SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

The Prevalence of Anemia among Pregnant Women in China: A Systematic Review and Meta-Analysis.

The systematic review and meta-analysis were conducted to ascertain the prevalence of anemia, iron deficiency (ID), and iron deficiency anemia (IDA) among Chinese pregnant women. A total of 722 articles on maternal anemia during pregnancy published between January 2010 and December 2020 were compiled, and a systematic review and meta-analysis were conducted on 57 eligible studies including 1,376,204 pregnant women to ascertain the prevalence of anemia and the prevalence in different subgroups. The results showed that the prevalence of anemia, ID, and IDA among pregnant women in China were 30.7% (95% CI: 26.6%, 34.7%), 45.6% (95% CI: 37.0%, 54.2%), and 17.3% (95% CI: 13.9%, 20.7%), respectively. All prevalence increased with the progression of the pregnancy. There were sizable regional variations in the prevalence of anemia, ID, and IDA. Generally, lower prevalence was observed in the economically more advanced eastern region of the country, while the prevalence of ID was higher in the eastern region than that in the western region. The prevalence of anemia and IDA in rural areas was higher than that in urban areas, but ID prevalence was higher in urban areas. In conclusion, the regional differences and urban-rural disparities in the prevalence of anemia indicate the need for more context-specific interventions to prevent and treat anemia. It was found that dietary factors were one of the major causes of anemia, and iron-containing supplements and nutrition counseling could be effective interventions to reduce the prevalence of anemia, ID, and IDA among Chinese pregnant women.

Maternal anaemia prevention and control in China: A policy review.

Maternal anaemia is a major public health problem. Developing maternal anaemia prevention and control policies is an important prerequisite for carrying out evidence-based interventions. This article reviews maternal anaemia prevention and control policies in China, identifies gaps, and provides references for other countries. We examined policies concerning maternal nutrition and other related literature in China, identified through key databases and government websites, and conducted a narrative review of the relevant documentations guided by the Smith Policy-Implementing-Process framework. A total of 65 articles and documents were identified for analysis. We found that Chinese government has committed to reducing maternal anaemia at the policy level, with established objectives and a clear time frame. However, most of policies were not accompanied by operational guidelines, standardized interventions, and vigorous monitoring and evaluation mechanisms, and 85% of the policies don't have quantifiable objectives on anaemia. Maternal anaemia prevention and control services offered in clinical settings were primarily nutrition education and anaemia screening. Population-based interventions such as iron fortification have yet to be scaled up. Furthermore, medical insurance schemes in some regions do not cover anaemia prevention and treatment, and in other regions that offer coverage, the reimbursement rate is low. The number and capacity of health professionals is also limited. Policy changes should focus on the integration of evidence-based interventions into routine antenatal care services and public health service packages, standardization of dosages and provision of iron supplementation, streamline of reimbursement for outpatient expenses, and capacity building of health professionals.

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Are 4f-Orbitals Engaged in Covalent Bonding Between Lanthanides and Triphenylphosphine Oxide? An Oxygen K-Edge X-ray Absorption Spectroscopy and Density Functional Theory Study.

The bonding covalency between trivalent lanthanides (Ln = La, Pr, Nd, Eu, Gd) and triphenylphosphine oxide (TPPO) is studied by X-ray absorption spectra (XAS) and density functional theory (DFT) calculations on the LnCl3(TPPO)3 complexes. The O, P, and Cl K-edge XAS for the single crystals of LnCl3(TPPO)3 were collected, and the spectra were interpreted based on DFT calculations. The O and P K-edge XAS spectra showed no significant change across the Ln series in the LnCl3(TPPO)3 complexes, unlike the Cl K-edge XAS spectra. The experimental O K-edge XAS spectra suggest no mixing between the Ln 4f- and the O 2p-orbitals in the LnCl3(TPPO)3 complexes. DFT calculations indicate that the amount of the O 2p character per Ln-O bond is less than 0.1% in the Ln 4f-based orbitals in all of the LnCl3(TPPO)3 complexes. The experimental spectra and theoretical calculations demonstrate that Ln 4f-orbitals are not engaged in the covalent bonding of lanthanides with TPPO, which contrasts the involvement of U 5f-orbitals in covalent bonding in the UO2Cl2(TPPO)2 complex. Results in this work reinforce our previous speculation that bonding covalency is potentially responsible for the extractability of monodentate organophosphorus ligands toward metal ions.

Dense Contrastive-based Federated Learning for Dense Prediction Tasks on Medical Images.

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma.

Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.

Harmonic Suppression Induced by Three-Electron Dynamics of Li in Strong Laser Fields.

