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.

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.

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 identify the risk factors in mortality of pediatric acute respiratory distress syndrome (PARDS) in pediatric intensive care unit (PICU). Methods: Second analysis of the data collected in the "efficacy of pulmonary surfactant (PS) in the treatment of children with moderate to severe PARDS" program. Retrospective case summary of the risk factors of mortality of children with moderate to severe PARDS who admitted in 14 participating tertiary PICU between December 2016 to December 2021. Differences in general condition, underlying diseases, oxygenation index, and mechanical ventilation were compared after the group was divided by survival at PICU discharge. When comparing between groups, the Mann-Whitney U test was used for measurement data, and the chi-square test was used for counting data. Receiver Operating Characteristic (ROC) curves were used to assess the accuracy of oxygen index (OI) in predicting mortality. Multivariate Logistic regression analysis was used to identify the risk factors for mortality. Results: Among 101 children with moderate to severe PARDS, 63 (62.4%) were males, 38 (37.6%) were females, aged (12±8) months. There were 23 cases in the non-survival group and 78 cases in the survival group. The combined rates of underlying diseases (52.2% (12/23) vs. 29.5% (23/78), χ²=4.04, P=0.045) and immune deficiency (30.4% (7/23) vs. 11.5% (9/78), χ²=4.76, P=0.029) in non-survival patients were significantly higher than those in survival patients, while the use of pulmonary surfactant (PS) was significantly lower (8.7% (2/23) vs. 41.0% (32/78), χ²=8.31, P=0.004). No significant differences existed in age, sex, pediatric critical illness score, etiology of PARDS, mechanical ventilation mode and fluid balance within 72 h (all P>0.05). OI on the first day (11.9(8.3, 17.1) vs.15.5(11.7, 23.0)), the second day (10.1(7.6, 16.6) vs.14.8(9.3, 26.2)) and the third day (9.2(6.6, 16.6) vs. 16.7(11.2, 31.4)) after PARDS identified were all higher in non-survival group compared to survival group (Z=-2.70, -2.52, -3.79 respectively, all P<0.05), and the improvement of OI in non-survival group was worse (0.03(-0.32, 0.31) vs. 0.32(-0.02, 0.56), Z=-2.49, P=0.013). ROC curve analysis showed that the OI on the thind day was more appropriate in predicting in-hospital mortality (area under the curve= 0.76, standard error 0.05,95%CI 0.65-0.87,P<0.001). When OI was set at 11.1, the sensitivity was 78.3% (95%CI 58.1%-90.3%), and the specificity was 60.3% (95%CI 49.2%-70.4%). Multivariate Logistic regression analysis showed that after adjusting for age, sex, pediatric critical illness score and fluid load within 72 h, no use of PS (OR=11.26, 95%CI 2.19-57.95, P=0.004), OI value on the third day (OR=7.93, 95%CI 1.51-41.69, P=0.014), and companied with immunodeficiency (OR=4.72, 95%CI 1.17-19.02, P=0.029) were independent risk factors for mortality in children with PARDS. Conclusions: The mortality of patients with moderate to severe PARDS is high, and immunodeficiency, no use of PS and OI on the third day after PARDS identified are the independent risk factors related to mortality. The OI on the third day after PARDS identified could be used to predict mortality.
Female ; Male ; Humans ; Child, Preschool ; Infant ; Child ; Critical Illness ; Pulmonary Surfactants/therapeutic use* ; Retrospective Studies ; Risk Factors ; Respiratory Distress Syndrome/therapy*

In order to judge the future development trend of science and technology, plan ahead and lay out the frontier technology fields and directions, China Association of Chinese Medicine(CACM) has launched consultation projects for collecting "major scienti-fic issues and engineering technology difficulties in traditional Chinese medicine(TCM)" for the industry for three consecutive years since 2019. Up to now, 18 projects have been selected as major issues for research, and some experience and achievements have been made. These projects have been applied in important scientific and technological work such as scientific and technological planning and deployment at all levels of national, local, and scientific research institutions, the selection and cultivation of major national scientific and technological projects, and the construction of innovation bases, giving full play to the role of the think tank advisory committee of CACM. This study reviewed the selection of major issues for the first time, systematically combed its application in the national layout of science and technology, and put forward the existing problems and improvement suggestions, aiming to provide new ideas for further improving the selection of major issues and research direction, providing a theoretical basis and decision support for the national scientific and technological layout in the field of TCM, and promoting scientific and technological innovation to facilitate the high quality development of TCM.
Medicine, Chinese Traditional ; Inventions ; China ; Drugs, Chinese Herbal

