Prediction model for cognitive frailty in older adults: A systematic review and critical appraisal

BackgroundSeveral prediction models for cognitive frailty (CF) in older adults have been developed. However, the existing models have varied in predictors and performances, and the methodological quality still needs to be determined.ObjectivesWe aimed to summarize and critically appraise the reported multivariable prediction models in older adults with CF.MethodsPubMed, Embase, Cochrane Library, Web of Science, Scopus, PsycINFO, CINAHL, China National Knowledge Infrastructure, and Wanfang Databases were searched from the inception to March 1, 2022. Included models were descriptively summarized and critically appraised by the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 1,535 articles were screened, of which seven were included in the review, describing the development of eight models. Most models were developed in China (n = 4, 50.0%). The most common predictors were age (n = 8, 100%) and depression (n = 4, 50.0%). Seven models reported discrimination by the C-index or area under the receiver operating curve (AUC) ranging from 0.71 to 0.97, and four models reported the calibration using the Hosmer–Lemeshow test and calibration plot. All models were rated as high risk of bias. Two models were validated externally.ConclusionThere are a few prediction models for CF. As a result of methodological shortcomings, incomplete presentation, and lack of external validation, the models’ usefulness still needs to be determined. In the future, models with better prediction performance and methodological quality should be developed and validated externally.Systematic review registrationwww.crd.york.ac.uk/prospero, identifier CRD42022323591

The Identification of ECG Signals Using Wavelet Transform and WOA-PNN

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%

Disability-adjusted life years associated with population ageing in China, 1990-2017

Abstract Background The Chinese population has aged significantly in the last few decades. Comprehensive health losses including both fatal and non-fatal health outcomes associated with ageing in China have not been detailed. Methods Based on freely accessible disability adjusted life years (DALYs) estimated by the Global Burden of Diseases (GBD) 2017, we adopted a robust decomposition method that ascribes changes in DALYs in any given country across two time points to changes resulting from three sources: population size, age structure, and age-specific DALYs rate per 100,000 population. Using the method, we calculated DALYs associated with population ageing in China from 1990 to 2017 and examined the counteraction between the effects of DALYs rate change and population ageing. This method extends previous work through attributing the change in DALYs to the three sources. Results Population ageing was associated with 92.8 million DALYs between 1990 and 2017 in China, of which 65.8% (61.1 million) were years of life lost (YLLs). Males had comparatively more DALYs associated with population ageing than females in the study period. The five leading causes of DALYs associated with population ageing between 1990 and 2017 were stroke (23.6 million), chronic obstructive pulmonary disease (COPD) (18.3 million), ischemic heart disease (13.0 million), tracheal, bronchus, and lung cancer (6.1 million) and liver cancer (5.0 million). Between 1990 and 2017, changes in DALYs associated with age-specific DALY rate reductions far exceeded those related to population ageing (− 196.2 million versus 92.8 million); 57.5% (− 112.8 million) of DALYs were caused by decreases in rates attributed to 84 modifiable risk factors. Conclusion Population ageing was associated with growing health loss in China from 1990 to 2017. Despite the recent progress in alleviating health loss associated with population ageing, the government should encourage scientific research on effective and affordable prevention and control strategies and should consider investment in resources to implement strategies nationwide to address the future challenge of population ageing

Table_1_Prediction model for cognitive frailty in older adults: A systematic review and critical appraisal.docx

BackgroundSeveral prediction models for cognitive frailty (CF) in older adults have been developed. However, the existing models have varied in predictors and performances, and the methodological quality still needs to be determined.ObjectivesWe aimed to summarize and critically appraise the reported multivariable prediction models in older adults with CF.MethodsPubMed, Embase, Cochrane Library, Web of Science, Scopus, PsycINFO, CINAHL, China National Knowledge Infrastructure, and Wanfang Databases were searched from the inception to March 1, 2022. Included models were descriptively summarized and critically appraised by the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 1,535 articles were screened, of which seven were included in the review, describing the development of eight models. Most models were developed in China (n = 4, 50.0%). The most common predictors were age (n = 8, 100%) and depression (n = 4, 50.0%). Seven models reported discrimination by the C-index or area under the receiver operating curve (AUC) ranging from 0.71 to 0.97, and four models reported the calibration using the Hosmer–Lemeshow test and calibration plot. All models were rated as high risk of bias. Two models were validated externally.ConclusionThere are a few prediction models for CF. As a result of methodological shortcomings, incomplete presentation, and lack of external validation, the models’ usefulness still needs to be determined. In the future, models with better prediction performance and methodological quality should be developed and validated externally.Systematic review registrationwww.crd.york.ac.uk/prospero, identifier CRD42022323591.</p

