U-Net is a convolutional neural network model developed in 2015 and has proven to be one of the most inspiring deep-learning models for image segmentation. Numerous U-Net-based applications have since emerged, constituting a heterogeneous set of tools that illustrate the current reproducibility crisis in the deep-learning field. Here we propose a solution in the form of Biom3d, a modular framework for deep learning facilitating the integration and development of novel models, metrics, or training schemes for 3D image segmentation. The new development philosophy of Biom3D provides an improved code sustainability and reproducibility in line with the FAIR principles and is available as a graphical user interface and an open-source deep-learning framework to target a large community of users, from end users to deep learning developers. ### Competing Interest Statement The authors have declared no competing interest.

BACKGROUND:COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism. METHODS:With the approval of NHS England, we used >24 million patients' primary care data from the OpenSAFELY-TPP platform. We used interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018-February 2020), COVID-19 restrictions (March 2020-February 2021) and recovery (March 2021-December 2022) periods. RESULTS:Prior to COVID-19 restrictions, antidepressant prescribing was increasing in the general population and in those with learning disability or autism. We did not find evidence that the pandemic was associated with a change in antidepressant prescribing trend in the general population (relative risk (RR) 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)) or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)).New prescribing post restrictions was 13% and 12% below expected had COVID-19 not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92)), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)). CONCLUSIONS AND IMPLICATIONS:In this England study, we did not see an impact of COVID-19 on overall antidepressant prescribing, although unique trends were noted, such as trends in new antidepressant prescriptions which increased in care homes over the pandemic and decreased in the general population and those with autism since recovery.

Understanding how viruses affect cellular pathways during infection may facilitate development of host cell-targeted therapeutics with broad-spectrum antiviral activity. The interferon (IFN) response is critical for reducing replication and pathogenesis of many viruses including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. Mounting evidence indicates that peroxisomes which are best known as metabolic organelles, function in the IFN response. Recently, we reported that the Wnt/β-catenin signaling pathway strongly suppresses peroxisome biogenesis. Here, we show that SARS-CoV-2 infection activates Wnt/β-catenin signaling and hypothesized that pharmacological inhibition of this pathway would result in increased peroxisome formation and enhanced IFN production. Indeed, Wnt/β-catenin signaling potently inhibits replication of SARS-CoV-2 and other pathogenic RNA viruses in vitro and reduces viral load, inflammation and clinical symptoms in a mouse model of COVID-19. As such, targeting this cellular pathway may have prophylactic and/or therapeutic value in reducing the disease burden caused by emerging viral pathogens.

The COVID-19 vaccines were developed and rigorously evaluated in randomized trials during 2020. However, important questions, such as the magnitude and duration of protection, their effectiveness against new virus variants, and the effectiveness of booster vaccination, could not be answered by randomized trials and have therefore been addressed in observational studies. Analyses of observational data can be biased because of confounding and because of inadequate design that does not consider the evolution of the pandemic over time and the rapid uptake of vaccination. Emulating a hypothetical "target trial" using observational data assembled during vaccine rollouts can help manage such potential sources of bias. This article describes 2 approaches to target trial emulation. In the sequential approach, on each day, eligible persons who have not yet been vaccinated are matched to a vaccinated person. The single-trial approach sets a single baseline at the start of the rollout and considers vaccination as a time-varying variable. The nature of the confounding depends on the analysis strategy: Estimating "per-protocol" effects (accounting for vaccination of initially unvaccinated persons after baseline) may require adjustment for both baseline and "time-varying" confounders. These issues are illustrated by using observational data from 2 780 931 persons in the United Kingdom aged 70 years or older to estimate the effect of a first dose of a COVID-19 vaccine. Addressing the issues discussed in this article should help authors of observational studies provide robust evidence to guide clinical and policy decisions.

