TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health.

Introduction: This paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives. Methods: To assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices. Results: Analysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises (r=0.523;p<0.001), Moreover, the presence of innovative environmental protection technologies on these platforms was found to positively influence the enforcement of environmental policies, with a calculated impact ratio of (a∗b/c=55.31%). An intermediary analysis highlighted that environmental innovation technology plays a mediating role in this process. Additionally, adjustment analysis showed that enterprises of various sizes and industries respond differently to digital platforms, indicating the need for tailored environmental policies. Discussion: These findings underscore the pivotal role of digital platforms in enhancing CSR efforts and public health by fostering improved environmental practices among corporations. The mediating effect of environmental innovation technologies suggests that digital platforms not only facilitate direct environmental actions but also enhance the efficiency and effectiveness of such initiatives through technological advances. The variability in response by different enterprises points to the importance of customizable strategies in policy formulation. By offering empirical evidence of digital platforms' potential to advance CSR and public health through environmental initiatives, this paper contributes to the ongoing dialogue on sustainable development goals. It provides practical insights for enterprises and policy implications for governments striving to craft more effective environmental policies and strategies.

The associations between functional dyspepsia and potential risk factors: A comprehensive Mendelian randomization study.

BACKGROUND: Previous cross-sectional studies have identified multiple potential risk factors for functional dyspepsia (FD). However, the causal associations between these factors and FD remain elusive. Here we aimed to fully examine the causal relationships between these factors and FD utilizing a two-sample MR framework. METHODS: A total of 53 potential FD-related modifiable factors, including those associated with hormones, metabolism, disease, medication, sociology, psychology, lifestyle and others were obtained through a comprehensive literature review. Independent genetic variants closely linked to these factors were screened as instrumental variables from genome-wide association studies (GWASs). A total of 8875 FD cases and 320387 controls were available for the analysis. The inverse variance weighted (IVW) method was employed as the primary analytical approach to assess the relationship between genetic variants of risk factors and the FD risk. Sensitivity analyses were performed to evaluate the consistency of the findings using the weighted median model, MR-Egger and MR-PRESSO methods. RESULTS: Genetically predicted depression (OR 1.515, 95% confidence interval (CI) 1.231 to 1.865, p = 0.000088), gastroesophageal reflux disease (OR 1.320, 95%CI 1.153 to 1.511, p = 0.000057) and years of education (OR 0.926, 95%CI 0.894 to 0.958, p = 0.00001) were associated with risk for FD in univariate MR analyses. Multiple medications, alcohol consumption, poultry intake, bipolar disorder, mood swings, type 1 diabetes, elevated systolic blood pressure and lower overall health rating showed to be suggestive risk factors for FD (all p<0.05 while ≥0.00167). The positive causal relationship between depression, years of education and FD was still significant in multivariate MR analyses. CONCLUSIONS: Our comprehensive MR study demonstrated that depression and lower educational attainment were causal factors for FD at the genetic level.

Clinical values of serum C5a in Alzheimer's disease patients with different dementia stages.

Alzheimer's disease (AD) is characterized by abnormal inflammatory responses, and complement C5a (C5a) is known to initiate inflammation. This study aimed to investigate the associations between serum C5a, inflammatory responses, and cognitive function in AD patients. A total of 242 CE patients and 132 age-matched controls were included. Enzyme-linked immunosorbent assay revealed increased levels of C5a, interleukin (IL)-4, IL-6, IL-10, IL-1ß, and tumor necrosis factor (TNF)-α with advancing stages of AD. Pearson correlation coefficient and receiver operating characteristic curve revealed positive correlations between serum C5a levels, inflammatory cytokine levels, Neuropsychiatric Inventory (NPI) and Activities of Daily Living (ADL) scores, and negative correlations with Mini-mental State Examination (MMSE) and Montreal cognitive assessment (MoCA) scores. Serum C5a above 68.68 pg/mL could aid in the diagnosis of AD. Multivariable logistic analysis revealed that serum C5a was an independent risk factor for IL-1ß/IL-6/IL-10/TNF-α and an independent protective factor for IL-4. Higher serum C5a levels were associated with lower MMSE and MoCA scores. In conclusion, elevated serum C5a levels were beneficial for AD diagnosis and predictive of inflammation and cognitive dysfunction.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

SR9009 attenuates inflammation-related NPMSC pyroptosis and IVDD through NR1D1/NLRP3/IL-1ß pathway.

