Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease.

Moyamoya disease (MMD) is a cerebrovascular narrowing and occlusive condition characterized by progressive stenosis of the terminal portion of the internal carotid artery and the formation of an abnormal network of dilated, fragile perforators at the base of the brain. However, the role of PANoptosis, an apoptotic mechanism associated with vascular disease, has not been elucidated in MMD. In our study, a total of 40 patients' genetic data were included, and a total of 815 MMD-related differential genes were screened, including 215 upregulated genes and 600 downregulated genes. Among them, DNAJA3, ESR1, H19, KRT18 and STK3 were five key genes. These five key genes were associated with a variety of immune cells and immune factors. Moreover, GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) showed that the different expression levels of the five key genes affected multiple signaling pathways associated with MMD. In addition, they were associated with the expression of MMD-related genes. Then, based on the five key genes, a transcription factor regulatory network was constructed. In addition, targeted therapeutic drugs against MMD-related genes were obtained by the Cmap drug prediction method: MST-312, bisacodyl, indirubin, and tropanyl-3,5-dimethylbenzoate. These results suggest that the PANoptosis-related genes may contribute to the pathogenesis of MMD through multiple mechanisms.

The impact of left-behind experience on urban identity of new-generation migrant workers.

We investigate the impact of left-behind experiences on the urban identity of new-generation migrant workers using data from the 2017 China Migrants Dynamic Survey. The results show the following: (1) The left-behind experience is an important factor undermining the urban identity of new-generation migrant workers, and the conclusion remains consistent after robustness checks, such as propensity score matching. (2) Left-behind experiences of both parents away from home had the most significant negative impact on urban identity. (3) The results of the mechanism tests indicate that the left-behind experience exerts an adverse impact on urban identity through the pathways of poorer physical health, more frequent migration, more challenging job search, and stronger dependence on preexisting social networks. The findings of this study also offer policy suggestions for promoting the urban identity of new-generation migrant workers.

Exploring the nexus between green finance and energy efficiency: Unravelling the impact through green technology innovation and energy structure.

This study delves into the intricate relationship between green finance and energy efficiency, focusing on how green technology innovation and energy structure transformations contribute to this dynamic. Utilizing panel data from China's provinces over the period 2015-2022, the research aims to uncover the nuances of how green finance can serve as a catalyst for enhancing energy efficiency across different regions. The objective is to quantify the impact of green finance on energy efficiency, considering the mediating roles of green technology innovation and shifts in energy structure. The analysis employs a sophisticated panel entropy weighting technique to analyze the data, ensuring a robust examination of the relationships between these variables. The results reveal a significant positive impact of green finance on energy efficiency, mediated by advances in green technology and modifications in the energy structure towards more sustainable forms. Specifically, regions with higher engagement in green finance initiatives demonstrated marked improvements in energy efficiency, attributed to substantial investments in green technologies and a gradual shift away from traditional, inefficient energy sources. These findings underscore the pivotal role of green finance in driving the transition towards a more energy-efficient and sustainable economic model. Policy implications drawn from this study suggest that targeted financial policies promoting green investments can significantly bolster energy efficiency.

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization.

Subcellular localization of mRNA is related to protein synthesis, cell polarity, cell movement and other biological regulation mechanisms. The distribution of mRNAs in subcellulars is similar to that of proteins, and most mRNAs are distributed in multiple subcellulars. Recently, some computational methods have been designed to predict the subcellular localization of mRNA. However, these methods only employed a sin-gle level of mRNA features and did not employ the position encoding of nucleotides in mRNA. In this paper, an ensemble learning prediction model is proposed, named MulStack, which is based on random forest and deep learning for multilabel mRNA subcellular localization. The proposed method employs two levels of mRNA features, including sequence-level and residue-level features, and position encoding is employed for the first time in the field of subcellular localization of mRNA. Random forest is employed to learn mRNA sequence-level feature, deep learning is employed to learn mRNA sequence-level feature and mRNA residue-level combined with position encoding. And the outputs of random forest and deep learning model will be weighted sum as the prediction probability. Compared with existing methods, the results show that MulStack is the best in the localization of the nucleus, cytosol and exosome. In addition, position weight matrices (PWMs) are extracted by convolutional neural networks (CNNs) that can be matched with known RNA binding protein motifs. Gene ontology (GO) enrichment analysis shows biological processes, molecular functions and cellular components of mRNA genes. The prediction web server of MulStack is freely accessible at http://bliulab.net/MulStack.

A Partially Flipped Physiology Classroom Improves the Deep Learning Approach of Medical Students.

This study aimed to compare the impact of the partially flipped physiology classroom (PFC) and the traditional lecture-based classroom (TLC) on students' learning approaches. The study was conducted over five months at Xiangya School of Medicine from February to July 2022 and comprised 71 students majoring in clinical medicine. The experimental group (n = 32) received PFC teaching, while the control group (n = 39) received TLC. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) was used to assess the impact of different teaching methods on students' learning approaches. After the PFC, students got significantly higher scores on deep learning approach (Z=-3.133, P<0.05). Conversely, after the TLC, students showed significantly higher scores on surface learning approach (Z=-2.259, P<0.05). After the course, students in the PFC group scored significantly higher in deep learning strategy than those in the TLC group (Z=-2.196, P<0.05). The PFC model had a positive impact on the deep learning motive and strategy, leading to an improvement in the deep approach, which is beneficial for the long-term development of students. In contrast, the TLC model only improved the surface learning approach. The study implies that educators should consider implementing PFC to enhance students' learning approaches.

Aminocyclopropenium as a novel hydrogen bonding organocatalyst for cycloaddition of carbon disulfide and epoxide to prepare cyclic dithiocarbonate.

The smallest Hückel aromatic ring cyclopropenium substituted by electron-donating C-amino groups produced a aminocyclopropenium electron-rich cation. A bifunctional aminocyclopropenium halide catalyst installed with bis-(hydroxyethyl) functions on the amino group was then designed. A typical (diethanolamino)cyclopropenium halide catalyst C5·I was screened optimally for the cycloaddition of carbon disulfide into an epoxide to produce cyclic dithiocarbonate with an excellent conversion (95%) and high selectivity (92%). The electrostatic enhancement of alkyl C-H HBD capability was implemented via vicinal positive charges on the cyclopropenium core, and the acidity of the terminal O-H hydrogen proton increased by intramolecular H-bonding between the two hydroxy groups on the diethanolamino group (O-H⋯O-H). Then, a hybrid H-bond donor comprising enhanced alkyl C-H and hydroxy O-H was formed. The hybrid HBD offered by aminocyclopropenium was vital in activating the epoxide and stabilizing the intermediate, resulting in reduced O/S scrambling. Moreover, weakly coordinated iodide anion served as a nucleophilic reagent to open the ring of the epoxide. The cooperative catalytic mechanism of the HBD cation and halide anion was supported by NMR titrations and control experiments. Eleven epoxides with various substituents were converted into the corresponding cyclic thiocarbonate with high conversion and selectivity under mild conditions (25 °C, 6 h) without a solvent. The cycloaddition of carbon disulfide with epoxides catalyzed by aminocyclopropenium developed a new working model for hydrogen bonding organocatalysis.

Integrative analysis of the bladder cancer from a cell cycle NCAM1 perspective at both single cell and bulk resolution.

