Meta-analysis approach as a gene selection method in class prediction: Does it improve model performance? A case study in acute myeloid leukemia

Background: Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. Results: Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. Conclusion: Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data

Factors affecting the accuracy of a class prediction model in gene expression data

Background: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. Results: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. Conclusions: We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Background: The COVID-19 pandemic has presented a series of new challenges to governments and healthcare systems. Testing is one important method for monitoring and controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries, not every country is able to employ widespread testing. Methods and Objective: Here, we have developed machine learning models for predicting the prevalence of COVID-19 cases in a country based on multilinear regression and neural network models. The models are trained on data from US states and tested against the reported infections in European countries. The model is based on four features: Number of tests, Population Percentage, Urban Population, and Gini index. Results: The population and the number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi-linear regression and 0.91 for the neural network model Conclusion: The model predicts that the actual prevalence of COVID-19 infection in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers

Loneliness of Older Immigrant Groups in Canada: Effects of Ethnic-Cultural Background

This study aimed to explore the loneliness of several groups of older immigrants in Canadacompared to native-born older adults. Data from the Canadian General Social Survey, Cycle 22 (Nolder adults = 3,692) were used. The dependent variable is the 6 item De Jong Gierveld lonelinessscale. Determinants of loneliness included country of birth, ethnic background (cultural context);belongingness (community context) and social networks (social context). Results showed that onlysome immigrant groups are significantly lonelier than older adults born in Canada. Immigrants withsimilar language and culture are not lonelier; while those from countries that differ in nativelanguage/culture are significantly higher on loneliness. Multivariate analyses showed the importanceof cultural background, of composition of the network of relatives and friends, and of localparticipation and feelings of belonging to the Canadian society in explaining loneliness of olderimmigrants

First insights into the phylogenetic diversity of Mycobacterium tuberculosis in Nepal

BACKGROUND: Tuberculosis (TB) is a major public health problem in Nepal. Strain variation in Mycobacterium tuberculosis may influence the outcome of TB infection and disease. To date, the phylogenetic diversity of M. tuberculosis in Nepal is unknown. METHODS AND FINDINGS: We analyzed 261 M. tuberculosis isolates recovered from pulmonary TB patients recruited between August 2009 and August 2010 in Nepal. M. tuberculosis lineages were determined by single nucleotide polymorphisms (SNP) typing and spoligotyping. Drug resistance was determined by sequencing the hot spot regions of the relevant target genes. Overall, 164 (62.8%) TB patients were new, and 97 (37.2%) were previously treated. Any drug resistance was detected in 50 (19.2%) isolates, and 16 (6.1%) were multidrug-resistant. The most frequent M. tuberculosis lineage was Lineage 3 (CAS/Delhi) with 106 isolates (40.6%), followed by Lineage 2 (East-Asian lineage, includes Beijing genotype) with 84 isolates (32.2%), Lineage 4 (Euro-American lineage) with 41 (15.7%) isolates, and Lineage 1 (Indo-Oceanic lineage) with 30 isolates (11.5%). Based on spoligotyping, we found 45 different spoligotyping patterns that were previously described. The Beijing (83 isolates, 31.8%) and CAS spoligotype (52, 19.9%) were the dominant spoligotypes. A total of 36 (13.8%) isolates could not be assigned to any known spoligotyping pattern. Lineage 2 was associated with female sex (adjusted odds ratio [aOR] 2.58, 95% confidence interval [95% CI] 1.42-4.67, p = 0.002), and any drug resistance (aOR 2.79; 95% CI 1.43-5.45; p = 0.002). We found no evidence for an association of Lineage 2 with age or BCG vaccination status. CONCLUSIONS: We found a large genetic diversity of M. tuberculosis in Nepal with representation of all four major lineages. Lineages 3 and 2 were dominating. Lineage 2 was associated with clinical characteristics. This study fills an important gap on the map of the M. tuberculosis genetic diversity in the Asian reg