Background: Treatments for COVID-19, including steroids, might exacerbate Strongyloides disease in patients with coinfection. We aimed to systematically review clinical and laboratory features of SARS-CoV-2 and Strongyloides coinfection, investigate possible interventions, assess outcomes, and identify research gaps requiring further attention. Methods: We searched two electronic databases, LitCOVID and WHO, up to August 2022, including SARS-CoV-2 and Strongyloides coinfection studies. We adapted the World Health Organization—Uppsala Monitoring Centre (WHO-UMC) system for standardized case causality assessment to evaluate if using corticosteroids or other immunosuppressive drugs in COVID-19 patients determined acute manifestations of strongyloidiasis. Results: We included 16 studies reporting 25 cases of Strongyloides and SARS-CoV-2 coinfection: 4 with hyperinfection syndrome; 2 with disseminated strongyloidiasis; 3 with cutaneous reactivation of strongyloidiasis; 3 with isolated digestive symptoms; and 2 with solely eosinophilia, without clinical manifestations. Eleven patients were asymptomatic regarding strongyloidiasis. Eosinopenia or normal eosinophil count was reported in 58.3% of patients with Strongyloides reactivation. Steroids were given to 18/21 (85.7%) cases. A total of 4 patients (19.1%) received tocilizumab and/or Anakirna in addition to steroids. Moreover, 2 patients (9.5%) did not receive any COVID-19 treatment. The causal relationship between Strongyloides reactivation and COVID-19 treatments was considered certain (4% of cases), probable (20% of patients), and possible (20% of patients). For 8% of cases, it was considered unlikely that COVID-19 treatment was associated with strongyloidiasis reactivations; the relationship between the Strongyloides infection and administration of COVID-19 treatment was unassessable/unclassifiable in 48% of cases. Of 13 assessable cases, 11 (84.6%) were considered to be causally associated with Strongyloides, ranging from certain to possible. Conclusions: Further research is needed to assess the frequency and risk of Strongyloides reactivation in SARS-CoV-2 infection. Our limited data using causality assessment supports recommendations that clinicians should screen and treat for Strongyloides infection in patients with coinfection who receive immunosuppressive COVID-19 therapies. In addition, the male gender and older age (over 50 years) may be predisposing factors for Strongyloides reactivation. Standardized guidelines should be developed for reporting future research.

Mid-SUN proteins are a neglected family of conserved type III membrane proteins of ancient origin with representatives in plants, animals, and fungi. Previous higher plant studies have associated them with functions at the nuclear envelope and the endoplasmic reticulum (ER). In this study, high-resolution confocal light microscopy is used to explore the localisation of SUN3 and SUN4 in the perinuclear region, to explore topology, and to study the role of mid-SUNs on endoplasmic reticulum morphology. The role of SUN3 in the ER is reinforced by the identification of a protein interaction between SUN3 and the ER membrane-bound transcription factor maMYB. The results highlight the importance of mid-SUNs as functional components of the ER and outer nuclear membrane.

This Community Resource paper introduces the range of materials developed by the INDEPTH (Impact of Nuclear Domains on Gene Expression and Plant Traits) COST Action made available through the INDEPTH Academy. Recent rapid growth in understanding of the significance of epigenetic controls in plant and crop science has led to a need for shared, high-quality resources, standardization of protocols, and repositories for open access data. The INDEPTH Academy provides a range of masterclass tutorials, standardized protocols, and teaching webinars, together with a rapidly developing repository to support imaging and spatial analysis of the nucleus and deep learning for automated analysis. These resources were developed partly as a response to the COVID-19 pandemic, but also driven by needs and opportunities identified by the INDEPTH community of ~200 researchers in 80 laboratories from 32 countries. This community report outlines the resources produced and how they will be extended beyond the INDEPTH project, but also aims to encourage the wider community to engage with epigenetics and nuclear structure by accessing these resources.