We build a model to elucidate the high harmonic generation in combined EUV and midinfrared laser fields by embodying the spin-resolved three-electron dynamics. The EUV pulse ionizes an inner-shell electron, and the midinfrared laser drives the photoelectron and steers the electron-ion rescattering. Depending on the spin of the photoelectron, the residual ion including two bound electrons can be either in a single spin configuration or in a coherent superposition of different spin configurations. In the latter case, the two electrons in the ion swap their orbits, leading to a deep valley in the harmonic spectrum. The model results agree with the time-dependent Schrödinger equation simulations including three active electrons. The intriguing picture explored in this work is fundamentally distinguished from all reported scenarios relied on spin-orbit coupling, but originates from the exchanges asymmetry of two-electron wave functions.

Double boron-oxygen-fused polycyclic aromatic hydrocarbons: skeletal editing and applications as organic optoelectronic materials.

An efficient one-pot strategy for the facile synthesis of double boron-oxygen-fused polycyclic aromatic hydrocarbons (dBO-PAHs) with high regioselectivity and efficient skeletal editing is developed. The boron-oxygen-fused rings exhibit low aromaticity, endowing the polycyclic aromatic hydrocarbons with high chemical and thermal stabilities. The incorporation of the boron-oxygen units enables the polycyclic aromatic hydrocarbons to show single-component, low-temperature ultralong afterglow of up to 20 s. Moreover, the boron-oxygen-fused polycyclic aromatic hydrocarbons can also serve as ideal n-type host materials for high-brightness and high-efficiency deep-blue OLEDs; compared to single host, devices using boron-oxygen-fused polycyclic aromatic hydrocarbons-based co-hosts exhibit dramatically brightness and efficiency enhancements with significantly reduced efficiency roll-offs; device 9 demonstrates a high color-purity (Commission International de l'Eclairage CIEy = 0.104), and also achieves a record-high external quantum efficiency (28.0%) among Pt(II)-based deep-blue OLEDs with Commission International de l'Eclairage CIEy < 0.20; device 10 achieves a maximum brightnessof 27219 cd/m2 with a peak external quantum efficiency of 27.8%, which representes the record-high maximum brightness among Pt(II)-based deep-blue OLEDs. This work demonstrates the great potential of the double boron-oxygen-fused polycyclic aromatic hydrocarbons as ultralong afterglow and n-type host materials in optoelectronic applications.

Comparison of Surgical and Colonoscopy Tissue to Establish Colorectal Patient-derived Organoids.

BACKGROUND: Patient-derived organoids (PDOs) are ex vivo models that retain the functions and characteristics of individualized source tissues, including a simulated tumor microenvironment. However, the potential impact of undiscovered differences between tissue sources on PDO growth and progression remains unclear. OBJECTIVE: This study aimed to compare the growth and condition of PDO models originating from surgical resection and colonoscopy and to provide practical insights for PDO studies. METHODS: Tissue samples and relevant patient clinical information were collected to establish organoid models. PDOs were derived from both surgical and colonoscopy tissues. The growth of the organoids, including their state, size, and success rate of establishment, was recorded and analyzed. The activity of the organoids at the end stage of growth was detected using calcein-AM fluorescence staining. RESULTS: The results showed that the early growth phase of 2/3 colonoscopy-derived organoids was faster compared to surgical PDOs, with a growth difference observed within 11-13 days of establishment. However, colonoscopy-derived organoids exhibited a diminished growth trend after this time. There were no significant differences observed in the terminal area and quantity between the two types of tissue-derived organoids. Immunofluorescence assays of the PDOs revealed that the surgical PDOs possessed a denser cell mass with relatively higher viability than colonoscopy-derived PDOs. CONCLUSION: In the establishment of colorectal patient-derived organoids, surgically derived organoids require a slightly longer establishment period, while colonoscopy-derived organoids should be passaged prior to growth inhibition to preserve organoid viability.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

Biological interactions of polystyrene nanoplastics: Their cytotoxic and immunotoxic effects on the hepatic and enteric systems.

