IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.

Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7 k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.

Organic Cocrystal Alloys: From Three Primary Colors to Continuously Tunable Emission and Applications on Optical Waveguides and Displays.

Multicolor luminescence of organic fluorescent materials is an essential part of lighting and optical communication. However, the conventional construction of a multicolor luminescence system based on integrating multiple organic fluorescent materials of a single emission band remains complicated and to be improved. Herein, organic alloys (OAs) capable of full-color emission are synthesized based on charge transfer (CT) cocrystals. By adjusting the molar ratio of electron donors, the emission color of the OAs can be conveniently and continuously regulated in a wide visible range from blue (CIE: 0.187, 0.277), to green (CIE: 0.301, 0.550), and to red (CIE: 0.561, 0.435). The OAs show analogous 1D morphology with smooth surface, allowing for full-color waveguides with low optical-loss coefficient. Impressively, full-color optical displays are easily achieved through the OAs system with continuous emission, which shows promising applications in the field of optical display and promotes the development of organic photonics.

Novel C-type lectin mediated non-specific cytotoxic cells killing activity through NCCRP-1 in nile tilapia (Oreochromis niloticus).

Non-specific cytotoxic cells (NCCs) are vital immune cells involved in teleost's non-specific immunity. As a receptor molecule on the NCCs' surface, the non-specific cytotoxic cell receptor protein 1 (NCCRP-1) is known to play a crucial role in mediating their activity. Nevertheless, there have been limited studies on the signal molecule that transmits signals via NCCRP-1. In this study, a yeast two-hybrid (Y2H) library of tilapia liver and head kidney was constructed and subsequently screened with the bait vector NCCRP-1 of Oreochromis niloticus (On-NCCRP-1) to obtain a C-type lectin (On-CTL) with an interacting protein sequence. Consequently, the full-length sequence of On-CTL was cloned and analyzed. The expression analysis revealed that On-CTL is highly expressed in the liver and is widely distributed in other tissues. Furthermore, On-CTL expression was significantly up-regulated in the brain, intestine, and head kidney following a challenge with Streptococcus agalactiae. A point-to-point Y2H method was also used to confirm the binding between On-NCCRP-1 and On-CTL. The recombinant On-CTL (rOn-CTL) protein was purified. In vitro experiments demonstrated that rOn-CTL can up-regulate the expression of killer effector molecules in NCCs via its interaction with On-NCCRP-1. Moreover, activation of NCCs by rOn-CTL resulted in a remarkable enhancement in their ability to eliminate fathead minnow cells, indicating that rOn-CTL effectively modulates the killing activity of NCCs through the NCC receptor molecule On-NCCRP-1. These findings significantly contribute to our comprehension of the regulatory mechanisms governing NCC activity, paving the way for future research in this field.

Movement recognition via channel-activation-wise sEMG attention.

CONTEXT: Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in rehabilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity. OBJECTIVE: Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition. METHOD: We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition. RESULTS: We compared the proposed method against existing methods on two Ninapro datasets consisting of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset.

Worm Generator: A System for High-Throughput in Vivo Screening.

Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nanogenerators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals' behaviors into friction deformation and result in a contact-separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug's identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics.

Global trade-driven transfer of atmospheric polycyclic aromatic hydrocarbon emissions and associated human inhalation exposure risk.

Polycyclic aromatic hydrocarbons (PAHs) are of significant public concern because of their toxicity and long-range transport potential. Extensive studies have been conducted to explore the source-receptor relationships of PAHs via atmospheric transport. However, the transfer of trade-driven regional and global PAHs is poorly understood. This study estimated the virtual PAHs emission transfer embodied in global trade from 2004 to 2014 and simulated the impact of international trade on global contamination and associated human inhalation exposure risk of PAHs. Results show that trade-driven PAHs flowed primarily from developed to less-developed regions, particularly in those regions with intensive heavy industries and transportation. As the result, international trade resulted in an increasing risk of lung cancer induced by exposure to PAHs (27.8% in China, 14.7% in India, and 11.3% in Southeast Asia). In contrast, we found decreasing risks of PAHs-induced lung cancer in Western Europe (63.2%) and the United States (45.9%) in 2004. Our findings indicate that final demand and emission intensity are the key driving factors contributing to rising and falling consumption-based PAHs emissions and related health risk respectively. The results could provide a useful reference for global collaboration in the reduction of PAHs pollution and related health risks.

