Direct to public peer support and e-therapy program versus information to aid self-management of depression and anxiety: protocol for a randomized controlled trial

Regardless of geography or income, effective help for depression and anxiety only reaches a small proportion of those who might benefit from it. The scale of the problem suggests a role for effective, safe, anonymised public health driven online services such as Big White Wall which offers immediate peer support at low cost. Objectives: Using RE-AIM methodology we will aim to determine the population reach, effectiveness, cost effectiveness, and barriers and drivers to implementation of Big White Wall (BWW) compared to online information compiled by the UK’s National Health Service (NHS Choices Moodzone) in people with probable mild to moderate depression and anxiety disorder. Method/Design: A pragmatic, parallel group, single blind RCT is being conducted using a fully automated trial website in which eligible participants are randomised to receive either 6 months access to BWW or signposted to the NHS Moodzone site. The recruitment of 2200 people to the study will be facilitated by a public health engagement campaign involving general marketing and social media, primary care clinical champions, healthcare staff, large employers and third sector groups. People will refer themselves to the study and will be eligible if they are over 16 years, have probable mild to moderate depression or anxiety disorders and have access to the internet. The primary outcome will be the Warwick-Edinburgh Mental Well-being Scale at six weeks. We will also explore the reach, maintenance, cost-effectiveness, barriers and drivers to implementation and possible mechanisms of actions using a range of qualitative and quantitative methods. Discussion: This will be the first fully digital trial of a direct to public on line peer support programme for common mental disorders. The potential advantages of adding this to current NHS mental health services and the challenges of designing a public health campaign and randomised controlled trial of two digital interventions using a fully automated digital enrolment and data collection process are considered for people with depression and anxiety

A direct-to-public peer support program (Big White Wall) versus web-based information to aid the self-management of depression and anxiety: Results and challenges of an automated randomized controlled trial

Background: Effective help for depression and anxiety reaches a small proportion of people who might benefit from it. The scale of the problem suggests the need for effective, safe web-based public health services delivered directly to the public. One model, the Big White Wall (BWW), offers peer support at low cost. As these interventions are delivered digitally, we tested whether a randomized controlled trial (RCT) intervention could also be fully delivered and evaluated digitally. Objective: This study aims to determine the reach, feasibility, acceptability, baseline costs, and outcomes of a public health campaign for an automated RCT of the BWW, providing digital peer support and information, compared with a standard website used by the National Health Service Moodzone (MZ), to people with probable mild-to-moderate depression and anxiety disorder. The primary outcome was the change in self-rated well-being at 6 weeks, measured using the Warwick-Edinburgh Mental Well-Being Scale. Methods: An 18-month campaign was conducted across Nottinghamshire, the United Kingdom (target population 914,000) to advertise the trial directly to the public through general marketing, web-based and social media sources, health services, other public services, and third-sector groups. The population reach of this campaign was examined by the number of people accessing the study website and self-registering to the study. A pragmatic, parallel-group, single-blind RCT was then conducted using a fully automated trial website in which eligible participants were randomized to receive either 6 months of access to BWW or signposted to MZ. Those eligible for participation were aged >16 years with probable mild-to-moderate depression or anxiety disorders. Results: Of 6483 visitors to the study website, 1510 (23.29%) were eligible. Overall, 790 of 1510 (52.32%) visitors participated. Of 790 visitors, 397 (50.3%) were randomized to BWW and 393 (49.7%) to MZ. Their mean age was 38 (SD 13.8) years, 81.0% (640/790) were female, 93.4% (738/790) were White, and 47.4% (271/572) had no contact with health services in the previous 3 months. We estimated 3-month productivity losses of £1001.01 (95% CI 868.75-1133.27; US $1380.79; 95% CI 1198.35-1563.23) per person for those employed. Only 16.6% (131/790) participants completed the primary outcome assessment. There were no differences in the primary or secondary outcomes between the 2 groups. Conclusions: Most participants reached and those eligible for this trial of digital interventions were White women not in recent contact with health services and whose productivity losses represent a significant annual societal burden. A fully automated RCT recruiting directly from the public failed to recruit and retain sufficient participants to test the clinical effectiveness of this digital intervention, primarily because it did not personally engage participants and explain how these unfamiliar interventions might benefit them

Impact of risk factors on the timing of first postpartum venous thromboembolism: a population-based cohort study from England

For women with preeclampsia, BMI >30 kg/m2, infection, or those having cesarean delivery, VTE risk remained elevated for 6 weeks postpartum.For women with postpartum hemorrhage or preterm birth, the relative rate of VTE was only increased for the first 3 weeks postpartum.Impact on the timing of first postpartum venous thromboembolism (VTE) for women with specific risk factors is of crucial importance when planning the duration of thromboprophylaxis regimen. We observed this using a large linked primary and secondary care database containing 222 334 pregnancies resulting in live and stillbirth births between 1997 and 2010. We assessed the impact of risk factors on the timing of postpartum VTE in term of absolute rates (ARs) and incidence rate ratios (IRRs) using a Poisson regression model. Women with preeclampsia/eclampsia and postpartum acute systemic infection had the highest risk of VTE during the first 3 weeks postpartum (ARs ≥2263/100 000 person-years; IRR ≥2.5) and at 4-6 weeks postpartum (AR ≥1360; IRR ≥3.5). Women with body mass index (BMI) >30 kg/m2 or those having cesarean delivery also had elevated rates up to 6 weeks (AR ≥1425 at 1-3 weeks and ≥722 at 4-6 weeks). Women with postpartum hemorrhage or preterm birth, had significantly increased VTE rates only in the first 3 weeks (AR ≥1736; IRR ≥2). Our findings suggest that the duration of the increased VTE risk after childbirth varies based on the type of risk factors and can extend up to the first 3 to 6 weeks postpartum

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Mutations in SLC39A14 disrupt manganese homeostasis and cause childhood-onset parkinsonism-dystonia.

Although manganese is an essential trace metal, little is known about its transport and homeostatic regulation. Here we have identified a cohort of patients with a novel autosomal recessive manganese transporter defect caused by mutations in SLC39A14. Excessive accumulation of manganese in these patients results in rapidly progressive childhood-onset parkinsonism-dystonia with distinctive brain magnetic resonance imaging appearances and neurodegenerative features on post-mortem examination. We show that mutations in SLC39A14 impair manganese transport in vitro and lead to manganese dyshomeostasis and altered locomotor activity in zebrafish with CRISPR-induced slc39a14 null mutations. Chelation with disodium calcium edetate lowers blood manganese levels in patients and can lead to striking clinical improvement. Our results demonstrate that SLC39A14 functions as a pivotal manganese transporter in vertebrates.Action Medical ResearchThis is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1160

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing