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BackgroundChina is the top global consumer, importer, and producer of coal, accounting for about half of the world’s totals. Yet despite all this progress in renewable energy, coal still provided 56 percent of China’s energy consumption, and the country has the fourth-largest reserve globally. However, coal mining also releases enormous amounts of methane, a very potent greenhouse gas. Additionally, it contributes over 70% to the total CO2 within the country.PurposeThis study evaluates the effect of green mining practices on green innovation, employee green organizational commitment, and corporate sustainable development in the mining industry of China.MethodologyThe data was collected through an online survey and distributed all over China. For this research, 511 responses were analyzed using SmartPLS 4.1.ResultsThe results indicate that green marketing practices have a direct positive and significant effect on green innovation (β = 0.493), organizational commitment (β = 0.476), and organizational sustainable development (β = 0.0.195). The study proves that green innovation significantly affects both organizations’ sustainable development (β = 0.262) and their commitment (β = 0.0.293). Additionally, green innovation and organizational commitment significantly mediate the relationship between green marketing practices and corporate sustainable development. Moreover, the study observed that corporate social responsibility significantly moderated the influence of green mining practices on green innovation, organizational commitment, and corporate sustainable development. Cumulatively green mining practices, direct and indirect effects of green innovation and organizational commitment, and moderation of corporate social responsibility explained 67.2% variance in the corporate sustainable development.ConclusionThe study results further attest that green practices and corporate social responsibility play an essential role in underpinning sustainability and innovation in the mining sector. They also provide important lessons for policymakers and other industrial stakeholders on improving sustainable development.

Background The Chinese educational sector is dynamic; hence, there is a need to anchor the factors that influence faculty job satisfaction and performance. These are channeled through organizational climate (OrgC) and employee happiness (EmH). The growing integration of artificial intelligence applications (AIAs)—like ChatGPT—into the learning environment raises questions about AIAs’ moderating role in the relationship between EmH at work and EJoS. Purpose This research empirically examines the influence of OrgC on EmH, the direct and mediated impacts of EmH on EJoS, and the moderating effect of AIAs on the influence of EmH on EJoS. Design/Methodology Data was collected from faculty members of various Chinese universities. Using SmartPLS version 4.1, I have analyzed six hypotheses and the corresponding research questions. Findings The outcomes include favorable effects of OrgC on EmH and EJoS. EmH significantly correlates with EJoS, partially mediating the relationship between OrgC and EJoS. Interestingly, the research did not find evidence that AIAs moderated the relationship (ChatGPT) between EmH and EJoS. The predictors (OrgC and EmH) and moderation of AIAs explained a 51.9% change in EJoS, and EJoS explained a 13.3% variance in employee job performance. Conclusion This study’s findings support a supportive OrgC as the key instrument for improving employees’ happiness and job satisfaction. AI assistants, such as ChatGPT, provide relative efficiency and support but do not significantly affect how EmH at work relates to job satisfaction.

Gastric cancer is the fifth most common malignancies and the third leading cause of cancer-related death worldwide, with more than 40% of new cases occurring in China. With the advancement of treatment methods, the application of adjuvant therapy and targeted drugs, the prognosis of patients with gastric cancer has been significantly improved. In recent years, more and more studies have reported that magnetic resonance imaging (MRI) showed great value in the clinical application among patients with gastric cancer, including preoperative staging, treatment response evaluation, predicting prognosis and histopathological features, treatment guidance, and molecular imaging. The remarkable research progress of MRI in gastric cancer will provide new evaluation and treatment approaches for clinical diagnosis and treatment. This article aims to review the current status of the application and research progress of MRI in patients with gastric cancer.

