Objective: In the present study, we compare the influence of oxidized lipoprotein(a) [Lp(a)] and unoxidized Lp(a) on plasminogen activation in the process of fibrinolysis and elucidate the potential atherogenic mechanisms of oxidized Lp(a), focusing on its role in thrombosis. Methods: Chromogenic substrate assays were conducted to study the kinetics of plasminogen activation. Fibrin clots were generated by incubating fibrinogen with thrombin, and plasminogen activation was triggered with tissue plasminogen activator (tPA). Experiments were performed in low and high concentrations of Lp(a) or oxidized Lp(a) to evaluate their respective effects on plasmin generation. Oxidized Lp(a) was prepared by chemical oxidation of isolated Lp(a) samples. Results: Low concentrations of Lp(a) enhanced plasminogen activation and fibrinolysis, reflecting its physiological role. However, at higher concentrations, oxidized Lp(a) exhibited a significant inhibitory effect on plasminogen activation. Compared to unoxidized Lp(a), oxidized Lp(a) led to earlier plateauing of plasmin generation and reduced overall plasmin levels. The inhibitory effects of oxidized Lp(a) are likely due to its structural similarity to plasminogen and higher oxidized phospholipid content, which competes with plasminogen for fibrin binding—the enhanced competition with fibrin fragments and tPA by oxidized Lp(a) further impaired fibrinolysis. Conclusion This study demonstrates that while low levels of Lp(a) may support fibrinolysis, oxidized Lp(a) impairs this process by inhibiting plasminogen activation through structural and functional competition. These findings highlight the atherogenic potential of oxidized Lp(a) and its contribution to thrombotic cardiovascular risk.

Objective: In the present study, we compare the influence of oxidized lipoprotein(a) [Lp(a)] and unoxidized Lp(a) on plasminogen activation in the process of fibrinolysis and elucidate the potential atherogenic mechanisms of oxidized Lp(a), focusing on its role in thrombosis. Methods: Chromogenic substrate assays were conducted to study the kinetics of plasminogen activation. Fibrin clots were generated by incubating fibrinogen with thrombin, and plasminogen activation was triggered with tissue plasminogen activator (tPA). Experiments were performed in low and high concentrations of Lp(a) or oxidized Lp(a) to evaluate their respective effects on plasmin generation. Oxidized Lp(a) was prepared by chemical oxidation of isolated Lp(a) samples. Results: Low concentrations of Lp(a) enhanced plasminogen activation and fibrinolysis, reflecting its physiological role. However, at higher concentrations, oxidized Lp(a) exhibited a significant inhibitory effect on plasminogen activation. Compared to unoxidized Lp(a), oxidized Lp(a) led to earlier plateauing of plasmin generation and reduced overall plasmin levels. The inhibitory effects of oxidized Lp(a) are likely due to its structural similarity to plasminogen and higher oxidized phospholipid content, which competes with plasminogen for fibrin binding—the enhanced competition with fibrin fragments and tPA by oxidized Lp(a) further impaired fibrinolysis. Conclusion This study demonstrates that while low levels of Lp(a) may support fibrinolysis, oxidized Lp(a) impairs this process by inhibiting plasminogen activation through structural and functional competition. These findings highlight the atherogenic potential of oxidized Lp(a) and its contribution to thrombotic cardiovascular risk.

Objective: In the present study, we compare the influence of oxidized lipoprotein(a) [Lp(a)] and unoxidized Lp(a) on plasminogen activation in the process of fibrinolysis and elucidate the potential atherogenic mechanisms of oxidized Lp(a), focusing on its role in thrombosis. Methods: Chromogenic substrate assays were conducted to study the kinetics of plasminogen activation. Fibrin clots were generated by incubating fibrinogen with thrombin, and plasminogen activation was triggered with tissue plasminogen activator (tPA). Experiments were performed in low and high concentrations of Lp(a) or oxidized Lp(a) to evaluate their respective effects on plasmin generation. Oxidized Lp(a) was prepared by chemical oxidation of isolated Lp(a) samples. Results: Low concentrations of Lp(a) enhanced plasminogen activation and fibrinolysis, reflecting its physiological role. However, at higher concentrations, oxidized Lp(a) exhibited a significant inhibitory effect on plasminogen activation. Compared to unoxidized Lp(a), oxidized Lp(a) led to earlier plateauing of plasmin generation and reduced overall plasmin levels. The inhibitory effects of oxidized Lp(a) are likely due to its structural similarity to plasminogen and higher oxidized phospholipid content, which competes with plasminogen for fibrin binding—the enhanced competition with fibrin fragments and tPA by oxidized Lp(a) further impaired fibrinolysis. Conclusion This study demonstrates that while low levels of Lp(a) may support fibrinolysis, oxidized Lp(a) impairs this process by inhibiting plasminogen activation through structural and functional competition. These findings highlight the atherogenic potential of oxidized Lp(a) and its contribution to thrombotic cardiovascular risk.

