Background: Bladder cancer (BLCA), particularly invasive BLCA, has become a medical burden worldwide as it is associated with recurrence and easy metastasis. There are specific differences in the expression of various miRNAs in tumor and normal tissues. Hence, miRNAs can be used as biomarkers for tumor diagnosis and prognostic evaluation. The current study aimed to predict the downstream target genes of BLCA-related miRNAs and explore their association with immune infiltration. Method: Data on BLCA-related mRNA and miRNA expression levels were downloaded from The Cancer Genome Atlas. Correlation analysis and Cox regression analysis were performed to validate the miRNA risk model. The infiltration of various immune cells should be compared to determine the distinct differences between the immunological microenvironment of the two risk groups. Results: A predictive framework of BLCA was established using the expression levels of two miRNAs. Cox regression analysis showed that the low-risk group had a better prognosis. Then, the target genes of miRNA were predicted, and the target genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Moreover, variations in immune cells and functions between the high- and low-risk groups were assessed. Conclusion: The prognostic features composed of two associated miRNAs (MIR-25, MIR-548AN) may help predict the overall survival of BLCA.

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.

We develop a four-party model involving banks, merchants, consumers, and platform firms to explain why large shopping malls can offer interest-free installment services to consumers. Using a collaboration between an online shopping platform firm and multiple banks to issue co-branded credit cards as a natural experiment, we find that consumers’ spending on the platform fulfills the requirements for merchants and banks to choose a four-way model. This study highlights the pivotal role of platform firms in the credit card market.

This paper investigates the effect of economic policy uncertainty (EPU) on the systemic risk of the fintech industry. To achieve this goal, we first estimate the evolution of system-wide systemic risk using the CatFin method. We further examine whether EPU significantly affects systemic risk. Our findings demonstrate that the systemic risk of the fintech industry is time-variant and sensitive to major crisis events. Systemic risk tends to increase after major crises, especially the outbreak of the COVID-19 pandemic. EPU has a considerable impact on systemic risk, notably during periods of turmoil.

Cancer treatment faces multiple challenges, including tumor heterogeneity, drug resistance, microenvironment influence, treatment side effects, and treatment cost. The heterogeneity of the tumor makes the effect of the same treatment vary in different patients, so the development of personalized treatment strategies is crucial. In addition, resistance of tumor cells to therapeutic drugs is a major challenge, and new strategies to overcome resistance are needed. As a cutting-edge field of science and technology, nanotechnology has brought great potential and opportunities for tumor treatment. Nanoparticle drug delivery systems improve drug efficacy and reduce side effects through precise targeted delivery and controlled release. Cell-membrane coated nanoparticles show great promise in tumor therapy. Nanoparticles coated with cell membranes have good biocompatibility, can reduce the obstacles of immune rejection and cell uptake, improve the accumulation and retention of drugs in tumor tissues, and have good drug delivery ability, drug stability and control release ability. This review discusses advances in the use of cell-membrane coated nanoparticles to target tumor drugs.

With the daily data from Nov 20, 2019 to Oct 31, 2022, this paper examines the dynamic nonlinear effects of RCEP on Dual Circulation and Greater Bay Area stock market from a quantile perspective. The rolling window quantile regressions detect the positive effects of RCEP on Dual Circulation and Greater Bay Area stock markets with significant time-varying characteristics. Meanwhile, QQ results show that the impacts from RCEP index are more significant under extreme conditions. In addition, we further use a nonparametric QC test to provide evidence on the predictive power of RCEP for Dual Circulation and Greater Bay Area with stock market.

The vigorous development of the digital economy has brought new opportunities and challenges to the construction of local fiscal revenue efficiency. Based on the panel data from 2011 to 2020, this paper uses the fixed effect model, instrumental variable method and other empirical studies to investigate the impact of the development of the digital economy on the efficiency of local fiscal revenue. The research results show that the development of the digital economy has directly improved the efficiency of local fiscal revenue, and the regression results of instrumental variables are still significant. The mechanism analysis shows that the development of digital economy mainly promotes local fiscal revenue such as efficiency improvement by cultivating tax sources.