As emerging pollutants in the environment, nanoplastics (NPs) can cross biological barriers and be enriched in organisms, posing a greatest threat to the health of livestock and humans. However, the size-dependent toxic effects of NPs in higher mammals remain largely unknown. To determine the size-dependent potential toxicities of NPs, we exposed mouse (AML-12) and human (L02) liver cell lines in vitro, and 6-week-old C57BL/6 mice (well-known preclinical model) in vivo to five different sizes of polystyrene NPs (PS-NPs) (20, 50, 100, 200 and 500 nm). We found that ultra-small NPs (20 nm) induced the highest cytotoxicity in mouse and human liver cell lines, causing oxidative stress and mitochondrial membrane potential loss on AML-12 cells. Unexpectedly in vivo, after long-term oral exposure to PS-NPs (75 mg/kg), medium NPs (200 nm) and large NPs (500 nm) induced significant hepatotoxicity, evidenced by increased oxidative stress, liver dysfunction, and lipid metabolism disorders. Most importantly, medium or large NPs generated local immunotoxic effects via recruiting and activating more numbers of neutrophils and monocytes in the liver or intestine, which potentially resulted in increased proinflammatory cytokine secretion and the tissue damage. The discrepancy in in vitro-in vivo toxic results might be attributed to the different properties of biodistribution and tissue accumulation of different sized NPs in vivo. Our study provides new insights regarding the hepatotoxicity and immunotoxicity of NPs on human and livestock health, warranting us to take immense measures to prevent these NPs-associated health damage.

Synthesis, Structure, and Theoretical Calculations on NpO2Br42.

The actinide-halogen complexes (AnO2X42-, X = Cl, Br, and I) are the simplest and most representative compounds for studying the bonding nature of actinides with ligands. In this work, we attempted to synthesize the crystals of NpO2X42- (X = Cl, Br, and I). The crystals of NpO2Cl42- and NpO2Br42- were successfully synthesized, in which the structure of NpO2Br42- was obtained for the first time. The crystal of NpO2I42- could not be obtained due to the rapid reduction of Np(VI) to Np(V) by I-. The molecular structures of NpO2Cl42- and NpO2Br42- were characterized by single-crystal X-ray diffraction and infrared, Raman, and UV-Vis-NIR absorption spectroscopy. The complexes of NpO2X42- (X = Cl, Br, and I) were also investigated by density functional theory calculations, and the calculated vibration frequencies and absorption features were comparable to the experimental results. Both the experimental results and theoretical calculations demonstrate the strengthened Np-O bonds and the weakened Np-X bonds across the NpO2X42- series; however, the population analysis on the frontier molecular orbitals (MOs) of NpO2X42- indicates a slight reduction in the Np-O bonding covalency and an enhancement in the Np-X bonding covalency from NpO2Cl42- to NpO2I42-. Results in this work have enriched the crystal database of the AnO2X42- family and provided insights into the bonding nature in the actinide complexes with soft- and hard-donor ligands.

The relationship between the local environment, N-type, spin state and catalytic functionality of carbon-hosted FeII/III-N4 for the conversion of CO2 to CO.

Iron and nitrogen codoped carbon (Fe-N-C) materials are promising alternatives to precious metal catalysts for the carbon dioxide electrochemical reduction reaction (CO2RR); however, the influence of the oxidation state, spin state, N-type and local environment of Fe-N on its catalytic activity remains poorly understood. In this study, we employed density functional theory (DFT) calculations to evaluate the catalytic activity of the pyridine-type FeIII/IIN4 motifs at the armchair and zigzag edges, the activity of the pyrrole-type FeIII/IIN4 sites in the bulk plane of carbon-based materials for the two-electron CO2RR by analyzing the stability of initial reactants, free-energy evolutions and energy barriers for the possible elementary reactions in the different spin states. The Fe ions in the armchair-edge pyridine-type FeN4 are mainly in the +2 oxidation state, and use the high spin state in the spin uncoupling manner to achieve the most efficient CO2-COOH-CO conversion. In contrast, the zigzag-edge pyridine-type FeIIN4 employs the medium spin state in the spin uncoupling manner to achieve the highest catalytic activity in the two-electron CO2RR. However, the Fe ions in the pyrrole-type bulk-hosted FeN4 mainly remain in the +3 valence state during the conversion process of CO2 to CO and utilize the medium spin state with spin coupling to obtain the highest catalytic activity. The corresponding kinetic analyses show that the armchair-edge pyridine-type FeIIN4 catalyst exhibited the best catalytic performance among the three cases. Consequently, these findings present significant insights into the design of Fe single-atom catalysts for enhancing CO2RR catalytic activity by producing more armchair-edge pyridine-type FeN4 sites, which may be constructed by introducing micropores in the carbon materials.

Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning.

Unsupervised clustering is an essential step in identifying cell types from single-cell RNA sequencing (scRNA-seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single-cell molecular heterogeneity. In particular, a silhouette coefficient-based indicator is developed to determine the optimization direction of the bi-objective function. In addition, a hierarchical autoencoder is employed to project the high-dimensional data onto multiple low-dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi-objective fruit fly optimization algorithm is designed to prune dynamically the low-quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA-seq datasets and one large real scRNA-seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post-transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms.

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning.

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/ .

Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.

