Adapting Emotional Support to Personality for Carers Experiencing Stress.

Carers - people who provide regular support for a friend or relative who could not manage without them - frequently report high levels of stress. Good emotional support (e.g. provided by an Intelligent Virtual Agent) could help relieve this stress. This study investigates whether adaptation to personality affects the amount and type of emotional support a carer is given and possible interaction effects with the stress experienced. We investigated the personality trait of Emotional Stability (ES) as it is interlinked with low tolerance for stress. Participants were presented with stressful scenarios experienced by a fictitious carer and description of their personality and asked to rank 6 emotional support messages. We predicted that people with low ES would be given more emotional support messages overall and that ES would affect the type of emotional support messages given in each scenario. We found that participants gave more praise to the high ES carer with a trend towards other support types for the low ES carer

The role of digital technologies during relationship breakdowns

Relationship breakdowns are undoubtedly difficult. Access to and use of technology can exacerbate the situation. In our networked society, shared lives generate vast amounts of shared digital data which can be difficult to untangle, whilst social media can provide an outlet to emotions that can take a public and often persistent form. In this paper, we report on a qualitative study that considered the role of technology in the process of a relationship breaking down. Four main themes emerged in our findings: communicating about the separation, change in social status, shared digital assets, and moving on. Opportunities for design are identified in reducing misunderstandings via CMCs, enhancing social media, supporting intimacy in distributed families, and refining service provision

Methionine restriction restores a younger metabolic phenotype in adult mice with alterations in fibroblast growth factor 21.

Methionine restriction (MR) decreases body weight and adiposity and improves glucose homeostasis in rodents. Similar to caloric restriction, MR extends lifespan, but is accompanied by increased food intake and energy expenditure. Most studies have examined MR in young animals; therefore, the aim of this study was to investigate the ability of MR to reverse age-induced obesity and insulin resistance in adult animals. Male C57BL/6J mice aged 2 and 12 months old were fed MR (0.172% methionine) or control diet (0.86% methionine) for 8 weeks or 48 h. Food intake and whole-body physiology were assessed and serum/tissues analyzed biochemically. Methionine restriction in 12-month-old mice completely reversed age-induced alterations in body weight, adiposity, physical activity, and glucose tolerance to the levels measured in healthy 2-month-old control-fed mice. This was despite a significant increase in food intake in 12-month-old MR-fed mice. Methionine restriction decreased hepatic lipogenic gene expression and caused a remodeling of lipid metabolism in white adipose tissue, alongside increased insulin-induced phosphorylation of the insulin receptor (IR) and Akt in peripheral tissues. Mice restricted of methionine exhibited increased circulating and hepatic gene expression levels of FGF21, phosphorylation of eIF2a, and expression of ATF4, with a concomitant decrease in IRE1α phosphorylation. Short-term 48-h MR treatment increased hepatic FGF21 expression/secretion and insulin signaling and improved whole-body glucose homeostasis without affecting body weight. Our findings suggest that MR feeding can reverse the negative effects of aging on body mass, adiposity, and insulin resistance through an FGF21 mechanism. These findings implicate MR dietary intervention as a viable therapy for age-induced metabolic syndrome in adult humans

The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

PURPOSE: Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. METHODS: We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. RESULTS: In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%-74%) and 87% (95% CI, 86%-88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91-0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25. CONCLUSIONS: This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. TRANSLATIONAL RELEVANCE: The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis

Development of an in-house ELISA to detect anti-HPV16-L1 antibodies in serum and dried blood spots

