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 , in two dimensions of resource supply (economic carrying capacity) and demand . (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. Public health and well-being have been a frontier issue of academic research and the focus of social service work of government departments. Especially in the context of the COVID-19 outbreak , which h“Health China 2030” Planning Outline to integrate health into all policies and safeguard and protect people’s health in an all-round and full-cycle way. The central government’s implementation of the “Healthy China” strategy requires local governments to optimize the allocation of health resources . The rationality of geographic distribution and spatial allocation of health resources has a great influence on the ability to protect and improve the health of the population. Therefore, it is of great significance to analyze the geographical pattern, spatio-temporal evolution dynamics, mismatch characteristics of health resources, and coping strategies for the formulating scientific health care policies, in order to plan and build a “Healthy China” .(1.(1RS) aThe spatial mismatch theory, created by Kain, was originally used to analyze the spatial mismatch between housing and employment of disadvantaged groups  and is nThis paper analyzes the spatial pattern of health resources and their mismatch by means of spatial clustering analysis, cold and hot spots analysis, and exploratory spatial data analysis (ESDA), which are all data-driven unsupervised learning methods and do not require a priori knowledge. The spatial clustering analysis is conducted by quantile method, and the study area is divided into high, medium, and low levels to analyze the spatial heterogeneity of medical resources. In other words, sort the data in descending order, the top 10 belong to high, 11–20 belong to medium, and 21–31 belong to low. Provinces of the same level share a high degree of similarity, while those at different levels have significant differences. In this paper, regions in statistically significant clusters are identified using the cold and hot spots analysis tool, and the spatial autocorrelation between the evolution trends of health resources and types of mismatches is analyzed using ESDA and characterized using Moran’s I. The global Moran’s I value are in the range of , and a larger absolute value indicates stronger spatial autocorrelation. A value greater than zero indicates spatial positive correlation, less than zero indicates spatial autocorrelation, and equal to zero indicates random distribution . We diviGeodetector, created by Wang Jinfeng, a professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and chief scientist of spatial analysis at the State Key Laboratory of Resource and Environmental Information System, is an emerging statistical analysis method for analyzing spatially driven mechanisms ,79. We uq and 22,565 (Inner Mongolia), 316,828 (Liaoning) and 187,106 (Jilin), and 188,100 (Zhejiang) and 113,000 (Fujian), respectively. In 2020, their thresholds are 35,447 (Hubei) and 25,616 (Jilin), 503,172 (Anhui) and 310,391 (Heilongjiang), and 361,300 (Zhejiang) and 216,800 (Fujian), respectively. In 2010 and 2020, the provinces of high level formed an “X” shaped agglomeration zone along Guangdong-Hebei and Sichuan-Jiangsu, including Guangdong, Hunan, Jiangxi, Hubei, Henan, Shandong, Shanxi, and Hebei. Provinces of medium level were distributed at the edge of the high-level zones, clustered in the Loess Plateau, Yunnan-Guizhou Plateau, Beibu Gulf, West Coast of the Strait, Yangtze River Delta, and northeast China. Most of the provinces of low level were clustered in northwest China, including Tibet, Qinghai, Xinjiang, Inner Mongolia, and Gansu . The GinThe change range of medical resources was characterized by clustering, and the spatial pattern of hospitals, doctors, and beds varied widely. Using the GIS spatial clustering quantile analysis tool, the change range of hospitals, doctors, and beds in 31 provinces from 2010 to 2020 were divided into three categories—high, medium, and low. The thresholds were 3460 (Guizhou) and 1178 (Hubei), respectively. 212,640 (Guizhou) and 89,469 (Shanghai), 177,200 (Zhejiang) and 81,500 (Gansu). From the perspective of hospitals, the high-level provinces were clustered in the eastern coastal regions and the Yunnan-Guizhou Plateau in the southwest; the medium-level provinces were distributed in a zonal pattern along the western and northern borders, while the low-level provinces were mostly clustered in the central, central south, and Loess Plateau regions. Shandong had the largest number of new hospitals , compared to the largest number of hospital reductions (−3317) in Hunan, and Liaoning, Heilongjiang, Henan, Shaanxi, and Gansu provinces saw varying degrees of reductions in the number of hospitals. It is worth noting that the northeast and northwest regions, such as Liaoning, Heilongjiang, and Gansu not only have serious population loss problems, but also experienced negative economic growth. From 2010 to 2020, their hospital reduction, doctors, and beds increase, whether this complex change phenomenon is reasonable needs to be further analyzed by other methods . From the perspective of hospitals, the high-level provinces were clustered in the eastern coastal regions and Pan-Pearl River Delta urban agglomeration; the medium-level provinces were distributed in the middle of the two high-level clusters, and the low-level provinces were in north (including northeast and northwest) and west China. The largest number of new doctors was in Guangdong , while the smallest was in Tibet . From the perspective of beds, the high-level provinces covered most of the eastern and central regions, the medium-level provinces were distributed in the periphery of those of high level, mostly clustered in the Yunnan-Guizhou Plateau in the southwest and Loess Plateau in the northwest, and the low-level provinces were clustered in the west and north. The largest increase in beds was in Sichuan , while the smallest was in Tibet  .The change speed of medical resources showed gradient agglomeration, and the high-middle-low level areas shaped a center-periphery spatial structure in geographical distribution. Using the GIS spatial clustering quantile analysis tool, the change speed of hospitals, doctors, and beds in 31 provinces from 2010 to 2020 were divided into three categories, i.e., high, medium, and low, with thresholds of 1.52% (Yunnan) and 0.75% (Jiangxi), 5.90 (Guangxi) and 4.75 (Henan), and 7.48% (Guangxi) and 5.87% (Hebei), respectively. In terms of hospitals, the high-level regions were scattered in distribution, including Guangdong, Hainan, Chongqing, Yunnan, Tibet, Shandong, Tianjin, Shanghai, Anhui, and Jilin. The medium-level provinces were on the periphery of those of high level, mostly clustered in west and north China. Most of the low-level provinces were clustered in central and south-central China, the Loess Plateau, and the Bohai Bay. Tibet recorded the largest average annual growth (3.41%), while Heilongjiang recorded the largest decline (−0.76%). The average of change speed for hospital was 1.14%, with 48.39% of provinces exceeding it. In terms of doctors, the high-level provinces were mostly clustered in the Yunnan-Guizhou Plateau in the southwest and the Qinghai-Tibet Plateau; the medium-level provinces were distributed in the periphery of those of high level, and the low-level provinces were distributed in north China . The largest average annual growth of doctors was in Tibet (9.41%), and the smallest was in Heilongjiang (1.69%). The average of change speed for doctor was 5.26%, with 45.16% of provinces exceeding it. In terms of beds, the provinces of high, medium and low level were clustered in a gradient distribution along the north–south direction, with the largest annual growth in Guizhou (10.13%) and smallest in Beijing (3.19%). The average of change speed for bed was 6.16%, with 61.29% of provinces exceeding it.In the hospital dimension, star provinces were clustered in the eastern coastal regions, cow provinces were in the central region, question provinces were mostly clustered along the western border, and dog provinces were clustered in north China. The hot and cold spots were clustered along the east–west direction in a gradient manner. Hot spot provinces were clustered in the middle and lower reaches of the Yangtze River, with secondary hot spots clustered in its periphery. Cold spot provinces were clustered in the northwest and northeast, with secondary cold spots clustered in north China, including Inner Mongolia, Shanxi, and Liaoning. Global Moran’s I was 0.14, indicating a positive spatial autocorrelation. According to local autocorrelation analysis, HH- and HL-type provinces were only Sichuan and Henan, LL-type provinces included Hainan, Anhui, Fujian, and Shanghai, and LH-type regions included Gansu and Xinjiang .In the doctor dimension, most of the star provinces were clustered in southwest China, including Yunnan, Guangxi, Guangdong, Sichuan, Shaanxi, Jiangsu, and Zhejiang. The cow provinces were clustered in the central China and extended to Hebei and Shandong. The question provinces were scattered in distribution, including Hunan, Chongqing, Ningxia, Qinghai, and Tibet. Most of the dog provinces were clustered in north China, especially in the northeastern region. The hot and cold spots were clustered along the north–south direction in a gradient manner. Hot spot provinces were clustered in the Yangtze River Economic Belt and its south, with secondary hot spots distributed in its periphery in the Yellow River Economic Belt. Cold spots were clustered in the northeast, with secondary cold spots clustered in the