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Introduction: Soft Tissue and Other Extraosseous Sarcomas (STOES) represent a rare and heterogeneous group of malignancies with significant clinical challenges due to their complexity and aggressiveness. Despite their low prevalence, the global impact of STOES is substantial, necessitating a detailed examination of their epidemiology and disease burden. Methods: This comprehensive analysis utilized data from the Global Health Data Exchange (GHDx) covering the years 1990 to 2021. We assessed the incidence, prevalence, mortality, and Disability-Adjusted Life Years (DALYs) for STOES, categorized by location, sex, and socio-demographic indices. Statistical methods included Estimated Annual Percentage Change (EAPC), Spearman correlation analysis, and Bayesian age-period-cohort modeling. Findings: In 2021, STOES cases reached a global prevalence of 480,473, a significant increase from 1990. High Socio-Demographic Index (SDI) regions exhibited the highest age-standardized incidence and prevalence rates (ASIR and ASPR) at 2.05 and 10.61 per 100,000 population, respectively. Notably, significant increases were also observed in Central Asia, Central Europe, and Southern Sub-Saharan Africa. Males consistently showed higher disease rates than females. The decomposition analysis highlighted population growth and aging as primary drivers of the observed trends. Forecasting suggests a decline in the global STOES burden by 2030, though disparities will persist, particularly among males. Conclusion: The study reveals critical geographic and demographic disparities in the burden of STOES, underscoring the ongoing higher risk among males and in certain global regions. Despite projected declines in overall disease burden by 2030, substantial disparities are expected to persist, necessitating targeted public health interventions and robust policies to effectively mitigate these differences and enhance global health outcomes. Plain Language Summary Why was the study done? This study was conducted to understand the global impact of rare cancers known as soft tissue and other extraosseous sarcomas (STOES). These cancers, although rare, significantly affect patients worldwide, making it important to monitor their occurrence and impact. What did the researchers do? We analyzed global health data from 1990 to 2021 to find out how many people are diagnosed with these cancers each year, how many live with them, and how many die from them. We examined these trends across different global regions and among various demographic groups. What did the researchers find? By 2021, approximately 480,473 people worldwide were living with these cancers, an increase from 1990. The highest rates of these cancers were in economically developed regions, with significant increases also noted in Central Asia, Central Europe, and Southern Sub-Saharan Africa. Men were more affected than women. The rise in cases is attributed to an aging population and overall population growth. We predict a decrease in these cancer cases by 2030, although regional and gender disparities will persist. What do the findings mean? The findings emphasize significant regional and demographic variations in the incidence of these rare cancers. Recognizing these patterns allows for better allocation of health resources and guides research efforts. Despite a projected decrease in cases, ongoing efforts are needed to address disparities through targeted health policies and improved early detection, diagnosis, and multidisciplinary management of STOES.

Background Complex immune-brain interactions that affect neural development, survival and function might have causal and therapeutic implications for psychiatric illnesses. However, previous studies examining the association between immune inflammation and schizophrenia (SCZ) have yielded inconsistent findings. Methods Comprehensive two-sample Mendelian randomization (MR) analysis was performed to determine the causal association between immune cell signatures and SCZ in this study. Based on publicly available genetic data, we explored causal associations between 731 immune cell signatures and SCZ risk. A total of four types of immune signatures (median fluorescence intensities (MFI), relative cell (RC), absolute cell (AC), and morphological parameters (MP)) were included. Comprehensive sensitivity analyses were used to verify the robustness, heterogeneity, and horizontal pleiotropy of the results. Results After FDR correction, SCZ had no statistically significant effect on immunophenotypes. It was worth mentioning some phenotypes with unadjusted low P -values, including FSC-A on NKT ( β  = 0.119, 95% CI  = 0.044 ~ 0.194, P  = 0.002), DN (CD4-CD8-) NKT %T cell ( β  = 0.131, 95% CI  = 0.054 ~ 0.208, P  = 9.03 × 10 − 4 ), and SSC-A on lymphocytes ( β  = 0.136, 95% CI  = 0.059 ~ 0.213, P  = 5.43 × 10 − 4 ). The causal effect of SCZ IgD on transitional was estimated to 0.127 (95% CI  = 0.051 ~ 0.203, P  = 1.09 × 10 − 3 ). SCZ also had a causal effect on IgD+ %B cell ( β  = 0.130, 95% CI  = 0.054 ~ 0.207, P  = 8.69 × 10 − 4 ), and DP (CD4 + CD8 + ) %T cell ( β  = 0.131, 95% CI  = 0.054 ~ 0.207, P  = 8.05 × 10 − 4 ). Furthermore, four immunophenotypes were identified to be significantly associated with SCZ risk: naive CD4 + %T cell ( OR  = 0.986, 95% CI  = 0.979 ~ 0.992, P  = 1.37 × 10 − 5 ), HLA DR on CD14 − CD16 − ( OR  = 0.738 (95% CI  = 0.642 ~ 0.849, P  = 2.00 × 10 − 5 ), CD33 dim HLA DR + CD11b − AC ( OR  = 0.631, 95% CI  = 0.529 ~ 0.753, P  = 3.40 × 10 − 7 ) and activated & resting Treg % CD4 Treg ( OR  = 0.937, 95% CI  = 0.906 ~ 0.970, P  = 1.96 × 10 − 4 ). Conclusions Our study has demonstrated the close connection between immune cells and SCZ by genetic means, thus providing guidance for future clinical research.

