Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

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 postoperative recurrence rates between Thulium laser en bloc resection of bladder tumor (ERBT) and traditional transurethral resection of bladder tumor (TURBT) in treating patients with non-muscle invasive bladder cancer (NMIBC).Methods:A retrospective analysis was conducted on the clinical data of 1 439 patients with NMIBC who underwent either Thulium laser ERBT or TURBT in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 2010 and March 2024. Among them, 201 patients received Thulium laser ERBT, while 1 238 patients underwent TURBT. Propensity score matching (PSM) was employed in a 1∶1 ratio to eliminate selection bias due to non-random assignment, ensuring the comparability of clinical baseline data such as gender, age, pathological diagnosis, T stage, tumor grade, tumor size, and tumor number between the two groups. Kaplan-Meier method was used to generate recurrence-free survival curves for the matched groups, and the log-rank test was conducted to compare differences between the groups. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors affecting postoperative recurrence.Results:After PSM matching, 193 patients were included in each group. There were no statistically significant differences between the two groups in terms of gender ( P=0.317), age ( P=0.207), pathological type ( P=0.756), T stage ( P=0.402), tumor grade ( P=0.965), tumor size ( P=0.821), or number of tumors ( P=0.421). The median follow-up time was 16.2(8.0, 33.9) months. Excluding patients with non-urothelial tumors such as adenocarcinoma and squamous cell carcinoma, there were 180 cases in the Thulium laser ERBT group and 184 cases in the TURBT group. Survival analysis showed that the postoperative recurrence rate of urothelial carcinoma patients in the Thulium laser ERBT group was lower than that in the TURBT group [20.0%(36/180) vs. 38.6%(71/184), P<0.001]. Stratified survival analysis indicated that in patients with tumor diameters ≤30 mm [22.3%(29/130) vs. 33.6%(45/134), P=0.017] or >30 mm [14.0%(7/50) vs. 52.0%(26/50), P=0.002], the Thulium laser ERBT group had lower postoperative recurrence rate compared to the TURBT group.Among patients with single tumor, the recurrence rate in the Thulium laser ERBT group was lower than in the TURBT group[10.5%(11/105) vs. 31.5%(35/111), P<0.001]. However, among patients with multiple tumors, there was no statistically significant difference in recurrence rates between the Thulium laser ERBT group and the TURBT group [35.7%(25/70) vs. 47.9%(34/71), P=0.061]. Univariate and multivariate Cox regression analyses indicated that Thulium laser ERBT treatment was an independent protective factor against postoperative recurrence in NMIBC patients ( HR=0.44, 95% CI 0.30-0.66, P<0.001). Patients with adenocarcinoma ( HR=5.85, 95% CI 2.07-16.51, P<0.001), squamous cell carcinoma ( HR=2.98, 95% CI 1.04-8.55, P=0.042), or other types of tumors ( HR=2.98, 95% CI 1.14-7.75, P=0.026) had higher risks of recurrence. High-grade tumor patients faced increased risks of postoperative recurrence ( HR=1.84, 95% CI 1.21-2.79, P=0.004). Additionally, tumors >30 mm had increased risks of postoperative recurrence compared to those ≤30 mm ( HR=2.00, 95% CI1.31-3.05, P=0.001). Patients with single tumor had significantly reduced risks of postoperative recurrence compared to those with multiple tumors ( HR=0.50, 95% CI 0.34-0.73, P<0.001). Conclusions:Regardless of tumor diameter (≤30 mm or >30 mm), Thulium laser ERBT significantly reduces the postoperative recurrence rate in patients with urothelial carcinoma compared to TURBT, with the advantage being more pronounced in patients with single bladder tumor. Additionally, patients with high-grade tumors, tumor diameters >30 mm, or multiple bladder tumors have higher risk of postoperative recurrence.

Objective To compare the three methods in intensity modulated radiation therapy(IMRT)dose verification using PTW Detector729.Methods A total of 50 patients with nasopharyngeal cancer,lung cancer,breast cancer,cervical cancer and whole brain radiation therapy who completed radiation treatment at some hospital from January to December 2022 were selected retrospectively.Two-dimensional(zero and actual gantry angles)and three-dimensional dose verifications were carried out for the IMRT plans using PTW Detector729 2D ionization chamber matrix combined with PTW RW3 solid water and PTW Ocavius 4D rotation unit.The dose assessment threshold was set to 10％,and the γ pass rates of the three verification methods were counted under four assessment criteria,namely 3％/1 mm,2％/2 mm,3％/2 mm and 3％/3 mm.SPSS 22.0 statistical software was used for data analysis.Results Under the 10％dose assessment threshold criterion,zero-gantry-angle 2D dose verification had the highest γ pass rate,and the differences were statistically significant(P＜0.05);actual-gantry-angle 2D dose verification had the γ pass rate higher than that of 3D verification,and the differences were statistically significant(P＜0.05).The γ pass rates of the three verification methods gradually increased under four criteria,namely,3％/1 mm,2％/2 mm,3％/2 mm and 3％/3 mm,and exceeded 90％under the 3％/2 mm criterion,and the results met the requirements of clinical radiotherapy.Conclusion The results of the three verification methods satisfy the requirements of the IMRT dose verification practice guidelines,and the selection of appropriate verification methods is of great significance to ensure the implementation of the treatment plan.[Chinese Medical Equipment Journal,2024,45(5):56-59]

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:In chronic hepatitis B (CHB) patients with previous 96-week treatment with tenofovir amibufenamide (TMF) or tenofovir disoproxil fumarate (TDF), we investigated the efficacy of sequential TMF treatment from 96 to 144 weeks.Methods:Enrolled subjects who were previously assigned (2:1) to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks received extended or switched TMF treatment for 48 weeks. Efficacy was evaluated based on virological, serological, biological parameters, and fibrosis staging. Statistical analysis was performed using the McNemar test, t-test, or Log-Rank test according to the data. Results:593 subjects from the initial TMF group and 287 subjects from the TDF group were included at week 144, with the proportions of HBV DNA<20 IU/ml at week 144 being 86.2% and 83.3%, respectively, and 78.1% and 73.8% in patients with baseline HBV DNA levels ≥8 log10 IU/ml. Resistance to tenofovir was not detected in both groups. For HBeAg loss and seroconversion rates, both groups showed a further increase from week 96 to 144 and the 3-year cumulative rates of HBeAg loss were about 35% in each group. However, HBsAg levels were less affected during 96 to 144 weeks. For patients switched from TDF to TMF, a substantial further increase in the alanine aminotransferase (ALT) normalization rate was observed (11.4%), along with improved FIB-4 scores.Conclusion:After 144 weeks of TMF treatment, CHB patients achieved high rates of virological, serological, and biochemical responses, as well as improved liver fibrosis outcomes. Also, switching to TMF resulted in significant benefits in ALT normalization rates (NCT03903796).