west, Beijing, Tianjin, and Hebei and their surrounding areas. Global Moran’s I was 0.15, indicating a positive spatial autocorrelation. According to the local autocorrelation analysis, only Hebei was an HH-type region; HL-type provinces included Guangxi, Anhui, and Shandong, LL-type regions included Hainan, Jiangxi, Chongqing and Guizhou, and LH--type regions included Inner Mongolia, Heilongjiang and Jilin.In the bed dimension, the star provinces were clustered in the Yangtze River Delta and Pan-Pearl River Delta urban agglomeration, the cow provinces were clustered in the Bohai Bay, the question provinces were clustered in Qinghai-Tibet and Loess Plateau regions and the west coast of the strait, and the dog provinces were clustered in north China. The hot and cold spots were clustered along the north–south direction in a gradient manner. Hot spot was clustered in clustered in south China, with secondary hot spots clustered in its periphery. The cold spot provinces were clustered along the border in west and north China, with secondary cold spots clustered in the middle and lower reaches of the Yellow River Basin and the Beijing-Tianjin-Hebei region. Global Moran’s I was 0.23, indicating a positive spatial autocorrelation. According to the local autocorrelation analysis, only Liaoning was an HH-type province, HL-type provinces included Guangxi, Anhui and Shandong, LL-type provinces included Hainan, Jiangxi, Chongqing and Guizhou, and LH-type provinces included Gansu, Inner Mongolia, and Heilongjiang.The analysis of spatio-temporal evolution trend based on Boston Consulting Group Matrix contributes to revealing the change trend of health resource allocation, for the purpose of developing differentiated and adaptive management strategies for different provinces. The strategies for future health resource supply include three types of development, stability, and retrenchment. The development strategy is to invest in additional health resources, expand the medical resource supply, and improve health services. The stabilization strategy is to stabilize the current supply mode and allocation status without additional investment, try to maximize the value, and efficiently use of health resources. The retrenchment strategy is to scale back the supply of health resources and address the problems of waste or extensive use. In general, star provinces should adopt development strategy, cow provinces should adopt stabilization strategy, question provinces can selectively implement development, stabilization or retrenchment strategy based on their actuality, and dog provinces can selectively adopt retrenchment or stabilization strategy. It should be noted that all provinces should choose the most appropriate strategy to maintain or reverse the development pattern or trend, according to the health resource supply and demand balance (mismatch or adaptation) .(1)Spatial Mismatch Type of PopulationIn the hospital dimension, 61.29% of the provinces fell into the dynamic matching type in 2010, further expanding to 70.97% in 2020. In 2010, 22.58% of the provinces were of the positive mismatch type, distributed in the middle and upper reaches of the Yellow River Basin in clusters; by 2020 the cluster area shrank significantly, with most of them in the lower reaches of the Yellow River Basin. There were the same number of provinces belonging to the negative mismatched type in 2010 and 2020, but their geographical distribution was changed from random to agglomerative (Yangtze River Delta urban agglomeration) . In 2010p > 0.05, the same below), indicating a shift from positive spatial autocorrelation to no correlation. In the dimension of local spatial autocorrelation, Zhejiang, Fujian, Chongqing, Hubei, Guangxi, and Hainan were HH-type regions in 2010, which were changed to Shandong, Beijing, and Zhejiang in 2020. Inner Mongolia and Hebei were HL-type provinces in 2010, which were expanded to most parts of the country in 2020. Provinces of LH- and LL-types were scarce and distributed in a random manner, including Hunan, Jiangxi, Guangdong, and Hebei.In the doctor dimension, 67.74% of provinces fell into the dynamic matching type in both 2010 and 2020, covering the western, northern, and central regions of China. In 2010, 12.9% of the provinces were of the positive mismatch type and relatively concentrated in the Bohai Bay, compared to a random distribution in 2020, including Jilin, Beijing, Shandong, Shanxi, and Zhejiang. Provinces of the negative mismatch type in 2010 shaped a belt-like agglomeration in the Pan-Pearl River Delta in the east–west direction and changed to a belt-like agglomeration in the north–south direction in 2020. In 2010, the hot spots were clustered in north China and the Bohai Bay, with secondary hot spots clustered in their western and southern periphery. The cold spots were clustered in the Pan-Pearl River Delta urban agglomeration, with secondary cold spots only including Yunnan, Sichuan, Jiangxi, and Zhejiang. In 2020, the hot spots covered north China and northeast China and extended to the Loess Plateau in northwest China, with secondary hotspots clustered in the west. The cold spots were clustered on the southeast coast and extended to Hunan and Chongqing, with secondary cold spots clustered in central China, including Anhui, Jiangxi, and Hubei. The global Moran’s I value for 2010 and 2020 were 0.15 and −0.10 *, respectively Mismatch Index Contribution Rate of PopulationIn 2010, the provinces with higher contribution to hospital mismatch were clustered in the center of China in a “T” shape, while the provinces of medium level were distributed in the periphery of the high level, and those of low level were distributed in the more peripheral northwest and northeast regions. High-medium-low level provinces shaped a closer center-periphery spatial structure in geographical distribution. In 2020, most of the provinces of high level were clustered in the eastern coast, the lower reaches of the Yangtze and Yellow rivers, with Jilin, Sichuan, and Shandong being isolated spots in the periphery. Provinces of medium level were clustered in the middle reaches of the Yangtze River and middle and upper reaches of the Yellow River, and those of low level were scattered in distribution, including Xinjiang, Qinghai, Ningxia, Heilongjiang, Liaoning, Fujian, Henan, Chongqing, Sichuan, Guangxi, and Hainan .In 2010, the provinces with higher contribution to doctor mismatch were clustered along Guangdong-Hubei-Liaoning in a north–south belt, and those of medium level were distributed in its periphery, including Fujian, Jiangxi, Zhejiang, Heilongjiang, Jilin, Shanxi, Shaanxi, Guangxi, Hainan, and Yunnan. Provinces of low level were clustered along the Loess Plateau–Qinghai-Tibet Plateau direction, including Gansu, Ningxia, Qinghai, Tibet, and Inner Mongolia. In 2020, three types of regions showed an east–west gradient distribution. Provinces of high level were still in the north–south belt-like agglomeration, but their regional coverage began to shrink, with those of medium level clustered in the middle area, and those of low level in the northwest and southwest China.In 2010, the provinces with higher contribution to bed mismatch were clustered in the central and coastal regions in an “X” shape, with those of medium of distributed in their periphery, as well as those of low level clustered in northwest China. Provinces of high level in 2020 were distributed in a band in the middle and upper reaches of the Yangtze River and the southeast coast, including Sichuan, Chongqing, Hubei, Hunan, Shandong, Fujian, Zhejiang, Hebei, and Liaoning. Most of the provinces of medium were clustered in the north and south of the high level, including Yunnan, Guizhou, and Guangxi in the southwest and Shaanxi, Henan, and Anhui in the central part of the country. Provinces of low level were mostly clustered in the Qinghai-Tibet Plateau and Loess Plateau, with a small number randomly distributed in the coastal areas, including Shandong and Jiangsu.(1)Spatial Mismatch Type of GDPIn the hospital dimension, 54.84% of the provinces fell into the dynamic matching type in 2010, further expanding to 64.52% in 2020. In 2010, 29.03% of the provinces fell into the positive mismatch type, mostly clustered in the middle and upper reaches of the Yellow River and middle reaches of the Yangtze River, but the cluster area shrank significantly to only the east and west ends by 2020. Provinces that fell into the negative mismatch type in 2010 and 2020 were essentially equivalent (about 15%), including Jiangsu, Zhejiang, and Guangdong . In 2010In the doctor dimension, about 50% of provinces fell into the dynamic matching type in both 2010 and 2020, with contraction in the north and expansion in the center. The same percentage of provinces were seen falling into the positive mismatch type in 2010 and 2020 (35.48%), mostly clustered in the Yunnan-Guizhou Plateau and Yellow River Basin. Provinces of the negative mismatch type in 2010 and 2020 were equivalent, clustered along the coast in a band. In 2010, the hot spots were clustered in the Yunnan-Guizhou Plateau in southwest China, with secondary hot spots clustered in the Loess Plateau and Beibu Gulf. The cold spots were clustered in the Shandong-Anhui-Zhejiang-Fujian region, with cold spots widely distributed. In 2020, the hot spots were clustered in the Yunnan-Guizhou Plateau–Loess Plateau-northeast direction, with secondary cold spots mostly in Bohai Bay. The cold spots were clustered in the Pearl River Delta–Yangtze River Delta region, with secondary cold spots clustered in the lower reaches of the Yellow River and west China (Tibet and Xinjiang). The global Moran’s I value for 2010 and 2020 were 0.30 and 0.46, respectively, indicating a positive spatial