Alternative bio actively chemicals may be found in natural goods and traditional herb medications, but only a few plant-information formulations have been rigorously studied and verified for their potential as medicinal therapies. The study of plant-derived elements' immunomodulation capabilities and their ability as provoke the immune system as combat various elemental disorders like cancer is, nonetheless, a promising area in current therapeutics information on plant-derived chemicals. This research showed how network pharmacology may be applied as define and validate natural individual elements or more complicated preparations as prospective cancer therapies information on their various aim capabilities in this research. We give a summary of the present state of understanding on network pharmacology, with a particular emphasis on various technical methods and their implications for cancer treatment.

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.

This research paper examines changes in patient care management in acute care hospitals between 2001 and 2011. During this time, there were two opposing factors at play: the competition effect of the reform and the policymaker’s decision to reduce public hospitals across France. By studying the trends, it is evident that there has been a significant overall shift in patient care management during this period. This change could be attributed to the global competition effect and the concentration of in-patients in specific public facilities. Through the difference-in-difference method, the study analyzed time variations in the intensity of local competition. It was found that local competition had a negligible impact on patient care management. Additionally, the study revealed that there was a significant positive competition effect on high-technical procedures for the private sector, which is in line with the market segment where private sector hospitals have a leadership position and the pro-competitive reform intensified this position. The study also uncovered a negative competition effect on the length of stay for public hospitals. Prior to the implementation of the DRG-based payment reform, public sector hospitals were paid a global budget. However, after the reform was implemented, they had to shorten the length of stay to increase the number of stays. For-profit hospitals have always been paid based on the number of stays. The results are robust and consistent when alternative measures of local competition are used.

Energy Performance Certificates are a key tool for achieving energy efficiency in the building sector. The existence of this type of legislation provides incentives for the energy renovation of buildings, increases energy-efficiency investments, and improves social welfare. At the same time, informational asymmetries are mitigated and energy consumption is reduced. However, the decarbonization of the building sector might influence regional energy poverty, especially because the rural energy poor are more vulnerable to energy price increases than their urban counterparts. Empirical research on rural-urban differences and the effects of Energy Performance Certificates is urgently needed, given the rising fuel prices and vulnerabilities of the energy market.

This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.

The objective of this research is to investigate, using time series data ranging from 1992 to 2021, the effects of information and communication technologies (ICTs) and foreign direct investment (FDI) on economic growth (GDP) in India. The stationarity of the data was examined by employing unit root tests, and an autoregressive distributed lag (ARDL) technique was used to investigate the link between the factors, taking both the long- and the short-run into consideration. According to the findings, an increase of one percent in both ICT and FDI will result in an increase of 0.56% and 0.71% in GDP over the long term, in addition to an increase of 0.11% and 0.29% over the short term. The findings of the investigation are of particular significance to policymakers because they can be utilized to establish sensible policymaking for long-term economic success, in addition to advancing the ICT sector and boosting FDI.

We generalize the 3-player game introduced by Mêgnigbêto (2018) to analyze the synergies existing between universities, the industry and the government in the Triple Helix, a model of research and innovation introduced by Leydesdorff and Etzkowitz (1995). We consider situations involving any number of actors and we allow for a differentiation of their contributions. The resulting game has nonnegative Harsanyi dividends, implying its convexity. The relative size of the core measures the extent of the synergies and the Shapley value measures the contribution of each actor to these synergies. Incidentally, the resulting game lends itself to a wide range of applications.

Foreign direct investment (FDI) is widely viewed as a key driving force behind China’s exceptional growth performance in the last four decades. This paper investigates several questions posed by China’s success in capturing gains from FDI. What explains that success? Can other countries replicate it, or is it unique to China? What lessons are from China for other countries? China indeed has advantages in attracting FDI such as huge market and cheap labor, the well-designed policy and strategy, however, seem to play more important role in the successful story. Our empirical estimates support on four hypotheses: FDI and GDP growth in China positively interact each other; FDI is helpful to China’s technological progress, FDI promotes China’s industrial development, and FDI stimulates China’s manufactured exports.

In the context of the continuous deepening reform of the foreign investment negative list system and the increasing pressure on financial risk prevention and resolution, it is of great significance to study the impact of the foreign investment negative list system on banking risks. This study is conducted on the basis of systematically sorting out the mechanism of the foreign negative list system affecting the risks of domestic banks. Based on the panel data of 27 provinces (cities, districts) in China from 2006 to 2016, and comprehensively adopting the synthetic control method, the difference-in-difference (DID) model, mediating effect model, and other methods, this article systematically examines the impact of the foreign negative list system on Shanghai banking risks. The study found that the implementation of the foreign investment negative list system has significantly reduced the credit risk of the Shanghai banking industry, and this reduction effect has gradually strengthened over time. Additionally, the implementation of the foreign investment negative list system will also significantly reduce the liquidity risk of the Shanghai banking industry; However, the effect of this positive influence is short-lived and subsequently weakens. The analysis of the mechanism of action indicates that the foreign investment negative list system primarily reduces the risk of Shanghai's banking industry by stimulating the inflow of foreign capital and reducing government intervention. However, the effect of improving the level of the rule of law to mitigate commercial bank risk is not apparent. Regarding other influencing factors, the growth of local government financial resources and the increase in new loans have significantly reduced banking risks, while the rise in residents' leverage has substantially increased the level of banking risks.

