Objective: The DSS was utilized to construct colitis model of mouse. The colitis mice were colonized with gut microbiota. The effects of gut microbial metabolites on colitis were studied. The mechanisms of gut microbial metabolites to improve intestinal immunity were also further explored. Methods: The male BALB/c mice were selected to construct colitis mouse model with DSS and colonized with gut microbiota. The content of short-chain fatty acids in intestinal metabolites of mice during modeling were detected with GC-MS. After the mice were sacrificed, the colon tissue was stained to observe the colitis in different groups of mice. The contents of IL-22 and IL-17 in colon tissue was determined with ELISA method. To study the mechanism of relieving colitis, qRT-PCR and western blotting were used to study the horizontal of Ffar2 gene and pSTAT3 and pAKT in colon tissue, respectively. The congenital lymphocytes were isolated and purified, and the migration ability of the congenital lymphocytes was examined by cell scratch plane migration test. Results: The colonization of the gut microbiota had significant effects on the contents of short-chain fatty acids in the intestinal metabolites of colitis mice, of which the effect on the content of acetic acid and butyric acid was more significant. The colonization of gut microbiota could effectively relieve colitis in mice and effectively promote the secretion of IL-22 in colon tissue. Studying the remission mechanism indicated that colonization of gut microbiota with colitis could effectively promote the expression of Ffar2 gene in colon tissue and increase the expression of pSTAT3 and pAKT protein. The migration ability of the lymphocyte was significantly upregulated in the model group compared with the other groups, demonstrating that DSS can effectively activate the lymphocyte; The migration of congenital lymphocyte in the experimental group was significantly alleviated than that in the model group, but it was up-regulated than that in the positive control group, and the colonic tissue of the positive control group was similar to that of the normal group. Conclusion: The short-chain fatty acids in the intestinal flora metabolites can promote the gene expression of the metabolite-sensitive receptor Ffar2. The effective combination of short-chain fatty acids and Ffar2 receptors can promote the phosphorylation of STAT3 and AKT proteins, effectively promote the secretion of IL-22 in intestinal ILC3 cells, alleviate colitis in mice, and thereby improve their intestinal immune function.

Poland has experienced a very sharp rise in income and wealth inequality after the economic transition. We measure the relative persistence of income inequality and intra-generational income mobility in Poland during the period 2008-2015. Our research is based on the panel survey data, our subsample includes 501 households. To measure the persistence of income inequality we calculate Shorrocks’s R coefficient. We find that if inequality is measured by the Gini index the relative persistence of income inequality in Poland is similar to Western Europe. In the case of the Theil index and the Mean Log Deviation (MLD), the relative persistence is higher than in a majority of Western Europe countries and similar to the United Kingdom or the United States. The income distribution in Poland is rather stable. Income mobility is lowest at the bottom and at the top of the income distribution. In the middle (3rd) quintile upward mobility is higher than downward mobility. The short-term income mobility in Poland has not changed during and after the Great Financial Crisis and is still medium in comparison with the rest of Europe.

When human civilization is thriving to the rapid economic and social development, the deteriorating ecological environment has also pressured the society to put environmental protection issues on the agenda future development. Therefore, green innovation is not merely a requirement for a corporate’s long-term development but the basics of sustainable development of human society. As an objective indicator to measure a company's commitment to social responsibility, corporate environmental responsibility has an extremely important impact on a corporate's business philosophy and innovation strategy. However, the existing literature rarely studies between these two variables. This paper collects the data of listed companies from 2010 to 2019 and empirically tests the impact of corporate environmental responsibility on corporate green innovation. The study finds that corporate environmental responsibility can promote green innovation by reducing financing constraints, improving corporate governance and increasing government subsidies and R&D investment, and the result is still significant under a series of robustness checks. The placebo test suggests that the Jiangsu enterprise responsibility construction pilot has facilitated the development of enterprises' green innovation to a great extent. The conclusions enrich existing literature on corporate environmental responsibility and corporate green innovation, providing implications for government policy orientation, social atmosphere guidance and corporate strategic decision-making process.

Previous literature shows that the price-discovery ability of options market varies substantially over time. Using data of Shanghai Stock Exchange 50 exchange-traded fund options, this paper shows that options prices contribute relatively less to price discovery during low-sentiment periods, but the price-discovery ability of options market remains unchanged during high-sentiment periods. These results suggest that change in aggregate investor sentiment is an important source of the time variation in options’ price discovery ability. Moreover, the options market experiences greater bid-ask spreads when investor sentiment is lower, supporting a “transaction costs mechanism.” This paper fulfills related literature on the time variation in options’ price-discovery ability.