Spence’s signaling model (Spence, 1973) suggests that education can signal workers’ unobserved ability to employers thereby mitigating discrimination. There have been several studies concerning education’s impact on labor market discrimination against minority or disadvantaged groups. Our approach in this inquiry is unique in that we utilize the data of PhD recipients, a group of people with the highest education attainment, to test Spence’s theory. Another novelty of this paper is that in addition to examining possible discrimination against women and foreign-born, as has been done in previous studies, we further explore possible discrimination against the physically challenged individuals. Our baseline results show conflicting results that Ph.D. education can reduce discrimination against disability and foreign-born but not against gender. Further analysis by the Blinder-Oaxaca Decomposition shows that the wage gaps of gender and disability come more from the unobserved part than the explained part, while the foreign-born wage gap come more from the observable human capital differences. Since prejudice is an unobserved factor and we know that the disadvantaged groups are likely to suffer from prejudice (Oaxaca, 1973; Blinder, 1973; Montes-Rojas et al., 2017; Deshpande and Khanna, 2018), we conjecture that prejudice might be attributable to the unexplained part of the wage gaps. Furthermore, prejudice might be deeply rooted in one’s mind, thus difficult to remove even with the influence of education. Hence, our results reveal that it would be hard for Ph.D. education to eradicate the discrimination against gender and disability, but not against foreign-born.

This study compares return and volatility performance of exchange-traded funds (ETFs) with high-ESG (Environment, Social, and Governance) rating vs. low-ESG rating. The paper also examines time-series data predictability by identifying their positive dependence and volatility asymmetry properties, and examines the performance of two combinations of short-memory models i.e., autoregressive moving average and exponential generalized autoregressive conditional heteroskedasticity (ARMA-EGARCH); autoregressive moving average and asymmetric power autoregressive conditional heteroskedasticity (ARMA-APARCH) and two long-memory models, autoregressive moving average and fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH); and autoregressive fractionally-integrated moving average and asymmetric power autoregressive conditional heteroskedasticity (ARFIMA-APARCH). The study found that low-ESG rating ETFs on average have slightly significant higher returns and also lower volatility compared to their high-ESG rating counterparts. Evidence of asymmetric volatility properties are also present on both high-ESG and low-ESG rating ETFs returns. The study also observed that for both high-ESG and low-ESG rating ETFs denote a stationarity, but non-invertible process in their returns. Results can provide fresh understanding in the topic of leverage effects and volatility that can open future research channels to academicians.

"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.

The Public Welfare Foundation of a listed company is a non-profit organization with independent legal personality. It is established and funded by a listed company. The purpose of establishing a foundation for a listed company is generally to carry out charitable activities and make important contributions to charitable causes. However, due to the special power separation model, the autonomy of the foundation is poor, and there may be serious agency conflicts, ultimately leading to listed companies becoming beneficiaries of the foundation's charitable activities. We have found from the perspectives of commercial credit, sales, and cooperative relationships that social responsibility initiating organizations (listed companies) can obtain potential benefits from social responsibility fulfilling organizations (listed company public welfare foundations) in supply chain commercial relationships, that is, listed companies that establish public welfare foundations have good supply chain commercial relationships. When a listed company and a public welfare foundation have a close relationship in terms of fund donation, decision-making, business, and naming, the public welfare foundation has a strategic preference in selecting project names, project service areas, project beneficiaries, and project service areas. It prioritizes the interests of the listed company, enabling the listed company to obtain better supply chain business relationships.

Information leakage in the stock market has been widely proven. Information disclosure is sometimes uneven, and there is significant information asymmetry between ordinary investors and professional institutional investors. In this paper, Regression Discontinuity design (RDD) model is first employed to analyze the information leakage issues. Based on the daily closing stock prices of 15 capital service listed companies, we analyze the difference between the market reaction time and the disclosure time of two stamp tax policies. We found that the sample policies information may leaked to the market about two days earlier. This paper provides a new method analyzing information leakage.

Numerous studies have explored the impact of family planning policies on income distribution, but the impact of policy relaxation remains largely unexplored. To address this gap, we investigate the effects of China's selective two-child policy, which was implemented in 2013, using provincial panel data from 2011 to 2016. Specifically, we employ a generalized difference-in-differences model to empirically analyze the impact and mechanism of the policy relaxation on residents' income distribution. Our findings are as follows. Firstly, the selective two-child policy has narrowed the income distribution gap. Secondly, male labor force participation, the urban-rural income gap, and disposable income serve as mechanisms through which the selective two-child policy affects income distribution. Thirdly, the robustness test confirms the robustness of our conclusions. Finally, we observe regional heterogeneity in the impact of the selective two-child policy on residents' income distribution. Specifically, the policy has had significant effects in the western and northeastern regions, but not in other regions. Overall, our results shed light on the impact of childbearing policies on income distribution, providing important insights for optimizing China's childbearing policies and promoting common prosperity.