Input of labile carbon may accelerate the decomposition of existing soil organic matter (priming effect), with the priming intensity depending on changes in soil nitrogen availability after permafrost thaw. However, experimental evidence for the linkage between the priming effect and post-thaw nitrogen availability is unavailable. Here we test the hypothesis that elevated nitrogen availability after permafrost collapse inhibits the priming effect by increasing microbial metabolic efficiency based on a combination of thermokarst-induced natural nitrogen gradient and nitrogen addition experiment. We find a negative correlation between the priming intensity and soil total dissolved nitrogen concentration along the thaw sequence. The negative effect is confirmed by the reduced priming effect after nitrogen addition. In contrast to the prevailing view, this nitrogen-regulated priming intensity is independent of extracellular enzyme activities but associated with microbial metabolic efficiency. These findings demonstrate that post-thaw nitrogen availability regulates topsoil carbon dynamics through its modification of microbial metabolic efficiency. Soil nitrogen availability may alter carbon dynamics after permafrost thaw, but experimental evidence for this carbon-nitrogen interaction is still lacking. Here the authors show that elevated post-thaw nitrogen availability inhibits soil carbon release through its enhancement in microbial metabolic efficiency.

Background 5-fluorouracil (5-FU)-based chemotherapy regimen has been widely used for the treatment of gastric cancer, but meanwhile the development of chemotherapeutic resistance remains a major clinical challenge. Tumor microenvironment (TME) frequently correlates with the development of chemoresistance in human cancer. As a major component of TME, the role of tumor-associated macrophages (TAMs) in the chemoresistance of gastric cancer has not been fully elucidated. Methods Immunohistochemistry (IHC) was applied to detect the density of TAMs in clinical samples of 103 patients with gastric cancer who had undergone 5-FU-based neoadjuvant chemotherapy. 5-FU-resistant gastric cell lines MKN45-R and HGC27-R were established, macrophages were then separately co-cultured with MKN45-R, HGC27-R cells and their parental cells. The effect of gastric cancer cells on the polarization of macrophages, the biological function of M2-polaried macrophages and the mechanism for promoting 5-FU-resistance were investigated. Then the correlation between the expression of CXC motif chemokine ligand 5 (CXCL5) and the infiltration of hemoglobin scavenger receptor (CD163) positive and mannose receptor (CD206) positive macrophages was analyzed, the prognostic value of CXCL5 expression in clinical samples was further explored. Results The high infiltration of macrophages marked by CD68 in gastric cancer samples was significantly associated with the resistance of gastric cancer to chemotherapy. Gastric cancer cells could modulate macrophages to M2-like polarization through indirect co-culture, and chemoresistant cells were more efficient in inducing macrophages polarization to M2 phenotype. Co-culturing M2-polarized macrophages in turn enhanced 5-FU-resistance of gastric cancer cells, and it was further verified that CXCL5 derived from M2-polarized macrophages promoted chemoresistance through activing the PI3K/AKT/mTOR pathway. Besides, high level of CXCL5 could recruit monocytes to form more M2-polarized macrophages. Clinically, high expression of CXCL5 in gastric cancer samples was associated with the high infiltration of CD163 positive macrophages and CD206 positive macrophages, and patients with high expression of CXCL5 presented lower overall survival (OS) rates than those with low expression of CXCL5. Conclusion Interaction between TAMs and gastric cancer cells promoted chemoresistance in gastric cancer via CXCL5/PI3K/AKT/mTOR pathway. Thus, targeting TAMs and blocking the cell–cell crosstalk between TAMs and gastric cancer cells may represent prospective therapeutic strategies for patients with gastric cancer.

