The latent transforming growth factor-beta (TGF-β) binding protein 1 (LTBP1) has been implicated in various cellular processes, but its role in triple-negative breast cancer (TNBC) remains unclear. In this study, we investigated the impact of LTBP1 on TNBC progression and its underlying mechanisms. Analysis of online datasets revealed elevated LTBP1 mRNA expression in breast cancer tissues compared to normal adjacent tissues. Kaplan-Meier Plotter analysis indicated that high LTBP1 expression was negatively correlated with relapse-free survival (RFS), distant-metastasis free survival (DMFS), and overall survival (OS) of breast cancer patients. Additionally, LTBP1 mRNA levels were associated with chemotherapy resistance. Functional assays in TNBC cells demonstrated that LTBP1 knockdown suppressed cell proliferation, induced apoptosis, and attenuated migration and invasion. In vivo studies confirmed that LTBP1 knockdown inhibited tumor growth in a xenograft mouse model. Mechanistically, LTBP1 positively correlated with genes involved in signaling regulation and organelle organization, with significant associations to GTPase binding and the RhoA/ROCK pathway. LTBP1 knockdown reduced RhoA activity and phosphorylation of Myosin Light Chain 2 (MLC2), suggesting inhibition of the RhoA/ROCK signaling pathway. Moreover, activation of the RhoA/ROCK pathway partially rescued the effects of LTBP1 knockdown on TNBC cell proliferation, apoptosis, migration, and invasion. In conclusion, our findings suggest that LTBP1 promotes TNBC progression through activation of the RhoA/ROCK signaling pathway, highlighting its potential as a therapeutic target for TNBC.

Since the Industrial Revolution, the emission of carbon dioxide and other greenhouse gases by human production activities has increased year by year, and the greenhouse effect has also increased. A series of problems, such as global warming, melting of the Antarctic continent, sea level rise and frequent occurrence of extreme climate, have attracted the attention of countries all over the world. As a developing country with the fastest economic growth rate, China's rapid economic growth is inseparable from the contribution of manufacturing industry. As an important engine of China's economic development, manufacturing industry inevitably brings environmental problems and a large number of greenhouse gas emissions while promoting rapid economic development. Therefore, manufacturing has become a major area for China to achieve its carbon emission reduction targets. With the "Made in China" to the world, the export trade of manufacturing industry plays a decisive role in China's total export trade and even the global trade. But whether the huge export demands will affect carbon emissions. Through the empirical study on carbon emissions of China's manufacturing export trade, the paper found that the increase of export demand of manufacturing industry will indeed bring a significant increase in carbon emissions, but also affected by some other factors. Finally, it provides reference for the high-quality development and export of China's manufacturing industry under the background of the target of “dual-carbon”.

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.

The implementation of environmental protection strategy necessarily requires mapping the amount of capital stock of environmental infrastructure. Through the Weibull distribution function and hyperbolic age-decreasing efficiency model, the provincial environmental infrastructure capital stock in China from 1980 to 2018 is measured cautiously, and its spatial dynamics with the generated pollutants is analyzed using the center of gravity method. It is found that: the spatial distribution of environmental infrastructure capital stock is uneven, and the unevenness in the east-west direction is greater than that in the north-south direction, but the unevenness in the east-west direction is narrowing while the north-south direction is widening; the spatial and temporal distribution of environmental infrastructure capital and environmental pollution vary greatly, and the spatial management of environmental pollution is less accurate.

We quantify financial returns to backers in reward-based crowdfunding projects on Kickstarter and show that such investments provide profitable opportunities in addition to non-monetary benefits. The average unconditional annualized return is 11.5% and the average return on successful projects is 30%. Hence, backing money near the end of a campaign, when the probability of success is already high, is a profitable strategy. The most attractive is the Design category, where successful projects yield 73%, on average. Short-term projects are more profitable than long-term ones. Financial return is an important type of extrinsic motivation in reward-based crowdfunding, which has generally been neglected in academic literature. Reward-based crowdfunding outperforms other forms of crowdfunding and other common alternative investments.

This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

This study focuses on the impact of trade in environmental goods (green trade) on the environment. We found that green trade can decrease pollution levels by exploiting a panel of 277 Chinese cities from 2004 to 2013 and using the instrumental variable (IV) strategy. However, total trade openness is far less favorable to the environment. We also found that both green imports and exports are conducive to the Chinese environment, while ordinary green trade performs better than green processing trade. Nevertheless, the effects of green trade are restricted by a city's purchasing power and absorptive capacity, as well as the classifications of environmental goods. Furthermore, green trade mainly promotes local green technological progress to benefit the environment.

