Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

This study constructed a LHCGR-CRE-luc-HEK293 transgenic cell line according to the activation of the cAMP signaling pathway after recombinant human chorionic gonadotropin binding to the receptor. The biological activity of recombinant human chorionic gonadotropin was assayed using a luciferase assay system. The relative potency of the samples was calculated using four-parameter model. And the method conditions were optimized to validate the specificity, relative accuracy, precision and linearity of the method. The results showed that there was a quantitative potency relationship of human chorinonic gonadotropin (hCG) in the method and it was in accordance with the four-parameter curve. After optimization, the conditions were determined as hCG dilution concentration of 2.5 μg·mL^-1, dilution ratio of 1∶4, cell number of 10 000-15 000 cells/well, and induction time of 6 h. The method had good specificity, relative accuracy with relative bias ranging from -8.9% to 3.4%, linear regression equation correlation coefficient of 0.996, intermediate precision geometric coefficient of variation ranging from 3.3% to 15.0%, and linearity range of 50% to 200%. This study successfully established and validated a reporter gene method to detect hCG biological activity, which can be used for hCG biological activity assay and quality control.

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: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. Methods: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. Results: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn’t change significantly with PT or PI. Moreover, the PMM FI was about 0.10–0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. Conclusion FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS patients.

Aim To study the effects of Taohong Siwu Tang(TSD)on serum lipid metabolites in rats with abnormal uterine bleeding(AUB),and to analyze the mechanism of action of TSD in improving lipid metabo-lism disorders in AUB.Methods The rat model of AUB was replicated by the method of incomplete abor-tion with drugs,and the lipid metabolites of serum were detected by applying UPLC-Q-Exactive Orbitrap/MS technology,and combined with the principal com-ponent analysis and orthogonal partial least squares-discriminant analysis to screen for differential lipids,the changes of lipids in serum before and after the in-tervention of TSD were clarified.Results A total of 11 differential lipids were screened,mainly phosphati-dyl inositol,phosphatidic acid,phosphatidyl ethanola-mine,phosphatidyl serine,sterol lipids,ceramide,acrylolipids and fatty acids.The screened differential lipids all tended to regress to normal after the adminis-tration of TSD intervention.Conclusion Improvement of AUB by TSD may be related to lipid metabolism such as phosphatidic acid,phosphatidyl inositol,phos-phatidyl ethanolamine,phosphatidyl serine,and ce-ramide.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

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: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. Methods: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. Results: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn’t change significantly with PT or PI. Moreover, the PMM FI was about 0.10–0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. Conclusion FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS 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: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. Methods: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. Results: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn’t change significantly with PT or PI. Moreover, the PMM FI was about 0.10–0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. Conclusion FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS patients.