With the development of smart agriculture and the growth of the global population, vegetable production is facing the dual challenges of diversified planting environments and increased concealment of diseases. Eggplant, as an important economic crop, has its disease detection accuracy directly affecting yield and quality. However, traditional detection methods fail to effectively capture small diseased areas. To address this issue, this paper proposes an improved deep learning small target detection model—the Efficient Deep-fusion Detection Model (EDDet), which is specifically optimized for the recognition of small diseased spots in eggplant disease detection. In the detection network, we innovatively designed the Pinwheel Fusion Feature Extractor (PFFE) framework, replacing the standard convolutions of the first two layers with Pinwheel Convolutions (PConv). By using asymmetric padding and parallel convolution kernel design, the receptive field is effectively expanded, the ability to capture underlying features is enhanced, and the detection of small diseased areas in eggplants is more precise. In the feature fusion stage, this paper designs a Cross-layer Attention Module (CAM), including Cross-layer Channel Attention (CCA) and Cross-layer Spatial Attention (CSA), which can efficiently interact and fuse features of different scales without additional sampling, alleviating the information loss caused by semantic gaps. In addition, to solve the instability caused by IoU fluctuations in the bounding box regression process, the model introduces Scale-based Dynamic Loss (SD Loss), which dynamically adjusts the loss weight based on the size of the target. By adaptively adjusting the proportion of IoU-based loss and location constraint loss, more precise localization and stable regression of small diseased areas in eggplants are achieved. Experimental results demonstrate that EDDet achieves a notable improvement in mAP50 (85.4%), outperforming the baseline by 2.8%.Importantly, EDDet also Maintains excellent efficiency with only 2.75 M parameters, 9.1 GFLOPs, and a high inference speed of 288.3 FPS, which is 37.5 FPS higher than the baseline.These results highlight the model’s strong potential for real-time deployment in complex agricultural scenarios where both precision and speed are critical.

Background The main types of PD-L1 in the blood include soluble PD-L1 (sPD-L1), exosomal PD-L1 (exoPD-L1), and PD-L1 in circulating tumor cells (CTCs). However, the predictive and prognostic values of these three indicators in patients with non-small cell lung cancer (NSCLC) undergoing immune checkpoint inhibitor (ICI) therapy are unclear, warranting a systematic meta-analysis. Methods A systematic literature search was performed in the PubMed, Cochrane Library, and Embase databases. The pooled hazard ratio (HR) and 95% confidence interval (CI) values were extracted from the included studies to investigate the correlation between the three PD-L1 indicators and overall survival (OS) or progression-free survival (PFS). The Newcastle–Ottawa Scale (NOS) was used to examine the quality of the included studies. Subgroup analyses were employed to investigate the heterogeneity. The publication bias of the included studies was assessed using Begg's and Egger's tests. P  < 0.05 was regarded as significantly different. Results The pooled results revealed that high pre-treatment sPD-L1 levels were significantly associated with inferior OS (HR = 2.32, 95% CI = 1.68–3.18, P  < 0.001) and PFS (HR = 2.52, 95% CI = 1.72–3.68, P  < 0.001). However, dynamic changes in sPD-L1 after immunotherapy were not statistically significant for OS (HR = 1.46, 95% CI = 0.65–3.26, P  > 0.05) or PFS (HR = 1.62, 95% CI = 0.92–2.86, P  > 0.05). Meanwhile, the upregulated pre-treatment exoPD-L1 levels were significantly associated with poor PFS (HR = 4.44, 95% CI = 2.87–6.89, P  < 0.001), whereas the post-treatment dynamic upregulation of exoPD-L1 was significantly correlated with superior PFS (HR = 0.36, 95% CI = 0.24–0.54, P  < 0.001) and OS (HR = 0.20, 95% CI = 0.07–0.53, P  < 0.001). For PD-L1 in CTCs, the pooled results indicated that PD-L1 expression in CTCs was not significantly correlated with OS (HR = 0.75, 95% CI = 0.49–1.13, P  = 0.170) and PFS (HR = 0.79, 95% CI = 0.59–1.06, P  = 0.12). Conclusions Blood-based PD-L1 analysis is a potential strategy for predicting treatment efficacy and prognosis in patients with cancer.

