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BackgroundExtracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts. ObjectiveTo relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction. MethodsSince syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences. ResultsWe evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, which is 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P

Recent scientific investigations have revealed the intricate mechanisms underlying bone formation, emphasizing the essential role of long non-coding RNAs (lncRNAs) as critical regulators. This process, essential for skeletal strength and functionality, involves the transformation of mesenchymal stem cells into osteoblasts and subsequent deposition of bone matrix. lncRNAs, including HOX transcript antisense RNA (HOTAIR), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), differentiation antagonizing non-coding RNA (DANCR), and maternally expressed gene 3 (MEG3), have emerged as prominent players in this regulatory network. HOTAIR modulates osteoblast differentiation by interacting with chromatin-modifying enzymes, while MALAT1 regulates osteogenic differentiation through microRNA interactions. DANCR collaborates with Runx2 to fine-tune osteoblast differentiation, and MEG3 orchestrates multiple signaling pathways crucial for bone formation. Moreover, other lncRNAs such as H19, lncRNA for enhancing osteogenesis 3, rhabdomyosarcoma 2-associated transcript, urothelial cancer associated 1, taurine up-regulated gene 1, and nuclear enriched abundant transcript 1 contribute to the complex regulatory network governing osteoblast activities. Understanding the precise roles of these lncRNAs offers promising avenues for developing innovative therapeutic strategies targeting bone-related disorders like osteoporosis. Overall, this review summarizes the pivotal role of lncRNAs in bone formation, highlighting their potential as targets for future research endeavors aimed at advancing therapeutic interventions in bone diseases.

On July 23, 2022, the World Health Organization declared the global mpox outbreak to be a public health emergency of international concern (https:// www.who.int/europe/news/item/23-07-2022-whodirector-general-declares-the- ongoing-monkeypoxoutbreak-a-pubbc-health-event- of-international-concern). No specific treatment is [...]

[Objective] In some resource-based cities in China, population shrinkage has already occurred, and the trend is more pronounced and distinctive in resource-exhausted cities. Clarifying its spatial change characteristics will be more conducive to useful exploration and theoretical support for the optimization of land space and the rational allocation of resources in resource-based cities. [Methods] This study took Zaozhuang City, the first prefecture-level resource-exhausted city in the eastern region of China, as a typical case. Based on the population, industry, and spatial data from different time periods, using ArcGIS spatial metrics and statistical analysis methods, taking the expansion of urban built-up areas in typical years as a basis, and using indicators such as urbanization intensity, center of gravity, and compactness, this study analyzed the characteristics of urban spatial change in the city from 1980 to 2020 and proposed smart shrinkage strategies based on the findings. [Results] The study found that: (1) The urban spatial expansion went through several phases, which experienced a process of steady growth, sharp increase and rapid decline. (2) The city’s spatial structure has evolved from the single center model of “city-mine integration” in the early days of the People’s Republic of China to the cluster model of “one main center and one sub-center, driven by two poles”, and the spatial structure is more dispersed and constrained by resources. The intensity of urban land use was not high, the urban gravity center generally showed a trend of moving westward and northward, the compactness of the built-up areas was gradually decreasing, and the change of urban spatial structure was closely related to the development of industrial land. (3) The signs of urban space shrinkage in Zaozhuang City are obvious, with significant characteristics of developing into shrinking cities. Since the depletion of coal resources in 2009, the population growth rate has slowed significantly; in the past five years, the resident population has declined significantly, indicating a state of population shrinkage. Urban space expansion was only 10.74 km2 in 2010-2020. Urban shrinkage is very likely to occur in the future, based on the trend of spatial change. [Conclusion] Smart development should be the primary direction for resource-exhausted cities. In the future, the potential urban shrinkage issue can be addressed through optimized allocation and orderly withdrawal of industrial land, compact and intensive urban development, and the construction of urban green spaces and a green infrastructure network.

Abstract Background Guidelines for thyroid surgery have evolved to reflect advances in medical knowledge and decrease the overdiagnosis of low-risk thyroid cancer. Our goal was to analyze the change made in operative thyroid management and the impact on thyroid cancer diagnosis. Background Guidelines for thyroid surgery have evolved to reflect advances in medical knowledge and decrease overdiagnosis of low risk thyroid cancer. Our goal was to study the evolution, over a long period, of pre- and postoperative management and the influence on histological cancer diagnosis and, more particularly, microcancer. Methods In this retrospective cohort study, we included 891 consecutive patients who underwent thyroid surgery between 2007 and 2020. Results Respectively 305, 290 and 266 patients underwent surgery over the 3 periods of 2007–2010, 2011–2015 and 2016–2020. In all three periods, women represented approximately 70% of the population, and the mean age was 54 years old (range: 67). Most surgeries (90%) involved total thyroidectomies. Over the study period, the proportion of preoperative fine needle aspiration (FNA) increased from 13 to 55%, p T1a increased significantly from 37% (2011–2015 period) to 81% (2016–2020 period), p = 0.001. Patients treated with radioiodine remained relatively stable (approximately 60%) but were more frequently treated with a low dose of radioiodine (p

In recent years, artificial intelligence (AI), such as image recognition and fixed-point and digital scanning, has been increasingly applied in dental orthodontics. ChatGPT is an emerging artificial intelligence technology that has attracted a considerable amount of attention from people of all walks of life since its introduction. This paper discusses the application potential of ChatGPT in the field of orthodontics from two perspectives—education and clinical practice—and analyzes its limitations. Current reports show that ChatGPT can improve the efficiency of teaching in orthodontic education as well as assist users in completing medical mock exams training, writing papers and conducting academic research, etc. In the clinic, ChatGPT can be used to engage patients in medical dialog, simplify clinical procedures and improve diagnostic and treatment efficiency. However, because ChatGPT is still in the early stages of research and application, there are problems such as insufficient data, inaccurate answers, and poor academic ethics and privacy. In the future, additional studies, training and optimization are needed to ensure the safety, ethics and benefit of AI, laying the foundation for improving the use of AI in the field of orthodontics.