Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Objective:To investigate the related factors affecting the survival of adult patients with newly diagnosed chronic myeloid leukemia in the chronic phase (CML-CP) treated with tyrosine kinase inhibitor (TKI) and the prognostic predictive effect of the nomogram constructed according to them.Methods:A retrospective cohort study was conducted. Clinical general information and laboratory index data of 243 newly diagnosed adult CML-CP patients treated with TKI in the First Hospital of Lanzhou University from December 2008 to June 2023 were collected, and they were divided into a training set (194 patients) and a validation set (49 patients) by complete randomization in the ratio of 8∶2. In the training set, variables affecting poor overall survival (OS) of patients were analyzed using univariate and multivariate Cox proportional hazards models by R4.3.2 software to obtain the independent influences of poor OS, on the basis of which the Cox regression model was constructed and the nomogram predicting the OS rate at 8 and 10 years was plotted. Kaplan-Meier method was applied to analyze the OS in all 243 patients and patients stratified by the screened independent influencing factors for poor OS, and log-rank test was used for comparison between the groups. In the training and validation sets, receiver operating characteristic (ROC) curve was used to analyze the effect of the nomogram on predicting 8- and 10-year OS rates of patients with actual survival as the gold standard; calibration curve was used to assess the accuracy of predictions of the nomogram; decision curve analysis (DCA) was used to assess the clinical utility of the nomogram.Results:The median age of 243 CML-CP patients [ M ( Q1, Q3)] was 46 (35, 58) years old, 150 cases (61.7%) were male and 9 cases (38.3%) were female, 119 cases (49.0%) had comorbidities, and the efficacy of 82 cases (33.7%) reached molecular response (MR) 5.0. Differences in patient compositions for age and gender, levels of major indicators for peripheral blood and bone marrow, spleen size, comorbidities, short-term efficacy, Sokal score, and long-term survival score of European Treatment and Outcomes Study between the training and validation sets were not statistically significant (all P > 0.05). Multivariate Cox regression analysis showed that patients with elevated age ( HR = 1.04, 95% CI: 1.01-1.08, P = 0.041), comorbidities (with vs. without, HR = 3.48, 95% CI: 1.23-9.86, P = 0.019), and those who did not achieve MR5.0 (achieved vs. unachieved, HR = 0.13, 95% CI: 0.02-0.97, P = 0.046) were independent risk factors for poor OS in TKI-treated newly diagnosed adult CML-CP patients. By the last follow-up (December 2023), the median follow-up was 72 months, with the range of 6-180 months. Kaplan-Meier method analysis showed that the 8- and 10-year OS rates of 243 patients were 83.7% and 81.6%, respectively; patients with age ≥46 years compared to <46 years, with comorbidities compared to without comorbidities, and who did not achieve MR5.0 in terms of efficacy compared to who achieved MR5.0, the OS was poorer, and the differences were statistically significant (all P < 0.01). The nomogram of 8- and 10-year OS rates in TKI-treated newly diagnosed adult CML-CP patients was constructed based on the screened independent influencing factors of poor OS. The area under the ROC curve was 0.910 and 0.851 in the training set and 0.778 and 0.764 in the validation set for the predicted 8- and 10-year OS rates based on the nomogram, respectively, and the calibration curve showed that the predicted 8- and 10-year OS rates based on the nomogram were in high agreement with the actual ones in the training and validation sets; the DCA showed that the nomogram within a certain prediction threshold could benefit the clinical decision-making in both the training and validation sets. Conclusions:Having comorbidities, not reaching MR5.0 in efficacy and old age are independent risk factors for poor survival of TKI-treated adult patients with newly diagnosed CML-CP, and the nomogram constructed based on these 3 factors has a good predictive ability for the survival of such patients.

Objective:To explore the impact of different segmentation methods on differential diagnostic efficiency of 18F-FDG PET/MR radiomics to distinguish Parkinson′s disease (PD) from multiple system atrophy (MSA). Methods:From December 2017 to June 2019, 90 patients (60 with PD and 30 with MSA; 37 males, 53 females; age (55.8±9.5) years) who underwent 18F-FDG PET/MR in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were retrospectively collected. Patients were randomized to training set and validation set in a ratio of 7∶3. The bilateral putamina and caudate nuclei, as the ROIs, were segmented by automatic segmentation of brain regions based on anatomical automatic labeling (AAL) template and manual segmentation using ITK-SNAP software. A total of 1 172 radiomics features were extracted from T 1 weighted imaging (WI) and 18F-FDG PET images. The minimal redundancy maximal relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used for features selection and radiomics signatures (Radscore) construction, with 10-fold cross-validation for preventing overfitting. The diagnostic performance of the models was assessed by ROC curve analysis, and the differences between models were calculated by Delong test. Results:There were 63 cases in training set (42 PD, 21 MSA) and 27 cases in validation set (18 PD, 9 MSA). The Radscore values were significantly different between the PD group and the MSA group in all training set and validation set of radiomics models ( 18F-FDG_Radscore and T 1WI_Radscore) based on automatic or manual segmentation methods ( z values: from -5.15 to -2.83, all P<0.05). ROC curve analysis showed that AUCs of 18F-FDG_Radscore and T 1WI_Radscore based on automatic segmentation in training and validation sets were 0.848, 0.840 and 0.892, 0.877, while AUCs were 0.900, 0.883 and 0.895, 0.870 based on manual segmentation. There were no significant differences in training and validation sets between Radiomics models based on different segmentation methods ( z values: 0.04-0.77, all P>0.05). Conclusions:The 18F-FDG PET/MR radiomics models based on different segmentation methods achieve promising diagnostic efficacy for distinguishing PD from MSA. The radiomics analysis based on automatic segmentation shows greater potential and practical value in the differential diagnosis of PD and MSA in view of the advantages including time-saving, labor-saving, and high repeatability.

