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 ConclusionThere are few books in the field of biological weapons defense in China and the content is relatively outdated.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 efficacy of Fuzheng Hejie Prescription(composed of Scutellariae Radix,Lonicerae Japonicae Flos,Agastachis Herba,Bupleuri Radix,Atractylodis Rhizoma,Glycyrrhizae Radix et Rhizoma,etc.)in the treatment of respiratory viral infections in children and to observe its effect on inflammatory factors and immune function.Methods A total of 203 children with respiratory viral infection of H1N1 virus were randomly divided into 101 cases in the observation group and 102 cases in the control group.Both groups were given the routine treatment for subsiding fever,maintaining water-electrolyte balance,and ensuring enough sleep.And additionally,the control group was given Ribavirin Granules and Ibuprofen Granules,and the observation group was given Fuzheng Hejie Prescription based on the treatment for the control group.The course of treatment covered 7 days.The changes of traditional Chinese medicine(TCM)syndrome scores and the levels of immunological indicators and inflammatory factors in the two groups were observed before and after the treatment.Moreover,the clinical efficacy,symptom resolution time and the incidence of adverse reactions were compared between the two groups of children.Results(1)In the course of the trial,one case fell off in the observation group and 2 cases fell off in the control group,and eventually 100 children in each group were included in the trial.(2)After 7 days of treatment,the total effective rate of the observation group was 93.00%(93/100),and that of the control group was 88.00%(88/100),and the intergroup comparison showed that the therapeutic effect of the observation group was superior to that of the control group,but the difference was not statistically significant(χ2= 1.454,P = 0.228).(3)After treatment,the scores of primary symptoms and secondary symptoms as well as the total TCM syndrome scores in the two groups were decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the time for the resolution of clinical symptoms such as fever,cough,expectoration and sore throat in the observation group was significantly shorter than that in the control group(P＜0.01).(5)After treatment,the levels of immunological indicators of T lymphocyte subset CD3+ and CD4+ in the two groups were increased compared with those before treatment(P＜0.05),and the levels of CD8+ and B cells were decreased compared with those before treatment(P＜0.05).The intergroup comparison showed that the increase in the levels of CD3+ and CD4+ as well as the decrease in the levels of CD8+ and B cells of the observation group was significantly superior to that of the control group(P＜0.01).(6)After treatment,the levels of inflammatory factors of serum amyloid A(SAA),C-reactive protein(CRP),serum tumor necrosis factor alpha(TNF-α),soluble interleukin 2 receptor(SIL-2R),and interleukin 6(IL-6)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the levels of interleukin 2(IL-2)and interferon γ(IFN-γ)ls were all significantly increased compared with those before treatment(P＜0.05).The intergroup comparison showed that the decrease of serum SAA,CRP,TNF-α,SIL-2R,and IL-6 levels and the increase of serum IL-2 and IFN-γ levels in the observation group were significantly superior to those in the control group(P＜0.01).(7)The incidence of adverse reactions in the observation group was 2.00%(2/100),which was significantly lower than that of 8.00%(8/100)in the control group,but the difference was not statistically significant(χ2 = 3.789,P = 0.052).Conclusion Fuzheng Hejie Prescription exerts certain effect in treating children with respiratory viral infection of H1N1 virus,which can effectively decrease children's TCM syndrome scores,regulate the inflammatory response,improve the immune function,accelerate the relief of clinical symptoms and shorten the course of the disease.

During the treatment of non-small cell lung cancer ( NSCLC) , many patients have developed drug resistance due to the use of targeted EGFR inhibitors. The main reasons for drug resistance are EGFR site mutations and bypass activation. Activation of ALK pathway is one of the major types of bypass activation. A recent authoritative study indicates that ALK is closely related to immunotherapy. This article reviews the treatment of ALK in tumors from three aspects: the structure and physiological function of ALK, the small molecule inhibitor of ALK, the biological function of ALK and its related treatment methods for NSCLC, and prospects future directions for better application of ALK in the treatment of NSCLC.

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: In the global phase 3 Study 309/KEYNOTE-775 (NCT03517449) at the first interim analysis, lenvatinib+pembrolizumab significantly improved progression-free survival (PFS), overall survival (OS), and objective response rate (ORR) versus treatment of physician’s choice chemotherapy (TPC) in patients with previously treated advanced/recurrent endometrial cancer (EC). This exploratory analysis evaluated outcomes in patients enrolled in East Asia at the time of prespecified final analysis. Methods: Women ≥18 years with histologically confirmed advanced, recurrent, or metastatic EC with progressive disease after 1 platinum-based chemotherapy (2 if 1 given in neoadjuvant/ adjuvant setting) were enrolled. Patients were randomized 1:1 to lenvatinib 20 mg orally once daily plus pembrolizumab 200 mg intravenously every 3 weeks (≤35 cycles) or TPC (doxorubicin or paclitaxel). Primary endpoints were PFS per RECIST v1.1 by blinded independent central review and OS. No alpha was assigned for this subgroup analysis. Results: Among 155 East Asian patients (lenvatinib+pembrolizumab, n=77; TPC, n=78), median follow-up time (data cutoff: March 1, 2022) was 34.3 (range, 25.1–43.0) months.Hazard ratios (HRs) with 95% confidence intervals (CIs) for PFS (lenvatinib+pembrolizumab vs. TPC) were 0.74 (0.49–1.10) and 0.64 (0.44–0.94) in the mismatch repair proficient (pMMR) and all-comer populations, respectively. HRs (95% CI) for OS were 0.68 (0.45–1.02) and 0.61 (0.41–0.90), respectively. ORRs were 36% with lenvatinib+pembrolizumab and 22% with TPC (pMMR) and 39% and 21%, respectively (all-comers). Treatment-related adverse events occurred in 97% and 96% (grade 3–5, 74% and 72%), respectively. Conclusion Lenvatinib+pembrolizumab provided clinically meaningful benefit with manageable safety compared with TPC, supporting its use in East Asian patients with previously treated advanced/recurrent EC.