INTRODUCTION: Bladder cancer (BLCA) is a prevalent and deadly form of urinary cancer, and there is a need for effective therapies, particularly for muscle-invasive bladder cancer (MIBC). Cell cycle inhibitors show promise in restoring control of the cell cycle in BLCA cells, but their clinical prognosis evaluation is limited. METHODS: Transcriptome and scRNA-seq data were collected from the Cancer Genome Atlas Program (TCGA)-BLCA and GSE190888 cohort, respectively. R software and the Seurat package were used for data analysis, including cell quality control, dimensionality reduction, and identification of differentially expressed genes. Genes related to the cell cycle were obtained from the genecards website, and a protein-protein interaction network analysis was performed using cytoscape software. Functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted using various tools and packages. BLCA cell lines were cultured and transfected for in vitro experimental assays, including RT-qPCR analysis, and CCK-8 cell viability assays. RESULTS: We identified 32 genes as independent risk or protective factors for BLCA prediction. Functional enrichment analysis revealed their involvement in cell cycle regulation, apoptosis, and various signaling pathways. Using these genes, we developed a nomogram for predicting BLCA survival, which displayed high prognosis stratification efficacy in BLCA patients. Four cell cycle associated key genes identified, including NCAM1, HBB, CKD6, and CTLA4. We also identified the main cell types in BLCA patients and investigated the functional differences between epithelial cells based on their expression levels of key genes. Furthermore, we observed a high positive correlative relationship between the infiltration of cancer-associated fibroblasts and the risk score value. Finally, we conducted in vitro experiments to demonstrate the suppressive role of NCAM1 in BLCA cell proliferation. CONCLUSION: These findings suggest that cell cycle associated genes could serve as potential biomarkers for predicting BLCA prognosis and may represent therapeutic targets for the development of more effective therapies. Hopefully, these findings provide valuable insights into the molecular mechanisms and potential therapeutic targets in BLCA from the perspective of cell cycle. Moreover, NCAM1 was a novel cell proliferation suppressor in the BLCA carcinogenesis.

Population identification and genetic diversity analysis of Fritillaria ussuriensis (Fritillaria) based on chloroplast genes atpF and petB.

The chloroplast genomes of five Fritillaria ussuriensis materials from different production areas were comparatively analyzed, atpF and petB were screened as specific DNA barcodes, and the population identification and genetic diversity of F. ussuriensis were analyzed based on them. The F. ussuriensis chloroplast genome showed a total length of 151 515-151 548 bp with a typical tetrad structure and encoded 130 genes. atpF and petB were used to amplify 183 samples from 13 populations, and they could identify 6 and 9 haplotypes, respectively. Joint analysis of the two sequences revealed 18 haplotypes, named H1-H18, with the most widely distributed and most abundant being H4. Ten haplotypes were unique for 7 populations that they could be used to distinguish from others. Haplotype diversity and nucleotide diversity were 0.99 and 2.09 × 10-3, respectively, indicating the genetic diversity was relatively rich. The results of the intermediary adjacency network showed that H5 was the oldest haplotype, and stellate radiation was centered around it, indicating that population expansion occurred in genuine production areas. This study lays a theoretical foundation for the population identification, genetic evolution, and breed selection of F. ussuriensis.

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China.

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.

Ginsenosides Rg1 and Rg2 Activate Autophagy and Attenuate Oxidative Stress in Neuroblastoma Cells Overexpressing Aß(1-42).

Alzheimer's disease is a neurodegeneration with protein deposits, altered proteolysis, and inflammatory and oxidative processes as major hallmarks. Despite the continuous search for potential therapeutic treatments, no cure is available to date. The use of natural molecules as adjuvants in the treatment of Alzheimer's disease is a very promising strategy. In this regard, ginsenosides from ginseng root show a variety of biological effects. Here, we dissected the role of ginsenosides Rg1 and Rg2 in modulating autophagy and oxidative stress in neuroblastoma cells overexpressing Aß(1-42). Key hallmarks of these cellular processes were detected through immunomethods and fluorometric assays. Our findings indicate that ginsenosides are able to upregulate autophagy in neuronal cells as demonstrated by increased levels of LC3II and Beclin-1 proteins and decreased amounts of p62. Simultaneously, an activation of lysosomal hydrolases was observed. Furthermore, autophagy activation promoted the clearance of Aß(1-42). Rg1 and Rg2 also reduced oxidative stress sources and macromolecule oxidation, promoting NRF2 nuclear translocation and the expression of antioxidant enzymes. Our data further clarify the mechanisms of action of Rg1 and Rg2, indicating new insights into their role in the management of disorders like Alzheimer's disease.