Green innovation, driven by China's new development concept, plays a crucial role in high-quality economic development. In line with the green development trend, businesses increasingly prioritize whether their corporate environmental responsibilities (CER) can effectively enhance corporate green innovation (CGI) levels. This paper examines the influence and mechanism of CER on CGI using a dynamic perspective, drawing from 1,640 manually-collected panel data of Shanghai and Shenzhen A-share listed companies between 2010 and 2017. The primary findings indicate that the impact of CER on CGI possesses phase-specific characteristics and a dual effect of "crowding in" and "crowding out." The current phase of CER negatively affects green innovation, while the lag phase has a positive effect. CER's impact on various CGI types is heterogeneous: specifically, it follows an "inverted-N" trajectory (inhibition-promotion-inhibition) for "strategic green innovation" and has a promotional effect on "substantive green innovation," which is stronger and has a longer time lag. The mechanism analysis reveals that financing constraints play a critical mediating role. A heterogeneity analysis based on multiple dimensions (ownership, industry, and location) suggests that CER has a more significant driving force for CGI among state-owned firms, high-polluting industries, and enterprises in inland areas. Finally, the paper presents corresponding suggestions for government and corporate entities.

The purpose of this paper is to examine and elucidate the effect of the announcement of winning a US federal contract by pharmaceutical companies, traded on the NASDAQ stock exchange on the share prices. The analysis is based on a sample of 327 event days from 12 pharmaceutical companies listed on the NASDAQ market for a given period (2010 – 2021). The sample is analysed by using the event study methodology and Capital Asset Pricing Model (CAPM) in order to examine the abnormal returns and cumulative abnormal returns during an event window of 10 days before the event and 10 days after the event. While prior researchers have identified significant positive abnormal returns following different types of contract announcements, this study aims to contribute to the existing literature by exploring the specific impact of the public contract in the pharmaceutical market on market shares. By investigating this unique aspect, the research fills a gap in previous studies and enhances our understanding of the relationship between public contract announcements and market performance. The data collection utilises secondary data from the official open data source of US federal spending information and daily stock prices. The collected data is analysed quantitatively using event study methodology, allowing for statistical examination of abnormal returns, such as ANOVA analysis, t-statistic, beta index, time series analysis and linear regression. The research findings indicate a significant impact of public contract-winning announcements on cumulative abnormal returns, suggesting a positive reaction from the market. However, average abnormal returns within the event window were positive but not statistically significant. These results imply that winning a government contract has a limited impact on the stock market. The implications of this research highlight the importance of public contract announcements in the pharmaceutical industry and their influence on share prices. The findings provide valuable insights for investors and stakeholders, aiding decision-making processes related to investment opportunities and market performance.

Ferroptosis, a recently identified form of programmed cell death, is characterized by the accumulation of lipid peroxidation, reactive oxygen species, and elevated free iron levels, involving the regulation of glutathione metabolism, iron metabolism, lipid metabolism, and oxidative stress biology. Tumor metastasis, a critical hallmark of malignancy and a key contributor to cancer recurrence and mortality, has been extensively linked to iron dysregulation, highlighting the potential of agents inducing iron-mediated cell death as promising strategies for preventing and treating metastasis. This review offers a comprehensive understanding the regulatory mechanisms underlying ferroptosis and its crucial role in the three distinct stages of metastasis: invasion, circulation, and colonization.

This study investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation's report on "Bank Failures in Brief - Summary 2001 through 2023". The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.

The enactment of policies that bolster new research and development (R&D) institutions stands as a pivotal strategy to catalyze urban innovation and development. Adopting a strategic lens of innovation chain management and employing the Differences-in-Differences (DID) method to scrutinize panel data from 43 Chinese cities spanning 2008 to 2019, this study probes the efficacy and underlying mechanisms of policies designed to support nascent R&D institutions in facilitating urban innovation. Empirical findings reveal that policies in support of new R&D institutions have markedly enhanced the three integral stages of the urban innovation chain: research and development, transfer, and application. Furthermore, the innovation ambiance within cities and the innovative activities of enterprises emerge as significant mediators between support policies and the output of urban innovation across these stages. A regional heterogeneity analysis unveils that the impact of support policies on the output of urban innovation diverges across regions, with a notably more pronounced effect observed in the eastern region compared to central and western regions. An objective appraisal of the policy's impact on urban innovation not only aids in delving into the profound implementation effects of policy instruments but also furnishes policy-makers with decision-making references for optimizing the utilization of support policies for new R&D institutions to advance the edification of the national innovation system.