autocorrelation with increasing strength. In the dimension of local spatial autocorrelation, Anhui fell into the HH-type in 2010, and no HH-type region was found in 2020. Provinces of HL-type were Yunnan and Guizhou in 2010, but expanded to Sichuan, Shaanxi, and Shanxi in 2020. In 2010, Tibet, Chongqing, and Hubei were LL-type regions, which were changed to Inner Mongolia, Liaoning, and Chongqing in 2020. The LH-type regions were clustered in the eastern coast, including Jiangsu, Shanghai, Zhejiang, and Fujian.In the bed dimension, 54.84% of the provinces fell into the dynamic matching type in 2010, expanding slightly to 58.06% in 2020, mostly clustered in west, north, and central China. About 35% of the provinces falling into the positive mismatch type in both 2010 and 2020, mostly clustered in the Yunnan-Guizhou Plateau and its surrounding areas. The regions that fell into the negative mismatch type were exactly the same in 2010 and 2020, including Jiangsu, Zhejiang, and Guangdong. In 2010, the hot spots were clustered in the Yunnan-Guizhou Plateau, with secondary hot spots clustered in the west of its periphery, the Loess Plateau, and Beibu Gulf. The cold spots included Zhejiang, Anhui, and Shandong, with secondary cold spots distributed in north, central, and south China as transitional zones. In 2020, the hot spots were still clustered in the Yunnan-Guizhou Plateau, with secondary hot spots clustered in the Loess Plateau and Beibu Gulf. The cold spots were clustered in the Yellow River and lower reaches of the Yangtze River, with secondary cold spots clustered in northwest, north, and central China. The global Moran’s I for 2010 and 2020 were 0.15 * and 0.19, respectively, indicating a shift from spatial uncorrelation to positive autocorrelation. In the dimension of local spatial autocorrelation, only Anhui fell into the HH-type in 2010, and no HH-type region was found in 2020. In 2010, Tibet, Qinghai, and Chongqing fell into the LL-type, which was further expanded to Shanxi, Tianjin, and Jilin in 2020. Yunnan and Guizhou maintained their status as HL-type regions in 2010 and 2020. The LH-type regions were Shanghai and Fujian in 2010, which were expanded to the Yangtze River Delta and west coast of the strait in 2020.(2)Mismatch Index Contribution Rate of GDPIn 2010 and 2020, the provinces with high contribution to hospital mismatch were clustered in the Yangtze River Delta, Pearl River Delta, and upper and lower reaches of the Yellow River, with those of medium level distributed in their periphery, low level clustered in west and north China, and middle reaches of the Yangtze River Economic Belt. In 2010, the provinces with high contribution to doctor mismatch were clustered in the lower reaches of the Yangtze River, Yellow River, and south China, with those of medium level distributed in the periphery and extending northwest; those of low level clustered in the Qinghai-Tibet Plateau, the middle reaches of the Yangtze River, and northeast China. Provinces of high level in 2020 shaped a coastal agglomeration in the east and extended north to Henan and Hebei and west to Yunnan and Sichuan. Provinces of medium level were clustered in northeast and northwest China, with those of low level clustered in the Qinghai-Tibet Plateau and middle reaches of the Yellow River. The provinces with high contribution to bed mismatch in 2010 were scattered in distribution, including Jiangsu, Zhejiang, Henan, Beijing, Guangdong, Hunan, Yunnan, Sichuan, and Xinjiang. Provinces of medium level were relatively clustered in north China and Beibu Gulf, with those of low level clustered in the Qinghai-Tibet Plateau, central China and Bohai Bay. In 2020, most of the provinces of high level were clustered in a belt-like pattern along the coast, with those of medium level clustered in southwest China and Bohai Bay, and those of low level clustered in the west, north, and central China .p < 0.05). With the mean value as the threshold, the influence factor with a force greater than the mean is defined as the key factor; the one smaller than the mean is defined as an important factor, and the one not statistically significant is defined as an auxiliary factor.The driving forces of influence factors on hospitals, doctors, and beds differed significantly, with the mean values of 0.52, 0.79, and 0.72, respectively , Hubei, Guangxi (maintaining the cow trend), and Shandong (maintaining the star trend). Hebei, Hubei, Sichuan, and Shanxi have long been in positive mismatch, and they should adopt an expansion strategy to push the evolution trend from cow to star. Shanghai, Zhejiang, Jiangsu, and Guangdong have been in negative mismatch for a long time, and they should adopt a smart shrink strategy to reduce the growth rate and push the evolution trend from question to dog and from star to cow. In terms of doctors and beds, Beijing and Tianjin are in dynamic matching, and they should introduce a stable development strategy to maintain the dog evolution trend. Heilongjiang, Sichuan, Guizhou, and Yunnan have long been in positive mismatch, and they should adopt a smart shrink strategy in the future to maintain the evolution trend of dog or cow type, or to change from question to dog.The comparison of spatio-temporal evolution trends and spatial mismatch relationships reveals the rationality of health resource supply and allocation schemes in the provinces and their improvement directions. For example, Zhejiang attaches great importance to investment in medical resources and, in recent years, has implemented the “13th Five-Year Plan” for the development of health care and plan for the medical service system in the province, which has contributed to the allocation of health resources in Zhejiang as a growth pole in China (in the evolution trend of star type). However, the spatial mismatch shifted from dynamic matching to negative mismatch from 2010 to 2020, indicating a shift from balance to imbalance between the large supply of health resources and population demand, with supply outstripping demand and extensive use of health facilities and resources. For example, the slowdown in investment in doctors and beds in Hebei in recent years has led to their evolution trend of cow-type. Notably, the type of spatial mismatch changed from dynamic matching in 2010 to positive mismatch in 2020, indicating the problem of undersupply of health facilities and resources, with the shift of supply-demand equilibrium of health services to disequilibrium, as a result of the long-term low investment in health care resources. In addition, despite the different evolution trends of health resources, such as question in Beijing and Tianjin, cow in Liaoning and Jiangxi, and dog in Inner Mongolia and Fujian, they all remained long in supply-demand balance of hospital allocation (in dynamic matching in 2010 and 2020).In addition, it is an innovation to include spatial effects in the analysis of influence factors and measure their interaction effects in this paper. Unlike the published articles, this paper incorporates spatial heterogeneity and autocorrelation into the influence factor analysis, based on the geographic detector method, to measure the force of influence factors on the geographical distribution of medical resources and reveal the interaction effects of different influence factors, thus further improving the accuracy of the driving mechanism analysis. This paper finds that factors such as children and elderly population, health care government investment, service industry added value, high quality population, medical insurance fund expenditure, and government revenue have a great direct influence on the geographical distribution of health resources, which further validates the findings of some scholars. For example, Li , Zheng , and GuoTo improve the uneven geographical distribution and spatial mismatch of health resources is one of the major challenges facing the government health sector. The government should redesign the spatial allocation scheme of medical resources and improve the supply and demand of health services, based on regional heterogeneity and spatial mismatch, under the guidance of spatial justice and regional health planning theories ,117. TheIn the hospital dimension, Beijing, Tianjin, Fujian, Jiangxi, Hubei, Chongqing, Guizhou, Guangxi, Hainan, Yunnan, Tibet, Qinghai, Xinjiang, Ningxia, Shaanxi, Inner Mongolia, Liaoning, and Heilongjiang are of dynamic matching. Hebei, Shandong, Shanxi, Henan, Hunan, Sichuan, Gansu, and Jilin are of positive mismatch, and Guangdong, Shanghai, Jiangsu, Zhejiang, and Anhui are of negative mismatch. According to the development trend, the future policy design should follow the guidance as below: Beijing, Tianjin, Chongqing, Guizhou, Yunnan, Tibet, Xinjiang, Hainan, Guangdong, Jiangsu, and Zhejiang should continue to maintain high growth and focus on cultivating provinces with high potential by carefully analyzing the development drivers to promote them as regional medical service centers. Fujian, Qinghai, Ningxia, Inner Mongolia, Heilongjiang, Hebei, Shanxi, Henan, Hunan, and Sichuan should maintain their slow growth or reduction in quantity. Shandong, Gansu, and Jilin should implement contractionary policies for strict control of the amount of growth and reverse the development trend of high growth, in order to avoid the waste or risk brought about by blind investment. Shanghai, Anhui, Jiangxi, Hubei, Guangxi, Shaanxi, and Liaoning should introduce innovation policies and invest their limited resources primarily in new demand creation or new supply development.In the doctor dimension, Tianjin, Hubei, Hunan, Chongqing, Tibet, Qinghai, Xinjiang, Ningxia, Inner Mongolia, and Liaoning are of dynamic matching. Beijing, Shandong, Shanxi, Henan, Shaanxi, Gansu, Sichuan, Yunnan, Guizhou, Guangxi, Liaoning, and