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Objectives: In this study we wished to determine the rank order of risk factors for endometrial cancer and calculate a pooled risk and percentage risk for each factor using a statistical meta-analysis approach. The next step was to design a neural network computer model to predict the overall increase or decreased risk of cancer for individual patients. This would help to determine whether this prediction could be used as a tool to decide if a patient should be considered for testing and to predict diagnosis, as well as to suggest prevention measures to patients. Design: A meta-analysis of existing data was carried out to calculate relative risk, followed by design and implementation of a risk prediction computational model based on a neural network algorithm. Setting: Meta-analysis data were collated from various settings from around the world. Primary data to test the model were collected from a hospital clinic setting. Participants: Data from 40 patients notes currently suspected of having endometrial cancer and undergoing investigations and treatment were collected to test the software with their cancer diagnosis not revealed to the software developers. Main outcome measures: The forest plots allowed an overall relative risk and percentage risk to be calculated from all the risk data gathered from the studies. A neural network computational model to determine percentage risk for individual patients was developed, implemented, and evaluated. Results: The results show that the greatest percentage increased risk was due to BMI being above 25, with the risk increasing as BMI increases. A BMI of 25 or over gave an increased risk of 2.01%, a BMI of 30 or over gave an increase of 5.24%, and a BMI of 40 or over led to an increase of 6.9%. PCOS was the second highest increased risk at 4.2%. Diabetes, which is incidentally also linked to an increased BMI, gave a significant increased risk along with null parity and noncontinuous HRT of 1.54%, 1.2%, and 0.56% respectively. Decreased risk due to contraception was greatest with IUD (intrauterine device) and IUPD (intrauterine progesterone device) at −1.34% compared to −0.9% with oral. Continuous HRT at −0.75% and parity at −0.9% also decreased the risk. Using open-source patient data to test our computational model to determine risk, our results showed that the model is 98.6% accurate with an algorithm sensitivity 75% on average. Conclusions: In this study, we successfully determined the rank order of risk factors for endometrial cancer and calculated a pooled risk and risk percentage for each factor using a statistical meta-analysis approach. Then, using a computer neural network model system, we were able to model the overall increase or decreased risk of cancer and predict the cancer diagnosis for particular patients to an accuracy of over 98%. The neural network model developed in this study was shown to be a potentially useful tool in determining the percentage risk and predicting the possibility of a given patient developing endometrial cancer. As such, it could be a useful tool for clinicians to use in conjunction with other biomarkers in determining which patients warrant further preventative interventions to avert progressing to endometrial cancer. This result would allow for a reduction in the number of unnecessary invasive tests on patients. The model may also be used to suggest interventions to decrease the risk for a particular patient. The sensitivity of the model limits it at this stage due to the small percentage of positive cases in the datasets; however, since this model utilizes a neural network machine learning algorithm, it can be further improved by providing the system with more and larger datasets to allow further refinement of the neural network.

Aims: The role of arts and music in supporting subjective wellbeing (SWB) is increasingly recognised. Robust evidence is needed to support policy and practice. This article reports on the first of four reviews of Culture, Sport and Wellbeing (CSW) commissioned by the Economic and Social Research Council (ESRC)-funded What Works Centre for Wellbeing (https://whatworkswellbeing.org/). Objective: To identify SWB outcomes for music and singing in adults. Methods: Comprehensive literature searches were conducted in PsychInfo, Medline, ERIC, Arts and Humanities, Social Science and Science Citation Indexes, Scopus, PILOTS and CINAHL databases. From 5,397 records identified, 61 relevant records were assessed using GRADE and CERQual schema. Results: A wide range of wellbeing measures was used, with no consistency in how SWB was measured across the studies. A wide range of activities was reported, most commonly music listening and regular group singing. Music has been associated with reduced anxiety in young adults, enhanced mood and purpose in adults and mental wellbeing, quality of life, self-awareness and coping in people with diagnosed health conditions. Music and singing have been shown to be effective in enhancing morale and reducing risk of depression in older people. Few studies address SWB in people with dementia. While there are a few studies of music with marginalised communities, participants in community choirs tend to be female, white and relatively well educated. Research challenges include recruiting participants with baseline wellbeing scores that are low enough to record any significant or noteworthy change following a music or singing intervention. Conclusions: There is reliable evidence for positive effects of music and singing on wellbeing in adults. There remains a need for research with sub-groups who are at greater risk of lower levels of wellbeing, and on the processes by which wellbeing outcomes are, or are not, achieved.