Intervertebral disc is a highly rhythmical tissue. As a key factor linking biorhythm and inflammatory response, the shielding effect of NR1D1 in the process of intervertebral disc degeneration remains unclear. Here, we first confirmed that NR1D1 in the nucleus pulposus tissue presents periodic rhythmic changes and decreases in expression with intervertebral disc degeneration. Second, when NR1D1 was activated by SR9009 in vitro, NLRP3 inflammasome assembly and IL-1ß production were inhibited, while ECM synthesis was increased. Finally, the vivo experiments further confirmed that the activation of NR1D1 can delay the process of disc degeneration to a certain extent. Mechanistically, we demonstrate that NR1D1 can bind to IL-1ß and NLRP3 promoters, and that the NR1D1/NLRP3/IL-1ß pathway is involved in this process. Our results demonstrate that the activation of NR1D1 can effectively reduce IL-1ß secretion, alleviate LPS-induced NPMSC pyroptosis, and protect ECM degeneration.

A novel mechanism of major ginsenosides from Panax ginseng against multiple organ aging in middle-aged mice: Phosphatidylcholine-myo-inositol metabolism based on metabolomic analysis.

Aging is a complex, degenerative process associated with various metabolic abnormalities. Ginsenosides (GS) is the main active components of Panax ginseng, which has anti-aging effects and improves metabolism. However, the anti-aging effect and the mechanism of GS in middle-aged mice has not been elucidated. In this study, GS after 3-month treatment significantly improved the grip strength, fatigue resistance, cognitive indices, and cardiac function of 15-month-old mice. Meanwhile, GS treatment reduced the fat content and obviously inhibited histone H2AX phosphorylation at Ser 139 (γ-H2AX), a marker of DNA damage in major organs, especially in the heart and liver. Further, the correlation analysis of serum metabolomics combined with aging phenotype suggested that myo-inositol (MI) upregulated by GS was positively correlated with left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS), the main indicators of cardiac function. More importantly, liver tissue metabolomic analysis showed that GS increased MI content by promoting the synthesis pathway from phosphatidylcholine (PC) to MI for the inhibition of liver aging. Finally, we proved that MI reduced the percentage of senescence-associated ß-galactosidase staining, γ-H2AX immunofluorescence staining, p21 expression, and the production of reactive oxygen species in H2O2-induced cardiomyocytes. These results suggest that GS can enhance multiple organ functions, especially cardiac function for promoting the healthspan of aging mice, which is mediated by the conversion of PC to MI in the liver and the increase of MI level in the serum. Our study might provide new insights into the potential mechanisms of ginsenosides for prolonging the healthspan of natural aging mice.

Deconstruction of Polymers through Olefin Metathesis.

The consumption of synthetic polymers has ballooned; so has the amount of post-consumer waste generated. The current polymer economy, however, is largely linear with most of the post-consumer waste being either landfilled or incinerated. The lack of recycling, together with the sizable carbon footprint of the polymer industry, has led to major negative environmental impacts. Over the past few years, chemical recycling technologies have gained significant traction as a possible technological route to tackle these challenges. In this regard, olefin metathesis, with its versatility and ease of operation, has emerged as an attractive tool. Here, we discuss the developments in olefin-metathesis-based chemical recycling technologies, including the development of new materials and the application of olefin metathesis to the recycling of commercial materials. We delve into structure-reactivity relationships in the context of polymerization-depolymerization behavior, how experimental conditions influence deconstruction outcomes, and the reaction pathways underlying these approaches. We also look at the current hurdles in adopting these technologies and relevant future directions for the field.

