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 compare the clinical therapeutic effect between electroacupuncture at "four points of sacral region" and transurethral Erbium laser in treatment of moderate to severe stress urinary incontinence after radical prostatectomy. METHODS: A total of 68 patients of moderate to severe stress urinary incontinence after radical prostatectomy were divided into an electroacupuncture group (34 cases) and an Erbium laser group (34 cases, 3 cases dropped off) according to the settings. In the electroacupuncture group, electroacupuncture was applied at "four points of sacral region", i.e. points of 0.5 cun beside bilateral sacrococcygeal joints and bilateral Huiyang (BL 35), with continuous wave, 2 Hz in frequency, 60 min each time, once every other day, 3 times a week, 12 times as one course of treatment. In the Erbium laser group, transurethral Erbium laser technology was given, once every 4 weeks as one course of treatment. Both groups were treated for 5 courses. The scores of the International Consultation on Incontinence questionnaire-short form (ICI-Q-SF) and the incontinence quality of life questionnaire (I-QOL) were observed before treatment, after each course of treatment and in follow-up after 1 and 2 months of treatment completion, respectively, and the clinical efficacy was evaluated after treatment in the two groups. RESULTS: Compared with those before treatment, the ICI-Q-SF scores were decreased while the I-QOL scores were increased after 5 courses of treatment and in follow-up after 1, 2 months of treatment completion in the two groups (P<0.01). The ICI-Q-SF score in follow-up after 2 months of treatment completion were higher than that after 5 courses of treatment in the Erbium laser group (P<0.05). After 3, 4, 5 courses of treatment and in follow-up after 1 and 2 months of treatment completion, the ICI-Q-SF scores in the electroacupuncture group were lower than those in the Erbium laser group (P<0.05, P<0.01); after 2, 3, 4, 5 courses of treatment and in follow-up after 1 and 2 months of treatment completion, the I-QOL scores in the electroacupuncture group were higher than those in the Erbium laser group (P<0.01). The change ranges of ICI-Q-SF score and I-QOL score between before treatment and after each course of treatment in the electroacupuncture group were lager than those in the Erbium laser group (P<0.01, P<0.05). The total effective rate was 61.8% (21/34) in the electroacupuncture group, which was superior to 19.4 (6/31) in the Erbium laser group (P<0.01). CONCLUSION Both electroacupuncture at "four points of sacral region" and transurethral Erbium laser can improve the clinical symptoms and the quality of life in patients of moderate to severe stress urinary incontinence after radical prostatectomy. The short-term efficacy and long-term efficacy of electroacupuncture are superior to the Erbium laser technology.
Male ; Humans ; Quality of Life ; Urinary Incontinence, Stress/therapy* ; Sacrococcygeal Region ; Electroacupuncture ; Erbium ; Prostatectomy/adverse effects*

Aim To evaluate the protective effect of α-asarone on microglials with cerebral ischemia/reperfusion injury by measuring the expression of polar transformation and related inflammatory proteins in BV2 cells in vitro and its mechanisms.Methods The cerebral ischemia/reperfusion injury BV2 cells were pretreated by α-asarone in vitro and simulated by OGD/R model.The effect of α-asarone on the viability of damaged cells in OGD/R model was determined by CCK-8; the morphological changes of cells were observed to analyze the general morphology of cells; the levels of proinflammatory factor IL-1β, IL-18 and anti-inflammatory factor IL-10, IL-4, and ROS activity secreted by BV2 cells were detected by ELISA; the protein expressions of TGF-β, TNF-α and inflammatory related protein NLRP3, caspase 1, p-NF-κB were detected by Western blot.Results The results of in vitro experiments were as follows: the activity of damaged cells in OGD/R model was significantly increased by α-asarone, with the increase of administration dose, the cells in the low, medium and high dose groups of α-asarone decreased, and the "amoeba-like" cells and the cell body were gradually became stereoscopic and full.From the results of cell morphology, it could be seen that α-asarone had a certain proliferative effect on normal cells; the release was significantly reduced of proinflammatory factor IL-1β, IL-18 and TNF-α in OGD/R injured BV2 cells pretreated with α-asarone, also increased the release of IL-10, IL-4 and TGF-β, with a dose-effect relationship, and the high dose(16 μmol·L-1)was the best; the expressions of inflammatory related protein NLRP3, caspase 1, NF-κB and ROS activity in injured cells of OGD/R model were significantly reduced after pretreatment with α-asarone.Conclusions α-asarone has a significant protective effect on cerebral ischemia/reperfusion injury, mainly by regulating ROS activity and inhibiting phosphorylation of NF-κB, in order to reduce the excessive activation of NLRP3 inflammatory corpuscles reducing the secretion of proinflammatory factor IL-1β and IL-18, promoting the secretion of anti-inflammatory factor IL-10 and IL-4, so as to protect cerebral ischemia/reperfusion injury by anti-inflammatory reaction.