Ubiquitin-specific peptidase 24 accelerates aerobic glycolysis and tumor progression in gastric carcinoma through stabilizing PLK1 to activate NOTCH1.

Ubiquitin-specific peptidase 24 (USP24), a member of the deubiquitinase family, plays an important role in tumor regulation. However, the role of USP24 in gastric cancer (GC) is unknown. The aim of our study was to explore the role of USP24 in GC to seek new therapeutic targets for GC. TCGA analysis showed that USP24 was upregulated in GC patients, and its high expression levels were associated with poor prognosis. It was found that overexpressed USP24 promoted GC cell proliferation and abnormal glycolysis. Further analysis and study showed that USP24 could promote the stability and increase the expression of oncogene PLK1. Knocking down USP24 can reduce the stability of PLK1 to reduce Notch 1 activity, thus inhibiting GC glycolysis, proliferation, and other processes and alleviating tumor progression. Knocking down USP24 can inhibit GC progression. In conclusion, USP24 was upregulated in GC and promoted the growth and glycolysis of GC cells, the mechanism of which was related to the deubiquitination of PLK1 and the increase of its stability. Silencing USP24 inhibited the GC process. This study suggests that the USP24/PLK1/Notch 1 axis may be a novel therapeutic target for gastric cancer.

Dynamic effective connectivity among large-scale brain networks mediates risk of anxiety.

Anxiety is characterized by altered brain networks. Directional information flows among dynamic brain networks concerning neuropathogenesis of anxiety have not yet been investigated. The role of directional influences between networks in gene-environment effects on anxiety remains to be further elucidated. In a large community sample, this resting-state functional MRI study estimated dynamic effective connectivity among large-scale brain networks based on a sliding-window approach and Granger causality analysis, providing dynamic and directional information for signal transmission in networks. We first explored altered effective connectivity among networks related to anxiety in distinct connectivity states. Due to the potential gene-environment effects on brain and anxiety, we further performed mediation and moderated mediation analyses to investigate the role of altered effective connectivity networks in relationships between polygenic risk scores, childhood trauma, and anxiety. State and trait anxiety scores showed correlations with altered effective connectivity among extensive networks in distinct connectivity states (p < .05, uncorrected). Only in a more frequent and strongly connected state, there were significant correlations between altered effective connectivity networks and trait anxiety (PFDR <0.05). Furthermore, mediation and moderated mediation analyses showed that the effective connectivity networks played a mediating role in the effects of childhood trauma and polygenic risk on trait anxiety. State-dependent effective connectivity changes among brain networks were significantly related to trait anxiety, and mediated gene-environment effects on trait anxiety. Our work sheds novel light on the neurobiological mechanisms underlying anxiety, and provides new insights into early objective diagnosis and intervention evaluation.

Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study.