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

Leber congenital amaurosis (LCA, MIM 204000) accounts for at least 5% of all inherited retinal disease 1 and is the most severe inherited retinopathy with the earliest age of onset 2 . Individuals affected with LCA are diagnosed at birth or in the first few months of life with severely impaired vision or blindness, nystagmus and an abnormal or flat electroretinogram (ERG). Mutations in GUCY2D (ref. 3 ), RPE65 (ref. 4 ) and CRX (ref. 5 ) are known to cause LCA, but one study identified disease-causing GUCY2D mutations in only 8 of 15 families whose LCA locus maps to 17p13.1 (ref. 3 ), suggesting another LCA locus might be located on 17p13.1. Confirming this prediction, the LCA in one Pakistani family mapped to 17p13.1, between D17S849 and D17S960 —a region that excludes GUCY2D . The LCA in this family has been designated LCA4 (ref. 6 ). We describe here a new photoreceptor/pineal-expressed gene, AIPL1 (encoding aryl-hydrocarbon interacting protein-like 1), that maps within the LCA4 candidate region and whose protein contains three tetratricopeptide (TPR) motifs, consistent with nuclear transport or chaperone activity. A homozygous nonsense mutation at codon 278 is present in all affected members of the original LCA4 family. AIPL1 mutations may cause approximately 20% of recessive LCA, as disease-causing mutations were identified in 3 of 14 LCA families not tested previously for linkage.

Endometrial cancer rates are increasing annually due to an aging population and rising rates of obesity. Currently there is no widely available, accurate, non-invasive test that can be used to triage women for diagnostic biopsy whilst safely reassuring healthy women without the need for invasive assessment. The aim of this systematic review and meta-analysis is to evaluate studies assessing blood and urine-based biomarkers as a replacement test for endometrial biopsy or as a triage test in symptomatic women. For each primary study, the diagnostic accuracy of different biomarkers was assessed by sensitivity, specificity, likelihood ratio and area under ROC curve. Forest plots of summary statistics were constructed for biomarkers which were assessed by multiple studies using data from a random-effect models. All but one study was of blood-based biomarkers. In total, 15 studies reported 29 different exosomal biomarkers; 34 studies reported 47 different proteomic biomarkers. Summary statistic meta-analysis was reported for micro-RNAs, cancer antigens, hormones, and other proteomic markers. Metabolites and circulating tumor materials were also summarized. For the majority of biomarkers, no meta-analysis was possible. There was a low number of small, heterogeneous studies for the majority of evaluated index tests. This may undermine the reliability of summary estimates from the meta-analyses. At present there is no liquid biopsy that is ready to be used as a replacement test for endometrial biopsy. However, to the best of our knowledge this is the first study to report and meta-analyze the diagnostic accuracy of different classes of blood and urine biomarkers for detection of endometrial cancer. This review may thus provide a reference guide for those wishing to explore candidate biomarkers for further research.

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

Previous studies have examined DNA methylation in different trinucleotide repeat diseases. We have combined this data and used a pattern searching algorithm to identify motifs in the DNA surrounding aberrantly methylated CpGs found in the DNA of patients with one of the three trinucleotide repeat (TNR) expansion diseases: fragile X syndrome (FRAXA), myotonic dystrophy type I (DM1), or Friedreich’s ataxia (FRDA). We examined sequences surrounding both the variably methylated (VM) CpGs, which are hypermethylated in patients compared with unaffected controls, and the nonvariably methylated CpGs which remain either always methylated (AM) or never methylated (NM) in both patients and controls. Using the J48 algorithm of WEKA analysis, we identified that two patterns are all that is necessary to classify our three regions CCGG* which is found in VM and not in AM regions and AATT* which distinguished between NM and VM + AM using proportional frequency. Furthermore, comparing our software with MEME software, we have demonstrated that our software identifies more patterns than MEME in these short DNA sequences. Thus, we present evidence that the DNA sequence surrounding CpG can influence its susceptibility to be de novo methylated in a disease state associated with a trinucleotide repeat.

ObjectiveTo review and assess effectiveness of sport and dance participation on subjective well-being outcomes among healthy young people aged 15–24 years.DesignSystematic review.MethodsWe searched for studies published in any language between January 2006 and September 2016 on PsychINFO, Ovid MEDLINE, Eric, Web of Science (Arts and Humanities Citation Index, Social Science and Science Citation Index), Scopus, PILOTS, CINAHL, SPORTDiscus and International Index to Performing Arts. Additionally, we searched for unpublished (grey) literature via an online call for evidence, expert contribution, searches of key organisation websites and the British Library EThOS database, and a keyword Google search. Published studies of sport or dance interventions for healthy young people aged 15–24 years where subjective well-being was measured were included. Studies were excluded if participants were paid professionals or elite athletes, or if the intervention was clinical sport/dance therapy. Two researchers extracted data and assessed strength and quality of evidence using criteria in the What Works Centre for Wellbeing methods guide and GRADE, and using standardised reporting forms. Due to clinical heterogeneity between studies, meta-analysis was not appropriate. Grey literature in the form of final evaluation reports on empirical data relating to sport or dance interventions were included.ResultsEleven out of 6587 articles were included (7 randomised controlled trials and 1 cohort study, and 3 unpublished grey evaluation reports). Published literature suggests meditative physical activity (yoga and Baduanjin Qigong) and group-based or peer-supported sport and dance has some potential to improve subjective well-being. Grey literature suggests sport and dance improve subjective well-being but identify negative feelings of competency and capability. The amount and quality of published evidence on sport and dance interventions to enhance subjective well-being is low.ConclusionsMeditative activities, group and peer-supported sport and dance may promote subjective well-being enhancement in youth. Evidence is limited. Better designed studies are needed.Trial registration numberCRD42016048745; Results.

