RESUMO
The association between oral microbiota and cancer development has been a topic of intense research in recent years, with compelling evidence suggesting that the oral microbiome may play a significant role in cancer initiation and progression. However, the causal connections between the two remain a subject of debate, and the underlying mechanisms are not fully understood. In this case-control study, we aimed to identify common oral microbiota associated with several cancer types and investigate the potential mechanisms that may trigger immune responses and initiate cancer upon cytokine secretion. Saliva and blood samples were collected from 309 adult cancer patients and 745 healthy controls to analyze the oral microbiome and the mechanisms involved in cancer initiation. Machine learning techniques revealed that six bacterial genera were associated with cancer. The abundance of Leuconostoc, Streptococcus, Abiotrophia, and Prevotella was reduced in the cancer group, while abundance of Haemophilus and Neisseria enhanced. G protein-coupled receptor kinase, H+-transporting ATPase, and futalosine hydrolase were found significantly enriched in the cancer group. Total short-chain fatty acid (SCFAs) concentrations and free fatty acid receptor 2 (FFAR2) expression levels were greater in the control group when compared with the cancer group, while serum tumor necrosis factor alpha induced protein 8 (TNFAIP8), interleukin-6 (IL6), and signal transducer and activator of transcription 3 (STAT3) levels were higher in the cancer group when compared with the control group. These results suggested that the alterations in the composition of oral microbiota can contribute to a reduction in SCFAs and FFAR2 expression that may initiate an inflammatory response through the upregulation of TNFAIP8 and the IL-6/STAT3 pathway, which could ultimately increase the risk of cancer onset.
RESUMO
AIMS AND OBJECTIVES: The main purpose of this study was to identify the best fall-risk assessment tool, among the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model, for a tertiary teaching hospital. The study also analysed fall-risk factors in the hospital, focusing on the items of each fall assessment tool. METHODS: Data on falls were obtained from the patient safety reports and electronic nursing records of a tertiary teaching hospital. A retrospective study was conducted to compare the sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, Youden index and accuracy of the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline for reporting case-control studies. RESULTS: By analysing the association between falls and the items included in the three tools, we identified significant fall-risk factors such as gait, dizziness or vertigo, changes in mental status, impulsivity, history of falling, elimination disorder, drugs affecting falls, and depression. CONCLUSIONS: The Hendrich II fall-risk Model had the best predictive performance for falls of the three tools, considering the highest in the area under the curve and the Youden index that comprehensively analysed sensitivity and specificity, while the Johns Hopkins fall-risk Assessment Tool had the highest accuracy. The most significant fall-risk predictors are gait, dizziness or vertigo, change in mental state, and history of falling. RELEVANCE TO CLINICAL PRACTICE: To improve the fall assessment performance of the Morse Fall Scale at the study hospital, we propose that it be supplemented with four most significant fall-risk predictors identified in this study.