In Vivo and In Vitro Interactions between Exopolysaccharides from Bacillus thuringensis HD270 and Vip3Aa11 Protein.

Bacillus thuringiensis (Bt) secretes the nutritional insecticidal protein Vip3Aa11, which exhibits high toxicity against the fall armyworm (Spodoptera frugiperda). The Bt HD270 extracellular polysaccharide (EPS) enhances the toxicity of Vip3Aa11 protoxin against S. frugiperda by enhancing the attachment of brush border membrane vesicles (BBMVs). However, how EPS-HD270 interacts with Vip3Aa11 protoxin in vivo and the effect of EPS-HD270 on the toxicity of activated Vip3Aa11 toxin are not yet clear. Our results indicated that there is an interaction between mannose, a monosaccharide that composes EPS-HD270, and Vip3Aa11 protoxin, with a dissociation constant of Kd = 16.75 ± 0.95 mmol/L. When EPS-HD270 and Vip3Aa11 protoxin were simultaneously fed to third-instar larvae, laser confocal microscopy observations revealed the co-localization of the two compounds near the midgut wall, which aggravated the damage to BBMVs. EPS-HD270 did not have a synergistic insecticidal effect on the activated Vip3Aa11 protein against S. frugiperda. The activated Vip3Aa11 toxin demonstrated a significantly reduced binding capacity (548.73 ± 82.87 nmol/L) towards EPS-HD270 in comparison to the protoxin (34.96 ± 9.00 nmol/L). Furthermore, this activation diminished the affinity of EPS-HD270 for BBMVs. This study provides important evidence for further elucidating the synergistic insecticidal mechanism between extracellular polysaccharides and Vip3Aa11 protein both in vivo and in vitro.

Metal oxide hybridization enhances room temperature phosphorescence of carbon dots-SiO2 matrix for information encryption and anti-counterfeiting.

Room temperature phosphorescent (RTP) carbon dot (CD) materials have been widely used in various fields, but it is difficult to achieve a long lifetime, high stability and easy synthesis. In particular, realizing the phosphorescence emission of CDs using a metal oxide (MO) matrix is a challenge. Here, solid gels are synthesized via in situ hydrolysis, and then RTP CDs are synthesized based on a SiO2 matrix (CDs@SiO2) and hybridized with a MO matrix (CDs@SiO2-MO) by high-temperature calcination. Among the materials synthesized, Al2O3 matrix RTP CDs (CDs@SiO2-Al2O3) have a long phosphorescence lifetime of 689 ms and can exhibit yellow-green light visible to the naked eye for 9 s after the UV light (365 nm) is turned off. Compared with the green phosphorescence of CDs@SiO2, the yellow-green phosphorescence lifetime of CDs@SiO2-Al2O3 is enhanced by 420 ms. In addition, CDs@SiO2-Al2O3 maintains good stability of phosphorescence emission in water, strongly oxidizing solutions and organic solvents. As a result, CDs@SiO2-Al2O3 can be applied to the field of information encryption and security anti-counterfeiting, and this work provides a new, easy and efficient synthesis method for MO as an RTP CD matrix.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Mix-Key: graph mixup with key structures for molecular property prediction.

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.

Gene cloning, expression and performance validation of nitric oxide dismutase.