Older adult loneliness: myths and realities

The focus in this paper is on the social domain of quality of life, and more particularly loneliness. The empirical literature on older adult loneliness is reviewed, thereby challenging three often-held assumptions that figure prominently in public debates on loneliness. The first assumption that loneliness is a problem specifically for older people finds only partial support. Loneliness is common only among the very old. The second assumption is that people in individualistic societies are most lonely. Contrary to this belief, findings show that older adults in northern European countries tend to be less lonely than those in the more familialistic southern European countries. The scarce data on Central and Eastern Europe suggest a high prevalence of older adult loneliness in those countries. The third assumption that loneliness has increased over the past decades finds no support. Loneliness levels have decreased, albeit slightly. The review notes the persistence of ageist attitudes, and underscores the importance of considering people’s frame of reference and normative orientation in analyses of loneliness

Older Norwegians' understanding of loneliness

This interpretive study explored older people's understanding of loneliness and what they considered appropriate and effective ways of dealing with it. Thirty elderly people were interviewed in-depth; 12 described themselves as “lonely” and 18 as “not lonely.” We found a striking difference in the way “lonely” and “not lonely” people talked about loneliness. The “not lonely” participants described loneliness as painful, caused by the person's negative way of behaving and a state they should pull themselves out of. The “lonely” participants also described loneliness as painful, and gave more detailed descriptions of loneliness as disconnection from others, from their former home and from today's society. The “lonely” participants were more reserved and subdued in trying to explain loneliness, attributing it partly to themselves, but mostly to the lack of social contact with important others. Some felt able to handle their loneliness, while others felt unable to cope. This study underlines the importance of subjective experiences in trying to understand a phenomenon like loneliness and of developing support for lonely older people unable to cope on their own

Development of a new therapeutic technique to direct stem cells to the infarcted heart using targeted microbubbles: StemBells

Successful stem cell therapy after acute myocardial infarction (AMI) is hindered by lack of engraftment of sufficient stem cells at the site of injury. We designed a novel technique to overcome this problem by assembling stem cell-microbubble complexes, named 'StemBells'.StemBells were assembled through binding of dual-targeted microbubbles (~ 3 μm) to adipose-derived stem cells (ASCs) via a CD90 antibody. StemBells were targeted to the infarct area

Interventions targeting social isolation in older people: a systematic review

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Targeting social isolation in older people is a growing public health concern. The proportion of older people in society has increased in recent decades, and it is estimated that approximately 25% of the population will be aged 60 or above within the next 20 to 40 years. Social isolation is prevalent amongst older people and evidence indicates the detrimental effect that it can have on health and wellbeing. The aim of this review was to assess the effectiveness of interventions designed to alleviate social isolation and loneliness in older people. METHODS: Relevant electronic databases (MEDLINE, EMBASE, ASSIA, IBSS, PsycINFO, PubMed, DARE, Social Care Online, the Cochrane Library and CINAHL) were systematically searched using an extensive search strategy, for randomised controlled trials and quasi-experimental studies published in English before May 2009. Additional articles were identified through citation tracking. Studies were included if they related to older people, if the intervention aimed to alleviate social isolation and loneliness, if intervention participants were compared against inactive controls and, if treatment effects were reported. Two independent reviewers extracted data using a standardised form. Narrative synthesis and vote-counting methods were used to summarise and interpret study data. RESULTS: Thirty two studies were included in the review. There was evidence of substantial heterogeneity in the interventions delivered and the overall quality of included studies indicated a medium to high risk of bias. Across the three domains of social, mental and physical health, 79% of group-based interventions and 55% of one-to-one interventions reported at least one improved participant outcome. Over 80% of participatory interventions produced beneficial effects across the same domains, compared with 44% of those categorised as non-participatory. Of interventions categorised as having a theoretical basis, 87% reported beneficial effects across the three domains compared with 59% of interventions with no evident theoretical foundation. Regarding intervention type, 86% of those providing activities and 80% of those providing support resulted in improved participant outcomes, compared with 60% of home visiting and 25% of internet training interventions. Fifty eight percent of interventions that explicitly targeted socially isolated or lonely older people reported positive outcomes, compared with 80% of studies with no explicit targeting. CONCLUSIONS: More, well-conducted studies of the effectiveness of social interventions for alleviating social isolation are needed to improve the evidence base. However, it appeared that common characteristics of effective interventions were those developed within the context of a theoretical basis, and those offering social activity and/or support within a group format. Interventions in which older people are active participants also appeared more likely to be effective. Future interventions incorporating all of these characteristics may therefore be more successful in targeting social isolation in older people.National Institute for Health Researc