Jilin are of positive mismatch. Shanghai, Jiangsu, Anhui, Fujian, Jiangxi, and Guangdong are of negative mismatch. Hebei and Zhejiang are of double mismatch. According to the development trend, the future policy design should follow the guidance as below: Jiangsu, Fujian, and Guangdong should continue to maintain high growth and cultivate them into regional integrated medical service centers. Shanghai, Anhui, Jiangxi, and Shandong should increase investment to further boost growth. Tianjin, Hubei, Hunan, Xinjiang, Inner Mongolia, Liaoning, Beijing, Shandong, Shanxi, Henan, Gansu, Liaoning, and Jilin should maintain a slow growth or reduction in quantity, while Tibet, Qinghai, Ningxia, Shaanxi, Sichuan, Yunnan, Guizhou, and Guangxi should introduce contractionary policies to strictly control the amount of growth and avoid waste or risk from blind investment. The current medical resources in Hebei fail to meet the actual demand and are beyond the economic carrying capacity, so external support is required in the future policy design. The current medical resources in Zhejiang are already excessive and far below the economic carrying capacity, so it should control the new investment and consider increasing external medical aid or cross-regional supportive transfer payments in the future policy design.In the bed dimension, Beijing, Tianjin, Shandong, Anhui, Shanghai, Jiangxi, Hainan, Chongqing, Shanxi, Shaanxi, Ningxia, Qinghai, Tibet, Xinjiang, Inner Mongolia, and Jilin are of dynamic matching. Liaoning, Heilongjiang, Henan, Hubei, Hunan, Guangxi, Guizhou, Yunnan, Sichuan, and Gansu are of positive mismatch. Shandong, Fujian, Zhejiang, and Jiangsu are of negative mismatch. Hebei is of double mismatch. In light of the development trend, the future policy design should follow the guidance as below: Anhui, Jiangxi, Hainan, Chongqing, Shaanxi, Qinghai, Tibet, Fujian, Zhejiang, and Jiangsu should continue to maintain high growth. Shandong should increase investment to further boost its growth rate. Beijing, Tianjin, Shandong, Shanghai, Shanxi, Ningxia, Xinjiang, Inner Mongolia, Jilin, Liaoning, and Heilongjiang should maintain a slow growth or reduction in quantity. Henan, Hubei, Hunan, Guangxi, Guizhou, Yunnan, Sichuan, and Gansu should implement contractionary policies and strict control of the amount of growth, in order to avoid waste or risk brought about by blind investment. The current medical resources in Hebei fail to meet the actual demand and are beyond the economic carrying capacity, so external support is required in the future policy design.In general, differentiated and adaptive management strategies should be adopted, depending on the relationship between health resource supply and demand, in order to maintain sustainable evolution trends or reverse unsound ones. For example, Hainan, Chongqing, Tibet, Qinghai, Ningxia, and Xinjiang, in dynamic matching, should implement a stable development strategy, with no need to change their current health resource allocation policy  and evolution trend. Sichuan and Henan, in positive mismatch, should implement an incremental expansion strategy. That is, they need to expand future investments in hospitals, doctors, and beds and drive the evolution trend to maintain or shift to question or star to address the problem of insufficient supply and to improve their health services and support capacity. Guangdong and Jiangsu, in negative mismatch, should implement a smart shrink strategy. In other words, it is necessary for them to reduce the future investment in health resources and promote the evolution trend to maintain or transform into dog or cow, in order to address the problem of oversupply and improve the efficiency of resource utilization. Hebei, Shanxi, Jilin, Heilongjiang, and Gansu, in double mismatch, should adopt an integrated control strategy. That is, due to the imbalance between the supply of health resources and population demand and the mismatch with economic carrying capacity, they should apply to the central government for inter-provincial transfer payments or medical assistance in the future to further improve their regional service capacity of health facilities and resources.Reducing the inequality of geographical distribution and spatial mismatch of medical resources is still a key policy goal of health authorities in most countries . Based oThis paper finds that the geographical pattern of medical resources in China is solid over time, and that the quantity of resources and its change amplitude and speed have high spatial agglomeration and differentiation. The spatio-temporal evolution of inter-provincial medical resources in China has become diversified, with similar regions having high geographic agglomeration and weak spatial autocorrelation, as well as a gradient distribution of cold hot spots in a core-periphery structure. Children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources, while social consumption, government revenue, high quality population, and medical insurance fund expenditure are important factors. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in nonlinear enhancement. Although per capita GDP, urbanization rate, and residents’ medical services consumption do not have statistically significant direct influence, they have high interaction influence with other factors as auxiliary factors that cannot be ignored. Most of the provinces’ medical resources are in dynamic matching, mainly concentrated in west and north China and the middle reaches of the Yangtze River. A path-dependent strategy should be adopted in the future policy design to maintain the current development. The mismatch in the supply of medical resources in China is more serious than that in demand, and the proportion of mismatch in hospitals and beds is decreasing; however, the opposite is true for doctors. For the regions of positive mismatch, hospital demand mismatches and hospital, doctor, and bed supply mismatches are clustered, while doctor and bed demand mismatches are randomly distributed, so smart contraction-type strategies should be adopted in the future policy design to moderately control the supply of new medical resources and avoid the waste of resources or investment risks. For the regions of negative mismatch, doctor and bed demand mismatches, as well as doctor, hospital, and bed supply mismatches, are clustered in bands, while hospital demand mismatches are randomly distributed, so smart growth-oriented strategies should be adopted in the future to increase the supply and investment in new medical resources to achieve sustainable development. The hot and cold spots of supply and demand mismatch are highly clustered, shaping a spatial structure of center-periphery or gradient change. Hospital demand mismatches and hospital, doctor, and bed demand mismatches all have positive spatial autocorrelation and are increasingly spatially correlated and dependent; however, doctor and bed demand mismatches have changed from positive spatial autocorrelation to uncorrelation.This paper is innovative in two areas. First, it introduces the spatial mismatch index to quantitatively measure the degree and type of medical resource mismatch at the provincial level in China from both the supply and demand perspectives and proposes a method for designing differential management policies, based on spatial zoning, which provides a basis for evidence-based decision making in health planning. Scholars are currently focusing on the study of mismatch of mobile factor resources, such as labor, capital, and commodities, and the immobility of health resources makes it more difficult and costly to correct their mismatch problems. In developing countries with limited health resources, incorporating economic carrying capacity and population demand power into health resource allocation models and health economics  and desiIn addition, this study is applicable to China; it also has great reference value for developing countries, such as Iran, Egypt, Ethiopia, South Africa, Tanzania, India, Malaysia, and Mongolia. As described above, they have very limited medical resources, with a striking problem of uneven geographical distribution and spatial mismatch. An empirical study of these countries, using the research framework and methodology provided in this paper, can help them design differentiated and adaptive health resource management policies. It should be noted that interest games and institution settings play an important role in the allocation of medical resources , but the


