Aim To explore the feasibility of the cloud exposure system for in vitro exposure experiments on inhalation toxicology.Methods Calu-3 cells cultured at the air-liquid interface(ALI)were exposed to three concentrations of lipopolysaccha-ride(LPS):high,medium,and low(800,400,200 mg·L-1)by the cloud exposure system,and phosphate buffer solu-tion(PBS)was used as a negative control group for one expo-sure,while the high concentration of LPS was used to expose Calu-3 cells for five times.Calu-3 cells were exposed to phos-phate buffer solution(PBS)once as negative control group and to high concentration of LPS solution for five times,and the ac-tivity of Calu-3 cells,the release of lactate dehydrogenase(LDH),TEER,mucin MUC5AC,and the expression of inflam-matory factors IL-6,IL-8 and TNF-α were detected 3 h and 24 h after the end of the exposure,respectively.Results Compared with the PBS-negative control group,after exposure to the Calu-3 cell model at the air-liquid interface with three concentrations of LPS,high,medium,and low,there were no significant changes in the activity and LDH release,but the cellular electrical resist-ance value was reduced,and the barrier function of the cells was impaired;with the increase of the exposure concentration,the LPS promoted the expression of the cellular mucin MUC5AC,which led to a decrease in the expression of cellular IL-6,IL-8,and a decrease in the expression of TNF-α.Expression of IL-6 and IL-8 decreased and TNF-α expression increased;as the fre-quency of exposure increased,LPS inhibited the expression of mucin and increased the expression of IL-6;an increase in the frequency of exposure along with a prolongation of post-exposure assay time resulted in an increase in the expression of cellular IL-8 and TNF-a.Conclusions The ALI cloud exposure ap-proach can effectively reflect the cellular response to positive subjects,and this in vitro exposure can be used in subsequent exposure experiments to evaluate the inhalation toxicity of com-pounds.

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.

The Dionex CaboPac^TM PA10 BioLC^TM Analyical 2 mm × 250 mm column was used with a protective column (Dionex CaboPac^TM PA10 BioLC^TM Guard 2 mm × 50 mm). 100 mmol·L^-1 sodium hydroxide solution was used as eluent; the flow rate was 0.25 mL·min^-1. Sample tray temperature: 35 ℃. The pulse amperometric detector was adopted, and the waveform was Gold CWE, Ag-AgCl RE, Carbo, Quad. The samples were cultured with 8 concentrations of glycogen substrates (0.31, 1.25, 2.5, 5, 10, 20, 30, and 40 mg·mL^-1). D-Glucose concentrations were measured at 5 different time points (T0, T1, T2, T3 and T4). The glucose concentration from T1 to T4 minus the glucose concentration at T0. The reaction rate was calculated at different glycogen substrate concentrations. These reaction rates are plotted against substrate concentrations using Michaelis-Menten equation. The kinetic parameters were expressed as V_max (nmol·mg^-1·min^-1) and K_m (mg·mL^-1). The RSD of glucose standard curve R² (n = 6, linear range: 1.25-500 μmol·L^-1) was 0.1% and the RSD (n = 6) of the slope of the standard curve was 2.2%. The mean limit of quantitation was 0.14 μmol·L^-1, and the mean limit of detection was 0.05 μmol·L^-1. The RSD of K_m and V_max were 4.4% and 4.6% respectively in three separate experiments. The durability of the method was good. The method was developed for the on-line automatic determination of the hydrolysis kinetics of acid α-glucosidase (GAA) for injection by ion chromatography. The method has good precision, repeatability and durability, and can be used for the determination of glycogen hydrolysis kinetics of GAA for injection, and could reference value for the enzyme kinetics evaluation of recombinant enzyme replacement therapy.

According to the requirements of the regulatory authorities, degree of modification (DP) should be included in the characterisation of the PEGylated protein drug substance, and is one of the critical quality attributes for quality control. In this study, based on the fundamental assumption that the refractive index (RI) signal and the ultraviolet (UV) signal of PEGylated protein are equal to the sum of the corresponding signal produced by the polyethylene glycol (PEG) and protein parts of the conjugates in their uncoupled state, we developed a method to determine the DP of PEGylated recombinant human growth hormone (inpegsomatropin). In this method, 20 μL of 1 mg·mL^-1 human growth hormone (hGH) standard, 2 mg·mL^-1 PEG reference substance and 1 mg·mL^-1 drug substance solution were each injected to size exclusion chromatographic (SEC) column for separation, detected with ultraviolet and refractive index (UV-RI) detectors in series. Finally, the DP was calculated as the formula derived from the fundamental assumption. The developed SEC-UV-RI method showed good specificity, repeatability (RSD = 0.63%, n = 9) and accuracy, with a recovery of 100.0%, compared with the result obtained from the simultaneously established classical acid hydrolysis method, demonstrating pretty handleability and accessibility, and is appropriate for quality control test of DP for this drug substance.

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 explore the relation of plasma Elabela with 3-month prognoses in large vessel occlusion-acute ischemic stroke (LVO-AIS) patients accepted endovascular thrombectomy (EVT).Methods:A prospective study was performed; 94 LVO-AIS patients aceepted EVT in Department of Neurology, Anhui Provincal Hospital, Anhui Medical University from August 2020 to August 2022 were selected. Plasma Elabela was detected before EVT, and 24 and 72 h after EVT. Modified Rankin scale (mRS) was used to evaluate the prognoses of the patients 3 months after EVT; differences in clinical data and plasma Elabela level between the good prognosis group and poor prognosis group were compared. Independent influencing factors for prognoses of LVO-AIS patients 3 months after EVT were analyzed by multivariate Logistic regression. Receiver operating characteristic (ROC) curve was used to analyze the efficacy of Elabela in predicting prognoses of patients with LVO-AIS 3 months after EVT.Results:Compared with the poor prognosis group, the good prognosis group had significantly lower percentages of patients with stroke history and diabetes, and lower NIHSS scores at admission ( P<0.05). Elabela level in the good prognosis group was significantly higher than that in the poor prognosis group 72 h after EVT ( P<0.05). Multivariate Logistic regression analysis showed that stroke history ( OR=0.148, P=0.037, 95% CI: 0.025-0.889), diabetes mellitus ( OR=0.148, P=0.037, 95% CI: 0.025-0.889), hypertension history ( OR=3.488, P=0.024, 95% CI: 1.177-10.339), and Elabela level 72 h after EVT ( OR=1.064, P=0.005, 95% CI: 1.019-1.111) were independent influencing factors for prognoses of LVO-AIS patients 3 months after EVT. ROC curve showed that area under ROC curve of plasma Elabela level 72 h after EVT in predicting prognosies of LVO-AIS patients 3 months after surgery was 0.718 ( P<0.001, 95% CI: 0.614-0.822). Conclusion:Plasma Elabela level 72 h after EVT may be a potential prognostic biomarker for LVO-AIS patients after EVT.