Acute suppurative thyroiditis(AST) is a rare thyroid disease, mostly caused by infections such as Staphylococcus aureus, and it is difficult to distinguish from subacute thyroiditis(SAT) at the beginning of the disease. Here we report the clinical data of a young male patient who was initially misdiagnosed as SAT, but was clinically diagnosed as AST with DNSIs accompanied by LS. The clinical features and treatment, combined with related literature, aim to enhance clinicians' understanding of this disease.

ObjectiveTo provide scientific support for the compilation of high-quality anti-nuclear， biological， and chemical （NBC） medical textbooks in China by retrieving books in the field of biological weapons defense in China， summarizing the publication time and distribution of publishing institutions， and categorizing content and key points of related books. MethodsRelevant subject terms in the field of biological weapons defense were searched through the official website of China National Digital Library and other websites， up until December 31， 2023， and were limited to books. Topic analysis was conducted on the introductions and contents of the books using the latent Dirichlet allocation （LDA） model. The number of topics was determined based on perplexity， and topics were identified according to the intertopic distance map， followed by a qualitative description of the core content of each topic. ResultsA total of 104 books were included in this study， among which four were identified as higher educational textbooks. The volume of publications increased during the periods 2002‒2004 and 2020‒2023. Research institutions accounted for the highest percentage of publishers （37.78%）， and 56.67% of the publishers were military institutions. The study identified six topics： "distribution， defense， and response to biological weapons"， "category， diagnosis， and treatment of biological warfare agents"， "response to biological public health emergencies"， "status of nuclear， biological， and chemical weapons internationally"， "biosafety risk management and prevention and control"， and "technologies and equipment related to biological hazard identification". ConclusionThere are few books in the field of biological weapons defense in China and the content is relatively outdated. In the future， the preparation of teaching materials should be aimed at practical emergency handling techniques for biological weapons， enhance the emphasis on biological weapons detection and biological warfare early warning， improve the fundamental theories at different training levels， and timely update the current research status in the field.

Objective To investigate the effect of silk fibroin hydrogel loaded with developmental endothelial locus-1(Del-1)nanoparticles on the healing of chronic skin wounds in mice.Methods The back skin of BALB/c mice(6-8 weeks old)was pressed with a magnet for 12 h and then relaxed for 12 h,for 4 consecutive days to establish a chronic pressure ulcer wound.After infliction,the mice were randomly divided into 3 groups(n=8),and the skin wounds were treated with PBS,silk fibroin hydrogel or Del-1 nanoparticles/silk fibroin hydrogel.The wound healing was recorded with camera to calculate the wound healing rate.In 9 d after treatment,HE and Masson staining were used to observe the wound healing,and immunofluorescence staining for CD 14 and TNF-α was used to compare the appearance frequency of skin macrophages and the expression of inflammatory factors.After Tert-butyl peroxide(TBHP)was used to stimulate mouse macrophage RAW 264.7 cells and mouse vascular endothelial C166 cells,C166 cells were transfected with lentival vector to overexpress Del-1.Crystal violet staining was used to observe the migration of macrophages.RT-qPCR was used to detect the expression of inflammatory factor IL-6.Results The wound healing was significantly faster in the Del-1 nanoparticles/silk fibroin hydrogel group than the silk fibroin hydrogel group and the PBS group(P＜0.01).The expression levels of TNF-α and CD14 in the wound surface were lower(P＜0.01),but collagen deposition and tissue repair were better in the Del-1 nanoparticles/silk fibroin hydrogel group than the silk fibroin hydrogel group and the PBS group(P＜0.01).In vitro experiments,macrophages migrated to endothelial cells stimulated by TBHP,but the migration rate of macrophages was significantly lower in the Del-1 overexpression group(P＜0.01).RT-qPCR confirmed that Del-1 inhibited the transcription of IL-6(P＜0.01).Conclusion Del-1 nanoparticles/silk fibroin hydrogel can significantly accelerate the healing of skin wounds,and its mechanism may be through promoting the regression of inflammation and tissue repair.

Objective:To explore influencing factors of the self-reported brief life quality satisfaction score(Brief-QoL) in patients with acromegaly and understand the persistent low Brief-QoL scores in cases achieving biochemical remission.Methods:This study included 836 acromegaly patients who were hospitalized at Peking Union Medical College Hospital between January 2012 and December 2020. We retrospectively examined how clinical characteristics, biochemical parameters, comorbidities, and symptoms influenced Brief-QoL. Among patients who achieved biochemical remission, differences in clinical symptoms and comorbidities were analyzed between the high and low quality of life groups.Results:Patients with well-controlled biochemical indicators at the last follow-up had generally high Brief-QoL. However, patients with symptoms such as headaches (47.8% in the low-score group vs 14.9% in the high-score group, P<0.001) and joint pain (69.6% in the low-score group vs 19.0% in the high-score group, P<0.001) had low Brief-QoL despite biochemical remission. Receiving combined treatment(52.4% in the low-score group vs 27.5% in the high-score group, P=0.030) and having comorbid diabetes or hyperlipidemia were significant factors leading to decreased quality of life. Conclusion:Brief-QoL is suitable for follow-up of outpatient patients. Early identification of factors affecting quality of life and timely intervention can facilitate the realization of standardized management.

[Objective]To understand"theory of sweat pore(Xuanfu)"and its relationship with dysuria,as well as explore Professor SUN Jie's clinical experience in using"theory of sweat pore"to treat dysuria.[Methods]Through the research method of grounded theory,this paper analyzed and constructed a theoretical model that used"theory of sweat pore"to differentiate and treat the dysuria.The clinical documents of 135 cases of"dysuria"came from Professor SUN Jie's clinical diagnosis and treatment database.Besides,according to the qualitative research method by grounded theory,it concluded the core and built the theoretical framework by the three-level coding analysis.[Results]This study concluded that Professor SUN Jie summarized his experience to conclude the core category of"paying attention to the deficiency and excess of sweat pore,regulating and promoting Qi and body fluid".Two categories of"treatment based on syndrome differentiation with theory of sweat pore"and"treatment based on the principle"of dredging and assisting sweat pore were extracted.Based on this,Professor SUN Jie's experience in distinguishing and treating dysuria was derived,which focused on syndrome differentiation and treatment with sweat pore and on distinguishing the deficiency and stagnation of sweat pore and the nature of evil stagnation,and establishing treatment principle of dredging and assisting sweat pore,dispelling evil and supplementing deficiency as the treatment method.When using herbs,he used the prescriptions represented by Cassia Twig and Poria Cocos as the core,and combined alleviating water to dredge and assist sweat pore,warming and dredging to assist sweat pore,particularly good at using herbs of activating Yang.[Conclusion]Based on the research and summary of"theory of sweat pore",this paper summarized Professor SUN Jie's experience in using"theory of sweat pore"to treat dysuria,and constructed a theoretical model for Professor SUN Jie's use of"theory of sweat pore"in the treatment of dysuria.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.