OBJECTIVE: To investigate the effect of chidamide on the killing activity of NK (Natural killer cell, NK) cells targeting K562 cells and its related mechanism. METHODS: K562 cells were pretreated with chidamide at different concentrations and cocultured with NK cells at different effect-target ratios. The killing effect of chidamide on K562 cells by NK cells, the expression of natural killer group 2 member D (NKG2D) ligands and apoptosis rate of K562 cells were detected by flow cytometry. RESULTS: The killing sensitivity of NK cells to K562 cells could be enhanced by chidamide. The expression of ULBP2 on K562 cell surface could be up-regulate, however, the expression of ULBP1 and MICA/MICB showed no statistically difference as compared with control group. Chidamide showed no obvious cytotoxicity to K562 cells. CONCLUSION Chidamide can significantly improve killing efficiency of NK cells on K562 cells, which may be related to the up-regulation of ULBP2 expression.
Aminopyridines ; Benzamides ; GPI-Linked Proteins ; Histocompatibility Antigens Class I ; Humans ; Intercellular Signaling Peptides and Proteins ; K562 Cells ; Killer Cells, Natural ; immunology ; NK Cell Lectin-Like Receptor Subfamily K

OBJECTIVE: To develop an improved version of the Quality-of-Life Assessment instrument for Lung Cancer Patients Based on Traditional Chinese Medicine (QLASTCM-Lu) and to evaluate its psychometric property. METHODS: The structured group method and the theory in developing rating scale were employed to revise the preliminary scale. The psychometric property (reliability, validity, and responsiveness) of the established QLASTCM-Lu (modified) were evaluated by quality of life data measured in 100 lung cancer patients. Statistical analyses were made accordingly by way of correlation analysis, factor analysis and paired t-test. RESULTS: The internal consistency reliability of the overall scale and all domains was from 0.80 to 0.94. Correlation and factor analyses demonstrated that the scale was good in construct validity. The criterion validity was formed with European Organization for Research and Treatment of Cancer-Quality of Life Questionnaire-Lung Cancer (EORTC QLQ-LC43) as the criterion. Statistically significant changes were found apart from such domain as "mental condition" and "social function", with the standardized response means being close to those of QLQ-LC43. CONCLUSION QLASTCM-Lu (modified) could be used to measure the quality of life of lung cancer patients with good reliability, validity and a certain degree of responsiveness.

Objective: To investigate the efficiency and safety of domestic tyrosine kinase inhibitor (TKI) dasatinib (Yinishu) as second-line treatment for patients with chronic myeloid leukemia in chronic phase (CML-CP). Methods: A retrospective analysis of clinical data of CML-CP patients who received domestic dasatinib as second-line treatment in the CML collaborative group hospitals of Hubei province from March 2016 to July 2018 was performed. The optimal response rate, the cumulative complete cytogenetic response (CCyR), the cumulative major molecular responses (MMR), progression free survival (PFS), event free survival (EFS) and adverse effects (AEs) of the patients were assessed at 3, 6 and 12 months of treatment. Results: A total of 83 CML-CP patients were enrolled in this study. The median follow-up time was 23 months. The optimal response rates at 3, 6 and 12 months in 83 CML-CP patients treated with dasatinib were 77.5% (54/71), 72.6% (61/75) and 60.7% (51/69), respectively. By the end of follow-up, the cumulative CCyR and MMR rates were 65.5% (55/80) and 57.1% (48/73), respectively. The median time to achieving CCyR and MMR was 3 months. During follow-up time, the PFS rate was 94.0% (79/83) and the EFS rate was 77.4% (65/83). The most common non-hematological AEs of dasatinib were edema (32.5%), rash itching (18.1%) and fatigue (13.3%). The common hematological AEs of dasatinib were thrombocytopenia (31.3%), leukopenia (19.3%) and anemia (6.0%). Conclusion: Domestic dasatinib was effective and safe as the second-line treatment of CML-CP patients and it can be used as an option for CML-CP patients.
Antineoplastic Agents ; Dasatinib/therapeutic use* ; Humans ; Imatinib Mesylate ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy* ; Protein Kinase Inhibitors ; Retrospective Studies ; Treatment Outcome