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Platelets, involved in the whole process of tumorigenesis and development, constantly absorb and enrich tumor-specific substances in the circulation during their life span, thus called “Tumor Educated Platelets” (TEPs). The alterations of platelet mRNA profiles have been identified as tumor markers due to the regulatory mechanism of post-transcriptional splicing. Small nuclear RNAs (SnRNAs), the important spliceosome components in platelets, dominate platelet RNA splicing and regulate the splicing intensity of pre-mRNA. Endogenous variation at the snRNA levels leads to widespread differences in alternative splicing, thereby driving the development and progression of neoplastic diseases. This review systematically expounds the bidirectional tumor-platelets interactions, especially the tumor induced alternative splicing in TEP, and further explores whether molecules related to alternative splicing such as snRNAs can serve as novel biomarkers for cancer diagnostics.

The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar imaging. In addition, it can better focus on target area and capture target information to boost model representation power.

The mixed caesium and formamidinium lead triiodide perovskite system (Cs 1 − x FA x PbI 3 ) in the form of quantum dots (QDs) offers a pathway towards stable perovskite-based photovoltaics and optoelectronics. However, it remains challenging to synthesize such multinary QDs with desirable properties for high-performance QD solar cells (QDSCs). Here we report an effective oleic acid (OA) ligand-assisted cation-exchange strategy that allows controllable synthesis of Cs 1 − x FA x PbI 3 QDs across the whole composition range ( x   =  0–1), which is inaccessible in large-grain polycrystalline thin films. In an OA-rich environment, the cross-exchange of cations is facilitated, enabling rapid formation of Cs 1 − x FA x PbI 3 QDs with reduced defect density. The hero Cs 0.5 FA 0.5 PbI 3 QDSC achieves a certified record power conversion efficiency (PCE) of 16.6% with negligible hysteresis. We further demonstrate that the QD devices exhibit substantially enhanced photostability compared with their thin-film counterparts because of suppressed phase segregation, and they retain 94% of the original PCE under continuous 1-sun illumination for 600 h. Mixed-cation perovskite quantum dot solar cells possess decent phase stability but considerably low efficiency. Here Hao et al. show that ligands are key to the formation of quantum dots with lower defect density and demonstrate devices that are more stable and efficient than their bulk counterparts.

Postoperative intracranial infection represents significant complications closely associated with a poor prognosis in patients with brain tumors. We aimed to investigate the value of cerebrospinal fluid (CSF) parameters in detection and monitoring of postoperative intracranial infection. This study assessed the diagnostic values CSF parameters for postoperative intracranial infection through the ROC curve and Fisher discriminant model, as well as the prognostic values through Kaplan-Meier analysis and multivariate Cox regression in brain tumor patients. 7 statistically significant CSF variables were extracted and identified, leading to the development of the infection discrimination model with a diagnostic sensitivity of 86.79% and a specificity of 77.01%. Its performance was in excellent agreement with clinical confirmation, as indicated by a κ value of 0.609, which also was validated by the external validation, with 92.86% sensitivity and 64.00% specificity, as well as a κ value of 0.602. Furthermore, elevated levels of CSF total protein, leukocytes, multinucleated cells, and multinucleated cell ratio, along with reduced glucose levels, were found to be associated with prolonged infection control, underscoring their prognostic significance. Multivariate Cox regression analyses identified elevated CSF total protein, reduced glucose and increased leukocyte count as independent predictors of prolonged time to infection control. We established and validated a Fisher model to objectively and quantitatively discriminate true-positive intracranial infection, as well as predicted the infection control time using CSF parameters.