Alzheimer’s disease (AD), due to its spread, has become a global health priority, and is characterized by senile dementia and progressive disability. The main cause of AD and other neurodegenerations  are aggregated protein accumulation and oxidative damage. Recent research on secondary metabolites of plants such as polyphenols demonstrated that they may slow the progression of AD. The flavonoids’ mechanism of action in AD involved the inhibition of acetylcholinesterase, butyrylcholinesterase, Tau protein aggregation, β-secretase, oxidative stress, inflammation, and apoptosis through modulation of signaling pathways which are implicated in cognitive and neuroprotective functions, such as ERK, PI3-kinase/Akt, NFKB, MAPKs, and endogenous antioxidant enzymatic systems. This review focuses on flavonoids and their role in AD, in terms of therapeutic potentiality for human health, antioxidant potential, and specific AD molecular targets. Alzheimer’s disease (AD) is the most common cause of senile dementia associated with progressive disability. The inherited disease, in an autosomal dominant way, generally leads to a lethal outcome after about 5–10 years from the onset of the first symptoms . GeneralOxidative stress is defined as an imbalance between oxidants and antioxidants that causes a rise in oxidant levels . AccordiTherefore, while the brain membrane phospholipids are composed of polyunsaturated fatty acids, this organ is particularly vulnerable to free radical attacks. Plasma levels of thiobarbituric acid are high in the early stages of AD ; lipid hProteins are major targets of reactive oxygen species (ROS). Protein oxidative modifications can induce unfolding or conformational changes that can lead to the loss of specific protein function  and the Nucleic acid damage also occurs early in AD. Significantly elevated levels of 8OHG and 4,6-diamino-5-formamidopyrimidine have been reported in post-mortem MCI brains relative to the age-matched controls . In addiAside from its presence in CNS, Aβ can be detected in platelets  and blooRecent studies have tested the power of natural compounds derived from plants against AD. Among these, flavonoids are ubiquitous compounds of plants, produced by plants for growth and defense against all kinds of stress, including cold tolerance. More than 6000 different flavonoids have been identified, the primary sources of which are apples, red fruits, onions, citrus fruits, nuts, and beverages such as tea, coffee, beer, and red wine. These compounds, derived from phenol, are particularly interesting for their ability to cross the blood–brain barrier and for their multi-target activity. Several studies have described flavonoids to exhibit relevant biologic activities involving the neuronal antioxidants, as well as anti-amyloidogenic properties, acting as metal chelators, showing anti-inflammatory properties, and ameliorating cognition and neuroprotection ,50,51,52From a chemical point of view, flavonoids consist of two benzene rings, called A and B, linked via a third pyranosic ring C. Flavonoids can be divided into a variety of subclasses that differ in terms of the structural characteristics of the B ring and the degree of hydroxylation and glycosylation of the third ring. Typically, ring B binds in position 2 on ring C, but can also bind in position 3 or 4. We can, therefore, distinguish the isoflavones in which the B ring binds in position 3 of the C ring and the neoflavonoids in which the B ring binds in position 4 of the C ring . The groIn general, since all flavonoids contain the same core scaffold, the functional differences between the various groups and subgroups are mainly due to the different substituent groups. These are weak polybasic acids of a polyphenolic nature, characterized by varying degrees of hydroxylation, methoxylation, glycosylation or glucuronidation, and this contributes to the great variety of biological properties of this large group of polyphenols ,60. In fFlavonoids are a wide group of secondary metabolites characterized by many interesting biological potentials, both in vitro and in vivo  and, in the last decades, they have emerged as a promising agents for neuroprotection ,66,67,68Flavonoids are widely distributed in the plant kingdom, and are characterized by chemical structures with the presence of several substituents that allow them to assume particular activities and exert beneficial effects for the wellness of organisms, as well as for their potential for therapeutic utilization. This does not indicate that every type of flavonoid is able to show biological potential, but only that those with particular characteristics can be employed for specific roles. For instance, one of the well-known and best-studied activities of flavonoids is its antioxidant activity, which is linked to the number of hydroxyl groups on the B ring. Generally, a greater number of free hydroxyl groups corresponds to a greater scavenging effect, but their location in the skeleton of flavonoids is a crucial structural element. These hydroxyl groups, through the donation of hydrogen atoms and electrons to radical species, favor the repair of the damage caused by ROS and reactive nitrogen species (RNS), reducing their degree of reactivity ,71. ThisThe ability of flavonoids to cross the blood–brain barrier suggests that these compounds can feasibly have a direct effect on the brain. Numerous studies have documented the bioactivity of flavonoids against neurodegenerative disorders such as AD, Parkinson’s, Huntington’s, and other neurological disorders ,91,92,93Flavonoids, including epicatechin-3-gallate, gossypetin, naringerin, quercetin, and myricetin are reported to block β-amyloid and Tau aggregation, scavenge free radicals, and sequester metal ions at clinically low concentrations ,99. In oQuercetin is a polyhydroxyflavonoid that belongs to the subclass of flavonols. Its chemical name is 3,3,4,5,7-pentahydroxyflavone, and the molecule contains five -OH groups, in positions 3, 3′, 5, 7 and 4′, which are crucial for potential biochemical–pharmacological activities. Quercetin is a natural antioxidant, widely used in healthcare for its beneficial role. Quercetin is found in flowers and fruits of edible plants; onions, apples, cherries, berries, asparagus, and red leaf lettuce have the highest levels, while tomatoes, peas, and broccoli have lower levels ,93. Expe2O2-induced p53 expression, and also significantly reduces apoptosis and caspase 3 activation [More specifically, Tau phosphorylation is under the control of several distinct kinases, such as Erk, Akt, p38, AMP activated protein kinase (AMPK), glycogen synthase kinase 3 beta (GSK3β), cyclin-dependent kinase 5 (cdk5), and protein phosphatase 2A (PP2A) ,103. Thrtivation . p53 regtivation ,110. In tivation ,110. In tivation ,112. A ttivation ,115,116.Hung et al. demonstrated that quercetin (10mM) pretreatment of human umbilical vein endothelial cells (HUVECs) suppressed the nuclear factor- kB (NF-kB) signal, suggesting that the drug is a powerful antiatherosclerotic . In addi2O2) and protein carbonyls levels, have been demonstrated [Naringenin is the aglycon of naringin, and belongs to the subclass of flavanones. It is abundant in citrus fruits , as well as vegetables, and especially in grapes, tomatoes, and cherries. Naringenin can be found in two forms. One is characterized by a bond with a sugar on C7, and one derives from the action of specific enzymes which are able to cleave this glycosidic bond by releasing the aglycone ,126. Botnstrated ,139,140.2O2 to chicken lymphocytes. After preincubation with EGCG, the compound restored H2O2′s harmful effects, suppressing the increase of ROS and restoring the antioxidant system by mRNA expression of SOD, CAT, and GPx [Epigallocatechin-3-gallate (EGCG), an ester of epigallocatechin and gallic acid, is the main bioactive polyphenol found in solid green tea extract ,142. EGC and GPx . EGCG en and GPx . In Figu2+ and Fe2+; this, on the one hand, strongly enhances its antioxidant activity, because the Fenton reaction is inhibited and, consequently, the ROS generation is reduced. On the other hand, myricetin acts directly on Aβ complexes, reducing their toxicity through the reduction in metal ions that can interact with them [Myricetin is a natural flavanol widely distributed in several vegetables and fruits, mainly including blackcurrant teas, red wines, and medical herbs ,155,156.ith them ,176,177.ith them . Finallyith them ,180. In Gossypetin  is a flavonol isolated from the flowers and the calyx of Hibiscus sabdariffa. Gossypetin has been shown to exert antioxidant, antimutagenic, antimicrobial, and anti-atherosclerotic activities ,183,184.Genistein  is an isoflavone distributed in several vegetables such as legumes, green peas, and peanuts, and is predominantly extracted from the Glycine max soybean ,187. SevThe antioxidant effects of genistein are associated with AMPK activation and the drug’s binding with estrogen receptor α (ERα), both of which promote the expression of antioxidant enzymes such as SOD, CAT, and GPx ,199,200.Apigenin  formally belongs to the flavone subclass, and is widely distributed in the plant kingdom, present principally in chamomile flowers. and in lower concentrations in vegetables, citrus fruits, herbs, and plant-based beverages  . In a raCyanidin 3-O-glucoside belongs to the anthocyanins class; it is present in plants such as berries and soybean fruits, where it is responsible for the red, purple, and blue pigments. Cyanidin has been reported to act as neuroprotector in several disorders, such as AD, Parkinson’s disease, and multiple sclerosis ,214,215.Unfortunately, there is still a lack of translational research and clinical evidence for these promising compounds, and we found only one clinical trial, which began in 2022 and will finish in 2024, studying the efficacy and safety of the Flos Gossypii flavonoid tablet in the treatment of Alzheimer’s disease . A totalThis review provides evidence that flavonoids have potential for treating AD, and are considered drug candidates for future clinical research. Although precise mechanisms are still unclear, flavonoids regulate several important physiological responses, which may contribute to neuroprotective effects in AD. The advantage of flavonoids over conventional targeting drugs is the possibility of administering these molecules as food supplements. Supplementation with flavonoids could allow for early protection, even at a young age. They can also be used without the need for a preclinical diagnosis, due to their low toxicity. Certainly, further long-term dietary intervention studies indicating the dosage and the times of drug assumption may contribute to fully evaluating the effectiveness of flavonoids as agents for the management of AD. It will be important to incorporate bioavailability and metabolism into experimental planning at all stages of preclinical research, in order to better clarify such mechanisms in vivo.


