Research hotspots and trends of nanomaterials in stomatology: A bibliometric analysis from 2000 to 2023.

Background: Nanomaterials (NMs) have emerged as highly promising candidates for stomatology due to their excellent quality and remarkable progress in recent years. However, with the rapid expansion of the research scale, challenges arise in the technological decision-making and research management processes, and therefore difficulty for researchers to understand the knowledge structure and research hotspots has increased significantly. This study aims to make a comprehensive summary of authors, institutions, journals, research topics, development trends, and research hotspots of NMs in stomatology through bibliometric analysis for the sake of providing references for scientific decision-making, research management, and academic exploration in this filed. Methods: Studies on research and application of NMs in stomatology were retrieved from the Web of Science Core Collection (WoSCC) from January 1, 2000 to April 27, 2023. Bibliometric analysis and visualization were conducted using CiteSpace and VOSviewer. Results: A total of 620 articles were included in this study, showing a gradual increase in the number of publications focusing on NMs in stomatology. Globally, China ranked first with 130 publications, and the United States (US) enjoyed the highest citation count (n = 5218) and average citation per paper (ACP) (n = 52.18). The top three institutions with the highest publication output were the University of Sao Paulo (n = 22), the Chinese Academy of Sciences (n = 20), and Shanghai Jiaotong University (n = 13). The journals MATERIALS and NANOMATERIALS emerged as the most popular in this field (n = 20), and BIOMATERIALS had the highest co-citations (n = 1597). The most prolific author was Dos Reis and Andrea Candido (n = 7), while Thomas J. Webster enjoyed the highest co-citations (n = 94). Burstness analysis of the references revealed a prominent research focus on nanoparticle drug delivery systems (specifically lipid nanoparticles). Keyword burstness analysis identified "oxide nanoparticle" as the primary frontier keyword in this field. Conclusion: This is the first study of using bibliometric analysis to summarize the research trends and frontiers of NMs in stomatology. With progressive advancements in the research and application of NMs in oral healthcare, their academic impact is steadily increasing. China and the US maintain a leading position in this field. Future directions could primarily focus on the development and application of nanoparticle drug delivery systems (especially lipid nanoparticles) and metal oxide nanoparticles (especially in antibacterial aspects). We hope that this bibliometric analysis could provide researchers with a panoramic view and useful references for future research, thus promoting the development of NMs in stomatology.

Tooth life expectancy and burden of tooth loss: Two cross-sectional studies in Taiwan.

Background/purpose: Life expectancy (LE) is a hypothetical measure to predict life longevity and the indicator of society's overall health. Tooth loss is a worldwide enigma; however, the LE for tooth (LET) are obscure. LET and the burden of tooth loss in Taiwan were estimated using the scheme of National Health Insurance (NHI). Materials and methods: Using NHI data, mortality rate, age-specific mortality rate, tooth-extraction rate, and age-specific tooth-extraction rate (ASTER) of Taiwanese in 2004 and 2013 were estimated. ASTER for the individual tooth (ASTER-T) was analyzed for each of 28 permanent teeth according to ID code and tooth location. LET and years lived with disability for tooth loss (YLDs-T) of each permanent tooth were estimated following Global Burden Disease study. Results: In 2004, 1,741,228 teeth extracted from 1,078,254 patients among 22,646,835 Taiwanese, whereas 2,012,907 teeth extracted from 1,254,746 patients among 23,344,670 in 2013. In both years, the ASTERs presented an increasing trend as age increased. However, the ASTER-Ts presented varied according to tooth types. The LET and YLDs-T were also varied. The maximum values of YLDs-T were noticed for the first molars. Conclusion: Our findings of this national survey highlight the need for public health policy, particular the early loss of first molars, aiming to increase awareness regarding oral health.

Deep LSAC for Fine-Grained Recognition.

Fine-grained recognition emphasizes the identification of subtle differences among object categories given objects that appear in different shapes and poses. These variances should be reduced for reliable recognition. We propose a fine-grained recognition system that incorporates localization, segmentation, alignment, and classification in a unified deep neural network. The input to the classification module includes functions that enable backward-propagation (BP) in constructing the solver. Our major contribution is to propose a valve linkage function (VLF) for BP chaining and form our deep localization, segmentation, alignment, and classification (LSAC) system. The VLF can adaptively compromise errors of classification and alignment when training the LSAC model. It in turn helps to update the localization and segmentation. We evaluate our framework on two widely used fine-grained object data sets. The performance confirms the effectiveness of our LSAC system.

PDE Learning of Filtering and Propagation for Task-Aware Facial Intrinsic Image Analysis.

