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.

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 observe the expression changes of nuclear factor erythroid 2 related factor 2 (Nrf2) and glutathione peroxidase (GPX4) in human pulmonary microvascular endothelial cells (HPMEC) under different experimental conditions, and to explore the role of Nrf2 in inhibiting ferroptosis in the process of alleviating hyperoxic lung injury(HLI).Methods:Hyperoxic model was established by hyperoxia exposure.HPMEC were treated with blank control (control group), oxygen exposure at the concentration of 950 mL/L (hyperoxia group), oxygen exposure at the concentration of 950 mL/L+ 10 μmol/L Ferrostatin (ferroptosis inhibitor group) and oxygen exposure at the concentration of 950 mL/L + 10 μmol/L ML385 (Nrf2 inhibitor group). Cell viability at 24 h and 48 h was tested by the Cell Counting Kit-8 assay, and reactive oxygen species (ROS) levels were detected by a commercial ROS kit.The mRNA and protein levels of Nrf2 and GPX4 were detected by real-time quantitative polymerase chain reaction and Western blot, respectively.Differences were analyzed using the Student′s t-test for a two-group comparison or one-way ANOVA test among groups. Results:(1)Compared with the control group, significantly decreased viability and increased ROS levels were detected in hyperoxia group.Meanwhile, the mRNA (24 h: 0.750±0.010 vs.1.010±0.160, 48 h: 0.690±0.050 vs.1.000±0.070) and protein levels of GPX4 (24 h: 0.160±0.010 vs.0.290±0.010, 48 h: 0.190±0.010 vs.0.250±0.010) at 24 h and 48 h were significantly downregulated, while the mRNA (24 h: 1.740±0.050 vs.1.000±0.050, 48 h: 2.130±0.020 vs.1.000±0.030) and protein levels of Nrf2 (24 h: 0.840±0.010 vs.0.480±0.010, 48 h: 0.840±0.010 vs.0.550±0.030) at 24 h and 48 h were significantly upregulated in hyperoxia group than those of control group (all P<0.05). (2)Compared with the hyperoxia group, significantly increased viability and decreased ROS levels were detected in ferroptosis inhibitor group.Meanwhile, the mRNA (24 h: 1.520±0.110, 48 h: 1.880±0.050) and protein levels of GPX4 (24 h: 0.290±0.010, 48 h: 0.250±0.004) at 24 h and 48 h were significantly upregulated, while the mRNA (24 h: 0.780±0.040, 48 h: 0.760±0.030) and protein levels of Nrf2 (24 h: 0.480±0.010, 48 h: 0.540±0.020) at 24 h and 48 h were significantly downregulated in ferroptosis inhibitor group than those of hyperoxia group (all P<0.05). (3)Compared with the hyperoxia group, significantly decreased viability and increased ROS levels were detected in Nrf2 inhibitor group.Meanwhile, the mRNA (24 h: 0.600±0.030, 48 h: 0.590±0.003) and protein levels of GPX4 (24 h: 0.150±0.001, 48 h: 0.180±0.001) at 24 h and 48 h were significantly downregulated, while the mRNA level of Nrf2 was significantly upregulated at 24 h (3.360±0.130), but downregulated at 48 h (1.430±0.130) (all P<0.05). No significant difference was detected in the protein level of Nrf2 at 24 h and 48 h between hyperoxia group and Nrf2 inhibitor group ( P>0.05). Conclusions:Ferroptosis is involved in the development of HLI, and Nrf2 is able to alleviate hyperoxic lung injury by inhibiting ferroptosis.Therefore, inhibition of ferroptosis by Nrf2 may provide a new therapeutic target for HLI.

To design a treatment plan for patients with epididymal obstruction, we explored the potential impact of factors such as body mass index (BMI) and age on the surgical outcomes of vasoepididymostomy (VE). In this retrospective study, 181 patients diagnosed with obstructive azoospermia (OA) due to epididymal obstruction between September 2014 and September 2017 were reviewed. All patients underwent single-armed microsurgical intussusception VEs with longitudinal two-suture placement performed by a single surgeon (KH) in a single hospital (Peking University Third Hospital, Beijing, China). Six factors that could possibly influence the patency rates were analyzed, including BMI, age, mode of anastomosis, site of anastomosis, and sperm motility and quantity in the intraoperative epididymal fluid. Single-factor outcome analysis was performed via Chi-square test and multivariable analysis was performed using logistic regression. A total of 159 (87.8%, 159/181) patients were followed up. The follow-up time (mean ± standard deviation [s.d.]) was 27.7 ± 9.3 months, ranging from 12 months to 48 months. The overall patency rate was 73.0% (116/159). The multivariable analysis revealed that BMI and age significantly influenced the patency rate (P = 0.008 and 0.028, respectively). Younger age (≤28 years; odds ratio [OR] = 3.531, 95% confidence interval [95% CI]: 1.397-8.924) and lower BMI score (<26.0 kg m^-2; OR = 2.352, 95% CI: 1.095-5.054) appeared to be associated with a higher patency rate. BMI and age were independent factors affecting the outcomes of microsurgical VEs depending on surgical expertise and the use of advanced technology.
Humans ; Male ; Adult ; Retrospective Studies ; Body Mass Index ; Epididymis/surgery* ; Vas Deferens/surgery* ; Treatment Outcome ; Sperm Motility ; Microsurgery ; Surgeons ; Vasovasostomy

OBJECTIVE: Jiedu Recipe (JR), a Chinese herbal remedy, has been shown to prolong overall survival time and decrease recurrence and metastasis rates in patients with hepatocellular carcinoma (HCC). This work investigated the mechanism of JR in HCC treatment. METHODS: The chemical constituents of JR were detected using liquid chromatography-mass spectrometry. The potential anti-HCC mechanism of JR was screened using network pharmacology and messenger ribonucleic acid (mRNA) microarray chip assay, followed by experimental validation in human HCC cells (SMMC-7721 and Huh7) in vitro and a nude mouse subcutaneous transplantation model of HCC in vivo. HCC cell characteristics of proliferation, migration and invasion under hypoxic setting were investigated using thiazolyl blue tetrazolium bromide, wound healing and Transwell assays, respectively. Image-iT™ Hypoxia Reagent was added to reveal hypoxic conditions. Stem cell sphere formation assay was used to detect the stemness. Epithelial-mesenchymal transition (EMT) markers like E-cadherin, vimentin and α-smooth muscle actin, and pluripotent transcription factors including nanog homeobox, octamer-binding transcription factor 4, and sex-determining region Y box protein 2 were analyzed using Western blotting and real-time polymerase chain reaction. Western blot was performed to ascertain the anti-HCC effect of JR under hypoxia involving the Wnt/β-catenin pathway. RESULTS: According to network pharmacology and mRNA microarray chip analysis, JR may potentially act on hypoxia and inhibit the Wnt/β-catenin pathway. In vitro and in vivo experiments showed that JR significantly decreased hypoxia, and suppressed HCC cell features of proliferation, migration and invasion; furthermore, the hypoxia-induced increases in EMT and stemness marker expression in HCC cells were inhibited by JR. Results based on the co-administration of JR and an agonist (LiCl) or inhibitor (IWR-1-endo) verified that JR suppressed HCC cancer stem-like properties under hypoxia by blocking the Wnt/β-catenin pathway. CONCLUSION JR exerts potent anti-HCC effects by inhibiting cancer stemness via abating the Wnt/β-catenin pathway under hypoxic conditions. Please cite this article as: Guo BJ, Ruan Y, Wang YJ, Xiao CL, Zhong ZP, Cheng BB, Du J, Li B, Gu W, Yin ZF. Jiedu Recipe, a compound Chinese herbal medicine, inhibits cancer stemness in hepatocellular carcinoma via Wnt/β-catenin pathway under hypoxia. J Integr Med. 2023; 21(5): 474-486.
Animals ; Mice ; Humans ; Carcinoma, Hepatocellular/genetics* ; beta Catenin/pharmacology* ; Liver Neoplasms/genetics* ; Drugs, Chinese Herbal/therapeutic use* ; RNA, Messenger/therapeutic use* ; Cell Line, Tumor ; Cell Proliferation ; Cell Movement ; Gene Expression Regulation, Neoplastic

Objectives: To investigate the current application status and implementation difficulties of extracorporeal cardiopulmonary resuscitation (ECPR) in children with sudden cardiac arrest. Methods: This cross-sectional survey was conducted in 35 hospitals. A Children's ECPR Information Questionnaire on the implementation status of ECPR technology (abbreviated as the questionnaire) was designed, to collect the data of 385 children treated with ECPR in the 35 hospitals. The survey extracted the information about development of ECPR, the maintenance of extracorporeal membrane oxygenation (ECMO) machine, the indication of ECPR, and the difficulties of implementation in China. These ECPR patients were grouped based on their age, the hospital location and level, to compare the survival rates after weaning and discharge. The statistical analysis used Chi-square test and one-way analysis of variance for the comparison between the groups, LSD method for post hoc testing, and Bonferroni method for pairwise comparison. Results: Of the 385 ECPR cases, 224 were males and 161 females. There were 185 (48.1%) survival cases after weaning and 157 (40.8%) after discharge. There were 324 children (84.2%) receiving ECPR for cardiac disease and 27 children (7.0%) for respiratory failure. The primary cause of death in ECPR patients was circulatory failure (82 cases, 35.9%), followed by brain failure (80 cases, 35.0%). The most common place of ECPR was intensive care unit (ICU) (278 cases, 72.2%); ECPR catheters were mostly inserted through incision (327 cases, 84.9%). There were 32 hospitals (91.4%) had established ECMO emergency teams, holding 125 ECMO machines in total. ECMO machines mainly located in ICU (89 pieces, 71.2%), and the majority of hospitals (32 units, 91.4%) did not have pre-charged loops. There were no statistically significant differences in the post-withdrawal and post-discharge survival rates of ECPR patients among different age groups, regions, and hospitals (all P>0.05). The top 5 difficulties in implementing ECPR in non-ICU environments were lack of ECMO machines (16 times), difficulty in placing CPR pipes (15 times), long time intervals between CPR and ECMO transfer (13 times), lack of conventional backup ECMO loops (10 times), and inability of ECMO emergency teams to quickly arrive at the site (5 times). Conclusion: ECPR has been gradually developed in the field of pediatric critical care in China, and needs to be further standardized. ECPR in non-ICU environment remains a challenge.
Child ; Female ; Humans ; Male ; Aftercare ; Cardiopulmonary Resuscitation/methods* ; Cross-Sectional Studies ; Death, Sudden, Cardiac/prevention & control* ; East Asian People ; Heart Arrest/therapy* ; Patient Discharge ; Retrospective Studies ; Surveys and Questionnaires