Bioorthogonal photocatalytic proximity labeling in primary living samples.

In situ profiling of subcellular proteomics in primary living systems, such as native tissues or clinic samples, is crucial for understanding life processes and diseases, yet challenging due to methodological obstacles. Here we report CAT-S, a bioorthogonal photocatalytic chemistry-enabled proximity labeling method, that expands proximity labeling to a wide range of primary living samples for in situ profiling of mitochondrial proteomes. Powered by our thioQM labeling warhead development and targeted bioorthogonal photocatalytic chemistry, CAT-S enables the labeling of mitochondrial proteins in living cells with high efficiency and specificity. We apply CAT-S to diverse cell cultures, dissociated mouse tissues as well as primary T cells from human blood, portraying the native-state mitochondrial proteomic characteristics, and unveiled hidden mitochondrial proteins (PTPN1, SLC35A4 uORF, and TRABD). Furthermore, CAT-S allows quantification of proteomic perturbations on dysfunctional tissues, exampled by diabetic mouse kidneys, revealing the alterations of lipid metabolism that may drive disease progression. Given the advantages of non-genetic operation, generality, and spatiotemporal resolution, CAT-S may open exciting avenues for subcellular proteomic investigations of primary samples that are otherwise inaccessible.

Emerging Role of Fibroblasts in Vitiligo: A Formerly Underestimated Rising Star.

Vitiligo is a disfiguring depigmentation disorder characterized by loss of melanocytes. Although numerous studies have been conducted on the pathogenesis of vitiligo, the underlying mechanisms remain unclear. Although most studies have focused on melanocytes and keratinocytes, growing evidence suggests the involvement of dermal fibroblasts, residing deeper in the skin. This review aims to elucidate the role of fibroblasts in both the physiological regulation of skin pigmentation and their pathological contribution to depigmentation, with the goal of shedding light on the involvement of fibroblasts in vitiligo. The topics covered in this review include alterations in the secretome, premature senescence, autophagy dysfunction, abnormal extracellular matrix, autoimmunity, and metabolic changes.

Pd@Pt Core-Shell Nanocrystal-Decorated ZnO Nanosheets for ppt-Level NO2 Detection.

Bimetallic nanocrystals (NCs) have obtained significant attention due to their unique advantages of the intrinsic properties of individual metals and synergistic enhancements resulting from the electronic coupling between two constituent metals. In this work, Pd@Pt core-shell NCs were prepared through a facile one-pot solution-phase method, which had excellent dispersion and uniform size. Concurrently, ZnO nanosheets were prepared via a hydrothermal method. To explore their potential in nitrogen dioxide (NO2) gas sensing applications, sensitive materials based on ZnO nanosheets with varying mass percentages of Pd@Pt NCs were generated through an impregnation process. The sensor based on 0.3 wt % Pd@Pt-ZnO exhibited remarkable performance, demonstrating a substantial response (Rg/Ra = 60.3) to 50 ppb of NO2 at a low operating temperature of 80 °C. Notably, this sensor reached an outstanding low detection limit of 300 ppt. The enhancement in gas sensing capabilities can be attributed to the sensitization and synergistic effects imparted by the exceptional catalytic activity of Pd@Pt NCs, which significantly promoted the reaction. This research introduces a novel approach for the utilization of core-shell structured bimetallic nanocrystals as modifiers in metal-oxide-semiconductor (MOS) materials for NO2 detection.

Recent advances in the application of Mendelian randomization to chronic kidney disease.