Reversible Cerebral Vasoconstriction Syndrome (RCVS) is a neurovascular condition characterized by a severe sudden-onset headache that may be associated with focal neurological deficits. On imaging, the suggestive finding corresponds to multifocal vasoconstriction of the cerebral arteries, with a spontaneous resolution of approximately 12 weeks. The identification of precipitating factors and diagnosis must be carried out early, so that adequate management is established and the patient has a good prognosis, given the risk of secondary complications and residual neurological deficits. This study consists of a literature review based on the analysis of articles published between 2017 and 2022 in PubMed, SciELO, and ScienceDirect on RCVS, intending to understand the clinical and radiological characteristics, diagnosis, treatment, and prognosis of patients with RCVS. The pathophysiology, drug management, and prognosis still lack solid evidence; therefore, further studies on RCVS are needed to expand medical knowledge and avoid underdiagnosis and inadequate treatment of this important condition. The Reversible Cerebral Vasoconstriction Syndrome (RCVS) is characterized by sudden headache and reversible multifocal cerebral vasoconstriction –4. It isThe most common manifestation is thunderclap headache, which has a sudden onset and peak intensity in 1 min , 12, 13,Reversible Cerebral Vasoconstriction Syndrome should be suspected when there is a history of sudden headaches with normal physical examination, cranial tomography (CT), and magnetic resonance imaging (MRI) of the head , 12. GivOnce the diagnosis is established, treatment should be initiated, mainly by discontinuing the triggering factor, if known , 13, 19,As the wrong or delayed diagnosis of RCVS usually results in unnecessary diagnostic tests, deleterious treatments, and increases the risk of neurological sequelae , 5, 21, This article presents a literature review based on scientific articles published from 2017 to 2022 in PubMed, SciELO, and ScienceDirect on RCVS.In the first step of the methodology, a search was carried out in Pubmed, SciELO, and ScienceDirect databases with the title “reversible cerebral vasoconstriction syndrome” and the filters “case reports”, “multicenter study”, “observational study”, “review”, and “systematic review” were applied. In addition to these filters, only studies involving humans and those published in the last 5 years were selected. The article should be available and in English. A total of 129 articles were found in PubMed, three in SciELO, and 30 in ScienceDirect , those with an impossibility of access (seven articles), or those that did not contain information relevant to the topic (four articles). A total of 74 articles remained at the end of this stage.In addition, 11 new articles were selected and manually added according to their relevance to the study. Therefore, a total of 85 articles were included in this study.Reversible Cerebral Vasoconstriction Syndrome, also known as Call-Fleming Syndrome, postpartum angiopathy, migrainous vasospasm, and benign central nervous system angiopathy , 24, 25,Radiologically, the disease is characterized by multifocal vasoconstriction of the cerebral arteries that can lead to multiple infarctions and usually resolves spontaneously within 3 months , 33–37. The largest studies suggest that 50–80% of patients will have a secondary cause for RCVS , 41, 44.Nasal decongestants, immunosuppressants , 29, 48,The puerperium is responsible for 10–50% of cases –11, 45. Posterior reversible encephalopathy syndrome (PRES) is a disease that can occur simultaneously with RCVS and many studies suggest overlapping pathophysiological mechanisms , 41. FurAlthough the cause of RCVS is not yet fully detailed in the literature, most studies point to a transient dysregulation in the control of brain vessel tone, which results in multifocal vasoconstriction , 57. It Thunderclap headache has a reported prevalence between 95% and 100% in individuals diagnosed with RCVS  and may In addition to headaches, patients often experience nausea, vomiting, photophobia, and phonophobia , 8, 10. Less frequently, RCVS may present with non-thunderclap and nonspecific headaches. It can be a single, recurrent, or progressive episode, and pain intensity varies from moderate to severe. There are also reports of patients who do not have any headaches , 65. TheReversible Cerebral Vasoconstriction Syndrome diagnosis is based on compatible clinical history, physical examination, and neuroimaging tests showing multifocal segmental vasoconstriction of cerebral arteries. Within 3 months, reversibility of this vasoconstriction occurs, evidenced in neuroimaging, however, some patients may persist with clinical deficits as well as complications, such as ischemic lesions and cerebral hemorrhage , 62, 64.The physical examination may be normal, but in some cases, there are neurological deficits that contribute to the suspicion, such as aphasia, hemiparesis, ataxia, and visual changes .Complementary laboratory tests such as a blood count, electrolyte analysis, and liver function tests are usually normal. However, inflammatory markers may occasionally be elevated when associated with a precipitating disease. Therefore, serum and urine drug toxicology should also be performed to screen for substances precipitating vasoconstriction . CerebroAs for imaging, digital subtraction angiography is the gold standard method, but it is invasive and can lead to complications , 13, 41,The characteristic diagnostic finding is the presence of “beading” or “pearls on a sting” in the cerebral arteries, which corresponds to the alternating pattern of intense vasoconstriction and dilatation observed , 17, 20.Although there is still no universal diagnostic criterion established , 17, 62,Reversible Cerebral Vasoconstriction Syndrome is contained in the group of diseases known to have a thunderclap headache. This group includes stroke, SAH, cervical artery dissection, intracranial aneurysm, cerebral venous thrombosis, and hypertensive crisis , 56, 73.Posterior reversible encephalopathy syndrome is also part of the set of diseases present in the differential diagnosis of RCVS , 68, 71.In addition, vasospasm secondary to SAH is also a differential diagnosis. Both RCVS and vasospasm secondary to SAH present severe headaches and occur more frequently in middle-aged females. On CT angiography, both conditions demonstrate subtle differences in segmental vasospasm .The treatment needs, initially, to go through discontinuation of the triggering factor of the symptoms, if known, such as illicit drugs, aggressive vasoactive drugs, and Valsalva maneuvers , 13, 46.In drug treatment, according to case reports and expert analyses, symptomatic patients are recommended to use multimodal analgesia, CCBs, antiepileptics in case of epileptic seizures, and antiemetics as necessary , 20. NonRegarding CCBs, intravenous and oral nimodipine, with a dosage of 30–60 mg every 4 h , is bestHowever, although nimodipine is usually the first choice for the treatment of RCVS, it has not been shown to improve long-term outcomes or prevent complications , 20, 41.The other choices of CCBs are verapamil and nicardipine , 76 oralIn addition to BCCs, headache is controlled by analgesics, such as aspirin, although there is still no strong evidence of the clinical efficacy of this use . In anotTherefore, it is worth noting that no guidelines or data from randomized studies are available in the literature to guide the management of RCVS effectively , 20.Reversible cerebral vasoconstriction syndrome has several complications, which include PRES, epileptic seizures, ischemic stroke, SAH, and intraparenchymal hemorrhage (IPH) , 67, 79.Posterior reversible encephalopathy syndrome, one of the most reported complications in the articles read in this review, has an incidence in RCVS ranging from 8% to 38% , 43, 66.Ischemic and hemorrhagic complications have also been reported in many studies in the scientific literature, leading to persistent neurological deficits and death in more severe cases , 30, 43.Stroke is also a complication associated with RCVS and usually occurs up to 3 weeks after the syndrome , 79. It Cognitive impairment, which may result from stroke, has been little described in the literature. In a case report, a 36-year-old female patient, after being diagnosed with RCVS, presented deficits in autobiographical memory, cognitive flexibility, verbal and non-verbal learning, and information processing. The involvement was verified bilaterally in the frontal and temporal lobes and in the same region of the vasoconstriction observed in the MRI .The prognosis of RCVS is uncertain, but most patients have self-limiting manifestations , 11, 28 In the literature, there are still reports of recurrence of RCVS , 62, 83,However, some patients remain with a chronic daily headache that is difficult to treat, since many medications to relieve these symptoms are triggering factors for RCVS . FurtherDue to the facts mentioned above, the main precipitating factors for RCVS are the postpartum state, nasal decongestants, and drugs with vasoactive properties. This understanding is fundamental to guide the treatment, since, initially, aggravating factors for the patient should be discontinued. In addition, although nimodipine is the drug of choice in most studies, in some cases, other drugs, such as verapamil and nicardipine, have better efficacy. The patient diagnosed with RCVS, especially combining the clinic with imaging tests, such as angiography, must undergo a thorough medical follow-up to avoid the appearance or, at least, the worsening of already known complications resulting from the syndrome.