Objective: In the present study, we compare the influence of oxidized lipoprotein(a) [Lp(a)] and unoxidized Lp(a) on plasminogen activation in the process of fibrinolysis and elucidate the potential atherogenic mechanisms of oxidized Lp(a), focusing on its role in thrombosis. Methods: Chromogenic substrate assays were conducted to study the kinetics of plasminogen activation. Fibrin clots were generated by incubating fibrinogen with thrombin, and plasminogen activation was triggered with tissue plasminogen activator (tPA). Experiments were performed in low and high concentrations of Lp(a) or oxidized Lp(a) to evaluate their respective effects on plasmin generation. Oxidized Lp(a) was prepared by chemical oxidation of isolated Lp(a) samples. Results: Low concentrations of Lp(a) enhanced plasminogen activation and fibrinolysis, reflecting its physiological role. However, at higher concentrations, oxidized Lp(a) exhibited a significant inhibitory effect on plasminogen activation. Compared to unoxidized Lp(a), oxidized Lp(a) led to earlier plateauing of plasmin generation and reduced overall plasmin levels. The inhibitory effects of oxidized Lp(a) are likely due to its structural similarity to plasminogen and higher oxidized phospholipid content, which competes with plasminogen for fibrin binding—the enhanced competition with fibrin fragments and tPA by oxidized Lp(a) further impaired fibrinolysis. Conclusion This study demonstrates that while low levels of Lp(a) may support fibrinolysis, oxidized Lp(a) impairs this process by inhibiting plasminogen activation through structural and functional competition. These findings highlight the atherogenic potential of oxidized Lp(a) and its contribution to thrombotic cardiovascular risk.

Objective:To investigate the current status of endoscopic treatment for gastroesophageal varices in portal hypertension in China, and to provide supporting data and reference for the development of endoscopic treatment.Methods:In this study, initiated by the Liver Health Consortium in China (CHESS), a questionnaire was designed and distributed online to investigate the basic condition of endoscopic treatment for gastroesophageal varices in portal hypertension in 2022 in China. Questions included annual number and indication of endoscopic procedures, adherence to guideline for preventing esophagogastric variceal bleeding (EGVB), management and timing of emergent EGVB, management of gastric and isolated varices, and improvement of endoscopic treatment. Proportions of hospitals concerning therapeutic choices to all participant hospitals were calculated. Guideline adherence between secondary and tertiary hospitals were compared by using Chi-square test.Results:A total of 836 hospitals from 31 provinces (anotomous regions and municipalities) participated in the survey. According to the survey, the control of acute EGVB (49.3%, 412/836) and the prevention of recurrent bleeding (38.3%, 320/836) were major indications of endoscopic treatment. For primary [non-selective β-blocker (NSBB) or endoscopic therapies] and secondary prophylaxis (NSBB and endoscopic therapies) of EGVB, adherence to domestic guideline was 72.5% (606/836) and 39.2% (328/836), respectively. There were significant differences in the adherence between secondary and tertiary hospitals in primary prophylaxis of EGVB [71.0% (495/697) VS 79.9% (111/139), χ2=4.11, P=0.033] and secondary prophylaxis of EGVB [41.6% (290/697) VS 27.3% (38/139), χ2=9.31, P=0.002]. A total of 78.2% (654/836) hospitals preferred endoscopic therapies treating acute EGVB, and endoscopic therapy was more likely to be the first choice for treating acute EGVB in tertiary hospitals (82.6%, 576/697) than secondary hospitals [56.1% (78/139), χ2=46.33, P<0.001]. The optimal timing was usually within 12 hours (48.5%, 317/654) and 12-24 hours (36.9%, 241/654) after the bleeding. Regarding the management of gastroesophageal varices type 2 and isolated gastric varices type 1, most hospitals used cyanoacrylate injection in combination with sclerotherapy [48.2% (403/836) and 29.9% (250/836), respectively], but substantial proportions of hospitals preferred clip-assisted therapies [12.4% (104/836) and 26.4% (221/836), respectively]. Improving the skills of endoscopic doctors (84.2%, 704/836), and enhancing the precision of pre-procedure evaluation and quality of multidisciplinary team (78.9%, 660/836) were considered urgent needs in the development of endoscopic treatment. Conclusion:A variety of endoscopic treatments for gastroesophageal varices in portal hypertension are implemented nationwide. Participant hospitals are active to perform emergent endoscopy for acute EGVB, but are inadequate in following recommendations regarding primary and secondary prophylaxis of EGVB. Moreover, the selection of endoscopic procedures for gastric varices differs greatly among hospitals.

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: 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.