OBJECTIVE: Chronic kidney disease (CKD) is a condition influenced by both genetic and environmental factors and has been a focus of extensive research. Utilizing Mendelian randomization, researchers have begun to untangle the complex causal relationships underlying CKD. This review delves into the advances and challenges in the application of MR in the field of nephrology, shifting from a mere summary of its principles and limitations to a more nuanced exploration of its contributions to our understanding of CKD. METHODS: Key findings from recent studies have been pivotal in reshaping our comprehension of CKD. Notably, evidence indicates that elevated testosterone levels may impair renal function, while higher sex hormone-binding globulin (SHBG) levels appear to be protective, predominantly in men. Surprisingly, variations in plasma glucose and glycated hemoglobin levels seem unaffected by genetically induced changes in the estimated glomerular filtration rate (eGFR), suggesting an independent pathway for renal function impairment. RESULTS: Furthermore, lifestyle factors such as physical activity and socioeconomic status emerge as significant influencers of CKD risk and kidney health. The relationship between sleep duration and CKD is nuanced; short sleep duration is linked to increased risk, while long sleep duration does not exhibit a clear causal effect. Additionally, lifestyle factors, including diet, exercise, and mental wellness activities, play a crucial role in kidney health. New insights also reveal a substantial causal connection between both central and general obesity and CKD onset, while no significant links were found between genetically modified LDL cholesterol or triglyceride levels and kidney function. CONCLUSION: This review not only presents the recent achievements of MR in CKD research but also illuminates the path forwards, underscoring critical unanswered questions and proposing future research directions in this dynamic field.

A comprehensive study on non-governmental actors in shaping grassland ecological compensation within legal frameworks.

Ecological compensation has emerged as a crucial institutional framework for managing the interplay between ecological preservation and economic development in China. This study focuses on the specific case of grassland ecological compensation to investigate the protection of rights and interests of non-governmental subjects. By utilizing data derived from questionnaire responses, this study examines the legal rights, obligations, and responsibilities associated with grassland ecological compensation. Statistical techniques such as Z-distribution, chi-square test, and non-parametric measures of correlation are employed to analyze the collected data, which are presented using tables and graphs. Furthermore, this research evaluates the current state of rights and interests of compensation subjects engaged in ecological compensation practices, aiming to enhance our comprehension and assessment of the extent to which the ecological compensation system safeguards the rights and interests of individuals. The findings show that a substantial number of respondents see current grassland ecological compensation methods in China as reasonable but insufficient, indicating a need for method diversification. There's a clear preference for a shared responsibility model over government-only funding, especially in regions with large grassland areas. This highlights the necessity for adaptable laws and a legal framework that accommodates diverse stakeholder needs. Additionally, the importance of clear property rights is emphasized for sustainable land use. The study suggests legislative reform towards a more equitable and effective approach to grassland conservation, providing valuable recommendations for refining and advancing the ecological compensation system.Author name 1 (Ziqi Liu) mismatch between ms and metadata. We have foolowed metadata. Kindly check and confirm.The metadata is right. Thank you.

Multiple mediation of the association between childhood emotional abuse and adult obesity by anxiety and bulimia - a sample from bariatric surgery candidates and healthy controls.

