(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of “regionalized governance-classified management”, in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), a chronic inflammatory disorder characterized by alcohol-independent hepatic lipid accumulation, remains poorly understood in terms of PANoptosis involvement. Methods: We integrated high-throughput sequencing data with bioinformatics to profile differentially expressed genes (DEGs) and immune infiltration patterns in MASLD, identifying PANoptosis-associated DEGs (PANoDEGs). Machine learning algorithms prioritized key PANoDEGs, while ROC curves assessed their diagnostic efficacy. Cellular, animal, and clinical validations confirmed target expression. Results: Three PANoDEGs (SNHG16, Caspase-6, and Dynamin-1-like protein) exhibited strong MASLD associations and diagnostic significance. Immune profiling revealed elevated M1 macrophages, naïve B cells, and activated natural killer cells in MASLD tissues versus controls. Further experiments verified the expression of the key PANoDEGs. Conclusions: This study provides new insights for further studies on the pathogenesis and treatment strategies of PANoptosis in MASLD.