Purpose Previous studies have been inconsistent concerning the associations of smoking and alcohol consumption with the prognosis of diffuse large B-cell lymphoma (DLBCL). This study aimed to investigate the associations of smoking and drinking status with overall survival (OS) in male patients with DLBCL. Methods A total of 371 male patients with newly diagnosed DLBCL were retrospectively enrolled from eight medical centers. Smoking and drinking status were assessed as binary variables (yes or no). Inverse probability of treatment weighting (IPTW) based on propensity scores was applied to adjust for potential confounders. Kaplan–Meier survival analysis and Cox proportional hazards models were used to assess associations. Results Overall, 17.3% ( n  = 64) were smokers, and 12.7% ( n  = 47) reported alcohol consumption. After weighting, smoking was not associated with OS ( HR  = 1.192, 95% CI: 0.546–2.605, P  = 0.665), nor was alcohol consumption ( HR  = 0.864, 95% CI: 0.174–4.288, P  = 0.808). Subgroup analyses showed interactions between smoking and age ( P for interaction = 0.003), and between drinking and EBV DNA status ( P for interaction = 0.004). Sensitivity analyses using complete-case data and three-category exposure variables yielded consistent findings. Conclusions In this multicenter cohort of male DLBCL patients, neither smoking nor alcohol consumption was associated with the prognosis of DLBCL. Among EBV-positive patients, alcohol use was associated with a trend toward poorer prognosis, highlighting the need for further research.

Vascular endothelial growth factor affects the invasiveness of solid tumors by regulating angiogenesis. However, it is not clear whether VEGF could be used to predict the prognosis of DLBCL in the era of rituximab-based immunotherapy. We conducted a retrospective study to explore response to therapy and the prognostic value of VEGF on DLBCL in the rituximab era. The subjects were 65 patients with a histological diagnosis of DLBCL from the Affiliated Hospital of Xuzhou Medical University. Kaplan–Meier analysis was performed to estimate the cumulative survival rate of patients with different VEGF and IPI levels, and comparisons between groups were made using the log-rank test. DLBCL patients with elevated VEGF were more likely to have extranodal involvement, advanced stage, Myc/Bcl-2 double expression, and a higher Ki-67 score. Elevated VEGF was associated with poor therapeutic response and survival. When patients were divided into low, low-intermediate, high-intermediate and high-risk groups using the V-IPI model based on VEGF and IPI, PFS rates were 94.4, 74.1, 40.6 and 14.8%, respectively. This model better identified low-risk patients than IPI (85.9, 88.9, 37 and 7.8%). Our results demonstrate that VEGF predicts therapeutic response in DLBCL and the V-IPI model accurately predicts PFS of low-risk DLBCL in the rituximab era.

The purpose of this study is to investigate whether mutations in key genes affect the prognosis of breast cancer patients, and base mutations is discussed by combining the physical properties of the electron–ion interaction pseudopotential. We study 994 breast cancer mutation samples, including 15,015 mutation genes, and use social network algorithm to screen key genes. In order to analyze the relationship between electron–ion interaction pseudopotential and mutation of key genes, this paper proposes the E difference formula. It not only analyses gene mutation at the physical level, but also explains the mutation rule of single base. Simultaneously, we use the Kaplan–Meier to analyze the overall survival rate of breast cancer samples and use Tarone-Ware to test. The results showed that mutations in GATA3, PIK3CA, and TP53 are significant difference in overall survival rate of breast cancer ( CI = 95 % , P < 0.05 ). Proteins often interact to form protein complexes, and driving protein function. Therefore, by constructing protein–protein interaction network and finding modular proteins of key genes, we can find genes closely related to key genes. Finally, the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology function enrichment analysis show that key genes played an important role in metabolic pathways and biological functions. In clinical medicine, screening for key gene mutations may help predict survival in breast cancer patients.