Metastasis is the major cause of cancer-related mortality. Metastasis is a process through which cancer spreads from its initial location to other sections of the body. Cancer cells' epithelial-mesenchymal transition (EMT), anoikis resistance, cell migration, and angiogenesis are all well-known steps in this process. Investigating the molecular processes that govern cancer metastatic progression may lead to more effective diagnostic and treatment strategies. Long non-coding RNAs (lncRNAs) have recently discovered to have a vital more than 200 nucleotides. A rising body of research indicates that lncRNAs have a role in a wide range of biological processes and diseases, including cancer. The usage of LncRNA in cancer metastasis has been widely researched. However, according to current studies, lncRNA is mostly associated with the EMT process. This review focuses on the processes behind lncRNA involvement in cancer metastasis.

Cardiovascular disease is still a disease with high incidence rate and mortality. Although advanced technology continues to increase our understanding of cardiovascular disease, its diagnosis and treatment still have limitations. As an emerging interdisciplinary method, nanotechnology has shown enormous clinical application potential. Nanomaterials have unique physical and chemical properties, which help to improve the sensitivity and specificity of biosensor technology and molecular imaging technology in the diagnosis of cardiovascular diseases. This paper first summarizes the versatility of nanomaterials, the physicochemical adjustability of biomolecular engineering, the design strategy of nanoparticles in cardio cerebral Vascular disease, the application of nanomaterials in the diagnosis and treatment of common cardiovascular diseases, and the use of nanomaterials can significantly improve the diagnostic sensitivity, specificity and therapeutic effect. Subsequently, the article summarized various nanomaterials. Finally, the article demonstrated the potential of the antioxidant/anti-inflammatory and photoelectric/photothermal properties of nanomaterials to be directly applied to the treatment of cardiovascular diseases.

Cancer, a pervasive threat to human health, presents formidable challenges to traditional treatment approaches. Tumor immunotherapy has emerged as a promising strategy for combating malignancies by bolstering the body's immune response to thwart tumor metastasis and recurrence. Nonetheless, the intricacies of tumors, patient heterogeneity, and the presence of tumor-immunosuppressive microenvironments have limited the overall efficacy of immunotherapy, achieving only approximately a 20% success rate. In recent years, nanomaterials have garnered increasing attention in the realm of tumor immunotherapy due to their inherent advantages, such as excellent biocompatibility, precise targeting, and controlled drug release. Nanomaterials empower immunostimulatory molecules and therapeutic agents with the ability to specifically target tumors, amplify drug accumulation at tumor sites, facilitate local immune modulation, alleviate immunosuppressive microenvironments, and thereby enhance the effectiveness of tumor immunotherapy. This review provides an overview of the current state of immunotherapy and offers insights into the ongoing research progress surrounding various nanomaterials aimed at augmenting the efficacy of immunotherapy.

We offer an extensive analysis of the significance of information within the realm of Gaussian quadratic economies. We build upon the seminal papers of Morris and Shin (2002, 2007) and consider a signal game of incomplete information. Particularly, we question the suitability of partial transparency portrayed by fragmented information in addition to the private signal in terms of welfare effects. We can summarize our findings in two main points. First, fragmented information, in conjunction with a private signal, can reduce the reliance on public signals. Second, a conflicting effect arises between increasing full disclosure and increasing the precision of fragmented (semi-public) information when examining different complex scenarios, involving for example endogenous private information or imperfect correlated signals. For a critical threshold, an optimal communication strategy designed by fragmented information should be implemented whenever that kind of information is acquired at a high precision.

This paper examines the link between informality and corruption, two interlinked phenomena that have negative impacts on economic development. The paper presents a neoclassical model simulation that illustrates how informality can drive corruption in the economy, emphasizing the incentives for corruption in an economy with informal and formal sectors. The model provides insights into the mechanisms that promote corruption and how policymakers can reduce it through formalisation. The paper reviews the existing literature on informality and corruption, highlighting the empirical evidence and theoretical models that support the relationship between the two. The research finds that countries with larger informal sectors tend to have higher levels of corruption. The study contributes to the ongoing debate on how to reduce corruption and promote formalisation, which are crucial for sustainable economic 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.