Enhancers are short non-coding DNA sequences outside of the target promoter regions that can be bound by specific proteins to increase a gene's transcriptional activity, which has a crucial role in the spatiotemporal and quantitative regulation of gene expression. However, enhancers do not have a specific sequence motifs or structures, and their scattered distribution in the genome makes the identification of enhancers from human cell lines particularly challenging. Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. Specifically, to characterize the hierarchical relationships of enhancer sequences, multi-source biological information and dynamic semantic information are fused to represent regulatory DNA enhancer sequences. Then, we implement a deep learning-based sequence network to learn the feature representation of the enhancer sequences comprehensively and to extract the implicit relationships in the dynamic semantic information. Ultimately, an ensemble machine learning classifier is trained based on the refined multi-source features and dynamic implicit relations obtained from the deep learning-based sequence network. Benchmarking experiments demonstrated that SMFM significantly outperforms other existing methods using several evaluation metrics. In addition, an independent test set was used to validate the generalization performance of SMFM by comparing it to other state-of-the-art enhancer identification methods. Moreover, we performed motif analysis based on the contribution scores of different bases of enhancer sequences to the final identification results. Besides, we conducted interpretability analysis of the identified enhancer sequences based on attention weights of EnhancerBERT, a fine-tuned BERT model that provides new insights into exploring the gene semantic information likely to underlie the discovered enhancers in an interpretable manner. Finally, in a human placenta study with 4,562 active distal gene regulatory enhancers, SMFM successfully exposed tissue-related placental development and the differential mechanism, demonstrating the generalizability and stability of our proposed framework.

Bufalin Inhibits Tumorigenesis, Stemness, and Epithelial-Mesenchymal Transition in Colorectal Cancer through a C-Kit/Slug Signaling Axis.

Colorectal cancer (CRC) is a major source of morbidity and mortality, characterized by intratumoral heterogeneity and the presence of cancer stem cells (CSCs). Bufalin has potent activity against many tumors, but studies of its effect on CRC stemness are limited. We explored bufalin's function and mechanism using CRC patient-derived organoids (PDOs) and cell lines. In CRC cells, bufalin prevented nuclear translocation of ß-catenin and down-regulated CSC markers (CD44, CD133, LGR5), pluripotency factors, and epithelial-mesenchymal transition (EMT) markers (N-Cadherin, Slug, ZEB1). Functionally, bufalin inhibited CRC spheroid formation, aldehyde dehydrogenase activity, migration, and invasion. Network analysis identified a C-Kit/Slug signaling axis accounting for bufalin's anti-stemness activity. Bufalin treatment significantly downregulated C-Kit, as predicted. Furthermore, overexpression of C-Kit induced Slug expression, spheroid formation, and bufalin resistance. Similarly, overexpression of Slug resulted in increased expression of C-Kit and identical functional effects, demonstrating a pro-stemness feedback loop. For further study, we established PDOs from diagnostic colonoscopy. Bufalin differentially inhibited PDO growth and proliferation, induced apoptosis, restored E-cadherin, and downregulated CSC markers CD133 and C-Myc, dependent on C-Kit/Slug. These findings suggest that the C-Kit/Slug axis plays a pivotal role in regulating CRC stemness, and reveal that targeting this axis can inhibit CRC growth and progression.

Regulation of different protonated states of two intimate histidine residues on the reductive half-reaction of glucose oxidase.

Glucose oxidase (GOx) can catalyze the oxidation of ß-D-glucose under mild conditions to directly convert biological energy into electrical energy, which has great potential for applications in the fields of enzyme biofuel cells and glucose biosensors. In enzymatic biofuel cells, GOx is often used as an anodic catalyst to improve the performance. The important role of two intimate histidine residues, His505 and His548 (PDB code 4YNU), in the GOx active center has been highlighted in the catalytic oxidation of ß-D-glucose, but there is still a lack of systematic examination on the influence of different protonated states of His505 and His548 on the catalytic oxidation of ß-D-glucose in GOx. Therefore, in the present work, the GOx active center under the possible protonated states of His548 and His505 is systematically examined by using ONIOM calculations, as well as the influence of remote Arg210 is considered. The calculations reveal that the intimate His505 and His548 can modulate the interaction of the ß-D-glucose substrate with isoalloxazine and then control the deprotonization of the hydroxyl group bound to the anomeric carbon of ß-D-glucose like controllers. The remote Arg210 provides the driving force for the transfer of two electrons from ß-D-glucose to isoalloxazine of FAD via the long-range electrostatic attraction like a horse. Specially, the protonated His505 can serve as a good helper of Arg210 to promote the occurring of the two-proton-coupled two-electron transfer from ß-D-glucose to isoalloxazine and His548 in the active center of GOx. These findings provide much insight into the catalytic reactions of GOx in a low pH environment, which may be beneficial to expand the applications of GOx.