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 ConclusionThere are few books in the field of biological weapons defense in China and the content is relatively outdated.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: 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]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: Our study aimed to conduct genomic characterization of Salmonella strains carrying the bla _NDM-1 gene in the intestinal tract of healthy individuals. The objectives were to underscore the importance of genomic surveillance for drug resistance in both commensal and pathogenic bacteria among healthy populations, and to establish protocols for regulating drug resistance plasmids based on the completion of a comprehensive map of drug resistance plasmid genomes. METHODS: We performed antimicrobial susceptibility testing and employed second- and third-generation sequencing techniques to analyze Salmonella strains harboring the bla _NDM-1 gene, to surveil drug-resistant bacteria in the intestines of healthy subjects. Sequence comparison was conducted using both core- and pan-genome approaches. Concurrently, conjugation experiments were carried out to assess the efficiency of plasmid transfer. RESULTS: We isolated a carbapenem-resistant Salmonella enterica serovar Typhimurium strain from a healthy food worker in China. This strain harbored an IncHI2/IncHI2A plasmid carrying bla _NDM-1 along with multiple antibiotic resistance genes (ARGs). Our findings highlight the potential for asymptomatic carriers to facilitate the transmission of ARGs. Pan-genomic analysis revealed that bla _NDM-1-positive plasmids could traverse bacterial species barriers, facilitating cross-host transmission. CONCLUSION This study marks the first detection of bla _NDM-1 in Salmonella strains isolated from healthy individuals. We underscore the risk associated with the transmission of conjugative hybrid plasmids carrying bla _NDM-1, which have the potential to be harbored and transmitted among healthy individuals. Enhanced surveillance of drug-resistant pathogens and plasmids in the intestinal microbiota of healthy individuals could provide insights into the risk of ARG transmission and pathways for population-wide dissemination via ARG transfer factors.
beta-Lactamases/genetics* ; Plasmids/genetics* ; Humans ; Anti-Bacterial Agents/pharmacology* ; China ; Microbial Sensitivity Tests ; Salmonella typhimurium/isolation & purification* ; Salmonella/isolation & purification* ; Salmonella Infections/microbiology*

ObjectiveTo explore the effect of early individualized rehabilitation on patients with severe mechanical ventilation. MethodsA total of 36 patients on mechanical ventilation admitted to the ICU of the Second Affiliated Hospital of Anhui Medical University from March, 2019 to February, 2020 were randomly divided into control group (n = 18) and rehabilitation group (n = 18). All the patients completed a rehabilitation assessment within 24 hours of admission, including clinical assessment, state of consciousness and muscle strength assessment. The control group was treated with intensive care routine treatment, including symptomatic treatment of primary disease, nutritional support, placement of normal limbs, active and passive movement of limbs. The rehabilitation group received early individualized rehabilitation in addition., including active and passive limb movements, transfer training, physical factor therapy, and respiratory muscle training after the specific evaluation. The mechanical ventilation duration and ICU length of stay, the hospitalization cost, Richmond Agitation and Sedation Scale (RASS), acute Physiology and Chronic Health Evaluation Ⅱ (APACHEⅡ), and the content of tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-8 were compared. ResultsThere was no significant difference in the weaning rate and hospitalization cost between two groups (P > 0.05). The mechanical ventilation duration and ICU length of stay were less in the rehabilitation group than in the control group (t > 2.067, P < 0.05). After treatment, the score of APACHEⅡ and the content of TNF-α and IL-6 decreased in the control group (t > 2.040, P < 0.05); the score of APACHEⅡ and the content of TNF-α, IL-6 and IL-8 decreased in the rehabilitation group (t > 4.141, P < 0.001); the content of TNF-α, IL-6 and IL-8 was less in the rehabilitation group than in the control group (t > 2.217, P < 0.05). The improvement of all the indexes was better in the rehabilitation group than in the control group (|Z| > 2.104, P < 0.05). ConclusionFor patients on mechanical ventilation, early individualized rehabilitation could improve the sedation, relieve the inflammatory reaction, accelerate the process of weaning, and reduce the length of stay with no extra cost.

Aim To study the therapeutic effect of 35% and 70% ethanol elution sites of Gardeniae Fructus extract on 2,4,6-trinitrobenzene sulfonic acid (TNBS)/ethanol induced ulcerative colitis (UC) in rats, and to identify the chemical composition of the active elution site using mass spectrometry. Methods The UC model induced by TNBS was used in rats, and the different eluted parts were administered by gavage at a dose of 2. lg/kg for 7 days. Body weight measurement , disease activity index (DAI) score, and pathological score of colon tissues were compared. Myeloperoxidase (MPO) , superoxide dismutase (SOD), malondialdehyde ( MDA ) , nitric oxide ( NO ) , tumor necrosis factor-a (TNF-a) , interleukin in mouse colon tissue -6 (IL-6), interleukin-1 (3 (IL-ip) levels were compared among groups. Liquid-mass spectrometry was used to identify the chemical components of the parts with better efficacy. Results Compared with model group, the weight loss in 35% elution site group was significantly improved, the DAI and histopathology scores were markedly reduced, and the contents of MPO, NO, MDA, TNF-a, IL-6 and IL-1(3 in tissues were apparently reduced. SOD content increased significantly (P <0. 01). A total of 19 chemical components were identified by LC-MS, 11 of which were iri- doids. Conclusions The 35% elution site of Gardeni- a has obvious therapeutic effect on UC rats, and the iridoid component may be the material basis for its function.