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.

Most bacterial cell surface glycans are structurally unique, and have been considered as ideal target molecules for the developments of detection and diagnosis techniques, as well as vaccines. Chemical synthesis has been a promising approach to prepare well-defined oligosaccharides, facilitating the structure-activity relationship exploration and biomedical applications of bacterial glycans. L-Galactosaminuronic acid is a rare sugar that has been only found in cell surface glycans of gram-negative bacteria. Here, an orthogonally protected L-galactosaminuronic acid building block was designed and chemically synthesized. A synthetic strategy based on glycal addition and TEMPO/BAIB-mediated C6 oxidation served well for the transformation of commercial L-galactose to the corresponding L-galactosaminuronic acid. Notably, the C6 oxidation of the allyl glycoside was more efficient than that of the selenoglycoside. In addition, a balance between the formation of allyl glycoside and the recovery of selenoglycoside was essential to improve efficiency of the NIS/TfOH-catalyzed allylation. This synthetically useful L-galactosaminuronic acid building block will provide a basis for the syntheses of complex bacterial glycans.
Carbohydrates ; Glycosides ; Oligosaccharides ; Oxidation-Reduction ; Polysaccharides/chemistry*

In this study, the critical quality attributes of Wuzhuyu Decoction reference sample were explored by using characteristic chromatogram, index component content and dry extract rate as indexes.The dissemination relationship of quantity value between medicinal materials-decoction pieces-reference sample was investigated to preliminarily formulate the quality standard of the reference sample.The characteristic chromatogram of 15 batches of Wuzhuyu Decoction was established by high performance liquid chromatography(HPLC) and the similarity analysis was conducted.Common peaks were demarcated and assigned to medicinal materials.Moreover, quantitative determination of limonin, evodiamine, rutaecarpine and ginsenoside Rb_1 of Wuzhuyu Decoction were performed.The dissemination of quantity value was explored combined with dry extract rate, similarity of characteristic chromatogram and transfer rate of index component content.A total of 18 common peaks were identified in the corresponding materials of Wuzhuyu Decoction reference sample, with the similarity of characteristic chromatogram greater than 0.9, and Fructus Evodiae, Radix Ginseng, Rhizoma Zingiberis Recens and Fructus Jujubae contributed 9, 5, 8 and 2 chromatographic peaks, respectively.The index component content of corresponding materials and the transfer rates of medicinal materials-decoction pieces and decoction pieces-reference sample of different batches of Wuzhuyu Decoction reference sample were as follows: the content of limonin was 0.16%-0.51%, and the transfer rates were 83.66%-115.60% and 38.54%-54.58%, respectively; the content of evodiamine was 0.01%-0.11%, the transfer rated were 80.80%-116.15% and 3.23%-12.93%, respectively; the content of rutaecarpine was 0.01%-0.05%, the transfer rates were 84.33%-134.53% and 5.72%-21.24%, respectively; the content of ginsenoside Rb_1 was 0.06%-0.11%, and the transfer rates were 90.00%-96.92% and 32.45%-67.24%, respectively.The dry extract rate of the whole prescription was 22.58%-29.89%.In this experiment, the dissemination of quantity value of Wuzhuyu Decoction reference sample was analyzed by the combination of characteristic chromatogram, index component content and dry extract rate.A scientific and stable quality evaluation method of the reference sample was preliminarily established, which provided basis for the subsequent development of Wuzhuyu Decoction and the quality control of related preparations.
Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal/chemistry* ; Ginsenosides/analysis* ; Limonins/analysis* ; Quality Control

Bacterial surface glycans perform a diverse and important set of biological roles, and have been widely used in the treatment of bacterial infectious diseases. The majority of bacterial surface glycans are decorated with diverse rare functional groups, including amido, acetamidino, carboxamido and pyruvate groups. These functional groups are thought to be important constituents for the biological activities of glycans. Chemical synthesis of glycans bearing these functional groups or their variants is essential for the investigation of structure-activity relationships by a medicinal chemistry approach. To date, a broad choice of synthetic methods is available for targeting the different rare functional groups in bacterial surface glycans. This article reviews the structures of naturally occurring rare functional groups in bacterial surface glycans, and the chemical methods used for installation of these groups.
Bacterial Infections ; Humans ; Polysaccharides/chemistry* ; Structure-Activity Relationship