Background Previous studies have been inconsistent concerning the association between smoking and risk of osteoarthritis (OA). This study aimed to explore the associations of smoking status and change in cartilage volume of OA in two longitudinal cohorts. Methods Subjects from the Osteoarthritis Initiative cohort (OAI, n = 593) and the Tasmanian Older Adult Cohort (TASOAC, n = 394) were included in this study. For both cohorts, participants were classified into three groups based on their smoking status, namely ‘never’, ‘former’, and ‘current’ smokers. The outcome measures were the annual rate of change of tibiofemoral cartilage volume over 2 years in OAI and of tibial cartilage volume over 2.6 years in TASOAC. Potential confounders were balanced using the inverse probability of treatment weighting (IPTW) method. Results Overall, 42.3% and 37.4% of participants were former smokers, and 5.7% and 9.3% were current smokers in the OAI and TASOAC cohorts, respectively. Compared to never smokers, neither former nor current smoking was associated with risk of the annual rate of change of tibiofemoral cartilage volume in OAI (former smoker: β=-0.068%/year, 95% confidence interval [CI] -0.824 to 0.688, p = 0.860; current smoker: β=-0.222%/year, 95% CI -0.565 to 0.120, p = 0.204) and tibial cartilage volume in TASOAC (former smoker: β = 0.001%/year, 95% CI -0.986 to 0.989, p = 0.998; current smoker: β=-0.839%/year, 95% CI -2.520 to 0.844, p = 0.329). Conclusions Our findings from two independent cohorts consistently showed that smoking was not associated with knee cartilage loss in older adults.

Background Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers. This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. Methods This prospective cohort study in China was conducted on critically ill patients to derivate and validate the prediction model. The derivation cohort, consisting of 400 patients admitted to the ICU of the Peking University Third Hospital(PUTH) between December 2020 and August 2023, was separated for training and internal validation, and an external data set of 160 patients at the FU YANG People's Hospital from August 2022 to August 2023 was employed for external validation. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen predictor variables. Multiple ML classification models were integrated to analyze and identify the best models. Several evaluation indexes were used to compare the model performance, including the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). SHapley Additive ex Planations (SHAP) is used to interpret ML models. Results 400 critically ill patients were included in the analysis, with 117 developing ARDS during follow-up. The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.762–0.910). For external validation, comprising 160 patients, 44 of whom developed ARDS, the AUC was 0.799 (95% CI: 0.723–0.875), significantly outperforming the LIPS score alone. Conclusion Combining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

Background To identify physical activity (PA) trajectories in adults with or at risk of knee osteoarthritis and to evaluate the association of PA trajectories with incident knee replacement (KR). Methods This study used data from the Osteoarthritis Initiative. The Physical Activity Scale for the Elderly and the KR were assessed annually from baseline to 9 years. Individuals were included if they did not undergo KR surgery at baseline and had data on PA at ≥ 1 visit before KR. Latent class growth mixture Modeling was used to identify the optimal trajectories of PA before KR. Log-binomial regression models were used to assess the association between PA trajectories and the risk of KR. Data analyses were conducted in all individuals and those with radiographic osteoarthritis (ROA) and significant knee pain (Western Ontario and McMaster Osteoarthritis Index pain score of ≥ 5 on a 0–20 scale) at baseline, respectively. Results Of 4731 participants (mean age 61.1 years, 58.5% female), four distinct and slightly declined PA trajectories were identified. Compared to individuals with a “Low” PA trajectory, those with “Medium-low”, “Medium-high”, or “High” PA trajectories were not significantly associated with the risk of KR (risk ratios: 0.97–1.19, all p  > 0.05). Similar PA trajectories and associations with the risk of KR were observed in the subgroups of individuals with radiographic osteoarthritis and those with significant knee pain at baseline, respectively. Conclusion In participants with or at risk of knee osteoarthritis, PA slightly declines over time and may play no role in the risk of KR.

Background Given the growing global burden of Alzheimer’s Disease and Other Dementias (ADODs) and inequalities in its distribution, it is of great necessity to analyze the trends and cross-country inequalities in the burden of ADODs globally and in China from 1990 to 2021. Methods Estimates and 95% uncertainty intervals (UIs) for incidence, prevalence, and disability-adjusted life years (DALYs) of ADODs were obtained from the Global Burden of Diseases Study (GBD) 2021. We described the epidemiology of ADODs at global, regional, and national levels, conducting temporal trend analyses using estimated annual percentage change, joinpoint regression, and age-period-cohort model. Additionally, we performed spatial autocorrelation, decomposition, and cross-country inequality analyses, comparing the burden in China to global trends. Results The global burden of ADODs increased overall from 1990 to 2021. GBD 2021 estimated 9,837,056 incident cases, 56,856,688 prevalent cases, and 36,332,687 DALYs for ADODs worldwide in 2021, with the highest case number in East Asia and highest age-standardized rate of DALYs in central sub-Saharan Africa. Spatial trends analyses showed hot spots in sub-Saharan Africa, the Middle East and North Africa, Europe, and High-income North America. Decomposition analysis revealed that changes in DALYs were primarily driven by population growth and aging, followed by prevalence, case fatality, and disease severity. Significant increases in absolute and relative sociodemographic index (SDI)-related inequalities in DALYs were observed from 1990 to 2021, with 318.19 to 759.97, and 0.43 to 0.49, respectively. Conclusion The burden of ADODs globally and in China showed overall increasing trends from 1990 to 2021. This increase was primarily driven by population growth and aging. Countries with high SDI values faced a disproportionately large burden of dementia, and SDI-related inequalities among countries exacerbated over time. These findings highlight significant challenges for the prevention and control of ADODs globally and in China.