Single-cell sequencing technology in diabetic wound healing: New insights into the progenitors-based repair strategies.

Diabetes mellitus (DM), an increasingly prevalent chronic metabolic disease, is characterised by prolonged hyperglycaemia, which leads to long-term health consequences. Although much effort has been put into understanding the pathogenesis of diabetic wounds, the underlying mechanisms remain unclear. The advent of single-cell RNA sequencing (scRNAseq) has revolutionised biological research by enabling the identification of novel cell types, the discovery of cellular markers, the analysis of gene expression patterns and the prediction of developmental trajectories. This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels. In this editorial, we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing. ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for intervention, offering hope for improved patient outcomes in the management of this challenging complication of DM.

Turnover intention of nurses in public hospitals and its association with quality of working life: a cross-sectional survey in six provinces in China.

Objectives: High turnover intention can exacerbate the workforce shortage of nurses. This study aimed to determine the level of turnover intention of public hospital nurses in China and its associated factors. Methods: A cross-sectional questionnaire survey of 2,863 nurses was conducted in 48 public hospitals across six provinces in mainland China, measuring the sociodemographic (gender, age, marital status, and monthly basic salary) and work characteristics (professional title, workload, night sleep deprivation, and workplace violence) of respondents, their quality of working life (QWL), and turnover intention. Multivariate logistic regression models were established to determine the association between QWL and turnover intention after adjustment for variations of the sociodemographic and work characteristics. Results: Overall, 42.8% of respondents reported turnover intention. Higher QWL scores (AOR = 0.824 for job and career satisfaction, p < 0.001; AOR = 0.894 for professional pride, p < 0.001; AOR = 0.911 for balance between work and family, p < 0.05) were associated with lower turnover intention. Workplace violence was the strongest predictor of higher turnover intention (AOR = 3.003-4.767) amongst the sociodemographic and work characteristics, followed by an age between 30 and 40 years (AOR = 1.457 relative to <30 years), and night sleep deprivation (AOR = 1.391-1.808). Senior professional title had a protective effect (AOR = 0.417 relative to no title) on turnover intention. Conclusion: High levels of turnover intention are evident across China in nurses employed by public hospitals, in particular in those aged between 30 and 40 years. Low QWL and poor work environment are significant predictors of turnover intention.

Effective Diagnosis of Alzheimer's Disease via Multimodal Fusion Analysis Framework.

Alzheimer's disease (AD) is a complex neurodegenerative disease involving a variety of pathogenic factors, and the etiology detection of this disease has been a major concern of researchers. Neuroimaging is a basic and important means to explore the problem. It is the main current scientific research direction for combining neuroimaging with other modal data to dig deep into the potential information of AD through the complementarities among multiple data points. Machine learning methods possess great potentiality and have reached some achievements in this research area. A few studies have proposed some solutions to the effects of multimodal data fusion, however, the overall analytical framework for data fusion and fusion result analysis has thus far been ignored. In this paper, we first put forward a novel multimodal data fusion method, and further present a new machine learning framework of data fusion, classification, feature selection, and disease-causing factor extraction. The real dataset of 37 AD patients and 35 normal controls (NC) with functional magnetic resonance imaging (fMRI) and genetic data was used to verify the effectiveness of the framework, which was more accurate in classification and optimal feature extraction than other methods. Furthermore, we revealed disease-causing brain regions and genes, such as the olfactory cortex, insula, posterior cingulate gyrus, lingual gyrus, CNTNAP2, LRP1B, FRMD4A, and DAB1. The results show that the machine learning framework could effectively perform multimodal data fusion analysis, providing new insights and perspectives for the diagnosis of Alzheimer's disease.