This paper specifically underscores the disparities among various ESG rating systems in China, highlighting their varied interpretations and emphasis on corporate financial factors. Analyzing data on Chinese listed firms from 2009-2022, we observe that while company size and leverage ratio uniformly correlate with ESG scores across rating agencies such as Bloomberg, Huazheng, Wind, and Hexun, the influence of factors like return on assets, cash flow, company age, and Tobin's Q is markedly inconsistent among these agencies. For instance, while operational cash flow and company age are positively associated with ESG ratings from Bloomberg, Huazheng, and Wind, they hold an inverse relationship with Hexun's ratings. This divergence underscores the unique data collection, weighting, and evaluation methodologies employed by each rating system. The study emphasizes the criticality of comprehending the nuances of each rating agency's approach when interpreting ESG scores and crafting ESG strategies. Moreover, it advocates for integrating insights from multiple rating systems to cater to the diverse expectations of stakeholders.

Access to electricity, a fundamental element of contemporary life, is essential for economic success. Its impact extends to the fundamental foundations of industrial development and has the power to improve a wide range of industries, including healthcare, transportation, utilities, and education. This article aims to analyse the effect of electricity access on primary education in Central Africa over the period 1997-2019. To this effect, we employ data mainly from the World Development indicator on 9 countries of Central Africa. Using the pooled ordinary least squares estimation technique, the results indicate that access to electricity contributes positively and significantly to primary educational attainment in Central Africa during the study period. The results are Robust to the use of alternative estimation strategy and eventual endogeneity problems in the results are account through the two stage least square estimation techniques which confirm our baseline results as well as the nature of the relationship between access to electricity and primary education in Central Africa. These results have important implications for policies in overcoming barriers to electricity access.

Skateparks, often called 'wheel parks,' are becoming increasingly common within communities worldwide. Despite this growth in parks, estimates show that the development of parks needs to catch up to users. Using a count data travel cost model, we estimate the adult user benefits associated with the Lauridsen Skatepark in Des Moines, Iowa – the largest in the United States. We estimate adult user benefits to be $61 per user per day and roughly $488,000 annually. This work contributes to the literature by being the first study to use econometric techniques to estimate skatepark user benefits. Second, we develop a simple and easy-to-apply method to assist municipalities in determining community skatepark needs. Third, our results support the literature showing that skateboarders are increasingly diverse.

This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as Oil and Financials provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

In this study, we examined the efficiency of cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), DASH, EOS, and MONERO from March 1, 2018, to March 1, 2023. We separated the sample into four subperiods for this purpose: a Tranquil period that includes the period from March 1, 2018, to December 31, 2019; a First Wave that includes the year 2020; a Second Wave that includes the year 2021; and a fourth subperiod that includes Russia's invasion of Ukraine in 2022-2023. The results are mixed, with some cryptocurrencies exhibiting equilibrium and others exhibiting autocorrelation and predictability in their pricing. When the sample is divided into subperiods, most digital currencies have long memories in their returns during the Tranquil period, BTC, LTC, and XRP exhibit efficiency during the First Wave of the pandemic, while BTC, ETH, and MONERO indicate efficiency during the Second Wave. Most assessed digital currencies showed equilibrium by 2022, with the exception of ETH and MONERO, which exhibit long memories, and LTC, which demonstrates anti-persistence. These results hold significance for investors in these alternative markets, as they suggest that some cryptocurrencies may be more predictable and therefore potentially profitable, whereas others may require greater caution and risk management strategies.

The vigorous development of the digital economy provides a new research perspective for alleviating local financial pressure. Based on the provincial panel data from 2013 to 2020, the mediating effect model and threshold regression model were used to explore the effect and mechanism of the digital economy on local fiscal pressure. The research shows that the digital economy has a significant alleviating effect on local financial pressure by increasing fiscal revenue, and this alleviating effect has a threshold effect on entrepreneurial activity. From the perspective of dimension, the alleviating effect of the digital economy development environment on local financial pressure is better than that of digital industrialization and industrial digitization. From a regional perspective, the digital economy has the most apparent impact on the western region. In addition, the quantile regression results show that with the increase in financial pressure, the easing effect of the digital economy shows an inverted U-shaped change. Therefore, in order to effectively alleviate the financial pressure on local governments, China should continue to promote the development of the digital economy, take multiple measures to increase entrepreneurial activity, and encourage various regions to implement differentiated digital economy development strategies according to local conditions.