sj-docx-2-dhj-10.1177_20552076241234628 - Supplemental material for Barriers and facilitators to acceptance and implementation of eMental-health intervention among older adults: A qualitative systematic review

Supplemental material, sj-docx-2-dhj-10.1177_20552076241234628 for Barriers and facilitators to acceptance and implementation of eMental-health intervention among older adults: A qualitative systematic review by Ruotong Peng, Xiaoyang Li, Yongzhen Guo, Hongting Ning, Jundan Huang, Dian Jiang, Hui Feng and Qingcai Liu in DIGITAL HEALTH</p

sj-docx-1-dhj-10.1177_20552076241234628 - Supplemental material for Barriers and facilitators to acceptance and implementation of eMental-health intervention among older adults: A qualitative systematic review

Supplemental material, sj-docx-1-dhj-10.1177_20552076241234628 for Barriers and facilitators to acceptance and implementation of eMental-health intervention among older adults: A qualitative systematic review by Ruotong Peng, Xiaoyang Li, Yongzhen Guo, Hongting Ning, Jundan Huang, Dian Jiang, Hui Feng and Qingcai Liu in DIGITAL HEALTH</p

Different microRNA profiles reveal the diverse outcomes induced by EV71 and CA16 infection in human umbilical vein endothelial cells using high-throughput sequencing

<div><p>Enterovirus 71 (EV71) and Coxsackievirus A16 (CA16) remain the predominant pathogens in hand, foot, and mouth disease (HFMD), but the factors underlying the pathogenesis of EV71 and CA16 infections have not been elucidated. Recently, the functions of microRNAs (miRNAs) in pathogen-host interactions have been highlighted. In the present study, we performed comprehensive miRNA profiling in EV71- and CA16-infected human umbilical vein endothelial cells (HUVECs) at multiple time points using high-throughput sequencing. The results showed that 135 known miRNAs exhibited remarkable differences in expression. Of these, 30 differentially expressed miRNAs presented opposite trends in EV71- and CA16-infected samples. Subsequently, we mainly focused on the 30 key differentially expressed miRNAs through further screening to predict targets. Gene ontology (GO) and pathway analysis of the predicted targets showed the enrichment of 14 biological processes, 9 molecular functions, 8 cellular components, and 85 pathways. The regulatory networks of these miRNAs with predicted targets, GOs, pathways, and co-expression genes were determined, suggesting that miRNAs display intricate regulatory mechanisms during the infection phase. Consequently, we specifically analyzed the hierarchical GO categories of the predicted targets involved in biological adhesion. The results indicated that the distinct changes induced by EV71 and CA16 infection may be partly linked to the function of the blood-brain barrier. Taken together, this is the first report describing miRNA expression profiles in HUVECs with EV71 and CA16 infections using high-throughput sequencing. Our data provide useful insights that may help to elucidate the different host-pathogen interactions following EV71 and CA16 infection and offer novel therapeutic targets for these infections.</p></div

Details of all miRNAs.

<p>(A) Principal component analysis (PCA) showing the distribution and clustering of the individual sample groups. Each spot represents a single array. EV71/CA16-infected samples were distinct from the Con sample. (B) Venn diagram representing Known differentially expressed miRNAs.(C) Time- and strain-specific regulation of differential miRNAs during EV71 and CA16 infections. The columns correspond to expression patterns of differentially expressed miRNAs during the EV71 and CA16 infections relative to Con samples at 6 hpi and 12 hpi. Significance was determined using a fold-change threshold of at least 2 and a P value cutoff of 0.05. The intensity of the miRNA expression is indicated in green (lower level of expression) and red (higher level of expression). Dendrograms between samples and between miRNAs are depicted, where the closest branches of the tree represents samples/miRNAs with the most similar expression pattern.</p