DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Amino acid metabolic reprogramming in the tumor microenvironment and its implication for cancer therapy.

Amino acids are essential building blocks for proteins, crucial energy sources for cell survival, and key signaling molecules supporting the resistant growth of tumor cells. In tumor cells, amino acid metabolic reprogramming is characterized by the enhanced uptake of amino acids as well as their aberrant synthesis, breakdown, and transport, leading to immune evasion and malignant progression of tumor cells. This article reviews the altered amino acid metabolism in tumor cells and its impact on tumor microenvironment, and also provides an overview of the current clinical applications of amino acid metabolism. Innovative drugs targeting amino acid metabolism hold great promise for precision and personalized cancer therapy.

Bioorthogonal Quinone Methide Decaging Enables Live-Cell Quantification of Tumor-Specific Immune Interactions.

Effective antitumor immunity hinges on the specific engagement between tumor and cytotoxic immune cells, especially cytotoxic T cells. Although investigating these intercellular interactions is crucial for characterizing immune responses and guiding immunotherapeutic applications, direct and quantitative detection of tumor-T cell interactions within a live-cell context remains challenging. We herein report a photocatalytic live-cell interaction labeling strategy (CAT-Cell) relying on the bioorthogonal decaging of quinone methide moieties for sensitive and selective investigation and quantification of tumor-T cell interactions. By developing quinone methide-derived probes optimized for capturing cell-cell interactions (CCIs), we demonstrated the capacity of CAT-Cell for detecting CCIs directed by various types of receptor-ligand pairs (e.g., CD40-CD40L, TCR-pMHC) and further quantified the strengths of tumor-T cell interactions that are crucial for evaluating the antitumor immune responses. We further applied CAT-Cell for ex vivo quantification of tumor-specific T cell interactions on splenocyte and solid tumor samples from mouse models. Finally, the broad compatibility and utility of CAT-Cell were demonstrated by integrating it with the antigen-specific targeting system as well as for tumor-natural killer cell interaction detection. By leveraging the bioorthogonal photocatalytic decaging chemistry on quinone methide, CAT-Cell provides a sensitive, tunable, universal, and noninvasive toolbox for unraveling and quantifying the crucial but delicate tumor-immune interactions under live-cell settings.

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.

Design and synthesis of dual functional NBD-fluorophore-incorporated naphthalene diimide derivatives as G-quadruplex ligands.

Nitrobenzoxadiazole (NBD)-incorporated naphthalene diimide derivatives were designed and synthesized as candidates of antitumor agents with cytotoxicity against human pancreatic cancer cell MIA PaCa-2. Among these, compounds 1NND and 3NND exhibited fluorescent "turn-off" property toward human telomeric G-quadruplex (G4), which allows the direct measurement of dissociation constant (Kd) of ligands against G4 by fluorescence titration method. Notably, the compound 1NND not only exhibited great cytotoxic activity against MIA PaCa-2 with a half maximal inhibitory concentration (IC50) of 77.9 nM, but also exhibited high affinity against G4 with Kd of 1.72 µM. Furthermore, the target binding properties were investigated by circular dichroism (CD) spectra and further studied by molecular docking methods.

Decaging-to-labeling: Development and investigation of quinone methide warhead for protein labeling.

Biomolecule labeling in living systems is crucial for understanding biological processes and discovering therapeutic targets. A variety of labeling warheads have been developed for multiple biological applications, including proteomics, bioimaging, sequencing, and drug development. Quinone methides (QMs), a class of highly reactive Michael receptors, have recently emerged as prominent warheads for on-demand biomolecule labeling. Their highly flexible functionality and tunability allow for diverse biological applications, but remain poorly explored at present. In this regard, we designed, synthesized, and evaluated a series of new QM probes with a trifluoromethyl group at the benzyl position and substituents on the aromatic ring to manipulate their chemical properties for biomolecule labeling. The engineered QM warhead efficiently labeled proteins both in vitro and under living cell conditions, with significantly enhanced activity compared to previous QM warheads. We further analyzed the labeling efficacy with the assistance of density functional theory (DFT) calculations, which revealed that the QM generation process, rather than the reactivity of QM, contributes more predominantly to the labeling efficacy. Noteworthy, twelve nucleophilic residues on the BSA were labeled by the probe, including Cys, Asp, Glu, His, Lys, Asn, Gln, Arg, Ser, Thr, Trp and Tyr. Given their high efficiency and tunability, these new QM warheads may hold great promise for a broad range of applications, especially spatiotemporal proteomic profiling for in-depth biological studies.