Background Ischemic stroke (IS) is a common cerebrovascular disease. Although the formation of atherosclerosis, which is closely related to oxidative stress (OS), is associated with stroke-related deaths. However, the role of OS in IS is unknown. Methods OS-related key genes were obtianed by overlapping the differentially expressed genes (DEGs) between IS and normal control (NC) specimens, IS-related genes, and OS-related genes. Then, we investigated the mechanism of action of key genes. Subsequently, protein–protein interaction (PPI) network and machine learning algorithms were utilized to excavate feature genes. In addition, the network between feature genes and microRNAs (miRNAs) was established to investigate the regulatory mechanism of feature genes. Finally, quantitative PCR (qPCR) was utilized to validate the expression of feature genes with blood specimens. Results A total of 42 key genes related to OS were acquired. Enrichment analysis indicated that the key genes were associated with oxidative stress, reactive oxygen species, lipid and atherosclerosis, and cell migration-related pathways. Then, 6 feature genes ( HSPA8 , NCF2 , FOS , KLF4 , THBS1 , and HSPA1A ) related to OS were identified for IS. Besides, 6 feature genes and 255 miRNAs were utilized to establish a feature genes-miRNA network which contained 261 nodes and 277 edges. At last, qPCR results revealed that there was a trend for higher expression of FOS , KLF4 , and HSPA1A in IS specimens than in NC specimens. Additionally, HSPA8 expression was significantly decreased in the IS specimens, which was consistent with the findings of the GEO database analysis. Conclusion In conclusion, 6 feature genes ( HSPA8 , NCF2 , FOS , KLF4 , THBS1 , and HSPA1A ) related to OS were mined by bioinformatics analysis, which might provide a new insights into the evaluation and treatment of IS. Clinical trial number : Not applicable.

Freeze-thaw cycles exert an important abiotic stress on plants at the beginning of winter and spring in mid-to-high latitudes. Here, we investigate whether the effects of freeze-thaw cycles are carried over into the growing season in wetlands. We conduct a temperature-controlled experiment under two freeze-thaw and two flooding conditions on a typical plant ( Scirpus planiculmis ) and soil from the Momoge wetland (China) and analyze the microbial nitrogen metabolism, based on metagenomic sequencing. We show that freeze-thaw cycles earlier in the year significantly inhibit plant sprouting and early growth. Specifically, they promote denitrification and thus reduce nitrogen levels, which in turn intensifies nitrogen limitation in the wetland soil. We find that plants tend to sprout later but faster after they are exposed to freeze-thaw cycles. Wetland flooding could alleviate these medium-term effects of freeze-thaw cycles. Our results suggest that wetland plants in mid-to-high latitudes have evolved sprouting and growth strategies to adapt to climatic conditions at the beginning of winter and spring.

IntroductionClimate policy uncertainty plays a crucial role in shaping corporate green strategic decisions. However, the mechanisms through which this uncertainty influences corporate green governance and its boundary conditions remain underexplored. This study aims to fill this gap by examining the effect of climate policy uncertainty on corporate green governance in China. Using data from Chinese A-share listed companies between 2009 and 2023, the paper investigates how this uncertainty drives changes in corporate green behavior.MethodTo examine the impact of climate policy uncertainty on corporate green governance, we employ a rigorous two-way fixed effects model.ResultsThe results reveal a significant positive impact of climate policy uncertainty on the level of corporate green governance. This effect is particularly pronounced in state-owned enterprises, firms with lower appeal to green investors, and companies operating in highly competitive sectors. Mechanism analyses indicate that the positive impact operates through three main channels: enhancing executives’ green cognition, reducing managerial myopia, and improving the quality of environmental information disclosure. Furthermore, the results show that these factors collectively optimize corporate green governance structures, contributing to improved corporate environmental behavior.DiscussionThe findings provide important theoretical and empirical insights into the role of climate policy uncertainty in shaping corporate environmental decisions. By identifying the channels through which uncertainty influences corporate behavior, the study contributes to a deeper understanding of the drivers behind corporate green governance. Additionally, the paper highlights the significance of government climate policies in fostering effective corporate environmental strategies, suggesting that policies should be designed with an understanding of the mechanisms that facilitate governance optimization under uncertainty.