Background Neoadjuvant chemotherapy is an important part of the comprehensive treatment of advanced gastric cancer (GC). The effect of neoadjuvant chemotherapy plays a key role in the prognosis of GC patients. Pathological response can represent the effect of neoadjuvant chemotherapy. However, evidence focused on pathological response and associated clinicopathological factors in GC patients is quite little. In this retrospective study, the clinicopathological factors affecting the effect of neoadjuvant chemotherapy in GC patients were investigated, and suggestions were proposed to improve the effect of neoadjuvant chemotherapy on GC. Methods Retrospective analysis was performed on GC patients who received radical surgery after neoadjuvant chemotherapy from February 2016 to December 2019 at Peking Union Medical College Hospital. Relevant clinicopathological data was collected to analyze the factors influencing the effect of neoadjuvant chemotherapy. Chi-square test was used for univariate analysis. Logistic regression was used for multivariate analysis. Receiver operating characteristic curve (ROC) was used to determine the cutoff value of variables which significantly influenced the effect of neoadjuvant chemotherapy. Results A total of 203 GC patients were included in the study. Analyses showed that patients < 60 years old (OR = 1.840 [1.016–3.332], P = 0.044), histological type of poor differentiation or signet-ring cell carcinoma (OR = 2.606 [1.321–5.140], P = 0.006), and weight loss during neoadjuvant chemotherapy (OR = 2.110 [1.161–3.834], P = 0.014) were independent risk factors for neoadjuvant chemotherapy effect. In ROC analysis of weight change and neoadjuvant chemotherapy effect, area under the curve (AUC) was 0.593 ( P = 0.024) and cutoff value of weight change was − 2.95%. Chi-square test showed that patients without weight loss during neoadjuvant chemotherapy had a higher rate of oral nutritional supplement (ONS) than patients with weight loss ( P = 0.039). Conclusions Patients <60 years old, histological type of poor differentiation or signet-ring cell carcinoma, and weight loss during neoadjuvant chemotherapy were independent risk factors for neoadjuvant chemotherapy effect in GC patients. Patients with weight loss > 2.95% during neoadjuvant may have a worse chemotherapy effect. Timely nutritional support such as ONS to maintain patients’ body weight is crucial for improving the effect of neoadjuvant chemotherapy.

Cholangiocarcinoma (CCA) is an aggressive solid tumour with a 5-year survival rate ranging from 7% to 20%. It is, therefore, urgent to identify novel biomarkers and therapeutic targets to improve the outcomes of patients with CCA. SPRY-domain containing protein 4 (SPRYD4) contains SPRY domains that modulate protein–protein interaction in various biological processes; however, its role in cancer development is insufficiently explored. This study is the first to identify that SPRYD4 is downregulated in CCA tissues using multiple public datasets and a CCA cohort. Furthermore, the low expression of SPRYD4 was significantly associated with unfavourable clinicopathological characteristics and poor prognosis in patients with CCA, indicating that SPRYD4 could be a prognosis indicator of CCA. In vitro experiments revealed that SPRYD4 overexpression inhibited CCA cells proliferation and migration, whereas the proliferative and migratory capacity of CCA cells was enhanced after SPRYD4 deletion. Moreover, flow cytometry showed that SPRYD4 overexpression triggered the S/G2 cell phase arrest and promoted apoptosis in CCA cells. Furthermore, the tumour-inhibitory effect of SPRYD4 was validated in vivo using xenograft mouse models. SPRYD4 also showed a close association with tumour-infiltrating lymphocytes and important immune checkpoints including PD1, PD-L1 and CTLA4 in CCA. In conclusion, this study elucidated the role of SPRYD4 during CCA development and highlighted SPRYD4 as a novel biomarker and tumour suppressor in CCA.

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric disorders. Conventional pharmacological treatments often have significant side effects, highlighting the need for safer alternatives. Traditional Chinese Medicine (TCM) offers potential solutions, with plant extracts like those from Nelumbo nucifera leaves showing promise due to their historical use and minimal side effects. This study employs a comprehensive computational biology approach to explore the anti-obesity effects of Nelumbo nucifera Leaf Bioactive Compounds. Sixteen active compounds from Nelumbo nucifera leaves were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Clustering analysis identified three representative molecules, and network pharmacology pinpointed PPARG as a common target gene. Molecular docking and machine learning models were used for inhibitors screening, and molecular dynamics simulations were futher used to investigate the inhibitory effects and mechanisms of these molecules on PPARG. Subsequent cellular assays confirmed the ability of Sitogluside to reduce lipid accumulation and triglyceride levels in 3T3-L1 cells, underscoring its potential as an effective and safer obesity treatment. Our findings provide a molecular basis for the anti-obesity properties of Nelumbo nucifera Leaf Bioactive Compounds and pave the way for developing new, effective, and safer obesity treatments.