Assessing breast disease with deep learning model using bimodal bi-view ultrasound images and clinical information.

Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists' scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer.

Advances of 3D Cell Co-Culture Technology Based on Microfluidic Chips.

Cell co-culture technology aims to study the communication mechanism between cells and to better reveal the interactions and regulatory mechanisms involved in processes such as cell growth, differentiation, apoptosis, and other cellular activities. This is achieved by simulating the complex organismic environment. Such studies are of great significance for understanding the physiological and pathological processes of multicellular organisms. As an emerging cell cultivation technology, 3D cell co-culture technology, based on microfluidic chips, can efficiently, rapidly, and accurately achieve cell co-culture. This is accomplished by leveraging the unique microchannel structures and flow characteristics of microfluidic chips. The technology can simulate the native microenvironment of cell growth, providing a new technical platform for studying intercellular communication. It has been widely used in the research of oncology, immunology, neuroscience, and other fields. In this review, we summarize and provide insights into the design of cell co-culture systems on microfluidic chips, the detection methods employed in co-culture systems, and the applications of these models.

KLSD: a kinase database focused on ligand similarity and diversity.

Due to the similarity and diversity among kinases, small molecule kinase inhibitors (SMKIs) often display multi-target effects or selectivity, which have a strong correlation with the efficacy and safety of these inhibitors. However, due to the limited number of well-known popular databases and their restricted data mining capabilities, along with the significant scarcity of databases focusing on the pharmacological similarity and diversity of SMIKIs, researchers find it challenging to quickly access relevant information. The KLIFS database is representative of specialized application databases in the field, focusing on kinase structure and co-crystallised kinase-ligand interactions, whereas the KLSD database in this paper emphasizes the analysis of SMKIs among all reported kinase targets. To solve the current problem of the lack of professional application databases in kinase research and to provide centralized, standardized, reliable and efficient data resources for kinase researchers, this paper proposes a research program based on the ChEMBL database. It focuses on kinase ligands activities comparisons. This scheme extracts kinase data and standardizes and normalizes them, then performs kinase target difference analysis to achieve kinase activity threshold judgement. It then constructs a specialized and personalized kinase database platform, adopts the front-end and back-end separation technology of SpringBoot architecture, constructs an extensible WEB application, handles the storage, retrieval and analysis of the data, ultimately realizing data visualization and interaction. This study aims to develop a kinase database platform to collect, organize, and provide standardized data related to kinases. By offering essential resources and tools, it supports kinase research and drug development, thereby advancing scientific research and innovation in kinase-related fields. It is freely accessible at: http://ai.njucm.edu.cn:8080.

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN's ability to extract intricate sequence features and BiLSTM's strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.

Analyzing the Functional Roles and Immunological Features of Chemokines in COAD.

Chemokines are key proteins that regulate cell migration and immune responses and are essential for modulating the tumor microenvironment. Despite their close association with colon cancer, the expression patterns, prognosis, immunity, and specific roles of chemokines in colon cancer are still not fully understood. In this study, we investigated the mutational features, differential expression, and immunological characteristics of chemokines in colon cancer (COAD) by analyzing the Tumor Genome Atlas (TCGA) database. We clarified the biological functions of these chemokines using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. By univariate and multivariate COX regression analyses, we developed chemokine-based prognostic risk models. In addition, using Gene Set Enrichment Analysis (GSEA) and Gene Set Variant Analysis (GSVA), we analyzed the differences in immune responses and signaling pathways among different risk groups. The results showed that the mutation rate of chemokines was low in COAD, but 25 chemokines were significantly differentially expressed. These chemokines function in several immune-related biological processes and play key roles in signaling pathways including cytokine-cytokine receptor interactions, NF-kappa B, and IL-17. Prognostic risk models based on CCL22, CXCL1, CXCL8, CXCL9, and CXCL11 performed well. GSEA and GSVA analyses showed significant differences in immune responses and signaling pathways across risk groups. In conclusion, this study reveals the potential molecular mechanisms of chemokines in COAD and proposes a new prognostic risk model based on these insights.

BRD4L cooperates with MYC to block local tumor invasion via suppression of S100A10.

Targeted therapy based on BRD4 and MYC shows promise due to their well-researched oncogenic functions in cancer, but their tumor-suppressive roles are less understood. In this study, we employ a systematic approach to delete exons that encode the low-complexity domain (LCD) of BRD4L in cells by using CRISPR-Cas9. In particular, the deletion of exon 14 (BRD4-E14) results in cellular morphological changes towards spindle-shaped and loosely packed. BRD4-E14 deficient cells show increased cell migration and reduced cell adhesion. The expression of S100A10 was significantly increased in cells lacking E14. BRD4L binds with MYC via the E14-encoded region of the LCD to inhibit the expression of S100A10. In cancer tissues, there is a positive correlation between BRD4 and MYC, while both of these proteins are negatively associated with S100A10 expression. Finally, knocking out the BRD4-E14 region or MYC promotes tumor growth in vivo. Together, these data support a tumor-suppressive role of BRD4L and MYC in some contexts. This discovery emphasizes the significance of a discreetly design and precise patient recruitment in clinical trials that testing cancer therapy based BRD4 and MYC.

In Situ Infrared Spectroscopic Evidence of Enhanced Electrochemical CO2 Reduction and C-C Coupling on Oxide-Derived Copper.

The reaction mechanism of CO2 electroreduction on oxide-derived copper has not yet been unraveled even though high C2+ Faradaic efficiencies are commonly observed on these surfaces. In this study, we aim to explore the effects of copper anodization on the adsorption of various CO2RR intermediates using in situ surface-enhanced infrared absorption spectroscopy (SEIRAS) on metallic and mildly anodized copper thin films. The in situ SEIRAS results show that the preoxidation process can significantly improve the overall CO2 reduction activity by (1) enhancing CO2 activation, (2) increasing CO uptake, and (3) promoting C-C coupling. First, the strong *COO- redshift indicates that the preoxidation process significantly enhances the first elementary step of CO2 adsorption and activation. The rapid uptake of adsorbed *COatop also illustrates how a high *CO coverage can be achieved in oxide-derived copper electrocatalysts. Finally, for the first time, we observed the formation of the *COCHO dimer on the anodized copper thin film. Using DFT calculations, we show how the presence of subsurface oxygen within the Cu lattice can improve the thermodynamics of C2 product formation via the coupling of adsorbed *CO and *CHO intermediates. This study advances our understanding of the role of surface and subsurface conditions in improving the catalytic reaction kinetics and product selectivity of CO2 reduction.

Identifying a locus in super-enhancer and its resident NFE2L1/MAFG as transcriptional factors that drive PD-L1 expression and immune evasion.

Although the transcriptional regulation of the programmed death ligand 1 (PD-L1) promoter has been extensively studied, the transcription factor residing in the PD-L1 super-enhancer has not been comprehensively explored. Through saturated CRISPR-Cas9 screening of the core region of the PD-L1 super-enhancer, we have identified a crucial genetic locus, referred to as locus 22, which is essential for PD-L1 expression. Locus 22 is a potential binding site for NFE2:MAF transcription factors. Although genetic silencing of NRF2 (NFE2L2) did not result in a reduction of PD-L1 expression, further analysis reveals that MAFG and NFE2L1 (NRF1) play a critical role in the expression of PD-L1. Importantly, lipopolysaccharides (LPS) as the major component of intratumoral bacteria could greatly induce PD-L1 expression, which is dependent on the PD-L1 super-enhancer, locus 22, and NFE2L1/MAFG. Mechanistically, genetic modification of locus 22 and silencing of MAFG greatly reduce BRD4 binding and loop formation but have minimal effects on H3K27Ac modification. Unlike control cells, cells with genetic modification of locus 22 and silencing of NFE2L1/MAFG failed to escape T cell-mediated killing. In breast cancer, the expression of MAFG is positively correlated with the expression of PD-L1. Taken together, our findings demonstrate the critical role of locus 22 and its associated transcription factor NFE2L1/MAFG in super-enhancer- and LPS-induced PD-L1 expression. Our findings provide new insight into understanding the regulation of PD-L1 transcription and intratumoral bacteria-mediated immune evasion.

N4BP1 regulates keratinocytes development and plays protective role in burn- and adhesive-related skin injury via MMP9.

Extensive studies have demonstrated critical roles of Regnase-1 in skin inflammation; however the role of N4BP1, a member of Regnase-1 family, in skin is largely unexplored. Here, we found that N4BP1 was highly expressed in skin and its expression was further increased upon skin injury. Compared to wildtype mice, N4BP1 deficient mice showed severe skin injury upon tape-stripping and burns. Overexpression of N4BP1 in HaCaT cells caused more cuboidal with higher cell-to-cell packing, while reduced expression of N4BP1 made cells become more spindle shaped and loosely packed. Overexpression of N4BP1 promoted cell migration, while silence of N4BP1 reduced migration. N4BP1 deficient HaCaT cells were more sensitive to heats compared to control cells. RNA profiling in N4BP1 genetically modified cells demonstrated that N4BP1 broadly affects cellular behaviors such as epithelium development. RNA profiling, RT-PCR verification, WB analysis and RNA immunoprecipitation demonstrated that MMP9 was one of N4BP1 targets that significantly increased in N4BP1 deficient HaCaT cells and skin tissues. Collectively, our results demonstrate a protective role of N4BP1 in skin injury through broadly affecting cellular behaviors of keratinocytes. Furthermore, we identified MMP9 is a target of N4BP1 in keratinocytes. Our findings provide new insight to understand how N4BP1 protects skin under injury.

A novel dictionary learning-based approach for Ultrasound Elastography denoising.

Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis.

Identification of Nine M6A-Related Long Noncoding RNAs as Prognostic Signatures Associated with Oxidative Stress in Oral Cancer Based on Data from The Cancer Genome Atlas.

Objective: Although the expression of long noncoding RNAs (lncRNAs) and N6-methyladenosine (M6A) is correlated with different tumors, it remains unclear how M6A-related lncRNA functioning affects the initiation and progression of oral squamous cell carcinoma (OSCC). Materials and Methods: Gene expression and clinical data were retrieved from The Cancer Genome Atlas. The prognostic value of M6A-related lncRNAs was determined using univariate Cox regression analyses. Gene set enrichment analysis of OSCC patient clusters revealed the pathways that elucidate the mechanism. Furthermore, a risk-based model was established. The difference in the overall survival (OS) between groups, including low-/high-risk groups, was determined by Kaplan-Meier analysis. Relationships among immune cells, clusters, clinicopathological characteristics, and risk scores were explored. Results: Among 1,080 M6A-related lncRNAs, 36 were prognosis-related. Patients with OSCC were divided into two clusters. T stage and the pathological grade were noticeably lower in cluster 2 than in cluster 1. Epithelial-mesenchymal transition showed greater enrichment in cluster 1. Nine hub M6A-related lncRNAs were identified for the model of risk score for predicting OSCC prognosis. The OS was longer in patients with a low-risk score than in patients with a high-risk score. The risk score was an independent prognostic factor of OSCC and was associated with the infiltration of different immune cells. T stages and the American Joint Committee on Cancer (AJCC) stages were more advanced in the high-risk score group than in the low-risk score group. Finally, expression correlation analysis showed that the risk score is associated with the expression of oxidative stress markers. Conclusion: M6A-related lncRNAs play an important role in OSCC progression. Immune cell infiltration was related to the risk score model in OSCC and could accurately predict OSCC prognosis.

Spherical Metal Oxides with High Tap Density as Sulfur Host to Enhance Cathode Volumetric Capacity for Lithium-Sulfur Battery.

Effective hosts of sulfur are essential for the application of lithium-sulfur batteries. However, various refined nanomaterials or carbon-based hosts possess low density, high surface area, and large porosity, leading to undesirable reduction on both gravimetric and volumetric energy densities. Herein, spherical metal oxides with high tap density are introduced as carbon-free hosts of sulfur for the first time. The ternary oxides show a superior synergistic effect of adsorption and electrocatalytic conversion of soluble intermediate polysulfides. Besides, oxide microspheres can build stable conductive frameworks and open channels in porous electrodes for fast transport of electrons and active diffusion of electrolyte. Such a synergistic effect and unique structural feature of porous electrodes are favorable for achieving good utilization and stable cycle performance of the sulfur cathode. Typically, the S/LiNi0.8Co0.1Mn0.1O2 composite exhibits good cycle stability with a low capacity decay rate (0.057% per cycle) during 500 cycles at 0.1 C. Importantly, due to the high tap density (1.81 g cm-3), the S/LiNi0.8Co0.1Mn0.1O2 composite delivers a larger volumetric capacity (1601.9 mAh cm-3-composite), almost 2.3 times of S/carbon composite (689.4 mAh cm-3-composite). Therefore, this work provides a feasible strategy to reach long life and high volumetric capacity of cathode based on metal oxides as sulfur hosts.

MicroRNA-125a-3p participates in odontoblastic differentiation of dental pulp stem cells by targeting Fyn.

Fyn is a member of the protein tyrosine kinase family and its overexpression is associated with various types of inflammation. MicroRNAs can regulate the expression of target genes and play an important role in varied physiological and pathological processes. Based on the important role of Fyn and microRNA-125a-3p (miR-125a-3p) in inflammation, and combined with the bioinformatics studies, we performed in this study and chose miR-125a-3p as the focus of our research. During the progression of inflammation, we found that the expression of miR-125a-3p was decreased while the expression of Fyn was up-regulated. Fyn formed a complex with Neuropilin-1, which inhibited odontoblastic differentiation and expanded inflammatory responses through nuclear factor-κB signal pathways in dental pulp stem cells (DPSCs). These findings suggested that miR-125a-3p plays an important role in odontoblastic differentiation of DPSCs by targeting Fyn, implying its therapeutic potential in dental caries.

Dysfunction of MiR-148a-NRP1 Functional Axis Suppresses Osteogenic Differentiation of Periodontal Ligament Stem Cells Under Inflammatory Microenvironment.

Periodontitis is a chronic inflammatory disease that can lead to the loss of periodontal bone tissue. The osteogenic potential of periodontal ligament stem cells (PDLSCs) is significantly decreased in periodontitis microenvironment. However, the mechanism is still unclear. We used Porphyromonas gingivalis lipopolysaccharide (LPS) as a stimulator of PDLSCs to mimic the periodontal inflammatory environment. The mineralization capability was restrained in LPS-stimulated PDLSCs, and the level of miR-148a increased, while the level of Neuropilin 1 (NRP1) decreased. Downregulation of miR-148a could reverse the osteogenesis deficiency of PDLSCs under LPS treatment. In addition, the expression of miR-148a in PDLSCs was negatively correlated with the expression of NRP1. Furthermore, overexpression of NRP1 upregulated the osteogenesis ability of LPS-stimulated PDLSCs, while inhibition of NRP1 eliminated the stimulative effect of miR-148a inhibitor on osteogenic differentiation. These data illustrated that the inflammatory environment mimicked by LPS inhibits osteogenesis by upregulation of miR-148a and subsequent downregulation of NRP1. We also found, compared to healthy periodontal tissues, miR-148a level increased, while NRP1 level decreased in periodontitis tissues. These two phenomena also exist in PDLSCs that come from the upper two types of tissues. To summarize, the decline of osteogenic potential of PDLSCs under inflammatory condition of periodontitis is related to miR-148a/NRP1 functional axis. This study may provide a novel strategy in the molecular aspect for the therapy of periodontitis.

Rosuvastatin Regulates Odontoblast Differentiation by Suppressing NF-κB Activation in an Inflammatory Environment.

Rosuvastatin is a synthetic statin of 3-hydroxy-methyl-3-glutamyl coenzyme A reductase inhibitor. It has pleiotropic characteristics including hepatic selectivity, minimal metabolism, inhibition of inflammation, and induction of osteoblast differentiation. In this study, dental pulp stem cells (DPSCs) were treated with lipopolysaccharide alone or with rosuvastatin. Then, we examined the accelerative effects of rosuvastatin on odontoblast differentiation and mineralized nodule formation by real-time polymerase chain reaction (RT-PCR), western blot, alizarin red S staining, and alkaline phosphatase staining. The extent of anti-inflammation was determined by RT-PCR and analysis of the expression of tumor necrosis factor α, interleukin 1ß (IL-1ß), and IL-6. Furthermore, the activation of nuclear factor kappa B (NF-κB) was determined by western blot. This study demonstrates that rosuvastatin may speed up odontoblast differentiation and rescue inflammatory reaction by suppressing the NF-κB signaling pathway. It is believed that our findings provide novel perceptions on odontogenic differentiation of DPSCs.

CKIP-1 suppresses odontoblastic differentiation of dental pulp stem cells via BMP2 pathway and can interact with NRP1.

AIM: Casein kinase 2 interacting protein-1 (CKIP-1) is a recently discovered intracellular regulator of bone formation, muscle cell differentiation, and tumor cell proliferation. Our study aims to identify the inhibition of BMP2-Smad1/5 signaling by CKIP-1 in odontoblastic differentiation of human dental pulp stem cells (DPSCs). MATERIALS AND METHODS: DPSCs infected CKIP-1 siRNA or transfected CKIP-1 full-length plasmid were cultured in odontoblastic differentiation medium or added noggin (200 ng/mL) for 21 days. We examined the effects of CKIP-1 on odontoblastic differentiation, mineralized nodules formation, and interaction by western blot, real-time polymerase chain reaction (RT-PCR), alkaline phosphatase (ALP) staining, alizarin red S staining, and immunoprecipitation. RESULTS: Firstly, we have demonstrated that CKIP-1 expression markedly decreased time-dependently along with cell odontoblastic differentiation. Indeed, the silence of CKIP-1 upregulated odontoblastic differentiation via BMP2-Smad1/5 signaling, while CKIP-1 over-expression had a negative effect on odontoblastic differentiation of DPSCs. Furthermore, CKIP-1 could interact with Neuropilin-1 (NRP1). CONCLUSIONS: This work provides data that advocates a novel perception on odontoblastic differentiation of DPSCs. Therefore, inhibiting the expression of CKIP-1 may be of great significance to the development of dental caries.

The Forkhead Box C1, a Novel Negative Regulator of Osteogenesis, Plays a Crucial Role in Odontogenic Differentiation of Dental Pulp Stem Cells.

The forkhead box C1 (Foxc1) protein, a member of the forkhead/winged helix transcription factor family, is required in stem cell developmental processes. Recently, multiple studies have indicated the crucial role of Foxc1 in mesenchymal stem cell differentiation, but the precise effects and mechanisms on dental pulp stem cells (DPSCs) remain unclear. In this study, we evaluate the role of Foxc1 on the odontogenic differentiation and proliferation of DPSCs. Our results show that Foxc1 decreases time dependently in odontogenic differentiation of DPSCs. Meanwhile, overexpression of Foxc1 could significantly inhibit the mineralization of DPSCs and the expression of odontogenic-related genes, such as runt-related transcription factor 2 (Runx2), dentin sialophosphoprote (DSPP), and dentin matrix acidic phosphoprotein 1 (DMP-1). Foxc1 overexpression does not significantly alter the proliferation of DPSCs. In addition, Foxc1 reduces the expression of p-Smad1/5, an important modulator of bone morphogenetic protein (BMP)/Smad signaling pathway, inhibiting BMP/Smad signaling pathway. In conclusion, our data demonstrated that Foxc1 inhibits odontogenic differentiation of DPSCs and odontogenic-related gene expression through the BMP/Smad signaling pathway which may be useful for the dental regeneration and repair.

