculate the area under the curve(AUC)for assessing the predictive perform-ance of imaging factors.Results The course of disease in stage Ⅲ to Ⅴ group was significantly lon-ger,and the Parkinson's Comprehensive Assessment Scale(UPDRS)score was significantly higher than that in stage Ⅰ to Ⅱ group(P＜0.05).The volume of pallidum,putamen and caudate nucleus in stage Ⅰ to Ⅱ and stage Ⅲ to Ⅴ groups were significantly lower,and those in stage Ⅲ to Ⅴgroup were significantly lower than that in stage Ⅰ to Ⅱ group(P＜0.05);the disappearance rate of substantia nigra-1 and the deposition amount of pallidum iron,putaminal iron as well as caudate i-ron in stage Ⅰ to Ⅱ and stage Ⅲ to Ⅴ groups were significantly higher,and those in stage Ⅲ to Ⅴgroup were significantly higher than that in stage Ⅰ to Ⅱ group(P＜0.05).Logistic regression analysis indicated that decreased caudate nucleus volume(OR=1.856,95％CI,1.234 to 2.564,P＜0.001),disappearance of substantia nigra-1(OR=2.235,95％CI,1.657 to 2.659,P＜0.001)and increased iron deposition in the globus pallidus(OR=1.562,95％CI,1.234 to 1.989,P＜0.001)were predictors of Hoehn-Yahr staging.ROC showed that the AUC of Hoehn-Yahr stage was 0.889 based on the combination of caudate volume,disappearance of substantia nigra-1 and depo-sition of pallidum.Conclusion SWI is an important non-invasive imaging tool for assessing different stages in PD patients with freezing of gait.Caudate nucleus volume,substantia nigra-1 and iron depo-sition in the globus pallidus are key indicators for predicting Hoehn-Yahr staging.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

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 investigate the change rules of the lumbar vertebral bone marrow fat fraction(FF)and T2* values at different bone mineral density(BMD)groups,and their relevances and the applications on the diagnosis of the primary osteoporosis. Methods The patients who underwent the dual energy X-ray absorptiometry(DXA)and the routine lumbar MRI scan,mDIXON-Quant scan were collected,and the BMD,T-score,FF and T2* values of L1-L4 were measured.According to the T-score,the vertebral bodies were divided into the normal group,the osteopenia group and the osteoporosis group.The differences of the FF,T2* values and BMD between each group were analyzed by one-way analysis of variance,and their correlations with BMD were analyzed by Pearson correlation,the diagnostic effects of them were evaluated by receiver operating characteristic curve(ROC curve).Results The differences of the FF ,T2 * values and BMD among the three groups were statistically significant (P < 0 .05) .The correlations of the FF and T2 * values with BMD were negative (r = - 0 .628 ,P < 0 .05 and r = - 0 .468 ,P < 0 .05 ,respectively) .The area under curve (AUC) of the FF and T2 * values were 0 .82 ± 0 .03 ,0 .79 ± 0 .03 ,respectively .Conclusion mDIXON-Quant which can accuratly quantify fat content could evaluate the lumbar vertebral bone marrow fat content ,reflect the changes of the bone quality ,provide valuable information for the diagnosis of osteoporosis ,and is helpful to predict the risks of the lumbar fragility fracture.

Objective To investigate the clinical diagnostic values of quantitative DTI and MRS in AIDS-related cerebral tuberculosis. Methods 17 cases confirmed with AIDS and brain tuberculosis,16 volunteers were recruited to perform routine MRI,DTI and MRS sequences. Morphological characteristics of lesions were observed.ADC,FA,rADC,rFA,NAA/Cho,NAA/Cr and Cho/Cr of the lesions solid areas,edematous areas,normal areas and contralateral corresponding normal areas were measured and their variances in different areas were analyzed. Results Significant differences of the values were observed among the three regions of AIDS-related brain tuberculosis,the results of multiple comparisons between the three areas had statistical significances (P <0.05)excepted NAA/Cr between edematous and contralateral areas.ADC,FA,rADC,rFA,NAA/Cho,NAA/Cr on solid areas were lower than that on edematous areas,Cho/Cr on solid areas was higher than that on other two areas.The diagnostic efficiency of rFA value to distinguish solid and edematous areas was the highest by ROC analysis(P <0.05).The normal areas of the two groups had statistical significances(P <0.05)excepted Cho/Cr.Conclusion DTI is valuable to display the lesions micro-structure changes and MRS can reflect the early pathology metabolites changes of AIDS-related tuberculosis.

Objective To investigate the value of X-ray,CT and MRI in diagnosing early spinal tuberculosis by constructing lum-bar vertebra tuberculosis model in New Zealand rabbits.Methods Forty healthy New Zealand rabbits were randomly divided into the infected group (n=30)and the control group (n= 10).All rabbits were performed lumbar vertebra surgery,and then underwent imaging and histopathologic examination at 4,6,8 weeks respectively.Results The sensitivity of X-ray,CT and MRI in detecting tuberculosis lesions were 46.13%,76.3%,and 92.3%,respectively.MRI and CT were better than X-ray for displaying the de-struction range limited in a single vertebra (P <0.05).Combination with routine MRI and diffusion-weighted imaging (DWI)could find the vertebra tuberculosis at the early stage.According to the HE staining,pus cells,epithelioid cells or necrosis were seen in the tissue sections of vertebra and paraspinal soft tissues in the infected group.Conclusion X-ray,CT and MRI could provide some information for the early diagnosis of spinal tuberculosis,and MRI is more sensitive.