Explore the vast range of titles available.

Based on the perspective of government-dominated and market-driven industrial co-agglomeration mode, the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control is explored by using spatial Durbin model, and the mediating effect of technological innovation is further tested. The results show that: (1) At the national level, the government-dominated industrial co-agglomeration only significantly promotes the local ecological environment pollution control, while the market-driven industrial co-agglomeration also can promote the ecological environment pollution control in the surrounding region through its spatial spillover effect. Moreover, there is a significant inverted “U-shaped” curve relationship between the economic development level and ecological environment pollution. Additionally, the environment regulation is also conducive to promoting the ecological environment pollution control, while the industrial structure and foreign direct investment will lead to more serious ecological environment pollution; (2) In the east region, the government-dominated and market-driven industrial co-agglomeration can promote the ecological environment pollution control in the local and surrounding regions, and the promotion effect and spatial spillover effect of market-driven industrial co-agglomeration are greater. However, in the central and west regions, the government-dominated industrial co-agglomeration and market-driven industrial co-agglomeration only promote the local ecological environment pollution control. (3) Technological innovation has partial mediating effect in the impact of government-dominated and market-driven industrial co-agglomeration on the ecological environment pollution control, namely that the government-dominated and market-driven industrial co-agglomeration not only can directly promote the ecological environment pollution control, but also can indirectly promote the ecological environment pollution control through the mediating effect of technological innovation.

Based on data from 30 provinces in China from 2013 to 2022, this paper employs the Spatial Durbin Model to analyze the effect of integration development of digital intelligence on the quantity and quality of green technology innovation and its regional heterogeneity. The moderating effects of degree of nationalization and green purchasing are further explored. The results show the following: (1) The integration development of digital intelligence can not only increase the quantity of green technology innovation, but also significantly improve the quality of green technology innovation. Meanwhile, the integration development of digital intelligence has a negative spatial spillover effect on the quantity and quality of green technology innovation in neighboring regions. (2) There is significant regional heterogeneity in the improvement effect of digital intelligence integration development on the quantity and quality of green technology innovation and its spatial spillover effect. Moreover, the integration development of digital intelligence realizes the “quantity increase and quality improvement” of green technology innovation mainly by generating a resource allocation effect, scale economy effect and technology promotion effect. (3) Degree of nationalization negatively moderates the impact of integration development of digital intelligence on the quantity and quality of green technology innovation, while green purchasing positively moderates the impact of integration development of digital intelligence on the quantity and quality of green technology innovation, both of which have significant spatial spillover effects.

The rapid increase in household leverage in China has led to potential financial risks and threatened socio-economic stability. In mitigating household debt risks, the effectiveness of monetary policy regulation varies significantly with differences in household financial literacy. Based on micro-level household financial data from China, this paper delves into the impact of monetary policy on household leverage and its underlying mechanisms and analyzes the role of financial literacy in the transmission of monetary policy. The findings reveal that expansionary monetary policy helps reduce household leverage, while contractionary monetary policy leads to an increase. Monetary policy affects household leverage through the “income effect,”“wealth effect” and “substitution effect.” Notably, low financial literacy amplifies the impact of contractionary monetary policy on leverage, whereas high financial literacy mitigates this effect. This paper suggests strengthening financial regulation and risk warning systems, optimizing the design of monetary policy transmission, promoting multi-tiered financial product supply, and deepening the promotion of financial literacy education to achieve an effective balance between “stable growth” and “risk prevention.”

The objective of this retrospective study was to determine whether lymph node metastasis has a prognostic impact on patients with stage IV breast cancer. Seven thousand three hundred and seventy-nine patients with de novo stage IV breast cancer diagnosed from 2004 to 2013 were identified. Kaplan-Meier estimate method was fitted to measure overall survival and breast cancer-specific survival (BCSS). Cox proportional hazard analysis was used to evaluate the association between N stage and BCSS after controlling variables such as other patient/tumor characteristics. The primary site of M1 tumors was mainly upper-outer quadrant and overlapping lesion of the breast. Patients with N1 disease had better overall survival and BCSS than did those without lymph node metastasis. The overall survival and BCSS of M1 patients with N3 disease were significantly lower than that of those with N0, N1 and N2 disease, whereas patients with N2 and N0/N1 involvement showed no significant difference with survival. Multivariate analysis showed that lymph node metastasis was an important prognostic factor for M1 patients (N1 versus N0, hazard ratio [HR] = 0.902, 95% confidence interval [CI]: 0.825-0.986, p = 0.023; N3 versus N0, HR = 1.161, 95% CI: 1.055-1.276, p = 0.002). For M1 patients, age, race, marital status, primary site, ER, PR and HER2 were the independent prognostic factors. The cohort study provides an insight into de novo stage IV breast cancer with lymph node metastasis. Our results indicated that accurate lymph node evaluation for stage IV patients is still necessary to obtain important prognostic information.