In the behavioral domain, this study discloses the pattern between microblogging-opinionated information and yields on the investment. This phenomenon is particularly related to the political instability in the Pakistan’s economy through the multivariate techniques. Pre-political crisis, the pessimistic sentiments were priced in yields on the investment. In environments of political instability, the intensity of decline in yields was more responsive against an incline in the negative opinions. Meanwhile, the intensity of increase in returns was less responsive against an incline in the positive opinions. The findings were further supported by the Bayesian approach. Before the political instability takes place in the Pakistan’s economy, the occurrence of yields was noted in response to the bearish market period. Post-political instability, there was a higher posterior probability for occurrence of returns against the bearish market period. Conversely, a higher posterior likelihood was noted for occurrence of investment’s yields in response to the bullish market period, but this relevance was not completely probable. From the impulse response analysis, the response of returns was reported against the standard deviation shocks in the microblogging sentiment indicators. The analysis may have potential implications in terms of disclosing the investors’ behavior from a political perspective.

With the improvement of people's living standards and the increasing emphasis on health, new and higher demands are also placed on the medical industry. The diagnosis and treatment methods combining artificial intelligence and medicine are bound to receive more and more attention and promotion. At present, drug design is stepping into the era of artificial intelligence, and academia and industry are actively applying artificial intelligence technology to all aspects of drug research and development, including drug screening, molecular characterization, drug activity and druggability optimization, chemical reaction design, etc. Relying on the big data information of millions of patients, the artificial intelligence system can quickly and accurately mine and screen out suitable drugs. Through computer simulations, artificial intelligence can make predictions about drug activity, safety, and side effects, and find the best drug that matches the disease. This technology will shorten the drug development cycle, reduce the cost of new drugs and improve the success rate of new drug development.

Cancer is the leading cause of death in the world, throughout the global researches of cancer treatment, people have a deeper understanding of cancer, and the treatment methods are constantly breakthrough. Conventional surgery, chemotherapy and radiotherapy have serious adverse effects and patients' quality of life is not significantly improved. Now, photodynamic therapy, photothermal therapy andthermodynamic therapy based on nanotechnology and materials technology are booming, and the development of these novel cancer therapies and their combination therapies brings more possibilities for cancer treatment. This review summarizes the research progress of novel cancer therapies based on nano and material technology from the aspects of mechanism of action and therapeutic methods, hoping to provide reference for their clinical application.

This study analyzed whether the level of corporate governance of companies listed on the Brazilian capital market is related to the investment decisions of Brazilian pension funds. We start from the assumption that the corporate governance system mitigates the moral risk, that may arise in the relationship between corporate managers and investors and respective shareholders, Brazilian pension funds. Shareholding dispersion would enable opportunism for corporate managers, making decisions focused on their interests to the detriment of shareholders or investors. The governance system would signal that this behavior would be controlled, signaling safer behavior for investors. Thus, the hypothesis is that the level of corporate governance would attract pension funds that would understand this system as reducing investment risk. To test this, we used panel data, with a sample of 1,582 observations of companies listed on the Brazilian Stock Exchange (B3) from 2010 to 2017. The statistical results show that corporate governance positively correlates with investment in pension funds. This leads to the conclusion that, for Brazilian pension funds, companies' signaling about their corporate governance practices is a risk-mitigating factor. Furthermore, these results allow us to infer that the company's governance level associated with leverage is a differentiating signaling element as the evidence points to a positive relationship.

With the continuous advancement of technological revolution and industrial transformation, digital finance supported by big data and artificial intelligence has become an important engine for promoting carbon neutrality. We measured the industrial structure transformation index (ISU) of 30 provinces in China, and discussed the spatial spillover effect and transmission mechanism between digital finance and ISU using the spatial Durbin model. The research results demonstrate that the digital finance development can significantly improve the local ISU. Interestingly, the impact of digital finance on the ISU of adjacent areas has a significantly negative spatial spillover effect, which still exists under the different spatial weight matrix. Digital finance can improve ISU by improving green technology innovation, upgrading industrial structure, and alleviating capital allocation. We also found that the higher degree of marketization and environmental regulation can increase the positive influences of digital finance on ISU. This research proves the effectiveness of the digital finance in improving energy efficiency, and it encourages policymakers around the world to rely on digital finance to promote ecological governance and achieve high-quality economic development.

Based on the provincial panel data of China from 2011 to 2019 for a total of 9 years, this paper uses the entropy method to construct a comprehensive measure of the digital economy and common prosperity. We theoretically analyzed the impact mechanism of the digital economy on common prosperity using the econometric model. The study found that the digital economy can significantly promote the realization of common prosperity. The digital economy has a significant impact on the development and sustainability of common prosperity. The results of the impact mechanism show that the digital economy promotes common prosperity by increasing entrepreneurial activity. Heterogeneity research shows that the role of digital economy in promoting common prosperity changes with different regions. Specifically, the digital economy in the central and western regions can significantly promote the realization of common prosperity, but this impact is not significant in the eastern region. From the regression results of the digital economy, we can see that industrial digitalization and digital infrastructure play a greater role in promoting common prosperity. Economic openness affects the promotion of digital economy to common prosperity. The conclusion of this work provides an important research reference for the development of digital economy and the realization of common prosperity.