Objective: In the global phase 3 Study 309/KEYNOTE-775 (NCT03517449) at the first interim analysis, lenvatinib+pembrolizumab significantly improved progression-free survival (PFS), overall survival (OS), and objective response rate (ORR) versus treatment of physician’s choice chemotherapy (TPC) in patients with previously treated advanced/recurrent endometrial cancer (EC). This exploratory analysis evaluated outcomes in patients enrolled in East Asia at the time of prespecified final analysis. Methods: Women ≥18 years with histologically confirmed advanced, recurrent, or metastatic EC with progressive disease after 1 platinum-based chemotherapy (2 if 1 given in neoadjuvant/ adjuvant setting) were enrolled. Patients were randomized 1:1 to lenvatinib 20 mg orally once daily plus pembrolizumab 200 mg intravenously every 3 weeks (≤35 cycles) or TPC (doxorubicin or paclitaxel). Primary endpoints were PFS per RECIST v1.1 by blinded independent central review and OS. No alpha was assigned for this subgroup analysis. Results: Among 155 East Asian patients (lenvatinib+pembrolizumab, n=77; TPC, n=78), median follow-up time (data cutoff: March 1, 2022) was 34.3 (range, 25.1–43.0) months.Hazard ratios (HRs) with 95% confidence intervals (CIs) for PFS (lenvatinib+pembrolizumab vs. TPC) were 0.74 (0.49–1.10) and 0.64 (0.44–0.94) in the mismatch repair proficient (pMMR) and all-comer populations, respectively. HRs (95% CI) for OS were 0.68 (0.45–1.02) and 0.61 (0.41–0.90), respectively. ORRs were 36% with lenvatinib+pembrolizumab and 22% with TPC (pMMR) and 39% and 21%, respectively (all-comers). Treatment-related adverse events occurred in 97% and 96% (grade 3–5, 74% and 72%), respectively. Conclusion Lenvatinib+pembrolizumab provided clinically meaningful benefit with manageable safety compared with TPC, supporting its use in East Asian patients with previously treated advanced/recurrent EC.

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: In the global phase 3 Study 309/KEYNOTE-775 (NCT03517449) at the first interim analysis, lenvatinib+pembrolizumab significantly improved progression-free survival (PFS), overall survival (OS), and objective response rate (ORR) versus treatment of physician’s choice chemotherapy (TPC) in patients with previously treated advanced/recurrent endometrial cancer (EC). This exploratory analysis evaluated outcomes in patients enrolled in East Asia at the time of prespecified final analysis. Methods: Women ≥18 years with histologically confirmed advanced, recurrent, or metastatic EC with progressive disease after 1 platinum-based chemotherapy (2 if 1 given in neoadjuvant/ adjuvant setting) were enrolled. Patients were randomized 1:1 to lenvatinib 20 mg orally once daily plus pembrolizumab 200 mg intravenously every 3 weeks (≤35 cycles) or TPC (doxorubicin or paclitaxel). Primary endpoints were PFS per RECIST v1.1 by blinded independent central review and OS. No alpha was assigned for this subgroup analysis. Results: Among 155 East Asian patients (lenvatinib+pembrolizumab, n=77; TPC, n=78), median follow-up time (data cutoff: March 1, 2022) was 34.3 (range, 25.1–43.0) months.Hazard ratios (HRs) with 95% confidence intervals (CIs) for PFS (lenvatinib+pembrolizumab vs. TPC) were 0.74 (0.49–1.10) and 0.64 (0.44–0.94) in the mismatch repair proficient (pMMR) and all-comer populations, respectively. HRs (95% CI) for OS were 0.68 (0.45–1.02) and 0.61 (0.41–0.90), respectively. ORRs were 36% with lenvatinib+pembrolizumab and 22% with TPC (pMMR) and 39% and 21%, respectively (all-comers). Treatment-related adverse events occurred in 97% and 96% (grade 3–5, 74% and 72%), respectively. Conclusion Lenvatinib+pembrolizumab provided clinically meaningful benefit with manageable safety compared with TPC, supporting its use in East Asian patients with previously treated advanced/recurrent EC.