Background: Metabolic syndrome (MetS) is a syndrome that comprises central obesity, increased serum triglyceride (TG) levels, decreased serum HDL cholesterol (HDL) levels, raised blood pressure (BP), and impaired glucose regulation, including prediabetic and diabetic glycaemic levels. Recently, the association with endometrial cancer (EC) has been described but it is unclear if the risk associated with MetS is higher than the individual effect of obesity alone. This study investigates the association between MetS components and differing MetS definitions on EC risk and compares the risk of MetS with the risk posed by obesity alone. It also analyses how MetS affects the risk of EC development in the pre- and post-menopausal subgroups. Methods: A prospective cohort study was undertaken using data from the UK biobank. Multivariable Cox proportional risk models with the time to diagnosis (years) were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of MetS and its components on the risk of EC. A subgroup analysis was also undertaken for pre- and post-menopausal participants. Kaplan–Meier (KM) was undertaken to assess the difference in the risk of EC development in differing BMI classes, and in pre- and post-menopausal subgroups. Results: A total of 177,005 females from the UK biobank were included in this study. Of those participants who developed EC (n = 1454), waist circumference > 80 cm, BMI > 30 kg/m2, hypertension > 130/80 mmHg, hyperlipidaemia and diabetes (HbA1C > 48 mmol/L were significant predictors of EC development, with waist circumference being the strongest predictor (HR = 2.21; 95% CI: 1.98–2.47, p < 0.001). Comparing the pre- and post-menopausal subgroup, hypertriglyceridaemia and diabetes were the strongest predictors of EC in the pre-menopausal subgroup (HR = 1.53; 95% CI: 1.18–1.99 and HR = 1.51; 95% CI: 1.08–2.12, p < 0.05, respectively). Raised waist circumference was not a significant independent predictor in the pre-menopausal subgroup. A KM curve analysis showed a clear distinction between those with and without MetS in the pre-menopausal group, suggesting a benefit of testing for MetS components in pre-menopausal women with obesity. Conclusions: Components of MetS, both independently and in combination, significantly increase the risk of EC. Screening those with obesity for MetS in their pre-menopausal years may help to identify those at the highest risk.

Lentivirus vectors are effective for treatment of genetic disease. However, safety associated with vector related genotoxicity is of concern and currently available models are not reliably predictive of safety in humans. We have developed hInGeTox as the first human in vitro platform that uses induced pluripotent stem cells and their hepatocyte like cell derivatives to better understand vector-host interactions that relate vectors to their potential genotoxicity. Using lentiviral vectors carrying the eGFP expression cassette under SFFV promoter activity, that only differ by their LTR and SIN configuration, we characterised vector host interactions potentially implicated in genotoxicity. To do this, lentiviral infected cells were subjected to an array of assays and data from these was used for multi-omics analyses of vector effects on cells at early and late harvest time points. Data on the integration sites of lentiviral vectors in cancer genes and differential expression levels of these genes, showed that both vector configurations are capable of activating cancer genes. Through IS tracking in bulk infected cell populations, we also saw an increase in the viral sequence count in cancer genes present over time which were differentially regulated. RNASeq also showed each vector had potential to generate fusion transcripts with the human genome suggestive of gene splicing or vector mediated readthrough from the internal SFFV promoter. Initially, after infection, both vector configurations were associated with differential expression of genes associated cytokine production, however, after culturing over time there were differences in differential expression in cells infected by each LV. This was marked in particular by the expression of genes involved in the response to DNA damage in cells transduced by the SIN vector, suggesting effects likely to prevent tumour development, in contrast to the expression of genes involved in methylation, characteristic of tumour development, in cells transduced by the LTR vector. Both sets of lentiviral infected cells were also found associated with differential expression of MECOM and LMO2 genes known to be associated with clonal dominance, supporting their potential genotoxicity. Alignment of transcriptomic signatures from iPSC and HLC infected cultures with known cancer gene signatures showed the LTR vector with a higher cancer score than the SIN vector over time in iPSC and also in HLC, which further suggests higher genotoxic potential by the LTR configuration lentivirus. By application of hInGeTox to cells infected with LV at the pre-clinical stage of development, we hope that hInGeTox can act as a useful pre-clinical tool to identify lentivirus-host interactions that may be considered contributory to genotoxicity to improve safer lentiviral vector design for gene therapy.