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.

This study aims to investigate the intervention effect and mechanism of Tongxie Yaofang in regulating tumor-associated macrophage polarization on colorectal cancer under chronic stress. BALB/C mice were randomized into blank, control, model, mifepristone, and low-, medium-, and high-dose Tongxie Yaofang groups. The other groups except the blank and model groups were subjected to chronic restraint stress and subcutaneous implantation of colon cancer cells for the modeling of colon cancer under stress. Du-ring this period, the body mass and tumor size of each group of mice were recorded. The degree of depression in mice was assessed by behavioral changes. Enzyme-linked immunosorbent assay was employed to determine the levels of cortisol(CORT), 5-hydroxytryptamine(5-HT), norepinephrine(NE), M1-associated inflammatory cytokines [interleukin(IL)-1β, IL-12, and tumor necrosis factor(TNF)-α], and M2-associated inflammatory cytokines(IL-4 and IL-10) in the serum. The tumor growth of mice in each group was regularly monitored by in vivo imaging. The histopathological changes of tumors in each group of mice were observed by hematoxylin-eosin staining. The proportions of CD86 and CD206 in the tumor tissue were detected by flow cytometry and immunofluorescence staining. Western blot was employed to determine the protein levels of Janus kinase(JAK)1, JAK2, JAK3, signal transducer and activator of transcription(STAT)3, and STAT6 in the tumor tissue. The results showed that chronic stress increased the immobility time of mice, elevated the serum levels of CORT, IL-4, and IL-10, lowered the levels of 5-HT, NE, IL-1β, IL-12, and TNF-α, and promoted the growth of subcutaneous tumors. The tumor cells in the tumor tissue grew actively, with obvious atypia and up-regulated protein levels of CD206, JAK1, JAK2, JAK3, STAT3, and STAT6, and down-regulated protein level of CD86. The treatment with Tongxie Yaofang shortened the immobility time of mice, lowered the serum levels of CORT, IL-4, and IL-10, elevated the serum levels of 5-HT, NE, IL-1β, IL-12, and TNF-α, and inhibited the growth of subcutaneous tumors in mice. Moreover, the treatment caused different degrees of necrosis in the tumor tissues, down-regulated the protein levels of CD206, JAK1, JAK2, JAK3, STAT3, and STAT6, and up-regulated the protein level of CD86. In summary, Tongxie Yaofang can promote the transformation of M2 macrophages to M1 macrophages and change the tumor microenvironment under chronic stress to inhibit the development of colorectal cancer, which may be related to the JAK/STAT signaling pathway.
Mice ; Animals ; Interleukin-10 ; Tumor-Associated Macrophages/metabolism* ; Tumor Necrosis Factor-alpha ; Interleukin-4 ; Serotonin ; Mice, Inbred BALB C ; Cytokines/metabolism* ; Interleukin-12 ; Colonic Neoplasms ; Colorectal Neoplasms ; Tumor Microenvironment

In this study, alkali-soluble polysaccharide was extracted from Poria residue, and the structure of alkali-soluble polysaccharide was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), and differential scanning calorimetry (DSC). The physical morphology of alkali-soluble polysaccharide and ethyl cellulose (EC) was investigated by scanning electron microscopy (SEM), and the focus on angle of repose, bulk density, tapped density, Carr index, interparticle porosity, cohesion index, Hausner ratio, etc. The physical fingerprints were drawn, and the powder properties were evaluated by multivariate analysis. Diclofenac sodium extended-release tablets were prepared by direct compression method using alkali-soluble polysaccharide and EC as insoluble backbone materials to evaluate the basic properties of the extended-release tablets, investigate the in vitro drug release behavior and study the release mechanism. The results showed that alkali-soluble polysaccharide is a semi-crystalline polymer with smooth lamellar structure, and its stacking and compressibility are stronger than EC. The in vitro release experiments showed that the slow release performance of alkali-soluble polysaccharide is stronger than EC, and the release behavior of the prepared slow release tablets is in accordance with the Higuchi model. The pore structure is formed inside the tablets during the release process, and the release mode is pore diffusion release. The results of this study are of great significance for the development of new slow-release materials and the rational use of resources.

