Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.

BACKGROUND: With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE: The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS: This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS: To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS: The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Nurses' acceptance of nursing information systems: A multi-center cross-sectional study in China.

BACKGROUND: In China, nursing information systems (NIS) implementation can face numerous barriers to acceptance, including the attitudes of potential users. However, few studies have evaluated this acceptance. OBJECTIVE: The aim of this study was to explain the acceptance of NIS utilizing a survey based on unified theory of acceptance and use of technology. METHODS: A multi-center cross-sectional study utilizing an online survey was conducted. SPSS AMOS was used to conduct a structural equation modelling analysis. This research followed the STROBE Checklist. RESULTS: A total of 3973 Nurses participated in the study between January 2023 and March 2023. The acceptance of NIS among nurses was overall moderate to high. The proposed model has been rigorously tested and validated using empirical data, ensuring its credibility and dependability. Performance expectancy (PE), social influence (SI), and attitude significantly and positively affected intentions to use NIS. Effort expectancy (EE) did not show any significant effects in the sample. Facilitating conditions (FCs) was found to have a negative relationship with the intention to use NIS. There was a statistically significant difference BI between the different age groups, working years, and computer training experience. The model demonstrates a good fit with the observed data. CONCLUSIONS: This study identified PE, SI, and attitude as facilitators of nurses' intentions to use NIS. The findings about EE indicates that the ease of using NIS does not seem to be a concern among nurses. Moreover, high FC might be perceived as indicative of a complex system or extensive usage, that can lead to increased workload and reduced behavioural intention (BI). The significant differences in BI among various demographic groups highlight the need for more studies understanding the preferences and barriers faced by different, levels of experience and training backgrounds. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Graphene-modified graphite paper cathode for the efficient bioelectrochemical removal of chromium.

Metal-free electrocatalysts have been widely used as cathodes for the reduction of hexavalent chromium [Cr(VI)] in microbial fuel cells (MFCs). The electrocatalytic activity of such system needs to be increased due to the low anodic potential provided by bacteria. In this study, graphite paper (GP) was treated by liquid nitrogen to form three-dimensional graphite foam (3DGF), improving the Cr(VI) reduction by 17% and the total Cr removal by 81% at 30 h in MFCs. X-ray absorption spectroscopy confirmed the Cr(VI) reduction product as Cr(OH)3. Through the spectroscopy characterizations, electrochemical measurements, and density functional theory calculations, the porous structures, edges, and O-doped defects on the 3DGF surface resulted in a higher electroconducting rate and a lower mass transfer rate, which provide more active sites for the Cr(VI) reduction. Additionally, the scrolled graphene-like carbon nanosheets and porous structures on the 3DGF surface might limit the OH- diffusion and result in a high local pH, which accelerated the Cr(OH)3 formation. The results of this study are expected to provide a simple method to manipulate the carbon materials and insights into mechanisms of Cr(VI) reduction in MFCs by the 3DGF with in situ exfoliated edges and O-functionalized graphene.

Impacts of chloride ions on the electrochemical decomplexation and degradation of Cr(III)-EDTA: Reaction mechanisms of HO• and RCS.

The removal of Cr(III)-organic complexes, encompassing both decomplexation and ligand degradation, presents significant challenges in industrial wastewater treatment. As one of the most common anions in wastewater, Cl- significantly improves the efficiency of electrochemically removing Cr(III)-organic complexes through generated reactive chlorine species (RCS). In the electrochemical chlorine (EC/Cl2) process, extensive experimentation revealed that ClO• plays a dominant role in the degradation of Cr(III)-EDTA, surpassing the effects of free chlorine, direct electrooxidation, HO•, and other RCS. Density functional theory calculations indicated that RCS, primarily Cl• and ClO•, preferentially oxidize the ligand in Cr(III)-EDTA via H-abstraction, whereas HO• trends to attack the Cr atom through electron transfer. The influential factors on the degradation efficiency of Cr(III)-EDTA, Cr(VI) yield, and total organic carbon removal in EC/Cl2 were also assessed, including Cl- concentration, current density, and pH. Real industrial wastewater was employed as a reaction matrix to evaluate the application of the EC/Cl2 process for treating Cr(III)-EDTA, accompanied by energy efficiency calculations. Additionally, a two-chamber reactor was established to simultaneously oxidize Cr(III)-EDTA at the anode and reduce Cr(VI) at the cathode. This study provided insight into developing RCS-dominated AOPs to effectively decomplex and decompose organic Cr(III)-complexes in Cl--containing industrial wastewater.

