The prevalence of gastric cancer (GC) in China, India, and the USA has different epidemiology, but comparative analyses with regard to their time trends and causes are underdeveloped. Using the 2023 Global Burden of Disease (GBD) database (1990–2023), we evaluated GC trends via Joinpoint regression. Das Gupta decomposition and comparative risk assessment quantified demographic/epidemiological drivers and behavioral risks. ARIMA models projected the burden through 2040. Globally, gastric cancer age-standardized incidence rates (ASIR) declined from 26.31 to 14.00 per 100,000 between 1990 and 2023, whereas absolute incident cases increased from 1.05 to 1.28 million. China maintained the highest disease burden in 2023 (ASIR: 25.04/100,000) despite achieving the steepest reductions in age-standardized incidence, mortality, and DALY rates. India demonstrated the slowest decline in age-standardized rates, while incident cases nearly doubled from 42,800 to 85,400 during the study period. Joinpoint analysis identified significant upward ASIR trends after 2020 in China (APC = 3.62) and globally (APC = 1.61). Smoking remained the leading attributable risk factor, particularly in China. Decomposition analysis showed that population aging and growth were the principal drivers of increasing absolute burden. ARIMA projections suggested that China will continue to carry the highest gastric cancer burden through 2040. Despite declining standardized rates, population aging and growth continue to drive absolute case numbers upward. To mitigate this evolving burden, China requires optimized early screening and stringent behavioral risk control; India needs expanded cancer registry coverage and equitable rural healthcare access; while the USA should prioritize targeted strategies to mitigate internal disparities among subpopulations.
Feng Lin, Xue-Ying Wei, Li-Huan Song et al.· Discover Oncology· 0 citations
DeepVoyager-VL is proposed, a long-horizon multimodal deep-search framework for vision-in-the-loop search that constructs a multimodal event graph to drive data synthesis, yielding problems with intermediate visual dependencies and long reasoning chains.
Huan-Yao Zhang, Jie-Peng Zhou, Ru Zhao et al.· 0 citations
SearchArt is introduced, a scalable framework for training long-horizon search agents through verification-driven task synthesis and a multi-stage post-training pipeline, which exhibits adaptive search planning, iterative evidence aggregation, and complex reasoning over extended interaction horizons.
Lang Mei, Xiaohan Yu, Chong Chen et al.· arXiv.org· 1 citation
A one-round study provides initial evidence for PRD-guided self-evolution, motivating validation at larger scales and in industrial settings, and presents AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), and To-Employee (ToE) applications.