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.

AIM To establish a UPLC method for the simultaneous content determination of chlorogenic acid,protocatechuic acid,protocatechualdehyde,cryptochlorogenic acid,syringic acid,1,3-dicaffeoylquinic acid,cynaroside,isochlorogenic acid B,neochlorogenic acid,isochlorogenic acid A,hesperidin,caffeic acid,isochlorogenic acid C and rosmarinic acid in Waigan Fengsha Granules.METHODS The analysis was performed on a 30℃ thermostatic Waters ACQUITY UPLC.HSS C18 column(2.1 mm×100 mm,1.8 μm),with the mobile phase comprising of acetonitrile-0.2%phosphoric acid flowing at 0.2 mL/min in a gradient elution manner,and the detection wavelengths were set at 275,327 nm.Subsequently,chemometric analysis was performed.RESULTS Fourteen constituents showed good linear relationships within their own ranges(r≥0.999 6),whose average recoveries were 95.68%-104.8%with the RSDs of 0.7%-3.0%.Seven batches of samples were clustered into two types,three principal components demonstrated the acumulative contribution rate of 93.031%.CONCLUSION This accurate and reliable method can be used for the quality control of Waigan Fengsha Granules.

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 investigate the clinical efficacy of modified Fuzheng Yiliu Decoction(composed of Astragali Radix,Codonopsis Radix,Ligustri Lucidi Fructus,Hedyotis Diffusae Herba,Moutan Cortex,Visci Herba,etc.)combined with XELOX regimen(Oxaliplatin plus Capecitabine)for the treatment of postoperative patients with advanced gastric cancer of qi and yin deficiency type.Methods A total of 80 postoperative patients with advanced gastric cancer of qi and yin deficiency type were randomly divided into the Chinese medicine group and the control group,with 40 cases in each group.Both groups received chemotherapy with XELOX regimen,while the Chinese medicine group was given modified Fuzheng Yiliu Decoction.Three weeks constituted a course of treatment,the medication of Chinese medicine decoction lasted for two weeks or more in each course of treatment,and a total of 8 courses of treatment were performed.The incidence of adverse reactions during chemotherapy was monitored and changes in serum tumor markers of serum carcinoembryonic antigen(CEA),carbohydrate antigen 199(CA199)and alpha-fetoprotein(AFP)were observed in the two groups before and after treatment.Moreover,the patients'quality of life was assessed by the scores of Karnofsky's Performance Status(KPS)and World Health Organization Quality of Life Measurement Scale(WHOQOL-100).Long-term follow-up was carried out for the evaluation of the prognostic indicators such as overall survival and one-year and 2-year overall survival rates.Results(1)Patients in the two groups were all followed up,and the median follow-up time was 27 months(95%CI:23.59-27.86).(2)After treatment,the levels of serum CEA and AFP in the Chinese medicine group were significantly lower than those before treatment(P＜0.05 or P＜0.01),while serum CA199 tended to decrease compared with those before treatment,but the difference was not statistically significant(P＞0.05);in the control group,the levels of serum CEA,CA199,and AFP were not significantly decreased after treatment(P＞0.05).The intergroup comparison showed that the decrease of serum CEA,CA199 and AFP levels in the Chinese medicine group was significantly superior to that in the control group(P＜0.05 or P＜0.01).(3)The adverse reactions during chemotherapy in the two groups mainly involved bone marrow suppression,gastrointestinal reactions and liver function abnormalities,etc.The incidences of all adverse reactions in the Chinese medicine group tended to be lower than those in the control group,but the differences were not statistically significant(P＞0.05).(4)After treatment,the KPS scores of patients in both groups were improved compared with those before treatment(P＜0.01),and the improvement in the Chinese medicine group was significantly superior to that in the control group(P＜0.01).(5)After treatment,the scores of the four dimensions of WHOQOL-100 such as health status,mobility,life feelings,and other activities of daily life in the Chinese medicine group were significantly improved compared with the pre-treatment(P＜0.05),whereas there was no significant improvement in the control group(P＞0.05).The intergroup comparison showed that the improvement of the scores of each dimension of the WHOQOL-100 in the Chinese medicine group was significantly superior to that in the control group(P＜0.05 or P＜0.01).(6)The median survival in the Chinese medicine group was 29.0 months(95%CI:25.95-31.70)and that in the control group was 22.0 months(95%CI:19.67-25.58),indicating that the median survival was significantly prolonged in Chinese medicine group(P＜0.01).The one-year and 2-year postoperative survival rates were 97.5%and 77.5%in the Chinese medicine group and 92.5%and 47.5%in the control group,respectively.The intergroup comparison showed that the one-year and 2-year postoperative survival rates in the Chinese medicine group were higher than those in the control group(P＜0.01).Conclusion Modified Fuzheng Yiliu Decoction can effectively alleviate the adverse reactions during adjuvant chemotherapy for postoperative patients with advanced gastric cancer of qi and yin deficiency type,improve the quality of life of patients,and prolong the survival time of patients.

