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.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Aim To establish limited sampling strategy to esti-mate area under the drug concentration versus time curve(AUC0-t)of lymphoma patients treated with autologous stem cell transplantation(ASCT)who had busulfan intravenous infu-sion.Methods Twelve lymphoma patients treated with ASCT received a conditioning regimen containing busulfan 105 mg·m-2,Ⅳ infusion for 3 h.Blood samples were obtained 1 h after the start of the first dose of the busulfan infusion,at 5 min,1 h,2 h,4 h,6 h and 18 h after the end of the drug administration.LC-MS/MS was used to determine the busulfan serum concentra-tion.After obtaining the clinical pharmacokinetic parameters of busulfan by traditional pharmacokinetic method,multiple linear stepwise regression analysis was used to establish the AUC0-t es-timation model of busulfan based on limited sampling method.The model was further verified by Jackknife and Bootstrap meth-od.Bland-Altman plots were used to evaluate the consistency between the limited sampling method and the classical pharma-cokinetic method.Results The multiple linear regression equa-tion analysis of C60min,C180min and C300min was obtained by the limited sampling method.The regression equation was AUC0-t=295.003C60min+233.050C180min+273.163C300min-1202.713,r2=0.995,MPE=-0.87％,RMSE=2.40％.Conclusion The limited sampling model with three-point estimation can be used to estimate the AUC0-t of busulfan exposure in lymphoma patients with ASCT to provide reference for clinical application of busulfan.

Objective To investigate the changing distribution and antibiotic resistance profiles of clinical isolates of Staphylococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Staphylococcus according to the unified protocol of CHINET(China Antimicrobial Surveillance Network)using disk diffusion method and commercial automated systems.The CHINET antimicrobial resistance surveillance data from 2015 to 2021 were interpreted according to the 2021 CLSI breakpoints and analyzed using WHONET 5.6.Results During the period from 2015 to 2021,a total of 204,771 nonduplicate strains of Staphylococcus were isolated,including 136,731(66.8％)strains of Staphylococcus aureus and 68,040(33.2％)strains of coagulase-negative Staphylococcus(CNS).The proportions of S.aureus isolates and CNS isolates did not show significant change.S.aureus strains were mainly isolated from respiratory specimens(38.9±5.1)％,wound,pus and secretions(33.6±4.2)％,and blood(11.9±1.5)％.The CNS strains were predominantly isolated from blood(73.6±4.2)％,cerebrospinal fluid(12.1±2.5)％,and pleural effusion and ascites(8.4±2.1)％.S.aureus strains were mainly isolated from the patients in ICU(17.0±7.3)％,outpatient and emergency(11.6±1.7)％,and department of surgery(11.2±0.9)％,whereas CNS strains were primarily isolated from the patients in ICU(32.2±9.7)％,outpatient and emergency(12.8±4.7)％,and department of internal medicine(11.2±1.9)％.The prevalence of methicillin-resistant strains was 32.9％in S.aureus(MRSA)and 74.1％in CNS(MRCNS).Over the 7-year period,the prevalence of MRSA decreased from 42.1％to 29.2％,and the prevalence of MRCNS decreased from 82.1％to 68.2％.MRSA showed higher resistance rates to all the antimicrobial agents tested except trimethoprim-sulfamethoxazole than methicillin-susceptible S.aureus(MSSA).Over the 7-year period,MRSA strains showed decreasing resistance rates to gentamicin,rifampicin,and levofloxacin,MRCNS showed decreasing resistance rates to gentamicin,erythromycin,rifampicin,and trimethoprim-sulfamethoxazole,but increasing resistance rate to levofloxacin.No vancomycin-resistant strains were detected.The prevalence of linezolid-resistant MRCNS increased from 0.2％to 2.3％over the 7-year period.Conclusions Staphylococcus remains the major pathogen among gram-positive bacteria.MRSA and MRCNS were still the principal antibiotic-resistant gram-positive bacteria.No S.aureus isolates were found resistant to vancomycin or linezolid,but linezolid-resistant strains have been detected in MRCNS isolates,which is an issue of concern.