Adequate lymph node evaluation is recommended for optimal staging in patients with malignant neoplasms including breast cancer. However, the role of negative lymph nodes (LNs) remains unclear in breast cancer according to N substage (N1, N2, and N3). In this study, for the first time, we analyzed the prognostic significance of negative LNs in breast cancer patients. A critical relationship was observed between negative LN count and survival, independent of patient characteristics and other related molecular variables including estrogen receptor (PR) status, progesterone receptor (ER) status, human epidermal growth factor receptor 2 (HER2) status, depth of tumor invasion and degree of differentiation. This research is of great importance in providing more information about the prognosis of breast cancer by statistical analysis of negative lymph nodes and can serve as a useful supplement to the current pathological system.

Green innovation, which combines “innovation-driven” and “green development,” is one of the most powerful ways to overcome resource and environmental constraints and enhance manufacturing industry sustainability. Based on the innovation value chain perspective, the green innovation process of manufacturing industry is decomposed into two stages: green scientific and technological R&D and achievement transformation. Then, using the three-stage DEA and Malmquist index model to measure the green innovation performance of China’s manufacturing industry, and compare its regional heterogeneity from the dual perspectives of static efficiency and dynamic productivity. In addition, this paper further discusses the improvement path of green innovation performance of China’s manufacturing industry. The findings are as follows: (1) The green innovation efficiency of manufacturing industry in China is at a comparatively low degree and has great potential for improvement. Moreover, it shows apparent regional heterogeneity: The green innovation efficiency in the eastern region is higher than that in the western region, and both are higher than that in the center region, confirming the phenomenon of “central collapse”. (2) The green innovation productivity of China’s manufacturing industry shows a “W-type” dynamic evolution tendency, with green technological progress as the key driving factor, while the green technical efficiency does not clearly exhibit a “catch-up effect”. Additionally, it shows significant regional heterogeneity: green innovation productivity in the western region is higher than that in the central and eastern regions, indicating a potential “backwardness advantage”. (3) The eastern region of China is located in combination IV, which indicates that it has a high rate of green innovation efficiency but a low rate of green innovation productivity; the central region is located in combination III, which indicates that it has a low rate of both green innovation efficiency and productivity; and the western region is located in combination II, which indicates that it has a low rate of green innovation efficiency but a high rate of green innovation productivity. Last but not least, this paper puts forward three kinds of paths for the improvement of the green innovation performance of China’s manufacturing industry: unilateral breakthrough, step-by-step and stimulating jumping type.

The double-wheel driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity. Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that there is a U-shaped curve relationship between industrial co-agglomeration and TFEE. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Meanwhile, industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect on the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.

Background With the improvement of treatment and prognosis for patients with late malignant diseases, certain malignancies with distant metastasis (M1 category) have been further classified into M1a (single metastatic site) and M1b (multiple metastatic sites) category in the staging system. We aimed to assess the feasibility of sub-classifying metastatic pancreatic adenocarcinoma (mPA) into M1a and M1b category depending on the number of metastatic organs. Methods Patient records were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). Univariable and multivariable analyses were performed using the Cox regression model. Then survival analysis was determined using the Kaplan–Meier method. Results A total of 11,885 patients were included in this analysis, including 9425 patients with single metastasis and 2460 patients with multiple metastases. Multivariable analysis showed that gender, age, marital status, grade, surgery, chemotherapy, and radiotherapy were independent prognostic factors for patients with single metastasis; gender, age, marital status, grade, chemotherapy and radiotherapy were independent prognostic factors for patients with multiple metastases. Notably, surgery was an independent prognostic factor for patients with single metastasis ( P  < 0.001) but not for patients with multiple metastases ( P  = 0.134). Kaplan–Meier analysis showed that patients with single metastasis (M1a) had better survival outcomes than patients with multiple metastases (M1b) ( P  < 0.001). Conclusions PA patients with M1 diseases could be divided into M1a (single metastasis) category and M1b (multiple metastases) category by the number of metastatic organs. The subclassification would facilitate individualized treatment for late PA patients. Surgery was associated with lower mortality in M1a patients but not significantly in M1b patients.

The double-wheel-driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity; Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that: There is a U-shaped curve relationship between industrial co-agglomeration and TFEE, namely that industrial co-agglomeration first shows a certain inhibitory effect on TFEE, and then plays a significant role in promoting. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect in the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 −8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine . The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.