Objective This study aimed to evaluate whether the onset of the plateau phase of slow hepatitis B surface antigen decline in patients with chronic hepatitis B treated with intermittent interferon therapy is related to the frequency of dendritic cell subsets and expression of the costimulatory molecules CD40,CD80,CD83,and CD86. Method This was a cross-sectional study in which patients were divided into a natural history group(namely NH group),a long-term oral nucleoside analogs treatment group(namely NA group),and a plateau-arriving group(namely P group).The percentage of plasmacytoid dendritic cell and myeloid dendritic cell subsets in peripheral blood lymphocytes and monocytes and the mean fluorescence intensity of their surface costimulatory molecules were detected using a flow cytometer. Results In total,143 patients were enrolled(NH group,n = 49;NA group,n = 47;P group,n = 47).The results demonstrated that CD141/CD1c double negative myeloid dendritic cell(DNmDC)/lymphocytes and monocytes(%)in P group(0.041[0.024,0.069])was significantly lower than that in NH group(0.270[0.135,0.407])and NA group(0.273[0.150,0.443]),and CD86 mean fluorescence intensity of DNmDCs in P group(1832.0[1484.0,2793.0])was significantly lower than that in NH group(4316.0[2958.0,5169.0])and NA group(3299.0[2534.0,4371.0]),Adjusted P all<0.001. Conclusion Reduced DNmDCs and impaired maturation may be associated with the onset of the plateau phase during intermittent interferon therapy in patients with chronic hepatitis B.

Objective To explore characteristics of clinical parameters and cytokines in patients with drug-induced liver injury(DILI)caused by different drugs and their correlation with clinical indicators. Method The study was conducted on patients who were up to Review of Uncertainties in Confidence Assessment for Medical Tests(RUCAM)scoring criteria and clinically diagnosed with DILI.Based on Chinese herbal medicine,cardiovascular drugs,non-steroidal anti-inflammatory drugs(NSAIDs),anti-infective drugs,and other drugs,patients were divided into five groups.Cytokines were measured by Luminex technology.Baseline characteristics of clinical biochemical indicators and cytokines in DILI patients and their correlation were analyzed. Results 73 patients were enrolled.Age among five groups was statistically different(P=0.032).Alanine aminotransferase(ALT)(P=0.033)and aspartate aminotransferase(AST)(P=0.007)in NSAIDs group were higher than those in chinese herbal medicine group.Interleukin-6(IL-6)and tumor necrosis factor alpha(TNF-α)in patients with Chinese herbal medicine(IL-6:P<0.001;TNF-α:P<0.001)and cardiovascular medicine(IL-6:P=0.020;TNF-α:P=0.001)were lower than those in NSAIDs group.There was a positive correlation between ALT(r=0.697,P=0.025),AST(r=0.721,P=0.019),and IL-6 in NSAIDs group. Conclusion Older age may be more prone to DILI.Patients with NSAIDs have more severe liver damage in early stages of DILI,TNF-α and IL-6 may partake the inflammatory process of DILI.

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.

Background/Aims: Oral EDP-514 is a potent core protein inhibitor of hepatitis B virus (HBV) replication, which produced a >4-log viral load reduction in HBV-infected chimeric mice with human liver cells. This study evaluated the safety, pharmacokinetics, and antiviral activity of three doses of EDP-514 in treatment-naive viremic patients with HBeAgpositive or -negative chronic HBV infection. Methods: Patients with HBsAg detectable at screening and at least 6 months previously were eligible. HBeAg-positive and -negative patients had a serum/plasma HBV DNA level ≥20,000 and ≥2,000 IU/mL, respectively. Twenty-five patients were randomized to EDP-514 200 (n=6), 400 (n=6) or 800 mg (n=7) or placebo (n=6) once daily for 28 days. Results: A dose-related increase in EDP-514 exposure (AUClast and Cmax) was observed across doses. At Day 28, mean reductions in HBV DNA were –2.9, –3.3, –3.5 and –0.2 log10 IU/mL with EDP-514 200 mg, 400 mg, 800 mg, and placebo groups, respectively. The corresponding mean change from baseline for HBV RNA levels was –2.9, –2.4, –2.0, and –0.02 log10 U/mL. No virologic failures were observed. No clinically meaningful changes from baseline were observed for HBsAg, HBeAg or HBcrAg. Nine patients reported treatment emergent adverse events of mild or moderate severity with no discontinuations, serious AEs or deaths. Conclusions In treatment-naïve viremic patients, oral EDP-514 was generally safe and well-tolerated, displayed PK profile supportive of once-daily dosing, and markedly reduced HBV DNA and HBV RNA.