Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

Objective To establish a weightlessness simulation animal model using miniature pigs, leveraging the characteristic of multiple systems’ tissue structures and functions similar to those of humans, and to observe pathophysiological changes, providing a new method for aerospace research. Methods Nine standard-grade miniature pigs were selected and randomly divided into an experimental group (n=7) and a control group (n=2). The experimental group was fixed using customized metal cages, with canvas slings suspending their hind limbs off the ground, and the body positioned at a -20° angle relative to the ground to simulate unloading for 30 days (24 hours a day). Data on body weight, blood volume, and blood biochemistry indicators were collected at different time points for statistical analysis of basic physiological changes. After the experiment, the miniature pigs were euthanized and tissue samples were collected for histopathological observation of the cardiovascular, skeletal and muscle systems HE and Masson staining. Statistical analysis was also conducted on the thickness of arterial vessels and the diameter of skeletal muscle fibers. Additionally, western blotting was employed to detect the expression levels of skeletal muscle atrophy-related proteins, including muscle-specific RING finger protein 1 (MuRf-1) and muscle atrophy F-box (MAFbx, as known as Atrogin-1), while immunohistochemistry was used to detect the expression of glial fibrillary acidic protein (GFAP), an indicator of astrocyte activation in the brain, reflecting the pathophysiological functional changes across systems. Results After hindlimb unloading, the experimental group showed significant decreases in body weight (P<0.001) and blood volume (P<0.01). During the experiment, hemoglobin, hematocrit, and red blood cell count levels significantly decreased (P<0.05) but gradually recovered. The expression levels of alanine aminotransferase and γ-glutamyltransferase initially decreased (P<0.05) before rebounding, while albumin significantly decreased (P<0.001) and globulin significantly increased (P<0.01). Creatinine significantly decreased (P<0.05). The average diameter of gastrocnemius muscle fibers in the experimental group significantly shortened (P<0.05), with a leftward shift in the distribution of muscle fiber diameters and an increase in small-diameter muscle fibers. Simultaneously, Atrogin-1 expression in the gastrocnemius and paravertebral muscles significantly increased (P<0.05). These changes are generally consistent with the effects of weightlessness on humans and animals in space. Furthermore, degenerative changes were observed in some neurons of the cortical parietal lobe, frontal lobe, and hippocampal regions of the experimental group, with a slight reduction in the number of Purkinje cells in the cerebellar region, and a significant enhancement of GFAP-positive signals in the hippocampal area (P<0.05). Conclusion Miniature pigs subjected to a -20° angle hind limb unloading for 30 days maybe serve as a new animal model for simulating weightlessness, applicable to related aerospace research.

A questionnaire was used to investigate the emergency training needs of novel coronavirus pneumonia of disease prevention and control institutions in provinces, deputy provincial level regions and cities specifically designated in the state plan, and the effect evaluation of emergency training activities conducted by Chinese Center for Disease Control and Prevention (China CDC). The results showed that 67.4% of 47 disease prevention and control institutions (31/46) believed that the emergency training at the initial stage of the epidemic should be conducted as soon as possible, and the form of network training should be given priority. The training should focus on the urgently needed technologies such as epidemiological investigation, formulation and response of prevention and control strategies, laboratory testing, etc. The teaching materials should highlight pertinence and practicability and be presented in the form of electronic video. The average satisfaction score of the video training conducted by China CDC was (8.81±1.125) and the score of audio-video courseware was (8.97±0.893). The needs analysis and evaluation of novel coronavirus pneumonia prevention and control in disease prevention and control institutions could provide reference for the follow-up training and improve the emergency training management.
COVID-19 ; China/epidemiology* ; Humans ; Pneumonia/prevention & control* ; SARS-CoV-2 ; Surveys and Questionnaires

Objective To understand the effect of anti-cysticercus therapy for patients with cerebral cysticercosis and the changes of cysticercus on CT image after treatment. Methods The patients with cerebral cysticercosis were classified by the presentation of their brain CT image before treatment, then the effect of anti-cysticercus therapy on them after treatment was analyzed and the presentations of their brain CT images between before and after treatment were compared. Results There were different changes on CT image of cysticercus in brain tissues after anti-cysticercus therapy for different types of patients with cerebral cysticercosis. Type Ⅰ: the focus was absorbed completely after treatment in the majority of patients and calcificated in the minority. Almost all the patients were cured clinically after anticysticercus therapy. Type Ⅱ: the focus was absorbed completely in the minority, and one to two or more calcification dots were observed in the majority of patients. Anti-cysticercus therapy was effective. Type Ⅲ and Ⅳ: the absorption of focus was not very good and the effect of anti-cysticercus therapy was lower relatively. Conclusion The changes of CT image such as absorption, calcification, has important significance in forecasting prognosis and instructing clinical usage.