Nitrous oxide (N2O) is a significant contributor to global warming and possesses an ozone-depleting impact nearly 298 times that of CO2. To reduce N2O emissions, the newly-discovered nod gene which can directly convert NO into N2 and O2 was successfully cloned from the anaerobic denitrification sludge. The recombinant plasmid containing the nod gene was built, and the expression of nod gene in Escherichia coli was determined, leading to the construction of recombinant engineering bacteria. Results showed that the recombinant engineering bacteria E. coli BL21 (DE3)-pET28a-nod could autonomously degrade NO, with a degradation rate of 72 % within 48 h, and could produce 2479.72 ppm of N2 and 75.12 mL of O2. The cumulative O2 production of the sludge sample and recombinant E. coli within 8 h was 1.75 mL and 8.45 mL, respectively. The cumulative O2 production of recombinant E. coli was at least 4.82 times higher than that of the sludge sample. The investigation proposed a new biodegradation pathway for nitrogen pollution.

Study on the treatment of osteoarthritis by acupuncture combined with traditional Chinese medicine based on pathophysiological mechanism: A review.

Osteoarthritis (OA) is a major contributor to disability and social costs in the elderly. As the population ages and becomes increasingly obese, the incidence of the disease is higher than in previous decades. In recent years, important progress has been made in the causes and pathogenesis of OA pain. Modern medical treatment modalities mainly include the specific situation of the patient and focus on the core treatment, including self-management and education, exercise, and related weight loss. As an important part of complementary and alternative medicine, TCM has remarkable curative effect, clinical safety, and diversity of treatment methods in the treatment of OA. Traditional Chinese Medicine treatment of OA has attracted worldwide attention. Therefore, this article will study the pathophysiological mechanism of OA based on modern medicine, and explore the treatment of OA by acupuncture combined with Chinese Medicine.

Ginseng polysaccharide components attenuate obesity and liver lipid accumulation by regulating fecal microbiota and hepatic lysine degradation.

The increasing incidence of obesity has led to widespread attention in the exploration of natural ingredients. Ginseng polysaccharides (PGP), the main components from Panax ginseng, have been reported potential effect to attenuate obesity and regulate lipid metabolism. In this study, we found that PGP inhibited the high-fat diet (HFD)-induced weight gain, fat ratio and fat tissue weight after 8-week administration. Serum and liver lipid analysis showed that PGP decreased the levels of triglyceride and total cholesterol, which was mediated by the inhibition of key genes for fatty acid and cholesterol metabolisms. Metabolomics studies showed that the inhibitory effect of PGP on liver lipid accumulation was significantly correlated with its regulation of citric acid cycle and lysine degradation. PGP regulated the expression of genes related to lysine degradation in both liver tissue and hepatocytes. In addition, PGP reshaped the composition of fecal microbiota at the genus and species levels in obese mice. Spearman's correlation analysis demonstrated that Staphylococcus sciuri, Staphylococcus lentus, and Pseudoflavonifractor sp. An85 may be the potential targets that PGP maintains the abundance of l-lysine against obesity. It concluded that PGP can attenuate obesity and liver lipid accumulation by regulating fecal microbiota and hepatic lysine degradation.

Periodontal disease and risk of Alzheimer's disease: A two-sample Mendelian randomization.

BACKGROUND: Evidence from observational studies and clinical trials suggests an association between periodontal disease and Alzheimer's disease (AD). However, the causal relationship between periodontal disease and AD remains to be determined. METHODS: We obtained periodontal disease data from the FinnGen database and two sets of AD data from the IEU consortium and PGC databases. Subsequently, we conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between periodontal disease and AD. RESULTS: The results of the random-effects IVW analysis revealed no evidence of a genetic causal relationship between periodontal disease and AD, regardless of whether the AD data from the IEU consortium or the AD data from the PGC database were utilized. No heterogeneity, multiple effects of levels, or outliers were observed in this study. CONCLUSIONS: Our findings indicate that there is no causal relationship between periodontal disease and AD at the genetic level.

Overcoming neutrophil-induced immunosuppression in postoperative cancer therapy: Combined sialic acid-modified liposomes with scaffold-based vaccines.