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.

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.

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.

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 5 mo 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, whereas 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 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.NEW & NOTEWORTHY In this article, we compare the impact of the partially flipped classroom (PFC) and the traditional lecture classroom (TLC) in a physiology course on medical students' learning approaches. We found that the PFC benefited students by significantly enhancing their deep learning motive, strategy, and approach, which was good for them. However, the TLC model only improved the surface learning motive and approach.

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.

Artificial Channels Based on Bottlebrush Polymers: Enhanced Ion Transport Through Polymer Topology Control.

Synthetic structures mimicking the transport function of natural ion channel proteins have a wide range of applications, including therapeutic treatments, separation membranes, sensing, and biotechnologies. However, the development of polymer-based artificial channels has been hampered due to the limitation on available models. In this study, we demonstrate the great potential of bottlebrush polymers as accessible and versatile molecular scaffolds for developing efficient artificial ion channels. Adopting the bottlebrush configuration enhanced ion transport activity of the channels compared to their linear analogs. Matching the structure of lipid bilayers, the bottlebrush channel with a hydrophilic-hydrophobic-hydrophilic triblock architecture exhibited the highest activity among the series. Functionalized with urea groups, these channels displayed high anion selectivity. Additionally, we illustrated that the transport properties could be fine-tuned by modifying the chemistry of ion binding sites. This work not only highlights the importance of polymer topology control in channel design, but also reveals the great potential for further developing bottlebrush channels with customized features and diverse functionalities.

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.

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.

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.

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.

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

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.

Organic acids released by submerged macrophytes with damaged leaves alter the denitrification microbial community in rhizosphere.

Submerged macrophytes have important impacts on the denitrification and anaerobic ammonia-oxidizing (anammox) processes. Leaf damage in these plants probably changes the rhizosphere environment, affecting organic acid release and denitrifying bacteria. However, there is a lack of comprehensive understanding of the specific changes. This study investigated these changes in the rhizosphere of Potamogeton crispus with four degrees of leaf excision. When 0 %, 30 %, 50 % and 70 % of leaves were excised, the concentrations of total organic acid were 31.45, 32.67, 38.26, and 35.16 mg/L, respectively. The abundances of nirS-type denitrifying bacteria were 2.10 × 1010, 1.59 × 1010, 2.54 × 1010, and 4.67 × 1010 copies/g dry sediment, respectively. The abundances of anammox bacteria were 7.58 × 109, 4.59 × 109, 3.81 × 109, and 3.90 × 109 copies/g dry sediment, respectively. The concentration of total organic acids and the abundance of two denitrification microorganisms in the rhizosphere zone were higher than those in the root zone and non-rhizosphere zone. With increasing leaf damage, the number of OTUs in the Pseudomonas genus of nirS-type denitrifying bacteria first increased and then decreased, while that of the Thauera genus was relatively stable. The overall increase in the OTU number of anammox bacteria indicated that leaf damage promotes root exudates release, thereby leading to an increase in their diversity. The co-occurrence network revealed that the two denitrification microorganisms had about 60.52 % positive connections in rhizosphere while 64.73 % negative connections in non-rhizosphere. The abundance and community composition of both denitrification microorganisms were positively correlated with the concentrations of various substances such as oxalic acid, succinic acid, total organic acids and NO2--N. These findings demonstrate that submerged plant damage has significantly impacts on the structure of denitrification microbial community in the rhizosphere, which may alter the nitrogen cycling process in the deposit sediment. SYNOPSIS: This study reveals leaf damage of macrophyte changed the rhizosphere denitrification microbial community, which is helpful to further understand the process of nitrogen cycle in water.