Objective:To compare the short-term effect and adverse reaction of venetoclax(VEN)combined with azacitidine(AZA)versus"7+3"regimen in newly diagnosed elder patients with acute myeloid leukemia(AML).Methods:From January 2021 to January 2022,the clinical data of seventy-nine newly diagnosed elder patients with AML at the Second Hospital of Shanxi Medical University and the Shanxi Bethune Hospital were retrospectively analyzed,including VEN+AZA group(41 cases)and"7+3"group(38 cases).The propensity score matching(PSM)method was used to balance confounding factors,then response,overall survival(OS),progression-free survival(PFS)and adverse reactions between the two groups were compared.Results:The ORR of VEN+AZA group and"7+3"group was 68％and 84％,respectively,and the CRc was 64％and 72％,respectively,the differents were not statistically significant(P＞0.05).In the VEN+AZA group,there were 5 non-remission(NR)patients,4 with chromosome 7 abnormality(7q-/-7),and 1 with ETV6 gene mutation.Median followed-up time between the two groups was 8 months and 12 months,respectively,and the 6-months OS was 84％vs 92％(P=0.389),while 6-months PFS was 84％vs 92％(P=0.258).The main hematological adverse reactions in two groups were stage Ⅲ-Ⅳmyelosuppression,and the incidence rate was not statistically different(P＞0.05).The median time of neutrophil recovery in two groups was 27(11-70)d,25(14-61)d(P=0.161),and platelet recovery was 27(11-75)d,25(16-50)d(P=0.270),respectively.The infection rate of VEN+AZA group was lower than that of"7+3"group(56％vs 88％,P=0.012).The rate of lung infections of two groups was 36％and 64％,respectively,the difference was statistically significant(P=0.048).Conclusion:The short-term effect of VEN+AZA group and"7+3"regimens in eldrly AML patients are similar,but the VEN+AZA regimen had a lower incidence of infection.The presence of chromosome 7 abnormality(7q-/-7)may be a poor prognostic factor for elderly AML patients treated with VEN+AZA.

Objective:To explore the therapeutic efficacy and prognostic factors of induction therapy for secondary central nervous system lymphoma(SCNSL).Methods:Clinical data of patients diagnosed with SCNSL from 2010 to 2021 at Guangdong Provincial People's Hospital were retrospectively collected.A retrospective cohort study was performed on all and grouped patients to analyze the efficacy and survival.Multivariate logistic regression analysis was used to explore the adverse prognostic factors.Results:Thirty-seven diffuse large B-cell lymphoma patients with secondary central involvement were included in the research.Their 2-year overall survival(OS)rate was 46.01％and median survival time was 18.1 months.The 2-year OS rates of HD-MTX group and TMZ group were 34.3％and 61％,median survival time were 8.7 and 38.3 months,and median progression-free survival time were 8.1 and 47 months,respectively.Multivariate logistic regression analysis showed that age,sex,IPI,Ann Arbor stage were correlated with patient survival time.The median survival time of patients with CD79B,KMT2D,CXCR4.ERBB2,TBL1XR1,BTG2,MYC,MYD88,and PIM1 mutations was 8.2 months,which was lower than the overall level.Conclusion:HD-MTX combined with TMZ as the first-line strategy may improve patient prognosis,and early application of gene sequencing is beneficial for evaluating prognosis.

Objective:To evaluate the efficiency of the four domestic language models,ERNIE Bot,ChatGLM2,Spark Desk and Qwen-14B-Chat,all with a massive user base and significant social attention,in response to consultations about PCa-related perio-perative nursing and health education.Methods:We designed a questionnaire that includes 15 questions commonly concerned by patients undergoing radical prostatectomy and 2 common nursing cases,and inputted the questions into each of the four language models for simulation consultation.Three nursing experts assessed the model responses based on a pre-designed Likert 5-point scale in terms of accuracy,comprehensiveness,understandability,humanistic care,and case analysis.We evaluated and compared the performance of the four models using visualization tools and statistical analyses.Results:All the models generated high-quality texts with no mis-leading information and exhibited satisfactory performance.Qwen-14B-Chat scored the highest in all aspects and showed relatively sta-ble outputs in multiple tests compared with ChatGLM2.Spark Desk performed well in terms of understandability but lacked comprehen-siveness and humanistic care.Both Qwen-14B-Chat and ChatGLM2 demonstrated excellent performance in case analysis.The overall performance of ERNIE Bot was slightly inferior.All things considered,Qwen-14B-Chat was superior to the other three models in con-sultations about PCa-related perioperative nursing and health education.Conclusion:In PCa-related perioperative nursing,large language models represented by Qwen-14B-Chat are expected to become powerful auxiliary tools to provide patients with more medical expertise and information support,so as to improve the patient compliance and the quality of clinical treatment and nursing.