This study employs empirical analysis using an econometric model that examines the interdependence among environmental degradation, exports, and economic development with energy use. It also provides an environmental Kuznets curve (EKC) for selected South Asian economies utilizing time-series data. The findings reveal a long-term, stable equilibrium link between energy demand and pollution. There exists a positive relationship between structural factors and pollution. Moreover, this study constructs a model of exports and pollution from an interdependent perspective. The three perspectives are tested: the scale and structure of energy consumption considering the twin constraints of export-trade and pollution, and the scale of pollution in export-trade constraint. These results show that the increase in energy use leads to higher CO2 emissions amidst export volume. However, in the presence of income, the scale of effect lowers a little. The analysis also supports the presence of Kuznets curve for south-Asian economies. The results imply substantial scope for development in the energy use and pollution structure within South Asia's current export trade process. This development can be attained by regulating energy use and enhancing system efficacy without necessitating changes to the scale effect or structural effect.

The No.1 document of the Central Government of China has been on the theme of "agriculture, rural areas and farmers" for nearly ten years, placing the solution of "agriculture, rural areas and farmers" at the forefront of the Chinese government's work. Located in the Central Plains, Henan Province in China is one of the major agricultural provinces in China and plays an extremely important role in the field of agricultural production in China. This paper uses an empirical approach and the DEA-Malquist model to study the efficiency of rural public financial inputs to rural revitalisation in Henan Province. The results show that due to different levels of regional economic development, the efficiency of rural public finance investment in Henan Province in promoting rural revitalisation varies, and there is a phenomenon of unreasonable allocation of funds. At the same time, the region's agricultural base also has a greater impact on the efficiency of financial investment. Therefore, Henan Province should adjust measures according to the different regional economic levels and agricultural bases to improve the efficiency of the use of fiscal funds.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

Breast cancer, as a malignant tumor with easy metastasis and poor prognosis, threatens the health of women around the world. Increasing studies have shown that the Bcl-2 family of apoptosis-related proteins is often expressed abnormally in breast cancer. The Bcl-2 homology 3 (BH3) mimetic peptide can bind and neutralize Bcl-2, preventing its binding to the apoptosis "effector" proteins Bak and Bax, thereby promoting the apoptosis process. However, there is a lack of effective intracellular delivery system for BH3 to exert its biological activity. Therefore, this study utilized an activatable supercharged polypeptide (ASCP) tumor-targeted delivery platform based on pH and protease response to achieve the targeted release of BH3 at the tumor site. Ultimately, intracellular delivery of BH3 was achieved and induced apoptosis of breast tumor cells, preventing the development of breast cancer.

The integration of digital technology and traditional finance has further achieved the goal of low-cost and sustainable financial services and contributed to high-quality economic development. Based on the data from 31 provinces in China from 2011 to 2021, this paper empirically analyzes the impact of digital finance on the urban-rural income gap. The results show that digital finance can significantly narrow the income gap between urban and rural residents. The quantile test shows that the greater the urban-rural income gap, the stronger the role of digital finance in narrowing the urban-rural income gap. The threshold effect test shows that the narrowing effect of digital finance on the urban-rural income gap will first increase and then weaken with the continuous improvement of the development level. Heterogeneity analysis shows that the secondary indicators of digital finance can significantly narrow the urban-rural income gap, and the effect of depth of usage is the most obvious. Moreover, digital finance has a significantly stronger effect on narrowing the urban-rural income gap in the eastern region than in other regions. Therefore, we should vigorously promote the development of digital finance and give full play to its effectiveness in narrowing the urban-rural income gap.

In order to avoid the real economy development lags behind and the deterioration of ecological problems in the process of traditional urbanization, China has been promoting city-industry deep integration, but the connection between city-industry integration and green economic growth, especially the spatial effect, has not been systematically explained. Based on the panel data from 2007 to 2018, this paper constructs an evaluation index system and uses the SEEA method to measure city-industry integration (CII) level and green economy growth (GEG) level. Then, by employing spatial Durbin model and intermediary effect model, it further systematically investigates the spatial impact of CII on GEG and the potential mechanism. The study found that: (1) On the whole, CII shows “slow-steady integration” trend, but regional heterogeneity was obvious and accompanied by “slow gap expansion”. GEG experienced “sharp increase” with “polarization” characteristic. (2) CII can directly promote regional GEG (with a marginal effect of 0.689), more effectively than traditional urbanization, and CII has obvious spatial spillover effects on GEG in both “local effect” and “neighboring effect”. (3) Interestingly, technological innovation and consumption structure upgrading are significant mediating mechanisms. (4) The direct positive effect of CII shows the regional imbalance characteristic. Finally, the corresponding policy implications are put forward.

The digital economy based on digital technology is an important force for high-quality economic growth and industrial transformation, and has great potential for value creation. Based on the data of 30 provinces in China from 2007 to 2017, this paper uses entropy method to calculate the industrial green transformation (IGT), and empirically analyzes the impact of Digital economy on IGT. The DE can significantly reduce IGT of local and neighboring areas after excluding the influence of macro system factors and replacing the spatial weight matrix. The DE can indirectly reduce the IGT by accelerating the accumulation of human capital, and green technology innovation. The impact of digital economy on IGT is a non-linear relationship. With the further improvement of environmental regulation, financial development and intellectual property protection, the role of digital economy in IGT is more obvious. To this end, it is necessary to speed up the construction and improvement of digital infrastructure, build an integrated layout of "digital infrastructure", give full play to the radiating role of the digital economy, and implement differentiated development paths based on regional comparative advantages.

This study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables, guiding sustainable development policies in South Asia.

Gold has been traditionally well recognized as a safe heaven for financial markets. Lately, Bitcoin has been gradually considered as a popular alternative. Since the outbreak of COVID-19 in early 2020, it has become even more necessary and critical to examine the diversification capability of them to hedge financial risks associated with an unexpected crisis comparable to the pandemic. This paper hence employs the wavelet analysis, complemented by the multivariate DCC-GARCH approach, to measure the coherence of the gold and Bitcoin prices with six representative stock market indices, three for developed economies and three for emerging economies, all of which are heavily affected by the pandemic. To have a more balanced and comprehensive analysis, two-year data are used, spanning from 12th April 2019 to 15th April 2021, which covers approximately one year before and one year after the announcement of the COVID-19 pandemic. The results suggest that the returns of both gold and Bitcoin are generally not strongly correlated with the market returns of all six indices, particularly for short-term investment horizons. That is, investors in all six indices can benefit through gold, as well as Bitcoin, in terms of hedging. Meanwhile, compared with Bitcoin, gold shows to be less correlated with the indices, particularly for long-term investment horizons. The findings hence suggest that gold and Bitcoin offer diversification benefits to investors in the market indices during a crisis such as the COVID-19 pandemic, especially for short-term investment horizons. The study also reminds policymakers thinking beyond the pandemic about the future of the earth, including air pollution and health, for sustainable development of the whole world.

Following the seminal paper on Optimum Currency Areas (OCA) by Mundell (1961) a wealth of literature has been published on the business cycles synchronisation and its main determinants. This work provides a systematic review of this research field both at country-level synchronization and regional level. The paper aims to evaluate the contribution of the related literature’s methodologies to the measurement of the business cycle and the estimation of the level of synchronization. The discussion of the collected papers is expected to substantially assist researchers, practitioners, and policy makers keen to employ themselves in the area of business cycles synchronisation.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

This paper evaluates the fit-for-purpose of the monetary policy measures implemented by the Bank of Ghana in response to the COVID-19 pandemic. We examine the effectiveness of the BoG’s policy interventions in the context of vector autoregressions augmented with macroeconomic and financial indicators. We demonstrate that the BoG’s monetary policy measures have had nominal, real, and financial effects. The monetary interventions have been successful, as evidenced by the gradual reduction in the sovereign spread, improved financial stability, and increased real economic activity. Our findings suggest that balance sheet actions are less effective and should be moderated in the conduct of monetary policy in jurisdictions without zero lower bound constraints. However, the analysis indicates that in times of crisis, central banks should deploy both standard and non-standard tools to stabilize dysfunctional financial markets and avoid a deflationary spiral. The historical and variance decomposition of the data reveal that monetary policy shocks have historically made the largest contributions to the targeted macro-financial aggregates during the pandemic episodes.

"Climate Economics and Finance" has risen to prominence as a crucial field, encompassing three primary dimensions: Climate Economics, Climate Finance, and the Intersection of Climate Change and Financial Markets. The adverse externalities arising from climate change, exemplifying a classic instance of "market failure" within market economic frameworks, have garnered global attention. Subsequently, we delve into the literature across three dimensions: the interplay between climate change and macroeconomics, the nexus of climate change and financial markets, and economic or financial resilience. In conclusion, we outline various promising avenues for prospective research in the realm of climate economics and finance.

Adenosine alterations to RNA, which are largely determined by RNA modification writers (RMWs), are critical for cancer growth and progression. These RMWs can catalyze different types of adenosine modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), alternative polyadenylation (APA), and adenosine-to-inosine (A-to-I) RNA editing. These modifications have profound effects on gene expression and function, such as immune response, cell development. Despite this, the clinical effects of RMW interactive genes on these cancers remain largely unclear. A comprehensive analysis of the clinical impact of these epigenetic regulators in pan-cancer requires further comprehensive exploration. Here, we systematically profiled the molecular and clinical characteristics of 26 RMWs across 33 cancer types using multi-omics datasets and validated the expression level of some RMWs in various cancer lines. Our findings indicated that a majority of RMWs exhibited high expression in diverse cancer types, and this expression was found to be significantly associated with poor patient outcomes. In the genetic alterations, the amplification and mutation of RMWs were the dominant alteration events. Consequently, the RNA Modification Writer Score (RMW score) was established as a means to assess the risk of RMWs in pan-cancer. We found that 27 of 33 cancers had significantly higher scores compared with normal tissues, and it was significantly correlated with prognosis. We also evaluated their impact on the tumor microenvironment and the response to immunotherapy and targeted therapy. These findings verified the important role of RMWs in different aspects of cancer biology, and provided biomarkers and personalized therapeutic targets for cancer.

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.

Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.