ObjectiveTo explore the effect and mechanism of Xiaojindan extract （XJD） on macrophage polarization. MethodLipopolysaccharide （LPS） and interleukin-4 （IL-4） were used to induce M1 and M2 polarization of RAW264.7 cells. The influence of 10-80 mg·L^-1 XJD on cell proliferation was detected by Cell Counting Kit-8 （CCK-8） assay. Nitric oxide （NO） and interleukin-6 （IL-6） release was explored by Griess assay and enzyme-linked immunosorbent assay （ELISA）， respectively. The mRNA expression of M1 and M2 macrophage markers was measured by real-time quantitative polymerase chain reaction （Real-time PCR）， and the CD206⁺ expression was determined by flow cytometry. The activation of phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） pathway was analyzed by western blot. Result10-80 mg·L^-1 XJD showed no marked cytotoxicity in LPS （0.5 mg·L^-1）- or IL-4 （20 μg·L^-1）-induced RAW264.7 cells. Compared with the control group， LPS significantly promoted the expression of M1 macrophage markers （P<0.01）， including increased NO and IL-6 release （P<0.01） and upregulated mRNA expression of interleukin-1β （IL-1β）， inducible nitric oxide synthase （iNOS）， cyclooxygenase-2 （COX-2） and tumor necrosis factor-α （TNF-α） （P<0.01）. Compared with LPS-induced group， 20-80 mg·L^-1 XJD decreased the release of NO and IL-6 in a dose-dependent manner （P<0.01）， and similarly 10-80 mg·L^-1 XJD suppressed the mRNA expression of IL-1β， iNOS， COX-2 and TNF-α （P<0.01）. Compared with the control group， IL-4 obviously increased the expression of M2 macrophage markers （P<0.01）， including increased CD206⁺ cell population and upregulated mRNA expression of arginine-1 （Arg-1）， interleukin-10 （IL-10）， interleukin-13 （IL-13） and transforming growth factor-β₁ （TGF-β₁）. Compared with IL-4-induced group， 10-80 mg·L^-1 XJD dose-dependently decreased CD206⁺ cell population （P<0.01） and inhibited the mRNA expression of Arg-1， IL-10， IL-13 and TGF-β₁ （P<0.01）. Western blot showed that XJD significantly downregulated the activation of PI3K/Akt pathway as compared to LPS- and IL-4-induced groups （P<0.05， P<0.01）. ConclusionXJD significantly inhibited the macrophage polarization in the LPS- and IL-4-induced RAW264.7 cells by targeting PI3K/Akt pathway.

Objective: The relationship between serum uric acid (SUA) levels and glycemic indices, including plasma glucose (FPG), 2-hour postload glucose (2h-PG), and glycated hemoglobin (HbA1c), remains inconclusive. We aimed to explore the associations between glycemic indices and SUA levels in the general Chinese population. Methods: The current study was a cross-sectional analysis using the first follow-up survey data from The China Cardiometabolic Disease and Cancer Cohort Study. A total of 105,922 community-dwelling adults aged ≥ 40 years underwent the oral glucose tolerance test and uric acid assessment. The nonlinear relationships between glycemic indices and SUA levels were explored using generalized additive models. Results: A total of 30,941 men and 62,361 women were eligible for the current analysis. Generalized additive models verified the inverted U-shaped association between glycemic indices and SUA levels, but with different inflection points in men and women. The thresholds for FPG, 2h-PG, and HbA1c for men and women were 6.5/8.0 mmol/L, 11.0/14.0 mmol/L, and 6.1/6.5, respectively (SUA levels increased with increasing glycemic indices before the inflection points and then eventually decreased with further increases in the glycemic indices). Conclusion An inverted U-shaped association was observed between major glycemic indices and uric acid levels in both sexes, while the inflection points were reached earlier in men than in women.
Aged ; Asian Continental Ancestry Group ; Blood Glucose/analysis* ; China/epidemiology* ; Cohort Studies ; Diabetes Mellitus/blood* ; Female ; Glucose Tolerance Test ; Glycated Hemoglobin A/analysis* ; Glycemic Index ; Humans ; Male ; Middle Aged ; Uric Acid/blood*