OBJECTIVE: To analyze the clinical features and prognosis of patients with Castleman's disease (CD) and improve the diagnosis and treatment of CD. METHODS: Clinical data of patients diagnosed with CD by pathological biopsy in Gansu Provincial Hospital from January 2009 to November 2020 were retrospectively analyzed. According to clinical classification, the patients were divided into two groups: UCD (unicentric CD) group (n=20) and MCD (multicentric CD) group (n=9). The clinical manifestations, laboratory examination, treatment regimens, pathological examination and follow-up data were statistically analyzed. RESULTS: There were no significant differences in average age and gender ratio between UCD group and MCD group. In UCD patients, 80.0% were hyaline vascular type, and 20.0% were plasma cell type. In MCD patients, 33.3% were hyaline vascular type, 55.6% were plasma cell type, and 11.1% were mixed type. There was significant difference in pathological classification between the two groups (P=0.039). The UCD patients usually presented asymptomatic single lymph node enlargement with mild clinical symptoms, while the MCD patients were characterized by multiple superficial and deep lymph node enlargement throughout the body. The incidences of asthenia, splenomegaly, serous effusion in MCD group were higher than those in UCD group (P<0.05). Meanwhile, the incidences of anemia, hypoproteinemia, increased ESR, elevated serum globulin and elevated β₂-microglobulin were significantly higher than those in UCD group too (P<0.05). There was no significant difference in the incidences of abnormal WBC, PLT and elevated LDH between the two groups (P>0.05). Among 20 patients with UCD, 13 cases reached complete remission (CR), 1 case achieved partial remission (PR). Among 9 patients with MCD, 3 cases received CR and 4 cases received PR. CONCLUSION Patients with CD requires pathological examination for diagnosis. Patients with UCD show mild clinical symptoms, good surgical treatment effect and good prognosis. Patients with MCD have diversified clinical manifestations and relatively poor prognosis, and these patients require comprehensive treatment.
Humans ; Castleman Disease/therapy* ; Retrospective Studies ; Prognosis ; Splenomegaly ; Anemia

OBJECTIVE: To investigate the fetal and maternal outcomes, risk factors of disease progression and adverse pregnancy outcomes (APOs) in patients with undifferentiated connective tissue disease (UCTD). METHODS: This retrospective study described the outcomes of 106 pregnancies in patients with UCTD. The patients were divided into APOs group (n=53) and non-APOs group (n=53). The APOs were defined as miscarriage, premature birth, pre-eclampsia, premature rupture of membranes (PROM), intrauterine growth restriction (IUGR), postpartum hemorrhage (PPH), and stillbirth, small for gestational age infant (SGA), low birth weight infant (LBW) and birth defects. The differences in clinical manifestations, laboratory data and pregnancy outcomes between the two groups were compared. Logistic regression analysis was performed to analyze the risk factors for APOs and the progression of UCTD to definitive CTD. RESULTS: There were 99 (93.39%) live births, 4 (3.77%) stillbirths and 3 (2.83%) miscarriage, 20 (18.86%) preterm delivery, 6 (5.66%) SGA, 17 (16.03%) LBW, 11 (10.37%) pre-eclampsia, 7 (6.60%) cases IUGR, 19 (17.92%) cases PROM, 10 (9.43%) cases PPH. Compared with the patients without APOs, the patients with APOs had a higher positive rate of anti-SSA antibodies (73.58% vs. 54.71%, P=0.036), higher rate of leukopenia (15.09% vs. 3.77%, P=0.046), lower haemoglobin level [109.00 (99.50, 118.00) g/L vs. 124.00 (111.50, 132.00) g/L, P < 0.001].Multivariate Logistic regression analysis showed that leucopenia (OR=0.82, 95%CI: 0.688-0.994) was an independent risk factors for APOs in UCTD (P=0.042). Within a mean follow-up time of 5.00 (3.00, 7.00) years, the rate of disease progression to a definite CTD was 14.15%, including 8 (7.54%) Sjögren's syndrome, 4 (3.77%) systemic lupus erythematosus (SLE), 4 (3.77%) rheumatoid arthritis and 1 (0.94%) mixed connective tissue disease. Multivariate Cox proportional risk regression analysis showed that Raynaud phenomenon (HR=40.157, 95%CI: 3.172-508.326) was an independent risk factor for progression to SLE. CONCLUSION Leukopenia is an independent risk factor for the development of APOs in patients with UCTD. Raynaud's phenmon is a risk factor for the progression of SLE. Tight disease monitoring and regular follow-up are the key measures to prevent adverse pregnancy outcomes and predict disease progression in UCTD patients with pregnancy.
Pregnancy ; Infant, Newborn ; Female ; Humans ; Pregnancy Outcome ; Retrospective Studies ; Abortion, Spontaneous/etiology* ; Undifferentiated Connective Tissue Diseases ; Pre-Eclampsia/epidemiology* ; Lupus Erythematosus, Systemic ; Risk Factors ; Leukopenia ; Pregnancy Complications/epidemiology* ; Disease Progression ; Connective Tissue Diseases/epidemiology*