Objective:To explore the feasibility of statistical parametric mapping (SPM) aided semi-quantitative analysis in 11C-Pittsburgh compound B (PIB) β-amyloid (Aβ) PET imaging acquired by hybrid PET/MR, and evaluate its possibility in assisting the diagnosis or differential diagnosis for cognitive impairment. Methods:From January 2018 to September 2019, 13 Alzheimer′s disease (AD) patients (4 males, 9 females; age (59.2±5.8) years) and 10 vascular cognitive disorders (VCD) patients (9 males, 1 female; age (59.5±11.5) years) who underwent 11C-PIB PET/MR in PET center of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were retrospectively analyzed. The standardized uptake value ratio (SUVR) of eight key brain regions (cerebral white matter, striatum, thalamus, posterior cingulate gyrus, frontal cortex, posterior parietal cortex, lateral temporal cortex and occipital cortex) to cerebellum cortex were obtained by manual delineation and SPM-aided semi-automatic segmentation with the help of synchronous three-dimensional T 1 weighted imaging (3D T 1WI). Pearson correlation analysis was carried out on the SUVR obtained by the two methods. Independent-sample t test and paired t test were used to analyze the data. Results:There was no significant difference between AD group and VCD group in age and Mini-Mental State Examination (MMSE) score (19.7±4.7 vs 21.7±3.8; t values: 0.095 and 1.098, both P>0.05). Except thalamus( r=0.179, P=0.413), there were good correlations between SUVR obtained by segmentation and delineation in the other 7 key regions ( r values: 0.678-0.893, all P<0.05). The SUVR of 8 key regions obtained by the two methods in AD group was significantly higher than that in VCD group (1.519-2.055 vs 1.105-1.618; t values: 2.799-11.582, all P<0.01). The SUVR of striatum (1.942±0.205), posterior cingulate gyrus (1.915±0.249), frontal lobe (1.983±0.264), parietal lobe (2.008±0.296) and temporal cortex (1.931±0.254) in AD group was significantly higher than that of cerebral white matter (1.746±0.192; t values: 3.793-6.992, all P<0.01). But in VCD group, there was no region with the SUVR higher than that of cerebral white matter. Conclusions:Hybrid PET/MR can acquire the PET and MRI images synchronously, which can realize the accurate brain segmentation and obtain the semi-quantitative data of key brain regions aided by SPM. The method can analyze the characteristics and differences of amyloid imaging in AD and VCD, which is expected to provide an accurate imaging analysis method for the diagnosis and differential diagnosis of cognitive disorders.

Tuberculosis(TB) caused by the Mycobacterium tuberculosis(Mtb) is a worldwide public health threat.Microbiota in body affects human health and is involved in human diseases, and its clinical importance is begi-nning to be understood.In this review, studies on the relationship between the establishment of Mtb infection and microbiota as well as the development and antibiotic treatment of Mtb infection were discussed.Studies have shown that: (1) microbiota influences the establishment of Mtb infection; (2) co-infection of Helicobacter pylori alters susceptibility to Mtb infection and progression of active TB; (3) microbiota influences the progression of TB by regulating the nutritio-nal, metabolic and immune status of the host; (4) susceptibility to reinfection increases in TB patients treated with antibiotics, possibly due to T-cell epitope depletion of common intestinal non-Mtb Mycobacterium, the effects of antibio-tics are long-term in patients; (5) the occurrence of childhood TB is age-related and many factors such as co-infection and vaccine inoculation increase risk.An in-depth study of the relationship between the microbiota and TB will provide a new perspective on the prevention of TB.

Objective To analyze the early risk factors of overweight and obesity in preschool children.Methods Using stratified cluster sampling,the data of 1 335 preschool children's physical examination in High-tech Zone,Urumqi,Xinjiang were collected,and the case group had 153 overweight and obese children,the control group had 1 182 non-overweight and obese children;a case-control study was conducted.The basic data of mothers and the basic data of neonatal birth were analyzed retrospectively.The univariate and unconditional multivariate logistic regression analysis was performed.Results The prevalence of overweight and obesity in preschool children in High-tech Zone in Urumqi was 11.5％.Non-conditional multivariate logistic regression analysis showed that children's age (OR=1.31,95％ CI:1.07-1.61),mother's pre-pregnancy BMI (OR=1.11 95 ％,CI:1.06-1.17) and whether mothers had gestational hypertension (OR=1.99 95％,CI:1.03-3.85) were the risk factors for overweight and obesity in preschool children (P＜0.05).Conclusion In Urumqi high school district preschool children's overweight and obesity rate was high;mothers with high BMI before pregnancy,and those with high blood pressure during pregnancy can increase the risk of overweight and obesity in children,preschool children's increased age may increase the risk of overweight and obesity in children.