Receptor Ligand-Free Mesoporous Silica Nanoparticles: A Streamlined Strategy for Targeted Drug Delivery across the Blood-Brain Barrier.

Mesoporous silica nanoparticles (MSNs) represent a promising avenue for targeted brain tumor therapy. However, the blood-brain barrier (BBB) often presents a formidable obstacle to efficient drug delivery. This study introduces a ligand-free PEGylated MSN variant (RMSN25-PEG-TA) with a 25 nm size and a slight positive charge, which exhibits superior BBB penetration. Utilizing two-photon imaging, RMSN25-PEG-TA particles remained in circulation for over 24 h, indicating significant traversal beyond the cerebrovascular realm. Importantly, DOX@RMSN25-PEG-TA, our MSN loaded with doxorubicin (DOX), harnessed the enhanced permeability and retention (EPR) effect to achieve a 6-fold increase in brain accumulation compared to free DOX. In vivo evaluations confirmed the potent inhibition of orthotopic glioma growth by DOX@RMSN25-PEG-TA, extending survival rates in spontaneous brain tumor models by over 28% and offering an improved biosafety profile. Advanced LC-MS/MS investigations unveiled a distinctive protein corona surrounding RMSN25-PEG-TA, suggesting proteins such as apolipoprotein E and albumin could play pivotal roles in enabling its BBB penetration. Our results underscore the potential of ligand-free MSNs in treating brain tumors, which supports the development of future drug-nanoparticle design paradigms.

MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.

Obtaining an accurate segmentation of the pulmonary nodules in computed tomography (CT) images is challenging. This is due to: (1) the heterogeneous nature of the lung nodules; (2) comparable visual characteristics between the nodules and their surroundings. A robust multi-scale feature extraction mechanism that can effectively obtain multi-scale representations at a granular level can improve segmentation accuracy. As the most commonly used network in lung nodule segmentation, UNet, its variants, and other image segmentation methods lack this robust feature extraction mechanism. In this study, we propose a multi-stride residual 3D UNet (MRUNet-3D) to improve the segmentation accuracy of lung nodules in CT images. It incorporates a multi-slide Res2Net block (MSR), which replaces the simple sequence of convolution layers in each encoder stage to effectively extract multi-scale features at a granular level from different receptive fields and resolutions while conserving the strengths of 3D UNet. The proposed method has been extensively evaluated on the publicly available LUNA16 dataset. Experimental results show that it achieves competitive segmentation performance with an average dice similarity coefficient of 83.47 % and an average surface distance of 0.35 mm on the dataset. More notably, our method has proven to be robust to the heterogeneity of lung nodules. It has also proven to perform better at segmenting small lung nodules. Ablation studies have shown that the proposed MSR and RFIA modules are fundamental to improving the performance of the proposed model.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five- fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods. The code is available at https://github.com/lanbiolab/LGCDA.

Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data.

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods.

Overcoming the Blood-Brain Tumor Barrier with Docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma.

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (DTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and DTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, DTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free DTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by DTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate DTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations.

Two-dimensional correlation infrared spectroscopy study on vanadoborate anionic skeleton regulated by countercations.

Two novel vanadoborate compounds, [Cu(en)2]3[Li(H2O)]4[Li(H2O)3]2[V12B18O50(OH)10(H2O)]2·33.5H2O (1) and (H2en)4[Li(H2O)]4[V12B18O55(OH)5(H2O)]·14H2O (2), were synthesized via hydrothermal synthesis under identical conditions except for temperature. Structural analysis revealed that although both contain [V12B18O60]n- cluster anion, the different countercations potentially lead to variations in the [V12B18O60]n- cluster anion skeletons. In compound 1, the V4+/V5+ ratio was 10:2; while in compound 2 the ratio was 11:1. It is speculated that different countercations may influence the valence states of cluster anions. In this study, quantum chemical calculations revealed that the aromaticity and activity of the two compounds were different, and two-dimensional correlation infrared spectroscopy (2D-COS-IR) under magnetic perturbation confirmed that distinct response peaks of functional group vibrations to the magnetic field due to the different V4+/V5+ ratios and aromaticity of the two compounds. An electrochemical analysis revealed that compound 2 exhibits higher electrocatalytic activity. The results of quantum chemical calculations are aligned not only with the changes in the 2D-COS-IR spectra but also with the conclusions obtained from experiments on electrochemical properties. Overall, this work proposes a novel strategy for interpreting the alteration of vanadoborate anionic skeleton due to the introduction of different countercations by combining 2D-COS-IR with quantum chemical calculations.

Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection.

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (DL) models e.g., convolutional neural network (CNN) and long short-term memory (LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed ), for time-series anomaly detection. explicitly captures the pairwise correlations via a correlation learning (MTCL) module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network (GCN) that exploits one-and multihop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available at https://github.com/huankoh/CST-GL.

In situ monitoring of ROS secretion from single cells with a dual-nanopore biosensor.

We report here a dual-nanopore biosensor based on modulation of surface charge density coupled with a microwell array chip for in situ monitoring of ROS secretion from single MCF-7 cells.

The mediating role of organizational commitment between workplace bullying and turnover intention among clinical nurses in China: a cross-sectional study.

BACKGROUND: Workplace bullying experienced by clinical nurses is a critical and pervasive issue that not only detrimentally impacts nurses but also poses a significant threat to the overall quality of nursing services and patient care. This study aimed to determine the mediating role of organizational commitment in the relationship between workplace bullying and turnover intention among clinical nurses in China. METHODS: Participants were recruited from 40 hospitals in various provinces of China from December 2, 2021 to February 25, 2023, using convenience sampling. After obtaining hospital ethical approval and participants' informed consent, clinical nurses (n = 585) from different nursing departments in different hospitals completed the questionnaire. The Socio-demographic Questionnaire, Negative Acts Qestionnaire, Chinese Workers' Organizational Commitment Scale and Turnover Intention Questionnaire were used to collect general demographic data of nurses and assess workplace bullying they experienced, their level of organizational commitment and turnover intention. Descriptive statistics, Pearson correlation analyses and structural equation model were adopted to analyze the data. RESULTS: Pearson's correlation analysis showed that that workplace bullying was significantly negatively correlated with organizational commitment (r = - 0.512, P<0.01) and significantly positively correlated with turnover intention (r = 0.558, P<0.01), organizational commitment was significantly negatively correlated with turnover intention (r = - 0.539, P<0.01). Mediation analysis indicated organizational commitment partially mediated the association between workplace bullying and turnover intention. The total effect (ß = 0.69) of workplace bullying on turnover intention consisted of its direct effect (ß = 0.41) and the indirect effect mediated through organizational commitment (ß = 0.280), with the mediating effect accounting for 40.58% of the total effect. CONCLUSION: Organizational commitment mediated the associations of workplace bullying and turnover intention. Therefore, healthcare organizations and nursing managers should develop appropriate strategies to enhance nurses' organizational commitment in order to reduce their turnover intention.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

The effects of different surgical approaches on the psychological status, medical coping mode and quality of life of patients with lung cancer.

Objective: This study aimed to compare the effects of robot-assisted thoracic surgery (RATS), video-assisted thoracic surgery (VATS), and thoracotomy on the psychological status, medical coping mode, and quality of life of patients with lung cancer. Methods: A total of 158 patients with lung cancer were selected from the thoracic surgery center of a third-grade hospital in Hunan Province, China, from September to November 2020. The Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Medical Coping Modes Questionnaire (MCMQ), and Medical Outcomes Study (MOS) 36-item Short Form Health Survey (SF-36) were used to assess the effects of the surgical approaches on the study parameters before and 48-96 h after surgery. The t-test and analysis of variance were used to analyze the data. Results: The results revealed that the patients' depression increased, their short-term quality of life decreased, and they tended to adopt a positive coping mode after surgery (p < 0.05). The RATS and VATS groups differed in avoidance dimension of medical coping modes (p < 0.05). The VATS and thoracotomy groups differed in the body pain dimension of quality of life (p < 0.05). Different surgical approaches had no effect on the psychological status, medical coping modes except the avoidance dimension, and quality of life except the body pain dimension. Conclusion: Surgical approaches have little effect on the psychological status, medical coping modes, and quality of life of patients with lung cancer; however, their depression increased and quality of life decreased after the surgery.

Class activation attention transfer neural networks for MCI conversion prediction.