Bulimia, which means a person has episodes of eating a very large amount of food (bingeing) during which the person feels a loss of control over their eating, is the most primitive reason for being overweight and obese. The extended literature has indicated that childhood emotional abuse has a close relationship with adverse mood states, bulimia, and obesity. To comprehensively understand the potential links among these factors, we evaluated a multiple mediation model in which anxiety/depression and bulimia were mediators between childhood emotional abuse and body mass index (BMI). A set of self-report questionnaires, including the Childhood Trauma Questionnaire (CTQ), Beck Anxiety Inventory, Beck Depression Inventory (BDI), and Eating Disorder Inventory (EDI), was sent out. Clinical data from 37 obese patients (age: 29.65 ± 5.35, body mass index (BMI): 37.59 ± 6.34) and 37 demographically well-matched healthy people with normal body weight (age: 31.35 ± 10.84, BMI: 22.16 ± 3.69) were included in the investigation. We first performed an independent t-test to compare all scales or subscale scores between the two groups. Then, we conducted Pearson correlation analysis to test every two variables' pairwise correlation. Finally, multiple mediation analysis was performed with BMI as the outcome variable, and childhood emotional abuse as the predictive variable. Pairs of anxiety, bulimia, and depression, bulimia were selected as the mediating variables in different multiple mediation models separately. The results show that the obese group reported higher childhood emotional abuse (t = 2.157, p = 0.034), worse mood state (anxiety: t = 5.466, p < 0.001; depression: t = 2.220, p = 0.030), and higher bulimia (t = 3.400, p = 0.001) than the healthy control group. Positive correlations were found in every pairwise combination of BMI, childhood emotional abuse, anxiety, and bulimia. Multiple mediation analyses indicate that childhood emotional abuse is positively linked to BMI (ß = 1.312, 95% CI = 0.482-2.141). The model using anxiety and bulimia as the multiple mediating variables is attested to play roles in the relationship between childhood emotional abuse and obesity (indirect effect = 0.739, 95% CI = 0.261-1.608, 56.33% of the total effect). These findings confirm that childhood emotional abuse contributes to adulthood obesity through the multiple mediating effects of anxiety and bulimia. The present study adds another potential model to facilitate our understanding of the eating psychopathology of obesity.

Evaluation of cerebrovascular reactivity in moyamoya disease using oxygen-dependent magnetic resonance imaging.

Moyamoya disease (MMD) is a rare cerebrovascular disorder marked by progressive stenosis of the internal carotid arteries. Assessing cerebral hemodynamics, specifically cerebrovascular reactivity (CVR), is vital for MMD management and prognosis. In this study, fMRI was performed in a prospective cohort of 47 patients with MMD and 32 healthy controls to investigate its utility in evaluating CVR and to explore the influence of cerebral posterior circulation compensation on CVR in MMD. The regions where the CVR values of participants with MMD were lower than those of healthy controls were primarily concentrated in the frontal, parietal, and temporal lobes (p < 0.05). In certain regions mainly supplied by posterior circulation, the CVR values of compensatory-normal subgroup tended to exceed those of compensatory-poor subgroup. fMRI can detect a significant decrease in CVR values in patients with MMD compared to healthy controls. Compensation for the posterior cerebral circulation may affect cerebrovascular reactivity.

Integrating spatial and single-cell transcriptomics to characterize the molecular and cellular architecture of the ischemic mouse brain.

Neuroinflammation is acknowledged as a pivotal pathological event after cerebral ischemia. However, there is limited knowledge of the molecular and spatial characteristics of nonneuronal cells, as well as of the interactions between cell types in the ischemic brain. Here, we used spatial transcriptomics to study the ischemic hemisphere in mice after stroke and sequenced the transcriptomes of 19,777 spots, allowing us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of ischemia-associated gene expression in the peri-infarct area of the ischemic hemisphere. Analysis of ligand-receptor interactions in cell communication revealed galectin-9 to cell-surface glycoprotein CD44 (LGALS9-CD44) as a critical signaling pathway after ischemic injury and identified microglia and macrophages as the main source of galectins after stroke. Extracellular vesicle-mediated Lgals9 delivery improved the long-term functional recovery in photothrombotic stroke mice. Knockdown of Cd44 partially reversed these therapeutic effects, inhibiting oligodendrocyte differentiation and remyelination. In summary, our study provides a detailed molecular and cellular characterization of the peri-infact area in a murine stroke model and revealed Lgals9 as potential treatment target that warrants further investigation.

Community assembly and microbial interactions in an alkaline vanadium tailing pond.

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

Exploration of the prognostic effect of costimulatory genes in bladder cancer.

BACKGROUND: A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets. METHOD: The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner. RESULTS: This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response. CONCLUSION: A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.