【Objective】 To perform pre-transfusion examination and major crossmatch test using CD47 anti-idiotypic antibody (CD47 AID) (method 1) and reagent lack of anti-IgG4 anti-human globulin(method 2) in patients treated with CD47 monoclonal antibodies, and evaluate the feasibility of method 1 by comparing the transfusion efficacy of patients after cross matching with two methods. 【Methods】 Post-drug samples were collected from 18 clinical subjects treated with CD47 monoclonal antibody in our hospital. Antibody screening and major crossmatch test were performed using method 1 and method 2, and the difference of ΔHb (post-transfusion Hb minus pre-transfusion Hb) was compared after transfusion. The differences in ΔHb after transfusion were analyzed between the test group using method 1 and the control group without CD47 monoclonal antibody using ordinary microcolumn gel method. 【Results】 There was no significant difference in ΔHb between the test group using method 1 and test group using method 2 (8.40±0.71 vs 7.36±0.94, P>0.05). No significant difference was noticed in ΔHb between the test group using method 1 and the control group without CD47 monoclonal antibody (8.40±0.71 vs 6.59±0.77, P>0.05). 【Conclusion】 In the test group, major crossmatch test with method 1 has the same transfusion efficacy as the test with method 2. Method 1 is simple and easy to operate, and the results are objective and accurate. It is recommended to use method 1 for pre-transfusion antibody screening and major crossmatch tests for patients using CD47 monoclonal antibody.

Objective To evaluate the safety and effectiveness of excessive dynamic airway collapse(EDAC)treated by laser.Methods 13 patients with EDAC confirmed by bronchoscopy from January 2018 to August 2022 were selected and divided into a simple EDAC group(6 cases)and an EDAC combined with tracheobronchomalacia(TBM)group(7 cases)based on whether they were combined with TBM.All patients underwent laser tracheobronchoplasty under bronchoscope.Symptoms,airway collapse,oxygenation index,modified version of British Medical Research Council dyspnoea scale(mMRC)and 6 min walking test before and after treatment were compared to evaluate the therapeutic effect.Results 13 patients underwent 17 times of laser tracheobronchoplasty with laser power of 8～15 W,and 4 patients underwent 2 times of laser tracheobronchoplasty.After treatment,the clinical symptoms of cough,sputum,shortness of breath and dyspnea were improved in all patients.1 week post-treatment,the EDAC group showed a significant improvement in airway lumen stenosis,with a significant statistical difference(P<0.05),1 month post-treatment,enhancements were observed in airway stenosis,oxygenation index,FEV1%,6-minute walk test,and mMRC,which remained stable over a 6 month follow-up.In the EDAC + TBM group,significant enhancements in airway stenosis,oxygenation index,and mMRC were noted 1 week post-treatment,with statistical significance(P<0.05).Between 8 d～6 months post-treatment,some patients exhibited a recurrence of airway stenosis,necessitating comprehensive interventions like balloon dilation,cryotherapy,and stent insertion.Local necrosis and granuloma occurred in some patients after laser therapy,and no serious complications associated with laser intervention were found in all patients.Conclusion Laser tracheobronchoplasty is a safe and effective technique for the treatment of EDAC.For patients with EDAC alone,the therapeutic effect is good,but for patients with EDAC combined with TBM,the long-term effect is not good.

Objective To investigate whether the combination of conventional respiratory training and articulatory visual feedback training can improve respiratory function and diaphragmatic function in stroke patients.Methods This single-blind randomized controlled trial recruited a total of 30 stroke patients who were admitted to Department of Rehabilitation Medicine,Zhongshan Hospital,Fudan University,from Nov 2022 to Aug 2023,and divided them into two groups:a experimental group(n=15)and a control group(n=15).The experimental group received conventional respiratory training combined with articulatory visual feedback training,and the control group received conventional respiratory training.The training in the 2 groups was conducted 5 times per week for 4 weeks.Results Both groups significantly improved in maximum inspiratory pressure(MIP),peak inspiratory flow(PIF),maximum phonation time(MPT),maximum counting ability(MCA),and peak expiratory flow(PEF)in each of the two groups improved significantly after training(P<0.05).After training,compared with the control group,the experimental group showed significant differences in MIP[(46.04±13.58)cmH2O vs.(63.46±16.96)cmH2O;P=0.004;95%CI:-28.91,-5.93;effect size(ES)=1.13],PIF[(144.00±43.81)L/min vs.(190.20±75.01)L/min;P=0.049;95%CI:-1.54,0;ES=0.75],MCA[(7.06±3.25)s vs.(10.30±4.89)s;P=0.041;95%CI:-6.34,-0.13;ES=0.77],forced vital capacity(FVC)[(1.74±0.76)L vs.(2.26±0.57)L;P=0.04;95%CI:-1.03,-0.03;ES=0.77],forced expiratory volume in one second(FEV1)[(1.10±0.40)L vs.(1.60±0.50)L;P=0.004;95%CI:-0.85,-0.18;ES=1.1],and PEF[(83.40(55.80)L/min vs.171.12(94.80)L/min;P=0.012)].However,there were no statistically significant differences after training between the two groups in the maximum phonation time(MPT),vital capacity(VC),maximum voluntary ventilation(MVV),diaphragm mobility of the nonparetic side and paretic side,thickening fraction of the nonparetic side and paretic side.Conclusion Compared with conventional respiratory training alone,the combination of articulatory visual feedback training with conventional respiratory training is more effective in enhancing respiratory and lung function in stroke.

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 pathogenesis of erythrocytosis by detecting the key enzymes of glucose metabolism and glucose transporter in bone marrow erythrocytes of chronic mountain sickness (CMS), and analyzing its correlation with hemoglobin. METHODS: Twenty CMS patients hospitalized in Qinghai Provincial People's Hospital from January 2019 to December 2020 were selected as CMS group. Twenty males with leukocyte count > 3.5×10⁹/L who had accepted bone marrow aspiration and had normal result were taken as control group. The mRNA and protein expression of key enzymes and glucose transporter in glucose metabolism in bone marrow CD71⁺ erythrocytes were detected by real time qPCR and Western blot, respectively. Glucose, lactic acid and 2,3-diphosphoglycerate in the bone marrow supernatant and serum were tested by ELISA. The mRNA and protein expression of key enzymes and glucose transporter, glucose, lactic acid and 2,3-diphosphoglycerate of the two groups were compared. Pearson correlation was used to analyze the correlation between key enzymes, glucose transporter in glucose metabolism in bone marrow CD71⁺ erythrocytes and hemoglobin. RESULTS: The expression of HK2, GLUT1 and GLUT2 mRNA in the CMS group were higher than those in the control group (P<0.001), while the expression of HK1, OGDH and COX5B mRNA were not different. The expression of HK2, GLUT1 and GLUT2 protein in the CMS group were higher than those in the control group (P<0.05). The levels of glucose and lactic acid in the bone marrow supernatant and serum in the CMS group were not different from those in the control group, while the level of 2,3-diphosphoglycerate was higher (P<0.001). Both HK2 and GLUT2 proteins were positively correlated with hemoglobin (r=0.511, 0.717). CONCLUSION CMS patients may increase glycolysis by increasing the expression of HK2, and promote the utilization of glucose through high expression of GLUT1 and GLUT2 to meet the need of energy supply.
Male ; Humans ; Altitude Sickness/metabolism* ; Glucose Transporter Type 1 ; 2,3-Diphosphoglycerate ; Hemoglobins ; Chronic Disease ; RNA, Messenger ; Phenotype ; Glucose

In this study, untargeted metabolomics was conducted using the liquid chromatography-tandem mass spectrometry(LC-MS/MS) technique to analyze the potential biomarkers in the plasma of mice with heart failure with preserved ejection fraction(HFpEF) induced by a high-fat diet(HFD) and nitric oxide synthase inhibitor(Nω-nitro-L-arginine methyl ester hydrochloride, L-NAME) and explore the pharmacological effects and mechanism of Jiming Powder in improving HFpEF. Male C57BL/6N mice aged eight weeks were randomly assigned to a control group, a model group, an empagliflozin(10 mg·kg~(-1)·d~(-1)) group, and high-and low-dose Jiming Powder(14.3 and 7.15 g·kg~(-1)·d~(-1)) groups. Mice in the control group were fed on a low-fat diet, and mice in the model group and groups with drug intervention were fed on a high-fat diet. All mice had free access to water, with water in the model group and Jiming Powder groups being supplemented with L-NAME(0.5 g·L~(-1)). Drugs were administered on the first day of modeling, and 15 weeks later, blood pressure and cardiac function of the mice in each group were measured. Heart tissues were collected for hematoxylin-eosin(HE) staining to observe pathological changes and Masson's staining to observe myocardial collagen deposition. Untargeted metabolomics analysis was performed on the plasma collected from mice in each group, and metabolic pathway analysis was conducted using MetaboAnalyst 5.0. The results showed that the blood pressure was significantly lower and the myocardial concentric hypertrophy and left ventricular diastolic dysfunction were significantly improved in both the high-dose and low-dose Jiming Powder groups as compared with those in the model group. HE and Masson staining showed that both high-dose and low-dose Jiming Powder significantly alleviated myocardial fibrosis. In the metabolomics experiment, 23 potential biomarkers were identified and eight strongly correlated metabolic pathways were enriched, including linoleic acid metabolism, histidine metabolism, alpha-linolenic acid metabolism, glycerophospholipid metabolism, purine metabolism, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and pyrimidine metabolism. The study confirmed the pharmacological effects of Jiming Powder in lowering blood pressure and ameliorating HFpEF and revealed the mechanism of Jiming Powder using the metabolomics technique, providing experimental evidence for the clinical application of Jiming Powder in treating HFpEF and a new perspective for advancing and developing TCM therapy for HFpEF.
Male ; Mice ; Animals ; Heart Failure/metabolism* ; Powders ; Stroke Volume/physiology* ; Chromatography, Liquid ; NG-Nitroarginine Methyl Ester/therapeutic use* ; Mice, Inbred C57BL ; Tandem Mass Spectrometry ; Metabolomics ; Biomarkers ; Water