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Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is commonly defined as a transitional subclinical state between normal aging and dementia. A growing body of research indicates that health behaviors may play a protective role against cognitive decline and could potentially slow down the progression from MCI to dementia. The aim of this study is to conduct a bibliometric analysis of literature focusing on health behaviors and MCI to summarize the factors and evidence regarding the influence of health behaviors on MCI. Methods: The study performed a bibliometric analysis by retrieving publications from the Science Citation Index and Social Sciences Citation Index subdatabases within the Web of Science Core Collection. Utilizing VOSviewer and CiteSpace software, a total of 2,843 eligible articles underwent co-citation, cokeywords, and clustering analyses. This methodology aimed to investigate the current status, trends, major research questions, and potential future directions within the research domain. Results: The bibliometric analysis indicates that research on healthy behaviors in individuals with MCI originated in 2002 and experienced rapid growth in 2014, reflecting the increasing global interest in this area. The United States emerged as the primary contributor, accounting for more than one-third of the total scientific output with 982 articles. Journals that published the most articles on MCI-related health behaviors included "Journal of Alzheimer's Disease," "Neurobiology of Aging," "Frontiers in Aging Neuroscience," and other geriatricsrelated journals. High-impact papers identified by VOSviewer predominantly cover concepts related to MCI, such as diagnostic criteria, assessment, and multifactorial interventions. Co-occurrence keyword analysis highlights five research hotspots in health behavior associated with MCI: exercise, diet, risk factors and preventive measures for dementia, cognitive decline-related biomarkers, and clinical trials. Conclusion: This study provides a comprehensive review of literature on health behavior in individuals with MCI, emphasizing influential documents and journals. It outlines research trends and key focal points, offering valuable insights for researchers to comprehend significant contributions and steer future studies. [ABSTRACT FROM AUTHOR]

Purpose: Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies. Methods: A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review. Two independent reviewers used the CHARMS checklist for data extraction and the PROBAST tool for bias assessment. Results: From 12,457 records, 15 studies were selected, contributing 30 models targeting various CAD patient groups (AMI, CABG, ACS) from primarily China, the USA, and Canada. Models utilized varied methods such as logistic regression and machine learning, with performance predominantly measured by the c-index. Key predictors included age, gender, and hospital stay duration. Readmission rates in the studies varied from 4.8% to 45.1%. Despite high bias risk across models, several showed notable accuracy and calibration. Conclusion: The study highlights the need for thorough external validation and the use of the PROBAST tool to reduce bias in models predicting readmission for CAD patients. [ABSTRACT FROM AUTHOR]

Prediction of renewable energy consumption structure (RECS) can provide important guidance for energy development planning and energy structure transformation. The RECS refer to the proportion of various renewable energy consumptions and belong to compositional data, which could reflect the structural shapes of a complete system better. The multivariate compositional data's vector autoregressive model (CDVAR) on the basis of the Simplex space and its algebraic system is proposed in this study aiming at the multi-dimensional small sample size. Firstly, the algebraic system of the Simplex space is introduced and the statistics of the compositional data are defined. Secondly, the novel model with the form of the compositional data is obtained and the least square parameter estimation of the model is derived according to Aitchison geometry. Third, the validation of the novel model is verified by the data on RECS in countries (China, USA, and Canada). The validation presents that the proposed model performs better in fitting, prediction, stability, and applicability compared with other five models under transformation. Last, the proposed model is applied to analyze and forecast the RECS of the above countries in 2021–2025 to provide an important basis for the optimization of the RECS. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus sequence type (ST) 398 is a lineage affecting both humans and livestock worldwide. However, the mechanisms underlying its clonal evolution are still not clearly elucidated. We applied whole-genome sequencing (WGS) typing to 45 S. aureus strains from China and Canada between 2005 and 2014, in order to gain insight into their evolutionary pathway. Based on WGS phylogenetic analysis, 42 isolates were assigned to the human-associated clade (I/II-GOI) and 3 isolates to livestock-associated clade (IIa). Phylogeny of ϕSa3 sequences revealed five phage groups (Groups 1–5), with Group 1 carrying ϕSa3-Group 1 (ϕSa3-G1), Group 2 carrying ϕSa3-G2, Group 3 carrying ϕSa3-G3, Group 4 carrying ϕSa3-G4 and Group 5 lacking ϕSa3. ϕSa3-G1 was only found in strains that accounted for the most ancestral human clade I, while ϕSa3-G2, ϕSa3-G3 and ϕSa3-G4 were found restricted to sublineages within clade II-GOI. Some isolates of clade II-GOI were also found to be ϕSa3-negative or resistant to methicillin which are unusual characteristics for human-adapted isolates. This study demonstrated a strong association between phylogenetic grouping and phage type, suggesting an important role of ϕSa3 prophage in the evolution of human-adapted ST398 subclones. In addition, our results suggest that this subclone slowly began to adapt to animal hosts by losing ϕSa3 and acquiring methicillin resistance, which was observed in some strains of human-associated clade II-GOI, an intermediate human to livestock transmission clade. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic in 2020 required risk communications to mitigate the virus' spread. However, social media not only conveyed health information to minimize the contagion, but also distracted from the threat by linking it to an externalized 'other'-primarily those appearing to be of Chinese descent. This disinformation caused the attribution of blame to Chinese people worldwide. In Canada's Greater Toronto Area, Chinese individuals reported widespread public stigma that compounded their risk of contagion; to the degree that it was driven by cognitive dissonance, it generated experiences of social and cultural vulnerability. In this paper, we draw on the aforementioned study's findings to explain how the risk perception and threat appraisal of Chinese diaspora individuals were impacted by different cognitive dissonance pathways. These findings explore how storytelling is a viable intervention with which to target and mitigate cognitive dissonance. Indeed, the mechanisms of cognitive dissonance can modify risk perception and mitigate social and cultural vulnerability, thereby averting potential long-term negative consequences for one's mental health and well-being. We hope our guidance, training educators to target pathways of cognitive dissonance by drawing on storytelling (with humour), can assist them to better convey information in ways that are more inclusive during public health emergencies.

During the first wave of the COVID-19 pandemic, immigrants were among the most socially vulnerable in Western countries. The Chinese diaspora in Canada were one such group due to the widespread cultural stigma surrounding their purported greater susceptibility to transmit and become infected by COVID-19. This paper aims to understand the social vulnerability of the Chinese diaspora in the Greater Toronto Area, Canada, during the first wave of COVID-19 from an explanation of their risk perception and threat appraisal of risk communication. We conducted secondary data analysis of 36 interviews using critical realism. The participants self-identified as being of Chinese descent. The results were used to develop a model of how social vulnerability occurred. In brief, cognitive dissonance was discovered to generate conflicts of one's cultural identity, shaped by social structures of (i) stigma of contagion, (ii) ethnic stigma, and (iii) public sentiment, and mediated by participants' threat appraisal and (iv) self-reliance. We assert that risk communicators need to consider their audiences' diverse socialization in crafting messages to modify behaviors, create a sense of responsibility, and mitigate public health threats. A lack of awareness of one's cognitive dissonance driven by cultural vulnerability may heighten their social vulnerability and prevent them from taking action to protect themself from high-risk events.