Non-randomised feasibility study testing a primary care intervention to promote engagement in an online health community for adults with troublesome asthma: protocol.

INTRODUCTION: In the UK, approximately 4.3 million adults have asthma, with one-third experiencing poor asthma control, affecting their quality of life, and increasing their healthcare use. Interventions promoting emotional/behavioural self-management can improve asthma control and reduce comorbidities and mortality. Integration of online peer support into primary care services to foster self-management is a novel strategy. We aim to co-design and evaluate an intervention for primary care clinicians to promote engagement with an asthma online health community (OHC). Our protocol describes a 'survey leading to a trial' design as part of a mixed-methods, non-randomised feasibility study to test the feasibility and acceptability of the intervention. METHODS AND ANALYSIS: Adults on the asthma registers of six London general practices (~3000 patients) will be invited to an online survey, via text messages. The survey will collect data on attitudes towards seeking online peer support, asthma control, anxiety, depression, quality of life, information on the network of people providing support with asthma and demographics. Regression analyses of the survey data will identify correlates/predictors of attitudes/receptiveness towards online peer support. Patients with troublesome asthma, who (in the survey) expressed interest in online peer support, will be invited to receive the intervention, aiming to reach a recruitment target of 50 patients. Intervention will involve a one-off, face-to-face consultation with a practice clinician to introduce online peer support, sign patients up to an established asthma OHC, and encourage OHC engagement. Outcome measures will be collected at baseline and 3 months post intervention and analysed with primary care and OHC engagement data. Recruitment, intervention uptake, retention, collection of outcomes, and OHC engagement will be assessed. Interviews with clinicians and patients will explore experiences of the intervention. ETHICS AND DISSEMINATION: Ethical approval was obtained from a National Health Service Research Ethics Committee (reference: 22/NE/0182). Written consent will be obtained before intervention receipt and interview participation. Findings will be shared via dissemination to general practices, conference presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05829265.

Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.

Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions.

The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients' self-care and health.

FaceLift: a transparent deep learning framework to beautify urban scenes.

In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond predicting beauty, and tackle the challenge of recreating beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their 'black-box nature', these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework (which we name FaceLift) that is able to both beautify existing urban scenes (Google Street Views) and explain which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence (or absence) of elements that make urban spaces great. Upon a review of the urban planning literature, we identify five main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift has been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of the spaces we intuitively love.

How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities.

BACKGROUND: Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice. OBJECTIVE: The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time. METHODS: We conducted a longitudinal network analysis of anonymized data from 2 patients' online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively. RESULTS: The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users' posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice. CONCLUSIONS: In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers' engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management.

Spindle rotation in human cells is reliant on a MARK2-mediated equatorial spindle-centering mechanism.

The plane of cell division is defined by the final position of the mitotic spindle. The spindle is pulled and rotated to the correct position by cortical dynein. However, it is unclear how the spindle's rotational center is maintained and what the consequences of an equatorially off centered spindle are in human cells. We analyzed spindle movements in 100s of cells exposed to protein depletions or drug treatments and uncovered a novel role for MARK2 in maintaining the spindle at the cell's geometric center. Following MARK2 depletion, spindles glide along the cell cortex, leading to a failure in identifying the correct division plane. Surprisingly, spindle off centering in MARK2-depleted cells is not caused by excessive pull by dynein. We show that MARK2 modulates mitotic microtubule growth and length and that codepleting mitotic centromere-associated protein (MCAK), a microtubule destabilizer, rescues spindle off centering in MARK2-depleted cells. Thus, we provide the first insight into a spindle-centering mechanism needed for proper spindle rotation and, in turn, the correct division plane in human cells.