As the COVID-19 epidemic swept the world in early 2020, it is worth watching how it affects the total factor productivity of enterprises. Based on the theory of information asymmetry and the data of A-share listed companies from 2014 to 2021, this paper conducts an empirical study on the relationship between COVID-19 and the total factor productivity of enterprises. The results show that the impact of the COVID-19 epidemic can significantly reduce the total factor productivity of enterprises, and the conclusion is still true after a series of robustness tests. Taking the financing constraint as an intervening variable, it clearly reveals a transmission mechanism, that is to say, the impact of the COVID-19 epidemic exacerbates the financing constraint of the enterprise, thus it makes the total factor productivity of the enterprise reduce. It is further found that the above effects are more obvious for small and medium-sized enterprises and non-intelligent enterprises. Based on this, the government should implement some active economic development policies to create a free, relaxed and orderly market environment for enterprise financing. At the same time, enterprises should not only pay attention to digital and intelligent development, and transform into technology-intensive enterprises, but also strengthen the awareness of worry. Enterprises should store "more grain" for preparing for the cold winter.

You have a product or service for which the world has been waiting. You've scraped together enough cash to get your idea off the ground. You are confident that your business will succeed. And yet, it is often the case that a short time later you are out of business. Numbers from the U.S. Bureau of Labor Statistics indicate that about half of new businesses fail within the first five years. And with those business failures go the life savings and the dreams of the person who started the business. This article describes five finance and accounting pitfalls that should be avoided in order to increase the likelihood that a small business will succeed. Of course, the most common reason for a small business to fail is that customers don't want the product or service. But small businesses are also often killed because of predictable and preventable finance and accounting mistakes: insufficient capital, poor cash management, poor record keeping and controls, improper product pricing, and uncontrolled growth.

We analyze the annual microdata of China's 100 commercial banks from 2007 to 2018 using the mediating effect analysis method, analyze the negative impact of economic policy uncertainty on the credit scale of commercial banks, and then verify the intermediary effects of the operational risks and short-term capital liquidity of commercial banks in the impact of economic policy uncertainty on the scale of commercial bank credit, separately. Moreover, we find that non-state-owned commercial banks, large-scale commercial banks, low-leverage commercial banks, and commercial banks with low profitability are more susceptible to economic policy uncertainty affecting commercial bank credit growth through short-term capital liquidity, and vice versa.

Tripartite motif-containing (TRIM) proteins consist of over 80 proteins, the majority of which exhibit E3 ubiquitin ligase activity. E3 ligases have a critical role in various cellular processes by specifically recognizing and ubiquitinating substrate proteins to promote their proteasomal degradation or alter their activities. Numerous studies have indicated that TRIMs are involved in carcinogenesis through various mechanisms. However, the regulatory mechanisms delimitating TRIMs’ function as E3 ligases has not yet been specifically addressed in a previous review article. In this review, we focus on recent advancements in understanding how certain TRIMs function solely as E3 ligases during cancer cell proliferation, apoptosis, and metastasis. We comprehensively summarize the target proteins of TRIMs involved in disordered signaling pathways such as Wnt/β-catenin, PI3K/AKT, NF-κB, p53, ERK, and STAT3, as well as those regulating the cell cycle and glycolysis. Following ubiquitination modification by TRIM E3 ligases, these target proteins either undergo proteasome-mediating degradation, maintain steady levels, or get activated/inactivated. This review provides a foundation for the development of E3 ligase-based cancer treatments.

By introducing time to build, which creates a time-lag between government investment and the accumulation of productive capital, into an analysis of fiscal stimulus to the economy with financial frictions, we find that the effectiveness of fiscal policy is dampened. While the weakening effects of time to build become significantly weaker alongside with a higher fraction of government bonds allocated to leverage-constrained banks, which can be explained by a high correlation between time to build and financial frictions in both worsening balance sheet conditions of banks. Furthermore, the stimulus effects of public investment become stronger associated with shorter time-to-build period.

The progression in nanotechnology has revolutionized the biomedical sciences for diagnosis and treatment of diseases like cancer. There have been many kinds of nanomaterials but Inorganic nanomaterials have been considered potential candidates for anticancer activities due to their high biocompatibility, less toxicity, high stability, and high precision in targeting affected cells. Several synthesis approaches have been used to prepared these nanoparticles, such as physical, chemical, and biogenic methods. Due to higher toxicity and adverse effects of chemical methods, eco-friendly way such as biosynthesized inorganic nanomaterials have attained much attention for multiple application particularly treatment of diseases. This review presents a comprehensive and updated knowledge (2015-2023) regarding the cancer treatment. The article first categorizes biogenically synthesized inorganic nanoparticles into three main groups: metallic nanoparticles, metal oxide nanoparticles, and quantum dots and then successful stories related to cancer treatment. This will also provide very effective platform for researchers and academia to detail the biogenically synthesized inorganic nanoparticles’ morphology, their characterization, targeted cancer cells.

This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.