Accurate prediction of the trajectory of Alzheimer's disease (AD) from an early stage is of substantial value for treatment and planning to delay the onset of AD. We propose a novel attention transfer method to train a 3D convolutional neural network to predict which patients with mild cognitive impairment (MCI) will progress to AD within 3 years. A model is first trained on a separate but related source task (task we are transferring information from) to automatically learn regions of interest (ROI) from a given image. Next we train a model to simultaneously classify progressive MCI (pMCI) and stable MCI (sMCI) (the target task we want to solve) and the ROIs learned from the source task. The predicted ROIs are then used to focus the model's attention on certain areas of the brain when classifying pMCI versus sMCI. Thus, in contrast to traditional transfer learning, we transfer attention maps instead of transferring model weights from a source task to the target classification task. Our Method outperformed all methods tested including traditional transfer learning and methods that used expert knowledge to define ROI. Furthermore, the attention map transferred from the source task highlights known Alzheimer's pathology.

Negative life events and aggression among Chinese rural left-behind adolescents: do self-esteem and resilience mediate the relationship?

BACKGROUND: Left-behind adolescents (LBAs) are adolescents aged 11-18 years who are separated from their parents and left behind in local cities by one or both parents for a period of more than 6 months. LBAs in rural areas are likely to engage in aggressive behavior, which can affect interpersonal relationships, reduce academic performance, and even lead to anxiety and depression. To our knowledge, no studies have examined the mediating effect of resilience and self-esteem on the relationship between negative life events and aggression among Chinese rural LBAs. Therefore, this study aimed to explore the relationship between negative life events and aggression among Chinese rural LBAs and how self-esteem and resilience mediate the association. METHODS: Using a stratified random sampling method, 1344 LBAs in Hunan Province of China were investigated. Information was collected by a self-designed sociodemographic questionnaire, Adolescent Self-Rating Life Events Checklist, Resilience Scale Chinese Adolescent, Rosenberg Self-Esteem Scale and Aggression Scales to assess the psychology of LBAs. Data analysis was conducted using descriptive statistics, Pearson correlation, and regression analysis to estimate direct and indirect effects using bootstrap analysis. RESULTS: Negative life events were significantly related to self-esteem (r = - 0.338), resilience (r = - 0.359), and aggression (r = 0.441). Aggression was directly affected by self-esteem (ß = - 0.44) and resilience (ß = - 0.34). Negative life events were not only directly related to aggression (ß = 0.34, 95% CI: 0.275 ~ 0.398) but also showed an indirect effect on aggression through self-esteem and resilience. The direct effect, total effect and indirect effect of negative life events on aggression through self-esteem and resilience were 0.3364, 0.4344 and 0.0980, respectively. The mediating effect of self-esteem and resilience accounted for 22.56% of the relationship between negative life events and aggression. CONCLUSIONS: We found that self-esteem and resilience mediated most negative life events on aggression. It is imperative for educators and families to improve LBAs' self-esteem and resilience to reduce the occurrence of aggression. Future intervention studies should be designed to strengthen self-esteem and resilience.

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder.

The growing prevalence of neurological disorders, e.g., Autism Spectrum Disorder (ASD), demands robust computer-aided diagnosis (CAD) due to the diverse symptoms which require early intervention, particularly in young children. The absence of a benchmark neuroimaging diagnostics paves the way to study transitions in the brain's anatomical structure and neurological patterns associated with ASD. The existing CADs take advantage of the large-scale baseline dataset from the Autism Brain Imaging Data Exchange (ABIDE) repository to improve diagnostic performance, but the involvement of multisite data also amplifies the variabilities and heterogeneities that hinder satisfactory results. To resolve this problem, we propose a Deep Multimodal Neuroimaging Framework (DeepMNF) that employs Functional Magnetic Resonance Imaging (fMRI) and Structural Magnetic Resonance Imaging (sMRI) to integrate cross-modality spatiotemporal information by exploiting 2-dimensional time-series data along with 3-dimensional images. The purpose is to fuse complementary information that increases group differences and homogeneities. To the best of our knowledge, our DeepMNF achieves superior validation performance than the best reported result on the ABIDE-1 repository involving datasets from all available screening sites. In this work, we also demonstrate the performance of the studied modalities in a single model as well as their possible combinations to develop the multimodal framework.

Coming ecological risks of organochlorine pesticides and novel brominated flame retardants in the Yellow River Basin.