Correction to: European Radiology10.1007/s00330-022-09154-yThe original version of this article, published on 14 October 2022, unfortunately contained a mistake. The affiliation of the author Giuseppe Palma was incorrectly given as ‘Institute of Biostructure and Bioimaging, National Research Council, Naples, Italy’ and has now been corrected to ‘Institute of Nanotechnology, National Research Council, Lecce, Italy’. The original article has been corrected.


INAA was chosen for its ability to perform elemental analysis without any preliminary sample treatment that could introduce systematic errors. The distribution of major elements was relatively uniform, with the sampling locations having less influence. Concerning the trace elements, excepting the PCE Cr, Ni, Cu, Zn, As, and Sb, their distributions presented the same remarkable similarity to the Upper Continental Crust (UCC), North American Shale Composite (NASC), Average Bottom Load (ABL), and Average Dobrogea Loess (AVL), and were in good concordance with the location of the Serbian Danube River in the Pannonian Plain. In the case of considered PCE, both Enrichment Factor and Pollution Load Index showed values higher than the pollution threshold, which pointed towards a significant anthropogenic contamination, and rising concern to what extent the water quality and biota could be affected.To determine the nature and origin of the unconsolidated bottom sediments, as well as to demonstrate and quantify the presence of Presumably Contaminating Elements (PCE) in the Serbian Danube River, as a novelty, the mass fractions on nine major elements as oxides—SiO Within it, the Serbian section, which begins at the Serbian–Hungarian border and ends at the confluence with Timoc River at the Serbian–Bulgarian border, has a length of 588 km, i.e., 20.6% of the total river length [The Danube River, with a total length of 2857 km and a catchment basin of 817,000 kmAccording to its geomorphology, the Serbian section can be divided into three units, e.g., the Pannonian Danube between the Serbian–Hungarian border and Golubac, with a length of 391 km; the Iron Gate Danube between Golubac and Kladovo, with a length of 111 km; and the Lover Danube between Kladovo and Serbian-Bulgarian border on Timok River, with a length of 86 km .The Pannonian unit, which flows the Pannonian Basin, presents typical characteristics of a low-gradient fluvial river, such as a sandy riverbed, bifurcating courses with numerous meanders, sand islands, and sandbars. Sedimentary material shows a well-evidenced stratification, which should reflect also the Pannonian Basin geochemistry and mineralogy, the main source of depositional material.The second two units of the Serbian Danube River have significantly changed as a result of the construction of the Iron Gate Hydropower and Navigation System (IGHPNS), comprising two large dams, one at km. 943 and the other downstream at 862.8. These created two reservoirs that extended upstream of the dams by 300 and 80 km, respectively . Both daRegarding sediments, excepting the Danube River at the entrance in Serbia, there are another three important sources of depositional material transported by the Tisa, Sava, and Velika Morava, the main Danube tributaries along Serbian Sector . Given sIndeed, according to , the maiThe origin of depositional material can be attributed, in different proportions, to the bedrock lithology, of which age is known, while the rest of sedimentary material can be considered a mixture of different components, of which exact origin, due to a continuous erosion and deposition, could not be exactly traced back. According to , the bedAnother peculiarity of the investigated sector of the Danube River is related to the presence of human agglomeration centers, such as Novi Sad, with over 365,000 inhabitants [For this reason, in recent decades, the anthropogenic contamination of the Serbian sector of the Danube catchment basin, and especially Danube and Sava River sediments, has been the subjects of an appreciable number of studies ,13,14,15Besides the presence and quantification of PCE, the geochemistry of the Serbian sector of the Danube River has been less investigated, although this aspect is worth attention, taking into account that, according to , at the To fill this gap, 13 samples of unconsolidated superficial sediments were collected along the Serbian sector of the Danube River between Belgrade and Iron Gate 2 dam, including the confluence of Sava, Velika Morava, and Pek tributaries . All samFor a better understanding of sediment geochemistry, we have reported our data to some general systems, such as Upper Continental Crust (UCC) , North AGiven the diversity of depositional material sources, as well as the presence of urban and industrial potential contamination along the Serbian sector of the Danube River and its tributaries, the main goals of this study were:(i) To evidence any similarities or dissimilarities between the geochemistry of Danube River sediments (Serbian Sector) and crustal material, such as UCC  and NASC(ii) To quantify the contribution to environmental contamination of those PCE of which mass fractions were determined by INAA;Therefore, a related achievement of these objectives represents, in our opinion, a new approach, able to understand not only the geochemistry of Danube sediments in relation to their location, but also to evidence to what extent this sector of the Danube river is affected by anthropogenic contamination.The results of our study performed under these circumstances will be further presented and discussed.To accomplish this project, 11 samples of surface sediments (0–15 cm deep) and two samples of deeper sediments  were collected between Belgrade and the Iron Gate 2 dam . The sedhttps://www.fritsch-international.com/ (accessed on 1 September 2022)) at 400 rpm. After that, from each homogenized sample, six aliquots of about 0.1 g were selected and irradiated at the IBR-2 reactor to be independently investigated via INAA.At FLNP, all sample processing and INAA measurements were performed in the Sector of Neutron Activation Analysis and Applied Research (SNAAAR). Here, each sample was again homogenized for 15 min using a PULVERISETTE 6 planetary ball mill ) software and processed using a proprietary software [After irradiation, gamma spectra were recorded using a HPGe detector with a 1.9 keV resolution for the software . This pesoftware , calculaSpecial attention was paid to quality control. This was done by simultaneous use of more Standard Reference Materials (SRM), e.g., 1633c-Coal fly ash, 667-Estuarine sediment, 2710-Montana Soil, and 1547-Peach leaves, as well as 2709-Trace elements in soil, 1632c-Trace elements in coal, 690CC-Calcareous soil, 2709a-San Joaquin soil, and SRM-AGV2–Andesite for short- and long-living isotopes .Furthermore, all of them were reunited by forming the Group of Standard Sample (GSS) proprietary software ,31, withTM (https://www.statistica.com/en/ (accessed on 1 September 2022)) and PAST 4.09 [For a better description and characterization of the experimental data in relation with closer systems, such as UCC , NASC 22, ABL 2, and ADLAST 4.09  softwareThe experimental INAA data, e.g., average mass fractions and CSU  values, The final results concerning the presence of major, rock-forming elements are illustrated using the spider diagram reproduced in Violin diagrams were chosen for major elements as it permits the visualization of the entire distribution function, together with important statistical parameters, while a box-and-whiskers plot was preferred in the case of trace elements. In both cases, all data were normalized to UCC  as one oAs mentioned before, excepting CaO, the average values of the mass fractions of major, rock-forming elements were relatively close to UCC , NASC 2, ABL 2424, and A2, less than 2.5%, suggests an almost uniform origin of sedimentary material from the point of view of siliceous minerals.Silica, which represents the major component of the investigated sediments, presented an average value of 66.86 wt%, almost identical with 66.62 wt% for UCC , and eve2O3 showed an average mass fraction slightly smaller than UCC [2O were shown to be significantly lower than in the case of UCC [2O.At its turn, Althan UCC , NASC [2than UCC , and ABLthan UCC , but higthan UCC . On the e of UCC , NASC [2e of UCC , and ADLe of UCC . Differee of UCC  (Table 22O + K2O vs SiO2 (2O/Al2O3 vs. K2O/Al2O3 (2O − Al2O3 − Na2O + CaO (K-A-CN)  c and SiO2O + CaO d suggest2O + CaO c. It is As shown by the data presented in To detail this analysis, it was necessary to use multiple-sample ANOVA tests. In this regard, the Tukey’s Q test gave a probability equal to one to have the same