Measuring anti-HPV antibody levels is important for surveillance of the immunological response to both natural infection and vaccination. Here, an ELISA test for measurement of HPV-16L1 antibodies was developed and validated in sera and dried blood spots. An in-house ELISA was developed for measuring anti-HPV-16L1 IgA and IgG levels. The assay was standardized against WHO international standard serum and validated on serum, dried blood spots and cervical liquid based cytology samples from women attending colposcopy clinics in Scotland. Antibody avidity index was also measured in serum samples. The average HPV 16-L1 specific IgG and IgA levels measured in sera, in women attending a routine colposcopy service were 7.3 units/ml and 8.1 units/ml respectively. Significant correlations between serum and dried blood spot eluates for both IgG and IgA were observed indicating that the latter serve as a credible proxy for antibody levels. Average IgG Avidity Index was 35% (95% CI 25%-45%) suggesting previous, historical challenge with natural infection. This ELISA has potential for use in epidemiological and field studies of antibody prevalence and if coupled with avidity measurement may be of use in individual case monitoring of vaccine responses and failures

The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

Purpose: Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. Methods: We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. Results: In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%-74%) and 87% (95% CI, 86%-88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91-0.96). For image annotation, a maximal Dice coefficient (~0.6) was achieved with a consensus threshold of 0.25. Conclusions: This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. Translational Relevance: The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis

TAPESTRY: A De-centralized Service for Trusted Interaction Online

Engineering and Physical Sciences Research Council EPSRC Grant Ref: EP/N02799X/1 under the UKRI Digital Economy Programme

Acute pain pathways:protocol for a prospective cohort study

INTRODUCTION: Opioid analgesics are often used to treat moderate-to-severe acute non-cancer pain; however, there is little high-quality evidence to guide clinician prescribing. An essential element to developing evidence-based guidelines is a better understanding of pain management and pain control among individuals experiencing acute pain for various common diagnoses. METHODS AND ANALYSIS: This multicentre prospective observational study will recruit 1550 opioid-naïve participants with acute pain seen in diverse clinical settings including primary/urgent care, emergency departments and dental clinics. Participants will be followed for 6 months with the aid of a patient-centred health data aggregating platform that consolidates data from study questionnaires, electronic health record data on healthcare services received, prescription fill data from pharmacies, and activity and sleep data from a Fitbit activity tracker. Participants will be enrolled to represent diverse races and ethnicities and pain conditions, as well as geographical diversity. Data analysis will focus on assessing patients’ patterns of pain and opioid analgesic use, along with other pain treatments; associations between patient and condition characteristics and patient-centred outcomes including resolution of pain, satisfaction with care and long-term use of opioid analgesics; and descriptive analyses of patient management of leftover opioids. ETHICS AND DISSEMINATION: This study has received approval from IRBs at each site. Results will be made available to participants, funders, the research community and the public. TRIAL REGISTRATION NUMBER: NCT04509115

Monoallelic Expression of Multiple Genes in the CNS

The inheritance pattern of a number of major genetic disorders suggests the possible involvement of genes that are expressed from one allele and silent on the other, but such genes are difficult to detect. Since DNA methylation in regulatory regions is often a mark of gene silencing, we modified existing microarray-based assays to detect both methylated and unmethylated DNA sequences in the same sample, a variation we term the MAUD assay. We probed a 65 Mb region of mouse Chr 7 for gene-associated sequences that show two distinct DNA methylation patterns in the mouse CNS. Selected genes were then tested for allele-specific expression in clonal neural stem cell lines derived from reciprocal F1 (C57BL/6×JF1) hybrid mice. In addition, using a separate approach, we directly analyzed allele-specific expression of a group of genes interspersed within clusters of OlfR genes, since the latter are subject to allelic exclusion. Altogether, of the 500 known genes in the chromosomal region surveyed, five show monoallelic expression, four identified by the MAUD assay (Agc1, p (pink-eyed dilution), P4ha3 and Thrsp), and one by its proximity to OlfR genes (Trim12). Thrsp (thyroid hormone responsive SPOT14 homolog) is expressed in hippocampus, but the human protein homolog, S14, has also been implicated in aggressive breast cancer. Monoallelic expression of the five genes is not coordinated at a chromosome-wide level, but rather regulated at individual loci. Taken together, our results suggest that at least 1% of previously untested genes are subject to allelic exclusion, and demonstrate a dual approach to expedite their identification

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease