MiR-199a-5P promotes osteogenic differentiation of human stem cells from apical papilla via targeting IFIT2 in apical periodontitis

IntroductionPeriapical alveolar bone loss is the common consequence of apical periodontitis (AP) caused by persistent local inflammation around the apical area. Human stem cells from apical papilla (hSCAPs) play a crucial role in the restoration of bone lesions during AP. Studies have recently identified the critical role of microRNAs (miRNAs) involved in AP pathogenesis, but little is known about their function and potential molecular mechanism, especially in the osteogenesis of hSCAPs during AP. Here, we investigated the role of clinical sample-based specific miRNAs in the osteogenesis of hSCAPs.MethodsDifferential expression of miRNAs were detected in the periapical tissues of normal and patients with AP via transcriptomic analysis, and the expression of miR-199a-5p was confirmed by qRT-PCR. Treatment of hSCAPs with miR-199a-5p mimics while loaded onto beta-tricalcium phosphate (β-TCP) ceramic particle scaffold to explore its effect on osteogenesis in vivo. RNA binding protein immunoprecipitation (RIP) and Luciferase reporter assay were conducted to identify the target gene of miR-199a-5p.ResultsThe expression of miR-199a-5p was decreased in the periapical tissues of AP patients, and miR-199a-5p mimics markedly enhanced cell proliferation and osteogenic differentiation of hSCAPs, while miR-199a-5p antagomir dramatically attenuated hSCAPs osteogenesis. Moreover, we identified and confirmed Interferon Induced Protein with Tetratricopeptide Repeats 2 (IFIT2) as a specific target of miR-199a-5p, and silencing endogenous IFIT2 expression alleviated the inhibitory effect of miR-199a-5p antagomir on the osteogenic differentiation of hSCAPs. Furthermore, miR-199a-5p mimics transfected hSCAPs loaded onto beta-tricalcium phosphate (β-TCP) scaffolds induced robust subcutaneous ectopic bone formation in vivo.DiscussionThese results strengthen our understanding of predictors and facilitators of the key AP miRNAs (miR-199a-5p) in bone lesion repair under periapical inflammatory conditions. And the regulatory networks will be instrumental in exploring the underlying mechanisms of AP and lay the foundation for future regenerative medicine based on dental mesenchymal stem cells

DAFNet: A dual attention-guided fuzzy network for cardiac MRI segmentation

Background: In clinical diagnostics, magnetic resonance imaging (MRI) technology plays a crucial role in the recognition of cardiac regions, serving as a pivotal tool to assist physicians in diagnosing cardiac diseases. Despite the notable success of convolutional neural networks (CNNs) in cardiac MRI segmentation, it remains a challenge to use existing CNNs-based methods to deal with fuzzy information in cardiac MRI. Therefore, we proposed a novel network architecture named DAFNet to comprehensively address these challenges. Methods: The proposed method was used to design a fuzzy convolutional module, which could improve the feature extraction performance of the network by utilizing fuzzy information that was easily ignored in medical images while retaining the advantage of attention mechanism. Then, a multi-scale feature refinement structure was designed in the decoder portion to solve the problem that the decoder structure of the existing network had poor results in obtaining the final segmentation mask. This structure further improved the performance of the network by aggregating segmentation results from multi-scale feature maps. Additionally, we introduced the dynamic convolution theory, which could further increase the pixel segmentation accuracy of the network. Result: The effectiveness of DAFNet was extensively validated for three datasets. The results demonstrated that the proposed method achieved DSC metrics of 0.942 and 0.885, and HD metricd of 2.50mm and 3.79mm on the first and second dataset, respectively. The recognition accuracy of left ventricular end-diastolic diameter recognition on the third dataset was 98.42%. Conclusion: Compared with the existing CNNs-based methods, the DAFNet achieved state-of-the-art segmentation performance and verified its effectiveness in clinical diagnosis

Chronic kidney disease and valvular heart disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies conference

Chronic kidney disease (CKD) is a major risk factor for valvular heart disease (VHD). Mitral annular and aortic valve calcifications are highly prevalent in CKD patients and commonly lead to valvular stenosis and regurgitation, as well as complications including conduction system abnormalities and endocarditis. VHD, especially mitral regurgitation and aortic stenosis, is associated with significantly reduced survival among CKD patients. Knowledge related to VHD in the general population is not always applicable to CKD patients because the pathophysiology may be different, and CKD patients have a high prevalence of comorbid conditions and elevated risk for periprocedural complications and mortality. This Kidney Disease: Improving Global Outcomes (KDIGO) review of CKD and VHD seeks to improve understanding of the epidemiology, pathophysiology, diagnosis, and treatment of VHD in CKD by summarizing knowledge gaps, areas of controversy, and priorities for research

Disparities in London’s Public Transport Accessibility over a Decade: Impact of Race, Ethnicity, and Socioeconomic Status

Cross-sectional studies have indicated spatial inequalities in public transport accessibility in London, where low-skilled, low-income groups often experience limited accessibility, hindering their access to urban services and opportunities. However, how accessibility to public transport is distributed by demographic groups and how it changed over time have not been studied. This study examined the potential unequal distribution of public transport accessibility with a focus on demographic groups defined by ethnicity, age, and socioeconomic status over the past decade, at the LSOA level in the Greater London Area. After accounting for geographical features, car ownership, population density, and spatial autocorrelation in spatial lag models, the disparities for ethnicity were found, as the mixed and other ethnic groups were more disadvantaged both in 2011 and 2021, while the Asian ethnic groups had a more advantaged position. Income also played a role, as wealthier groups tended to have better access to public transport; however, these privileges decreased throughout the decade. The accessibility advantage of the middle-aged and older groups in 2011 diminished significantly by 2021. This was replaced by the median low-level age group, which had the most prominent advantage in tube accessibility. The research aims to inform policymakers on addressing disparities in public transport, optimising accessibility, and developing a fairer and more inclusive urban environment

Nuclear Delivery of Nanoparticle-Based Drug Delivery Systems by Nuclear Localization Signals

Nanomedicine 2.0 refers to the next generation of nanotechnology-based medical therapies and diagnostic tools. This field focuses on the development of more sophisticated and precise nanoparticles (NPs) for targeted drug delivery, imaging, and sensing. It has been established that the nuclear delivery of NP-loaded drugs can increase their therapeutic efficacy. To effectively direct the NPs to the nucleus, the attachment of nuclear localization signals (NLSs) to NPs has been employed in many applications. In this review, we will provide an overview of the structure of nuclear pore complexes (NPCs) and the classic nuclear import mechanism. Additionally, we will explore various nanoparticles, including their synthesis, functionalization, drug loading and release mechanisms, nuclear targeting strategies, and potential applications. Finally, we will highlight the challenges associated with developing nucleus-targeted nanoparticle-based drug delivery systems (NDDSs) and provide insights into the future of NDDSs

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

As an important indicator of terrestrial ecosystems, vegetation plays an important role in the study of global or regional ecological environmental changes. Northern Shaanxi is located in the ecologically fragile area of the Loess Plateau, which is affected by interactions between natural and human factors. Here, we used the Normalized Difference Vegetation Index (NDVI) as an indicator to study the temporal and spatial variations of vegetation in Northern Shaanxi from 2000 to 2018. Based on the geographic detector method which can detect spatial differentiation, we analyzed the spatial differentiation characteristics and driving forces of vegetation in Northern Shaanxi, and revealed the most appropriate range or type of influencing factors for promoting vegetation growth. The results showed that the overall vegetation coverage improved in the study area, and NDVI showed an increasing trend with a growth rate of 0.10/10 years from 2000 to 2018. Natural and human factors are crucial driving forces of NDVI change, among which gross domestic product, land-use type, slope, and temperature have the greatest influence. The interaction between natural and human factors on NDVI was dominated by nonlinear and mutual enhancement effects, and the influence of interactions among all factors was significantly higher than that of a single factor. The range or types of factors suitable for vegetation growth were analyzed in the study area, and the joint action of natural and human factors had a more significant impact on vegetation. These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope

An Efficient Method for Generating Adversarial Malware Samples

Deep learning methods have been applied to malware detection. However, deep learning algorithms are not safe, which can easily be fooled by adversarial samples. In this paper, we study how to generate malware adversarial samples using deep learning models. Gradient-based methods are usually used to generate adversarial samples. These methods generate adversarial samples case-by-case, which is very time-consuming to generate a large number of adversarial samples. To address this issue, we propose a novel method to generate adversarial malware samples. Different from gradient-based methods, we extract feature byte sequences from benign samples. Feature byte sequences represent the characteristics of benign samples and can affect classification decision. We directly inject feature byte sequences into malware samples to generate adversarial samples. Feature byte sequences can be shared to produce different adversarial samples, which can efficiently generate a large number of adversarial samples. We compare the proposed method with the randomly injecting and gradient-based methods. The experimental results show that the adversarial samples generated using our proposed method have a high successful rate

An Efficient Method for Generating Adversarial Malware Samples

Deep learning methods have been applied to malware detection. However, deep learning algorithms are not safe, which can easily be fooled by adversarial samples. In this paper, we study how to generate malware adversarial samples using deep learning models. Gradient-based methods are usually used to generate adversarial samples. These methods generate adversarial samples case-by-case, which is very time-consuming to generate a large number of adversarial samples. To address this issue, we propose a novel method to generate adversarial malware samples. Different from gradient-based methods, we extract feature byte sequences from benign samples. Feature byte sequences represent the characteristics of benign samples and can affect classification decision. We directly inject feature byte sequences into malware samples to generate adversarial samples. Feature byte sequences can be shared to produce different adversarial samples, which can efficiently generate a large number of adversarial samples. We compare the proposed method with the randomly injecting and gradient-based methods. The experimental results show that the adversarial samples generated using our proposed method have a high successful rate

Comparative Chloroplast Genome Analysis of Rhubarb Botanical Origins and the Development of Specific Identification Markers

Rhubarb is an important ingredient in traditional Chinese medicine known as Rhei radix et rhizome. However, this common name refers to three different botanical species with different pharmacological effects. To facilitate the genetic identification of these three species for their more precise application in Chinese medicine we here want to provide chloroplast sequences with specific identification sites that are easy to amplify. We therefore sequenced the complete chloroplast genomes of all three species and then screened those for suitable sequences describing the three species. The length of the three chloroplast genomes ranged from 161,053 bp to 161,541 bp, with a total of 131 encoded genes including 31 tRNA, eight rRNA and 92 protein-coding sequences. The simple repeat sequence analysis indicated the differences existed in these species, phylogenetic analyses showed the chloroplast genome can be used as an ultra-barcode to distinguish the three botanical species of rhubarb, the variation of the non-coding regions is higher than that of the protein coding regions, and the variations in single-copy region are higher than that in inverted repeat. Twenty-one specific primer pairs were designed and eight specific identification sites were experimentally confirmed that can be used as special DNA barcodes for the identification of the three species based on the highly variable regions. This study provides a molecular basis for precise medicinal plant selection, and supplies the groundwork for the next investigation of the closely related Rheum species comparing and correctly identification on these important medicinal species