mean, Kruskal–Wallis gave the same result for medians, and only non-parametric Mann–Whitney and Dunnet post hoc tests  evidenceMoreover, it should be pointed out that the investigated area, with a total length of 175 km (about 6.2% of the Danube River length), entirely passes through the Pannonian Plain. All above mentioned findings confirm previous conclusions, according to which, the investigated materials have, regardless of sampling points, the same geochemistry closer to crustal one.Two variants of INAA, Thermal and Epithermal Neutron Analysis, permitted determination of the mass fractions of at least 29 trace elements, including nine lanthanides, the presence of which was detailed discussed in . TherefoTrace elements are important as their distribution permits inferring the nature of depositional material and, in some instances, the anthropogenic influence manifested by an anomalous increase of the mass fraction of some PCE.Concerning trace elements Sc, Zr, La, Th, and U, their presence has almost never been associated with any anthropogenic contamination ,35, whil2 vs. Ni  [i,jEF represents the EF of the i-th element corresponding to the j sample, i,jc represents the mass fraction of the i-th element corresponding to the j sample, Sc,jc represents the mass fraction of the Sc corresponding to the j sample, i,UCCc represents the mass fraction of the i-th element in the UCC [Sc,UCCc represents the Sc mass fraction in the UCC [Under these circumstances, we have considered the Enrichment Factor (EF) , as one  the UCC , while t the UCC .Sc was chosen as reference element because its presence in not linked to any industrial or human activity.PLI) [EFs:n represents the number of considered PCE for each sample j.To evaluate the global contamination status of the sediments, we have used the Pollution Load Index (PLI) , definedEF ≥ 1, while the sediment could be considered as contaminated if PLI ≥ 1. In both cases, EF and PLI less than one signifies the absence of any anthropogenic contamination. It is worth mentioning that the selection of the UCC as zero contamination reference represents, as mentioned before, a conservative approach. This is not a universal criterion, as in some regions, such as Chaco, Argentina [According to ,44, a PCrgentina ; Hokuetsrgentina ; or the rgentina , the natrgentina  one. DepEF for all sampling points, as well as for the considered PCE, i.e., Cr, Ni, Cu, Zn, As and Sb, are reproduced in EF higher than one, which suggests, especially in the case of Sb, a significant degree of contamination. In addition, it should be remarked that antimony, regardless of whether it is an oxic or anoxic substrate, is partially soluble in water [Final results concerning the distribution of in water . Given iPLIs of sedimentary material collected from all other places were within one standard deviation closer, which, according to [On the other hand, excepting the sampling points 1S (confluence of Sava River with the Danube), 5 (Smederovo), and 6M (confluence of Morava River with Danube), and, to a lesser extent, 9P , the rding to , suggestIn our opinion, the increased degree of local contamination as evidenced for sediments collected at the confluence of Sava River with Danube—1S, Smderovo—5, and at the confluence of Morava River with Danube—6M needs a future detailed investigation in context of presumably local PCE sources.Almost all investigated PCE present a certain degree of solubility in water, mainly as organo-metallic compounds, increasing the necessity for a similar investigation of their presence in the Danube River water. This problem is even more important as the Danube catchment basin, to the Serbia–Bulgarian border, covers six countries, some of them among the most industrialized nations in Europe. Therefore, besides sediment contamination, a systematic investigation of Danube water is necessary to evidence any trans-boundary transport of contaminants ,50,51.The mass fraction distribution of nine major, rock-forming elements, Si, Ti, Al, Fe, Mn, Ca, Mg, Na, and K, as well as another 20 trace elements, i.e., Sc, V, Cr, Co, Ni, Cu, Zn, As, Rb, Sr, Zr, Sb, Cs, Ba, La, Hf, Ta, W, Th, and U, were determined by Instrumental Neutron Activation Analysis in 13 samples of shallow, unconsolidated sediments collected along the Eastern Serbian sector of the Danube River, from Belgrade to Iron Gate II dam.As a reference, the mass fractions of the same elements in Upper Continental Crust (UCC), North American Shale Composite (NASC), Average Bottom Load (ABL), and Average Dobrogea Losse (ADL) were used.The distribution of major elements revealed, on one hand, a relative uniformity of their presence, less influenced by the location of sampling points, and a striking similitude to their distribution in all above-mentioned reference systems on the other. This finding appears in good concordance with the location of the Serbian Danube River in the Pannonian Plain, of which sediments represent a mixture of depositional material of different ages from Neoproterozoic–early Paleozoic to Cenozoic and Quaternary.The distribution of incompatible elements, Sc, Zr, La, Hf, and Th, confirmed the crustal origin of sedimentary material, as well as its homogeneity along the entire Eastern Serbian sector of the Danube River.About 30% of the investigated trace elements could be categorized as Presumably Contaminating Elements as their mass fractions exceeded the UCC by 100% in the case of Cr and Ni, 250% for Cu and As, about 300% for Zn, and a maximum of 850% for Sb. These facts were confirmed by the Enrichment Factor and Pollution Load Index, of which, values were systematically much higher than one unit, the threshold for uncontaminated sedimentary material, suggesting the existence of a significant contamination level of the investigated sector of the Danube River.This last inference increases the necessity for a continuous investigation of the Danube River sediment contamination and especially the monitoring of the Danube River water along its path to the Black Sea to evidence the routes of transboundary contamination. In our opinion, such an investigation should be extended to the aquatic flora and fauna, including sturgeons as key indicators for the quality of aquatic environment.


In the published article, there was an error regarding the affiliations for Katarzyna Obarska, Maciej Skorko, and Mateusz Gola. As well as having affiliation 1, they should also have Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland.In the published article, there was an error regarding the affiliations for Alicja A. Binkowska. As well as having affiliation 1, she should also have DrugsTeam, NeuroCognitive Research Center, SWPS University of Social Sciences and Humanities, Warsaw, Poland.In the published article, there was an error regarding the affiliations for Karol Szymczak. As well as having affiliation 1, he should also have Institute of Psychology, The Maria Grzegorzewska University, Warsaw, Poland.In the published article, there was an error regarding the affiliations for Karol Lewczuk. As well as having affiliation 1, he should also have Institute of Psychology, Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland.In the published article, there was an error regarding the affiliations for Maria Banaszak. As well as having affiliation 1, she should also have Monar Association, Warsaw, Poland.In the published article, there was an error regarding the affiliations for Bohdan Woronowicz. As well as having affiliation 1, he should also have Consulting Center Akmed, Warsaw, Poland.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


In the published article, there was an error in affiliations 3 and 4. Instead of “3. Division of Clinical Research, Department of Medical Research and Division of Medical Oncology, Center for Immuno-oncology, Taipei, Taiwan, 4 Department of Oncology, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan”, it should be “3. Division of Clinical Research, Department of Medical Research and Division of Medical Oncology, Center for Immuno-oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, 4. National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan”.In the published article, an author name was incorrectly written as “Sean M.N. Wong”. The correct spelling is “Natalie S.M. Wong”.In the published article, there was an error in the Funding statement. An acknowledgement was missed. The correct Funding statement appears below.This work was supported in part by the Chang Gung Memorial Hospital CMRPG3J0971~3 CORPG3J0251~3. We thank to Zai Lab (Taiwan) Limited for the educational grant to Taiwan Society of Molecular Medicine. The funder Zai Lab (Taiwan) was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.