BACKGROUND: Noninvasive determination of Notch signaling is important for prognostic evaluation and therapeutic intervention in glioma. PURPOSE: To predict Notch signaling using multiparametric (mp) MRI radiomics and correlate with biological characteristics in gliomas. STUDY TYPE: Retrospective. POPULATION: A total of 63 patients for model construction and 47 patients from two public databases for external testing. FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T, T1-weighted imaging (T1WI), T2WI, T2 fluid attenuated inversion recovery (FLAIR), contrast-enhanced (CE)-T1WI. ASSESSMENT: Radiomic features were extracted from CE-T1WI, T1WI, T2WI, and T2FLAIR and imaging signatures were selected using a least absolute shrinkage and selection operator. Diagnostic performance was compared between single modality and a combined mpMRI radiomics model. A radiomic-clinical nomogram was constructed incorporating the mpMRI radiomic signature and Karnofsky Performance score. The performance was validated in the test set. The radiomic signatures were correlated with immunohistochemistry (IHC) analysis of downstream Notch pathway components. STATISTICAL TESTS: Receiver operating characteristic curve, decision curve analysis (DCA), Pearson correlation, and Hosmer-Lemeshow test. A P value < 0.05 was considered statistically significant. RESULTS: The radiomic signature derived from the combination of all sequences numerically showed highest area under the curve (AUC) in both training and external test sets (AUCs of 0.857 and 0.823). The radiomics nomogram that incorporated the mpMRI radiomic signature and KPS status resulted in AUCs of 0.891 and 0.859 in the training and test sets. The calibration curves showed good agreement between prediction and observation in both sets (P= 0.279 and 0.170, respectively). DCA confirmed the clinical usefulness of the nomogram. IHC identified Notch pathway inactivation and the expression levels of Hes1 correlated with higher combined radiomic scores (r = -0.711) in Notch1 mutant tumors. DATA CONCLUSION: The mpMRI-based radiomics nomogram may reflect the intratumor heterogeneity associated with downstream biofunction that predicts Notch signaling in a noninvasive manner. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing.

The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning.

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.

BACKGROUND: Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. RESULTS: In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. CONCLUSIONS: Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.

Interleukin-17A pretreatment attenuates the anti-hepatitis B virus efficacy of interferon-alpha by reducing activation of the interferon-stimulated gene factor 3 transcriptional complex in hepatitis B virus-expressing HepG2 cells.

BACKGROUND: Some cytokine signaling pathways can interact with interferon (IFN)-α pathway and thus regulate cell responses to IFN-α. Levels of the pro-inflammatory cytokine interleukin-17A (IL-17A) were found to be elevated in both the peripheral blood and liver in chronic hepatitis B (CHB) patients. However, how IL-17A affects the anti-HBV activity of IFN-α remains unclear. METHODS: The effects of IL-17A on anti-HBV activity of IFN-α were evaluated in HBV-expressing HepG2 cells (HepG2-HBV1.3) with IL-17A pretreatment and IFN-α stimulation. Culture supernatant levels of HBsAg, HBeAg, and HBV DNA, or intracellular expression of HBsAg and HBcAg were detected by ELISA, real-time quantitative PCR (RT-qPCR), or western blotting (WB). The expression of canonical IFN-α signaling pathway components, including the interferon-α/ß receptor (IFNAR), Janus Kinase 1 (JAK1), Tyrosine Kinase 2 (TYK2), the Interferon Stimulated Gene Factor 3 complex (ISGF3) and IFN-stimulated genes (ISGs), was also examined by RT-qPCR, Immunofluorescence or WB. The effects of IL-17A were further investigated by the suppression of the IL-17A pathway with a TRAF6 inhibitor. RESULTS: Compared to IFN-α stimulation alone, IL-17A pretreatment followed by IFN-α stimulation increased the levels of HBsAg, HBeAg, and HBV DNA, and decreased the levels of ISGF3 complex (phosphorylated (p)-signal transducer and activator of transcription (STAT1)/p-STAT2/IRF9) and antiviral-related ISGs (ISG15, ISG20 and Mx1). Interestingly, IL-17A pretreatment increased the expression of suppressor of cytokine signaling (SOCS) 1, SOCS3 and USP18, which were also the ISGs negatively regulating activity of ISGF3. Moreover, IFNAR1 protein expression declined more sharply in the group with IL-17A pretreatment than in the group with IFN-α stimulation alone. Blocking the IL-17A pathway reversed the effects of IL-17A on the IFN-α-induced activation of ISGF3 and anti-HBV efficacy. CONCLUSIONS: Our results demonstrate that IL-17A pretreatment could attenuate IFN-α-induced anti-HBV activity by upregulating negative regulators of the critical transcriptional ISGF3 complex. Thus, this might be a potential target for improving response to IFN-α therapy.

Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity.

Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at https://github.com/19990915fzy/DGNNMDA.

Quantification of active selenols in cells: a selenol-specific recognition europium-switched signal-amplification ICP-MS approach.

Selenium (Se) is a mysterious thus tempting element playing a dual bio-chemical function, mainly through selenol, during life processes. Quantification of the selenols is thus of great significance for understanding the biological roles of Se, but remains a big challenge. Herein we report a selenol-specific recognition-mediated and europium (Eu) signal-switched amplification inductively coupled plasma mass spectrometry (ICP-MS) approach for quantifying the free active selenols (act-SeH) in cells. A bifunctional molecule, 2,4-dinitrobenzenesulfonyl-piperidin-4-yl-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic europium (DNBS-DOTA-Eu), was designed and synthesized for the specific recognition and highly sensitive quantification of act-SeH via switching Se to more sensitive Eu ICP-MS signals. The limit of detection (LOD, 3σ) of 3.41 pg/mL (22.43 pmol/L), corresponding to the absolute mass LOD of 6.82 ag act-SeH per cell, is almost 25 times lower than 83.76 pg/mL (1.06 nmol/L), 167.52 ag, when monitoring 80Se. The results indicate that act-SeH in the selenite-precultured cancerous HepG2 and paracancerous HL7702 cells are 0.090 ± 0.002 pg/cell (n = 7) and 0.021 ± 0.006 pg/cell (n = 7), more than 4.28 times higher in HepG2 than in HL7702. Preliminary application of this approach to the cells from real hepatic tissue samples suggested that act-SeH has a positive relationship with the degree of hepatic disease. act-SeH in cells appears to be a very promising relevant index for understanding the biochemical functions of Se, besides the total Se in cells and blood serum and/or plasma.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.

BACKGROUND: Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. RESULTS: In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. CONCLUSION: The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model.

The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity α and ß, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (Dm : 1.14 ± 0.37 µm2 /ms; α: 0.904 ± 0.03, ß: 0.913 ± 0.06), GrIII (Dm : 0.88 ± 0.21 µm2 /ms; α: 0.888 ± 0.01, ß: 0.857 ± 0.06) and GrIV gliomas (Dm : 0.73 ± 0.22 µm2 /ms; α: 0.878 ± 0.01; ß: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.

RICK regulates the odontogenic differentiation of dental pulp stem cells through activation of TNF-α via the ERK and not through NF-κB signaling pathway.

Dental pulp stem cells (DPSCs) are capable of both self-renewal and multilineage differentiation, which play a positive role in dentinogenesis. Studies have shown that tumor necrosis factor-α (TNF-α) is involved in the differentiation of DPSCs under pro-inflammatory stimuli, but the mechanism of action of TNF-α is unknown. Rip-like interacting caspase-like apoptosis-regulatory protein kinase (RICK) is a biomarker of an early inflammatory response that plays a key role in modulating cell differentiation, but the role of RICK in DPSCs is still unclear. In this study, we identified that RICK regulates TNF-α-mediated odontogenic differentiation of DPSCs via the ERK signaling pathway. The expression of the biomarkers of odontogenic differentiation dental matrix protein-1 (DMP-1), dentin sialophosphoprotein (DSPP), biomarkers of odontogenic differentiation, increased in low concentration (1-10 ng/ml) of TNF-α and decreased in high concentration (50-100 ng/ml). Odontogenic differentiation increased over time in the odontogenic differentiation medium. In the presence of 10 ng/L TNF-α, the expression of RICK increased gradually over time, along with odontogenic differentiation. Genetic silencing of RICK expression reduced the expression of odontogenic markers DMP-1 and DSPP. The ERK, but not the NF-κB signaling pathway, was activated during the odontogenic differentiation of DPSCs. ERK signaling modulators decreased when RICK expression was inhibited. PD98059, an ERK inhibitor, blocked the odontogenic differentiation of DPSCs induced by TNF-α. These results provide a further theoretical and experimental basis for the potential use of RICK in targeted therapy for dentin regeneration.