To conduct ecological risk assessment, food, water and soil samples were collected from five densely populated irrigation areas in the Yellow River Basin (YRB), and analyzed for organochlorine pesticides (OCPs) and novel brominated flame retardants (NBFRs). The results showed that the OCP residues (∑14OCP were 0.748 ng L-1, 13.1 ng g-1 dw and 3.22 ng g-1 dw in water, soil and maize) were generally within moderate levels in the YRB. Dichlorodiphenyltrichloroethane (DDT) residues dominated the OCPs, and potential ecological risks to aquatic and terrestrial organisms likely stemmed from dichlorodiphenyldichloroethylene (DDE) pollution in the upper reaches of the YRB in the Ningxia and Hetao Plain. The NBFR concentrations in the YRB were 90.9 pg L-1 in water, 21.1 pg g-1 dw in soil and 3.81 pg g-1 dw in maize. Positive correlations were observed between soil and maize contamination in the five irrigated districts, indicating a potential threat to grain security and human health risks caused by OCPs and NBFRs. Pollutants detected in soil and maize primarily originated from historical use (36.3 %), while the sources in water were more complex (64.4 %) and new inputs could not be excluded. Integrated health risks of human exposure to agricultural products and water from the YRB were acceptable. However, the ecological risk of the Ningxia Plain would further deteriorate to the medium risk after 2032. The increasing ecological risk of DDT in water indicates that regular monitoring should strengthen to ensure grain and water safety in the YRB.

Overcoming Cytosolic Delivery Barriers of Proteins Using Denatured Protein-Conjugated Mesoporous Silica Nanoparticles.

Intracellular delivery of therapeutic proteins has increased advantages over current small-molecule drugs and gene therapies, especially in therapeutic efficacies for a broad spectrum of diseases. Hence, developing the protein therapeutics approach provides a needed alternative. Here, we designed a mesoporous silica nanoparticle (MSN)-mediated protein delivery approach and demonstrated effective intracellular delivery of the denatured superoxide dismutase (SOD) protein, overcoming the delivery challenges and achieving higher enzymatic activity than native SOD-conjugated MSNs. The denatured SOD-conjugated MSN delivery strategy provides benefits of reduced size and steric hindrance, increased protein flexibility without distorting its secondary structure, exposure of the cell-penetrating peptide transactivator of transcription for enhanced efficient delivery, and a change in the corona protein composition, enabling cytosolic delivery. After delivery, SOD displayed a specific activity around threefold higher than in our previous reports. Furthermore, the in vivo biosafety and therapeutic potential for neuron therapy were evaluated, demonstrating the biocompatibility and the effective antioxidant effect in Neuro-2a cells that protected neurite outgrowth from paraquat-induced reactive oxygen species attack. This study offers an opportunity to realize the druggable possibility of cytosolic proteins using MSNs.

LncRNA HCG18 Promotes Osteosarcoma Cells Proliferation, Migration, and Invasion in by Regulating miR-34a/RUNX2 Pathway.

Osteosarcoma (OS) is a type of tumor with high malignant behaviors. Increasing investigates have confirmed that long non-coding RNA HLA complex group 18 (lncRNA HCG18) acted as a tumor-promoting factor in multiple tumors. Nevertheless, the underlying mechanism of HCG18 on OS remains largely unclear. HCG18, miR-34a, and runt-related transcription factor 2 (RUNX2) expressions were detected by quantitative real-time PCR (RT-qPCR) or western blotting assays, respectively. The underlying tumorigenic phenotypes were detected by MTT, wound healing, transwell invasion, western blotting assays. Molecular interactions were verified by dual-luciferase report assay. HCG18 and RUNX2 were notably enhanced, whereas miR-34a was decreased in OS tumor tissues and cell lines. Functional experiments uncovered that HCG18 silencing significantly inhibited the capabilities of proliferation, migration, and invasion, while overexpression of HCG18 play the opposite roles. Furthermore, HCG18 directly bound to miR-34a, and miR-34a was confirm to be a negative regulator of RUNX2. Interestingly, the anti-tumor effects of HCG18 silencing were attenuated by miR-34a inhibitor and RUNX2 overexpression. Taken together, the present study suggested that HCG18 promoted the malignant biological behaviors of OS through regulating the miR-34a/RUNX2 pathway, implying HCG18 might serve as a new target for OS treatment.