Wedelolactone Enhances Odontoblast Differentiation by Promoting Wnt/ß-Catenin Signaling Pathway and Suppressing NF-κB Signaling Pathway.

Wedelolactone is a multitarget natural plant compound with many pharmacological activities, including anti-inflammatory, anticancer, and antiosteoporosis. In this study, dental pulp stem cells (DPSCs) were treated with or without wedelolactone. We found that wedelolactone stimulated odontoblast differentiation and mineralization. At the molecular level, wedelolactone directly promoted the nuclear accumulation of ß-catenin, and thereafter stimulated the expression of odontoblast-related marker genes containing dentin matrix protein-1 (DMP1), dentin sialophosphoprotein (DSPP), and runt-related transcription factor 2 (Runx2). Furthermore, wedelolactone upregulated the expression of IκBα and inhibited phosphonation and nuclear migration of p65. As a result, wedelolactone remarkably induced odontoblast differentiation through semaphorin 3A (Sema3A)/neuropilin-1 (NRP1) pathway-mediated ß-catenin activation and nuclear factor kappa B (NF-κB) pathway inhibition. Our findings provide novel perceptions on odontogenic differentiation of DPSCs.

The Role of Neuropilin-1-FYN Interaction in Odontoblast Differentiation of Dental Pulp Stem Cells.

Abnormal odontoblast differentiation of dental pulp stem cells (DPSCs) caused by inflammation is closely related to the development of dental caries. Neuropilin-1 (NRP1) is one of the members of neuropilin family. It can combine with disparate ligands involved in regulating cell differentiation. FYN belongs to the protein-tyrosine kinase family, which has been implicated in the control of cell growth, and the effect can be further strengthened by inflammatory factors. In our studies, we verified that NRP1 can form complexes with FYN and have the correlation changes in odontoblast differentiation of DPSCs. Therefore, we surmise that in the progress of dental caries, NRP1 interacts with FYN, by expanding inflammation and inhibition of odontoblast differentiation of DPSCs through nuclear factor kappa B (NF-κB) signaling pathway. In this subject, we first investigated the expression and interaction of NRP1 and FYN in DPSCs. And then, we researched the effect of this complex controlling downstream signal pathway in normal or inflammation stimulated DPSCs. Finally, we analyzed the relationship between this role and odontoblast differentiation of DPSCs. This research will provide the molecular mechanism of inflammation factors of dental caries through activating NF-κB signal regulating odontoblast differentiation in DPSCs for finding new potential drug targets for the clinical treatment of dental caries.

NRP1 Accelerates Odontoblast Differentiation of Dental Pulp Stem Cells Through Classical Wnt/ß-Catenin Signaling.

Neuropilin-1 (NRP1) is one of the members of neuropilin family. It can combine with disparate ligands involved in regulating cell proliferation, apoptosis, and differentiation. The binding of NRP1 to Sema3A stimulates osteoblast differentiation through the classical Wnt/ß-catenin pathway. However, the functions of NRP1 in dental pulp stem cells (DPSCs) are not clear. The aim of our study was to investigate how NRP1 controlled odontoblast differentiation in DPSCs and clarified the underlying mechanisms. NRP1 expression was increased in time-dependent manner along with cell odontoblast differentiation. Overexpression of NRP1 upregulated dentin matrix protein-1, dentin sialophosphoprotein, alkaline phosphatase protein level, and mineralization in DPSCs, while knockdown of NRP1 induced the opposite effects. SiNRP1 similar to DKK1 availably blocked classical Wnt/ß-catenin signaling and odontoblast differentiation. In summary, NRP1, as a promoter of odontoblast differentiation, regulates DPSCs via the classical Wnt/ß-catenin pathway.