Objective: To analyze the occurrence of recompensation conditions in patients with chronic hepatitis B virus-related decompensated cirrhosis after entecavir antiviral therapy. Methods: Patients with hepatitis B virus-related decompensated cirrhosis with ascites as the initial manifestation were prospectively enrolled. Patients who received entecavir treatment for 120 weeks and were followed up every 24 weeks (including clinical endpoint events, hematological and imaging indicators, and others) were calculated for recompensation rates according to the Baveno VII criteria. Measurement data were compared using the Student t-test or Mann-Whitney U test between groups. Categorical data were compared by the χ (2) test or Fisher's exact probability method between groups. Results: 283 of the 320 enrolled cases completed the 120-week follow-up, and 92.2% (261/283) achieved a virological response (HBV DNA 20 IU/ml). Child-Pugh and MELD scores were significantly improved after treatment (8.33 ± 1.90 vs. 5.77 ± 1.37, t = 12.70, P < 0.001; 13.37 ± 4.44 vs. 10.45 ± 4.58, t = 5.963, P < 0.001). During the 120-week follow-up period, 14 cases died, two received liver transplants, 19 developed hepatocellular cancer, 11 developed gastroesophageal variceal bleeding, and four developed hepatic encephalopathy. 60.4% (171/283) (no decompensation events occurred for 12 months) and 56.2% (159/283) (no decompensation events occurred for 12 months and improved liver function) of the patients had achieved clinical recompensation within 120 weeks. Patients with baseline MELD scores > 15 after active antiviral therapy achieved higher recompensation than patients with baseline MELD scores ≤15 [50/74 (67.6%) vs. 109/209 (52.2%), χ (2) = 5.275, P = 0.029]. Conclusion: Antiviral therapy can significantly improve the prognosis of patients with hepatitis B virus-related decompensated cirrhosis. The majority of patients (56.2%) had achieved recompensation. Patients with severe disease did not have a lower probability of recompensation at baseline than other patients.
Humans ; Hepatitis B virus/genetics* ; Hepatitis B, Chronic/drug therapy* ; Antiviral Agents/adverse effects* ; Esophageal and Gastric Varices/complications* ; Liver Cirrhosis/complications* ; Treatment Outcome ; Gastrointestinal Hemorrhage/complications* ; Hepatitis B/drug therapy*

We investigated the effects of decursin on the proliferation, apoptosis, and migration of colorectal cancer HT29 and HCT116 cells through the phosphatidylinositol 3-kinase(PI3K)/serine-threonine kinase(Akt) pathway. Decursin(10, 30, 60, and 90 μmol·L~(-1)) was used to treat HT29 and HCT116 cells. The survival, colony formation ability, proliferation, apoptosis, wound hea-ling area, and migration of the HT29 and HCT116 cells exposed to decursin were examined by cell counting kit-8(CCK8), cloning formation experiments, Ki67 immunofluorescence staining, flow cytometry, wound healing assay, and Transwell assay, respectively. Western blot was employed to determine the expression levels of epithelial cadherin(E-cadherin), neural cadherin(N-cadherin), vimentin, B-cell lymphoma/leukemia-2(Bcl-2), Bcl-2-associated X protein(Bax), tumor suppressor protein p53, PI3K, and Akt. Compared with the control group, decursin significantly inhibited the proliferation and colony number and promoted the apoptosis of HT29 and HCT116 cells, and it significantly down-regulated the expression of Bcl-2 and up-regulated the expression of Bax. Decursin inhibited the wound healing and migration of the cells, significantly down-regulated the expression of N-cadherin and vimentin, and up-regulated the expression of E-cadherin. In addition, it significantly down-regulated the expression of PI3K and Akt and up-regulated that of p53. In summary, decursin may regulate epithelial-mesenchymal transition(EMT) via the PI3K/Akt signaling pathway, thereby affecting the proliferation, apoptosis, and migration of colorectal cancer cells.
Humans ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; bcl-2-Associated X Protein ; Vimentin/metabolism* ; Cell Proliferation ; Signal Transduction ; Apoptosis ; Cell Line, Tumor ; Colorectal Neoplasms/genetics* ; Cadherins/genetics* ; Cell Movement

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*