The role of reactive phosphate species in the abatement of micropollutants by activated peroxymonosulfate in the treatment of phosphate-rich wastewater.

This study investigated the mechanisms of forming reactive species to degrade micropollutants through the activation of peroxymonosulfate (PMS) by phosphate, a prevalent ion in wastewater. Considering the density functional theory results, the formation of hydrogen bonds between phosphate and PMS molecules might be the crucial step in the overall reactions, which prefers producing ⋅OH and reactive phosphate species (RPS, namely H2PO4⋅, HPO4⋅-, and PO4⋅2-) to yielding SO4⋅-. Besides, in the phosphate (5 mM)/PMS system at pH = 8, HPO4⋅- was modeled to be the dominant radical with a steady-state concentration of 3.6 × 10-12 M, which was 666 and 773 times higher than those of ⋅OH and SO4⋅-. The contributions of 1O2, ⋅OH, SO4⋅-, and RPS to the micropollutant decomposition in phosphate/PMS were studied, and RPS were found to be selective for micropollutants with electron-donating moieties (such as phenolic and aniline groups). Additionally, the degradation pathways of bisphenol A, diclofenac, ibuprofen, and atrazine in phosphate/PMS were proposed according to the detected transformation products. Cytotoxicity analysis was carried out to evaluate the potential environmental impacts resulting from the degradation of micropollutants by phosphate/PMS. This study confirmed the significance of RPS for micropollutant degradation during PMS-based treatment in phosphate-rich scenarios.

Lysosomal dysfunction in carbon black-induced lung disorders.

Carbon black (CB), a component of environmental particulate pollution derived from carbon sources, poses a significant threat to human health, particularly in the context of lung-related disease. This study aimed to investigate the detrimental effects of aggregated CB in the average micron scale on lung tissues and cells in vitro and in vivo. We observed that CB particles induced lung disorders characterized by enhanced expression of inflammation, necrosis, and fibrosis-related factors in vivo. In alveolar epithelial cells, CB exposure resulted in decreased cell viability, induction of cell death, and generation of reactive oxidative species, along with altered expression of proteins associated with lung disorders. Our findings suggested that the damaging effects of CB on the lung involved the targeting of lysosomes. Specifically, CB promoted lysosomal membrane permeabilization, while lysosomal alkalization mitigated the harmfulness of CB on lung cells. Additionally, we explored the protective effects of alkaloids derived from Nelumbinis plumula, with a focus on neferine, against CB-induced lung disorders. In conclusion, these findings contribute to a deeper understanding of the pathophysiological effects of CB particles on the lungs and propose a potential therapeutic approach for pollution-related diseases.

Water Splitting-Biosynthetic Hybrid System for CO2 Conversion using Nickel Nanoparticles Embedded in N-Doped Carbon Nanotubes.

CO2 reduction has drawn increasing attention owing to the concern of global warming. Water splitting-biosynthetic hybrid systems are novel and efficient approaches for CO2 conversion. Intimate coupling of electrocatalysts and biosynthesis requires the catalysts possess both high catalytic performance and excellent biocompatibility, which is a bottleneck of developing such catalysts. Here, a complex of Ni nanoparticles embedded in N-doped carbon nanotubes (Ni@N-C) is synthesized as a hydrogen evolution reaction electrocatalyst and is coupled with a hydrogen oxidizing autotroph, Cupriavidus necator H16, to convert CO2 to poly-ß-hydroxybutyrate. In Ni@N-C, the Ni nanoparticles are encapsulated in N-C nanotubes, which prevents bacteria from direct contact with Ni and inhibits Ni2+ leaching. As a result, Ni@N-C exhibits excellent biocompatibility and stability. This work demonstrates that electrocatalysts and biosynthesis can be intimately coupled through rational catalyst design.