Immunotherapy is a promising approach for preventing postoperative tumor recurrence and metastasis. However, inflammatory neutrophils, recruited to the postoperative tumor site, have been shown to exacerbate tumor regeneration and limit the efficacy of cancer vaccines. Consequently, addressing postoperative immunosuppression caused by neutrophils is crucial for improving treatment outcomes. This study presents a combined chemoimmunotherapeutic strategy that employs a biocompatible macroporous scaffold-based cancer vaccine (S-CV) and a sialic acid (SA)-modified, doxorubicin (DOX)-loaded liposomal platform (DOX@SAL). The S-CV contains whole tumor lysates as antigens and imiquimod (R837, Toll-like receptor 7 activator)-loaded PLGA nanoparticles as immune adjuvants for cancer, which enhance dendritic cell activation and cytotoxic T cell proliferation upon localized implantation. When administered intravenously, DOX@SAL specifically targets and delivers drugs to activated neutrophils in vivo, mitigating neutrophil infiltration and suppressing postoperative inflammatory responses. In vivo and vitro experiments have demonstrated that S-CV plus DOX@SAL, a combined chemo-immunotherapeutic strategy, has a remarkable potential to inhibit postoperative local tumor recurrence and distant tumor progression, with minimal systemic toxicity, providing a new concept for postoperative treatment of tumors.

One-Step Preparation of High-Stability CsPbX3/CsPb2X5 Composite Microplates with Tunable Emission.

The core-shell structure is an effective means to improve the stability and optoelectronic properties of cesium lead halide (CsPbX3 (X = Cl, Br, I)) perovskite quantum dots (QDs). However, confined by the ionic radius differences, developing a core-shell packaging strategy suitable for the entire CsPbX3 system remains a challenge. In this study, we introduce an optimized hot-injection method for the epitaxial growth of the CsPb2X5 substrate on CsPbX3 surfaces, achieved by precisely controlling the reaction time and the ratio of lead halide precursors. The synthesized CsPbX3/CsPb2X5 composite microplates exhibit an emission light spectrum that covers the entire visible range. Crystallographic analyses and density functional theory (DFT) calculations reveal a minimal lattice mismatch between the (002) plane of CsPb2X5 and the (11¯0) plane of CsPbX3, facilitating the formation of high-quality type-I heterojunctions. Furthermore, introducing Cl- and I- significantly alters the surface energy of CsPb2X5's (110) plane, leading to an evolutionary morphological shift of grains from circular to square microplates. Benefiting from the passivation of CsPb2X5, the composites exhibit enhanced optical properties and stability. Subsequently, the white light-emitting diode prepared using the CsPbX3/CsPb2X5 composite microplates has a high luminescence efficiency of 136.76 lm/W and the PL intensity decays by only 3.6% after 24 h of continuous operation.

A psychobiographical analysis of Empress Dowager Cixi: exploring the femininity castrated complex.

In Chinese culture, there is a widely circulated phrase, 'A hen crows in the morning'. This phrase is used to humiliate women who steal power and engage in the political field. It demonstrates the complicated relationship between women and power in the context of Chinese culture. Women are not completely excluded from the politics, but women in power are often stigmatised. This study explores the life of Empress Dowager Cixi (1835-1908), the last female dominator in Chinese history, takes psychobiography as the research method, and attempts to understand the complicated relationship between women and power in Chinese culture through analysing Cixi's life from the perspective of complex and cultural complex theory which originated with C.G. Jung and analytical theory. The research findings show that humiliating and suppressing women with political talent can trigger their complexes, both personal and cultural. This study attempts to propose the femininity castrated complex to better describe the conscious and unconscious psychological dynamics impacting on women within patriarchal, political Chinese culture. This complex further relates to (1) denying her biological sex in order to avoid accusations of superego and, (2) the relationship with her son who is not only her son, but also her enemy regarding (political) power.

Utilizing data platform management to implement "5W" analysis framework for preventing and controlling corruption in grassroots government.

In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the "5W" (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.