Filtering and propagation are two basic operations in image analysis and rendering, and they are also widely used in computer graphics and machine learning. However, the models of filtering and propagation were based on diverse mathematical formulations, which have not been fully understood. This article aims to explore the properties of both filtering and propagation models from a partial differential equation (PDE) learning perspective. We propose a unified PDE learning framework based on nonlinear reaction-diffusion with a guided map, graph Laplacian, and reaction weight. It reveals that: 1) the guided map and reaction weight determines whether the PDE produces filtering or propagation diffusion and 2) the kernel of graph Laplacian controls the diffusion pattern. Based on the proposed PDE framework, we derive the mathematical relations between different models, including learning to diffusion (LTD) model, label propagation, edit propagation, and edge-aware filter. In practical verification, we apply the PDE framework to design diffusion operations with the adaptive kernel to tackle the ill-posed problem of facial intrinsic image analysis (FIIA). A flexible task-aware FIIA system is built to achieve various facial rendering effects, such as face image relighting and delighting, artistic illumination transfer, illumination-aware face swapping, or transfiguring. Qualitative and quantitative experiments show the effectiveness and flexibility of task-aware FIIA and provide new insights on PDE learning for visual analysis and rendering.

A Multi-Level Approach to Waste Object Segmentation.

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

Gnomoniopsis chinensis (Gnomoniaceae, Diaporthales), a new fungus causing canker of Chinese chestnut in Hebei Province, China.

Chinese chestnut (Castanea mollissima) is an important crop tree species in China. However, branch canker and fruit rot are two kinds of severe diseases, which weaken the host and decrease chestnut production. During our investigations into chestnut diseases in China, several fungi have been confirmed as casual agents in previous studies, namely Aurantiosacculus castaneae, Cryphonectria neoparasitica, Cry. parasitica, Endothia chinensis and Gnomoniopsis daii. In this study, a new canker pathogen is introduced based on morphology, phylogeny and pathogenicity. Typical Gnomoniopsis canker sign of wide, orange tendrils emerging from hosts' glaucous lenticels were obvious on the diseased trees in the field. Symptomatic branches or bark on stems from different chestnut plantations were sampled and isolated, then strains were identified by comparisons of DNA sequence data for the nuclear ribosomal internal transcribed spacer (ITS), partial translation elongation factor-1α (tef1) and ß-tubulin (tub2) gene regions as well as morphological features. As a result, these strains appeared different from any known Gnomoniopsis species. Hence, we propose a novel species named Gnomoniopsis chinensis. Pathogenicity was further tested using the ex-type strain (CFCC 52286) and another strain (CFCC 52288) on both detached branches and 3-year-old chestnut seedlings. The inoculation results showed that Gnomoniopsis chinensis is mildly pathogenic to Chinese chestnut. However, further studies are required to confirm its pathogenicity to the other cultivated Castanea species in America, Europe and Japan.

Identification of Biomarkers Based on Differentially Expressed Genes in Papillary Thyroid Carcinoma.

The incidence of papillary thyroid carcinoma (PTC) is increasing rapidly throughout the world. Hence, there is an urgent need for identifying more specific and sensitive biomarkers to explorate the pathogenesis of PTC. In this study, three pairs of stage I PTC tissues and matched normal adjacent tissues were sequenced by RNA-Seq, and 719 differentially expressed genes (DEGs) were screened. KEGG pathway enrichment analyses indicated that the DEGs were significantly enriched in 28 pathways. A total of 18 nodes consisting of 20 DEGs were identified in the top 10% of KEGG integrated networks. The functions of DEGs were further analysed by GO. The 13 selected genes were confirmed by qRT-PCR in 16 stage I PTC patients and by The Cancer Genome Atlas (TCGA) database. The relationship interactions between DEGs were analysed by protein-protein interaction networks and chromosome localizations. Finally, four newly discovered genes, COMP, COL3A1, ZAP70, and CD247, were found to be related with PTC clinical phenotypes, and were confirmed by Spearman's correlation analyses in TCGA database. These four DEGs might be promising biomarkers for early-stage PTC, and provide an experimental foundation for further exploration of the pathogenesis of early-stage PTC.

Facial skin beautification using adaptive region-aware masks.

In this paper, we propose a unified facial beautification framework with respect to skin homogeneity, lighting, and color. A novel region-aware mask is constructed for skin manipulation, which can automatically select the edited regions with great precision. Inspired by the state-of-the-art edit propagation techniques, we present an adaptive edge-preserving energy minimization model with a spatially variant parameter and a high-dimensional guided feature space for mask generation. Using region-aware masks, our method facilitates more flexible and accurate facial skin enhancement while the complex manipulations are simplified considerably. In our beautification framework, a portrait is decomposed into smoothness, lighting, and color layers by an edge-preserving operator. Next, facial landmarks and significant features are extracted as input constraints for mask generation. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Furthermore, the combinations of parameters can be optimized automatically, depending on the data priors and psychological knowledge. We performed both qualitative and quantitative evaluation for our method using faces with different genders, races, ages, poses, and backgrounds from various databases. The experimental results demonstrate that our technique is superior to previous methods and comparable to commercial systems, for example, PicTreat, Portrait+ , and Portraiture.