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Abstract Background N6-methyladenosine (m6A) is dysregulated in various cancers, including colorectal cancer (CRC). Herein, we assess the diagnostic potential of peripheral blood (PB) m6A levels in CRC. Methods We collected PB from healthy controls (HCs) and patients with CRC, analyzed PB RNA m6A levels and the expression of m6A-related demethylase genes FTO and ALKBH5, cocultured CRC cells with PB mononuclear cells (PBMCs), and constructed an MC38 cancer model. Results PB RNA m6A levels were higher in the CRC than that in HCs. The area under the curve (AUC) of m6A levels (0.886) in the CRC was significantly larger compared with carbohydrate antigen 199 (CA199; 0.666) and carcinoembryonic antigen (CEA; 0.834). The combination of CEA and CA199 with PB RNA m6A led to an increase in the AUC (0.935). Compared with HCs, the expression of FTO and ALKBH5 was decreased in the CRC. After coculturing with CRC cells, the PBMCs RNA m6A were significantly increased, whereas the expression of FTO and ALKBH5 decreased. Furthermore, m6A RNA levels in the PB of MC38 cancer models were upregulated, whereas the expression of FTO and ALKBH5 decreased. Conclusions PB RNA m6A levels are a potential diagnostic biomarker for patients with CRC.

Due to its exceptional quality as a biomedical metal, stainless steel is often utilized to produce a broad range of medical tools. The resistance of stainless steel to corrosion is a key indicator of how long and how effective it will serve its intended purpose, and it is an important factor in determining the biocompatibility of the material. However, due to the complex physiological environment within the human body, the corrosion management of medical-grade stainless steel is facing several challenges. In this article, an overview of the factors that influence the corrosion performance of medical-grade stainless steel is provided, and new technologies and methods that have been developed in recent years to improve the corrosion resistance are discussed. These cutting-edge methods are expected to improve the corrosion resistance and longevity of medical-grade stainless steel, providing strong support for the increased applicability of the material in the medical industry.

Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan–Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group ( P

This survey provides an overview of recent advancements in wireless soil sensing technologies, including the sensors and communication systems used to manage and enhance soil health and crop production for sustainable agricultural practices. The surveyed technologies are categorized by the target sensing parameters, such as moisture, temperature, salinity, pH, nutrient concentration, pressure, and heavy metal content. Special focus is placed on the passive and semi-passive sensing principles for soil data collection, communication systems for transmitting the sensed data, and machine learning approaches for data processing and analysis to assist in informed decision-making in the realization of sustainable agriculture. By comparing existing technologies, we highlight the advantages and disadvantages of these sensing mechanisms, summarize existing challenges of the surveyed soil sensing systems, and identify future research directions that need attention.

Abstract Objective To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies. Materials and methods We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering. Results The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes. Conclusion CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

BackgroundThe diagnosis and treatment of cancer triggers not only a negative psychological response for the patient, but also a positive psychological outcome. Positive dyadic coping, as a form of coping for mental health outcomes, can maintain or reestablish internal stability between the patient and his or her spouse, resulting in positive physical and psychological changes. However, there is a paucity of research on body image, dyadic coping, and post-traumatic growth in breast cancer patients. The purpose of this study was to explore the relationship and pathways between body image, dyadic coping, and post-traumatic growth in breast cancer patients.MethodsA cross-sectional study was conducted from November 2022 to November 2023 at a tertiary care hospital in Wuxi, Jiangsu, China. This study was conducted among 154 breast cancer patients treated at the Affiliated Hospital of Jiangnan University, all of whom completed demographic and clinical information questionnaires, Body image self-rating questionnaire for breast cancer (BISQ-BC), Dyadic Coping Inventory (DCI) and Post Traumatic Growth Inventory (PTGI). A Pearson correlation analysis was used to explore the relationship between body image, dyadic coping, and post-traumatic growth. Structural equation modeling was used to analyze the path relationships among the three and to explore the mediating role of dyadic coping.ResultsThe level of body image was negatively correlated with post-traumatic growth (r = −0.462, p

Summary: Interfacial investigation for fine-tuning microenvironment has recently emerged as a promising method to optimize the electrochemical CO2 reduction system. The electrical double layer located at the electrode-electrolyte interface presents a particularly significant impact on electrochemical reactions. However, its effect on the activity and selectivity of CO2 electrocatalysis remains poorly understood. Here, we utilized two-dimensional mica flakes, a material with a high dielectric constant, to modify the electrical double layer of Ag nanoparticles. This modification resulted in a significant enhancement of current densities for CO2 reduction and an impressive Faradaic efficiency of 98% for CO production. Our mechanistic investigations suggest that the enhancement of the electrical double layer capacitance through mica modification enriched local CO2 concentration near the reaction interface, thus facilitating CO2 electroreduction.

Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students' acquisition of skills and knowledge and positively mediated dental technology students' CAD performance.

A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%. [ABSTRACT FROM AUTHOR]