Objective To design an assisted patient conveying and vibration damping system to solve the problems of operator fatigue and patient bump during casualty evacuation.Methods The assisted patient conveying and vibration damping system was composed of several conveying straps and a vibration damping mechanism.The conveying straps were made up of a waist strap,two shoulder straps,a chest strap,adhesive straps and joint components,and the joint components included adjusting buckles,big buckles,small buckles,connecting buckles and hook mechanisms;the vibration damping mechanism adopted the technical form of extension handle combined with vibration absorber,in which the extension handle was made of rigid material and the vibration absorber was equipped with a scissor guiding mechanism.Tests were carried out on the system to record the operating time of the operators and to analyze the system's vibration damping characteristics.Results The system developed extended the operating time of the stretcher conveyers while reduced the vibration during casualty transport,with a maximum vibration reduction of 71.73％.Conclusion The system developed gains advantages in low vibration and low workload,and can be used for casualty conveying in poor road conditions.[Chinese Medical Equipment Journal,2024,45(1):15-24]

Objective To explore the potential value of three-dimensional fast spin echo(3D-SPACE)combined with multilayer spiral CT(MSCT)in the diagnosis of knee cruciate ligament injury,to provide a new direction for the optimization of subsequent clinical diagnosis.Methods A total of 120 patients with knee cruciate ligament injury were treated from April 2020 to April 2021,aged from 21 to 68 with an average of(41.52±4.13)years old.For all patients,separate MSCT scanner scans,3D-SPACE sequence scans alone and 3D-SPACE sequence combined with MSCT scans were used.The injury and classifica-tion of the anterior and posterior cruciate ligament of the knee were compared,the length of the anterior-medial bundle and posterolateral bundle and its angle of the knee with the horizontal plane were observed,the diagnostic value of 3 diagnostic methods in knee cruciate ligament injury were determined.Results There was no significant difference between the 3D-SPACE sequence scan alone and the MSCT test alone on the total diagnostic rate and grading total diagnostic rate(P＞0.05).The total diagnostic rate and grading total diagnostic rate of 3D-SPACE scan combined with MSCT were significantly higher than those of 3D-SPACE scan or MSCT alone(P＜0.05).The 3D-SPACE sequence scan alone and the MSCT detection alone had no signifi-cant difference in the measurement values related to the anterior and posterior cruciate ligaments of the knee joint(P＞0.05).3D-SPACE sequence scanning combined with MSCT detection on the knee joint anterior and posterior cruciate ligament related mea-surements were significantly higher than the 3D-SPACE sequence scan or MSCT detection alone(P＜0.05).The area under the ROC curve estimated by 3D-SPACE sequence scanning combined with MSCT was 0.960,which was significantly higher than that of 3D-SPACE sequence scanning and MSCT alone evaluating the area under the ROC curve line of 0.756 and 0.795.The com-bined 3D-SPACE sequence scanning and 3D-SPACE sequence scanning MSCT analysis and prediction models were statistically different(Z=2.236,P＜0.05),and MSCT alone and 3D-SPACE sequence scanning combined with MSCT analysis and prediction models were statistically different(Z=2.653,P＜0.05).Conclusion The application of 3D-SPACE sequence combined with MSCT scanning for knee cruciate ligament injury can improve the diagnosis rate of patients with knee cruciate ligament injury.It can be used as a diagnostic tool for patients with knee cruciate ligament injury and is worthy of clinical application.

Objective To explore the impact of pH value of the reaction system on the properties of bovine hemoglobin modified with aldehydeated polyethylene glycol(PEG-bHb).Methods PEG-bHb conjugates were synthesized under varying pH conditions(6.0,6.5,7.4 and 8.0)of the reaction system while consistent molar ratios,temperature,and reaction time were maintained.The structural and functional attributes of PEG-bHb were characterized.Results The proportion of methemoglobin decreased with an increase in pH.In a weakly acidic reaction environment,the PEG-bHb was found to be relatively highly modified.At pH 6.5,the average number of PEG chains attached to the bHb surface was 6.86±0.38.Selective PEG modification of the N-terminal α-NH2 groups was more pronounced under weakly acidic conditions.Specifically,at pH 6.5,the modification efficiency of the N-terminal α-NH2 groups of bHb by aldehyde-activated PEG reached 95.4％for the α-chains and 99.3％for the β-chains.The PEG modification influenced the heme region microenvi-ronment of bHb,with minimal structural impact observed at pH 6.5.After modification,the oxygen affinity of PEG-bHb was enhanced,the Hill coefficient was reduced,and there were significant increases in colloid osmotic pressure,viscosity,and particle size,all of which differed markedly from the unmodified bHb group(P＜0.001).Conclusion The synthesis of PEG-bHb under weakly acidic conditions can result in a high degree of selective modification of the N-terminal α-NH2 groups and an overall high degree of modification.

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: : Blood-blister aneurysms (BBAs) of the internal carotid artery (ICA) are challenging lesions with high morbidity and mortality rates. Although research on BBAs is well documented in different populations, the study of BBAs in the Tibetan population is extremely rare. This study aimed to evaluate the characteristics of BBAs and analyze the treatment modalities and long-term outcomes in the Tibetan population in comparison with the Han population. Methods: : The characteristics of patients with BBAs of the ICA from January 2009 to January 2021 at our institution were reviewed. The features of aneurysms, treatment modalities, complications, and follow-up outcomes were retrospectively analyzed. Results: : A total of 130 patients (41 Tibetan and 89 Han patients) with BBAs of the ICA who underwent treatment were enrolled. Compared with the Han group, the Tibetan group significantly demonstrated a high ratio of BBAs among ICAs (8.6%, 41/477 vs. 1.6%, 89/5563; p<0.05), a high ratio of vasospasm (34.1%, 14/41 vs. 6.7%, 6/89; p=0.001), a high risk of ischemic events (43.9%, 18/41 vs. 22.5%, 20/89; p<0.05), and a low ratio of good outcomes (modified Rankin scale, 0–2) at the 1-year follow-up (51.2%, 21/41 vs. 74.2%, 66/89; p<0.05). The multivariate regression model showed that ischemic events significantly contributed to the prediction of outcomes at 1 year. Further analysis revealed that microsurgery and vasospasm were associated with ischemic events. Conclusion : In comparison with Han patients, the Tibetan population had a high ratio of BBA occurrence, a high incidence of ischemic events, and a high ratio of poor outcomes. The endovascular approach showed more benefits in BBA patients.