Background Postoperative intracranial infection is a critical complication strongly associated with poor prognosis in brain tumor patients. This study aimed to develop and validate machine learning (ML) models for predicting intracranial infection using readily accessible postoperative cerebrospinal fluid (CSF) parameters. Method We retrospectively analyzed 657 brain tumor patients, with an independent cohort ( n  = 116) for external validation. Key predictors were identified through feature selection via LASSO regression combined with random forest. Eleven ML models were trained (70% data) with hyperparameter optimization via 10-fold cross-validation and bootstrap-based comparison, followed by evaluation on internal test set (30%) and external validation set. Results CSF polymorphonuclear cell percentage (PMN%), glucose (GLU) level and color were identified as the most significant predictors of postoperative intracranial infection. Among the tested models, the Gradient Boosting Decision Tree (GBDT) exhibited the strongest predictive performance, achieving AUC values of 0.98 (training set), 0.94 (internal validation), and 0.91 (external validation). The model also demonstrated excellent calibration, robust precision-recall discrimination, and meaningful clinical utility, as confirmed by decision curve analysis (DCA). Shapley Additive exPlanations (SHAP) interpretability analysis further validated PMN% as the most influential predictor. Subgroup analyses indicated that the model maintained robust performance in most key clinical subgroups, though some variability was observed in patients with brain metastasis. To facilitate clinical application, we developed a user-friendly, web-based calculator for estimating individualized infection risk in brain tumor patients. Conclusion The GBDT-based model enables accurate prediction of postoperative intracranial infection by leveraging readily available CSF parameters (PMN%, GLU, color). Characterized by rapidity, objectivity, and interpretability, it facilitates early risk stratification and personalized clinical intervention.

Quantum dots (QDs) of formamidinium lead triiodide (FAPbI3) perovskite hold great potential, outperforming their inorganic counterparts in terms of phase stability and carrier lifetime, for high‐performance solar cells. However, the highly dynamic nature of FAPbI3 QDs, which mainly originates from the proton exchange between oleic acid and oleylamine (OAm) surface ligands, is a key hurdle that impedes the fabrication of high‐efficiency solar cells. To tackle such an issue, here, protonated‐OAm in situ to strengthen the ligand binding at the surface of FAPbI3 QDs, which can effectively suppress the defect formation during QD synthesis and purification processes is selectively introduced. In addition, by forming a halide‐rich surface environment, the ligand density in a broader range for FAPbI3 QDs without compromising their structural integrity, which significantly improves their optoelectronic properties can be modulated. As a result, the power conversion efficiency of FAPbI3 QD solar cells (QDSCs) is enhanced from 7.4% to 13.8%, a record for FAPbI3 QDSCs. Furthermore, the suppressed proton exchange and reduced surface defects in FAPbI3 QDs also enhance the stability of QDSCs, which retain 80% of the initial efficiency upon exposure to ambient air for 3000 hours. An in situ surface ligand regulation strategy for deliberately controlling protonated‐oleylamine (OAm) dominated surface binding of formamidinium lead triiodide quantum dots (FAPbI3 QDs) is demonstrated. The QDs present reduced long‐chain insulating ligand density without compromising their structural integrity, leading to the corresponding QD solar cell a record power conversion efficiency of 13.8% for FAPbI3 QDSCs.

Identifying the effects of nitrogen (N) addition under key environmental factors and N addition methods can aid in understanding the paradigm of N addition in wetland ecosystems. In this study, we conducted a meta-analysis of 30 field studies of wetland ecosystems and selected 14 indicators. We found that the changes in soil TN and SOC contributed significantly to the changes in microbial community structure under N additions. The environmental factors and N addition methods altered the direction or size of N addition effects on wetland soil properties, microbial diversity and key C and N cycling genes. N-limited conditions and climate conditions determined the N addition effect direction on SOC, and saline-alkali conditions determined the N addition effect direction on microbial diversity and AOB abundance. Environmental heterogeneity and N addition methods determine the response of wetland soil to nitrogen application. Therefore, it is crucial to study the effects of environmental factors and N addition methods on the N deposition of wetland soils.