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 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.

BACKGROUND: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Humans ; Retrospective Studies ; Pneumonia/diagnostic imaging* ; Analysis of Variance ; Tomography, X-Ray Computed ; Lymphoma/diagnostic imaging*

Objective:To develop a prototype artificial intelligence immunofluorescence image recognition system for classification of antinuclear antibodies in order to meet the growing clinical requirements for an automatic readout and classification of immunof luorescence patterns for antinuclear antibody (ANA) images.Methods:Immunofluorescence images with positive results of ANA in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 2020 to December 2021 were collected. Three senior technicians independently and in parallel interpreted the Immunofluorescence images to determine the ANA results. Then the images were labeled according to the ANA International Consensus on Fluorescence Patterns (ICAP) classification criteria. There were 7 labeled groups: Fine speckled, Coarse speckled, Homogeneous, nucleolar, Centromere, Nuclear dots and Nuclear envelope. Each group was randomly divided into training dataset and validation dataset at a ratio of 9∶1 by using random number table. On the deep learning framework PyTORCH 1.7, the convolutional neural network (CNN) training platform was constructed based on ResNet-34 image classification network, and the automatic ANA recognition system was established. After the model was established, the test set was set up separately, the judgment results of the model were output by ranking the prediction probability, with the results of the 2 senior technicians was taken as "golden standard". Parameters such as accuracy, precision, recall and F1-score were used as indicators to evaluate the performance of the model.Results:A total of 23138 immunofluorescence images were obtained after segmentation and annotation. A total of 7 models were trained, and the effects of different algorithms, image processing and enhancement methods on the model were compared. The ResNet-34 model with the highest accuracy andswas selected as the final model, with the classification accuracy of 93.31%, precision rate of 91%, and recall rate of 90% and F1-score of 91% in the test set. The overall coincidence rate between the model and manual interpretation was 90.05%, and the accuracy of recognition of nucleolus was the highest, with the coincidence rate reaching 100% in the test set.Conclusion:The current AI system developed based on deep learning of the ANA immunofluorescence images in the present study showed the ability to recognize ANA pattern, especially in the common, typical, simple pattern.

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.

There were three new blood group systems including the KANNO blood group system, the Sid blood group system and the CTL2 blood group system (provisional status), have been registered by the International Society of Blood Transfusion (ISBT) registered Science August 2019. The main reason for this update is that the significant SNPs of the KANNO blood group system (rs1800014) and the Sid blood group system (rs7224888) have been found through genome-wide association studies and whole exome sequencing. The new genetic evidences are consistent with the current immunological findings. In addition, although CTL2 antigen has been found on erythrocyte ghost (erythrocyte membrane) since 2017, CTL2 blood group system is still in provisional status due to lack of serological and genetic evidence. In this review, the experimental research advances of these three ISBT blood group systems and discuss the clinical value of the relevant researches was summarized briefly.
Blood Group Antigens ; Blood Transfusion ; Genome-Wide Association Study ; Humans

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.