The SARS-CoV-2 Omicron variant is increasing in prevalence around the world. Accurate estimation of disease severity associated with Omicron is critical for pandemic planning. We found lower risk of accident and emergency (AE) attendance following SARS-CoV-2 infection with Omicron compared to Delta (HR: 0.39 (95% CI: 0.30 – 0.51; P<.0001). For AE attendances that lead to hospital admission, Omicron was associated with an 85% lower hazard compared with Delta (HR: 0.14 (95% CI: 0.09 – 0.24; P<.0001)).Conflicts of Interests Nothing to declare.Funding statement This work was supported by the Medical Research Council MR/V015737/1. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. Rosalind Eggo is funded by HDR UK (grant: MR/S003975/1), MRC (grant: MC_PC 19065), NIHR (grant: NIHR200908).### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis work was supported by the Medical Research Council MR/V015737/1. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency. Rosalind Eggo is funded by HDR UK (grant: MR/S003975/1), MRC (grant: MC_PC 19065), NIHR (grant: NIHR200908).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study was approved by the Health Research Authority (REC reference 20/LO/0651) and by the LSHTM Ethics Board (reference 21863).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesDetailed pseudonymised patient data is potentially re-identifiable and therefore not shared.

EDITORIAL article Front. Plant Sci., 03 August 2022Sec. Plant Cell Biology https://doi.org/10.3389/fpls.2022.998823

For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.

Extended data for: SARS-CoV-2 and the role of orofecal transmission: a systematic review.

Background The COVID-19 pandemic is likely to influence the rate of cardiometabolic and pulmonary complications. We set out to measure the overall rate of events over time. Methods Working on behalf of NHS England, we used data from the OpenSAFELY platform containing data from approximately 40% of the population of England. We selected the whole adult population of 17m patients and identified three mutually exclusive groups: hospitalised with COVID-19, tested positive for SARS-CoV-2, and everyone else. We then counted rates of death, DVT, PE, ischaemic stroke, MI, heart failure, AKI and diabetic ketoacidosis in each month between February 2019 and October 2020. Outcome events were defined using hospitalisations, GP records and cause of death data. Results For all outcomes except deaths there was a lower count of events in April 2020 compared to a year earlier. For most outcomes the minimum count of events was in April 2020, where the decrease compared to April 2019 in events ranged from 5.9% (PE) to 40.0% (heart failure). Deaths saw an increase of 73.4% between April 2019 and April 2020. Despite the hospitalised COVID-19 population making up just 0.14% of the population in April 2019, they accounted for a high proportion of events in that month (range of proportions 10.3% (DVT) to 33.5% (AKI)). When only counting the events in people with a record of COVID, the decrease was much greater, ranging from 23.0% (stroke) to 50.5% (heart failure). Interpretation We observed a large drop in recording of all cardiometabolic and pulmonary complications in the non-COVID general population, alongside frequent occurrence of these outcomes in patients hospitalised with COVID.

We propose a hierarchical framework based on our experience of systematically reviewing and synthesizing 378 primary studies for an evidence-based update of the modes of transmission for SARS-CoV-2. These studies revealed significant methodological shortcomings with a lack of standardization in the design, conduct, testing and reporting of SARS-CoV-2 transmission. While this situation is in part excusable at the outset of a pandemic, evidence rules of proof for assessing the transmission of this virus are needed for this and future pandemics of viral respiratory pathogens. We review the history of causality assessment related to microbial etiologies with a focus on respiratory viruses and suggest a hierarchy of evidence to integrate clinical, epidemiologic, molecular and laboratory perspectives on transmission. The hierarchy, if applied to future studies, should narrow the uncertainty over the twin concepts of causality and transmission of human respiratory viruses. We attempt to address the translational gap between the current research evidence and the assessment of causality in the transmission of respiratory viruses with a focus on SARS-CoV-2. Experimentation, consistency and independent replication of research alongside our proposed framework provide a chain of evidence that can reduce the uncertainty over the transmission of respiratory viruses and increase the level of confidence in specific modes of transmission and the measures that should be undertaken to prevent transmission

BackgroundLong COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created.AimTo describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time.Design and settingPopulation-based cohort study in English primary care.MethodWorking on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week.ResultsLong COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4).ConclusionCurrent recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians’ understanding of the diagnosis.