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The COVID-19 pandemic brought abrupt changes and disruption to the lives of children and young people. This qualitative study used semi-structured interviews to explore how participants navigated national lockdowns (including school closures), social restrictions, and the reintegration back into pre-pandemic routines. Twenty children, young people and parents were purposively sampled from the Mental Health of Children and Young People's Survey, 2020. We identified three major themes through thematic analysis, with participants discussing (1) learning experiences, (2) emotions and coping, and (3) decompression and discovery. Experiences during the pandemic were highly variable. Participants emphasised the importance of social and familial connections, access to engaging learning environments, and structure/routine in promoting health and well-being.

The Incredible Years Teacher Classroom Management (TCM) programme is an evidence-based training with documented positive effects on teachers' classroom management strategies and pupil mental health. However, programme effectiveness alone does not ensure sustainability. This study explored teachers' views on the sustainability of the TCM programme. One year after attending the TCM training, 25 UK primary schoolteachers participated in three focus groups and ten individual interviews. Data were thematically analysed using the Framework Method. One year after the training, teachers were still using TCM strategies, adapting their use to their new cohort of pupils and still reported positive effects on children, teacher-parent relationships, and themselves. However, some school-level and educational policy changes hindered their intended use of TCM strategies. Teachers had shared the TCM strategies with their colleagues, with mixed responses from the latter. To ensure the sustainability of the programme, teachers endorsed refresher sessions and a whole-school training approach.

In spite of challenges, initial teacher education in England may yet have a future, but only if policy makers come to recognise that a narrow training model can only achieve so much. Teacher education (as opposed to teacher training) recognises teaching as a 'professional endeavour', with teachers prepared in a way that will enable them to become competent and confident professionals who can take their rightful place as 'public intellectuals' (Cochran-Smith 2006), equipped to serve the needs not just of the pupils in their classrooms but also of the schools and communities in which they work and live. Cochran-Smith (2004) had previously discussed the 'problem' of teacher education, and shown that, at various stages, it has been conceptualised, in turn, as a 'training problem' a 'learning problem' and, finally a 'policy problem' (2004, 295). The authors would suggest that, with the recruitment of new teachers falling steadily year-on-year and a government-mandated model of teacher 'training' that imposes a narrowly conceived core content framework within a strict compliance model, teacher education, in England now risks becoming a 'professional' problem. The future of initial teacher education in England may therefore depend on those who advocate strongly for teaching as a 'professional endeavour' (Winch et al., 2015, 202), and devise ITE programmes which meet the stipulated minimum requirements but are not constrained by otherwise reductive conceptualisations of teacher professional learning.

Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved with a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214.

Abstract The structural architecture of coral reefs is a known predictor of species richness, fish biomass and reef resilience. At a smaller scale, three-dimensional (3D) surface area of corals is a fundamental determinant of physical and biological processes. Quantifying the 3D surface area of corals has applications for a broad range of scientific disciplines, including carbonate production estimates, coral predation studies, and assessments of reef growth. Here, we present morphotaxon-specific conversion metrics to estimate total 3D surface area and projected 2D surface area of individual colonies from simple field measurements of colony maximum diameter. Underwater photogrammetry techniques were used to quantify surface area and estimate conversion metrics. Bayesian models showed strong non-linear (power) relationships between colony maximum diameter and both total 3D surface area and projected 2D surface area for 13 out of 15 morphotaxa. This study presents a highly resolved and efficient method for obtaining critical surface area assessments of corals for various applications, including assessments of biotic surface area, tissue biomass, calcification rates, coral demographic rates, and reef restoration monitoring.

Abstract Background Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. Results The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. Conclusion HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

Abstract Breast cancer is the most frequently diagnosed cancer in females globally. However, we know relatively little about trends in males. This study describes United Kingdom (UK) secular trends in breast cancer from 2000 to 2021 for both sexes. We describe a population-based cohort study using UK primary care Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases. There were 5,848,436 eligible females and 5,539,681 males aged 18+ years, with ≥ one year of prior data availability in the study period. We estimated crude breast cancer incidence rates (IR), prevalence and survival probability at one-, five- and 10-years after diagnosis using the Kaplan–Meier method. Analyses were further stratified by age. Crude IR of breast cancer from 2000 to 2021 was 194.4 per 100,000 person-years for females and 1.16 for males. Crude prevalence in 2021 was 2.1% for females and 0.009% for males. Both sexes have seen around a 2.5-fold increase in prevalence across time. Incidence increased with age for both sexes, peaking in females aged 60–69 years and males 90+ . There was a drop in incidence for females aged 70–79 years. From 2003–2019, incidence increased > twofold in younger females (aged 18–29: IR 2.12 in 2003 vs. 4.58 in 2018); decreased in females aged 50–69 years; and further declined from 2015 onwards in females aged 70–89 years. Survival probability for females after one-, five-, and ten-years after diagnosis was 95.1%, 80.2%, and 68.4%, and for males 92.9%, 69.0%, and 51.3%. Survival probability at one-year increased by 2.08% points, and survival at five years increased by 5.39% from 2000–2004 to 2015–2019 for females, particularly those aged 50–70 years. For males, there were no clear time-trends for short-term and long-term survival probability. Changes in incidence of breast cancer in females largely reflect the success of screening programmes, as rates rise and fall in synchronicity with ages of eligibility for such programmes. Overall survival from breast cancer for females has improved from 2000 to 2021, again reflecting the success of screening programmes, early diagnosis, and improvements in treatments. Male breast cancer patients have worse survival outcomes compared to females, highlighting the need to develop male-specific diagnosis and treatment strategies to improve long-term survival in line with females.

Burn care quality indicators are used to monitor and improve quality of care and for benchmark purposes. The perspectives of burn survivors, however, are not included in current sets of quality indicators while patient-centred care gains importance. The aim of this study was to explore burn survivors’ perspectives on quality aspects of burn care, which was used to translate their perspectives into patient-centred quality of care indicators. Qualitative descriptive research was conducted in a patient panel group. First, thematic analysis was applied to the focus groups to identify overarching themes. Second, patient-centred quality indicators, informed by burn survivors’ valued aspects of care, were defined. Ten burn survivors with an average age of 54 years (SD = 11; range 38–72 years) and mean TBSA burned of 14% (SD = 11%; range 5–35%) participated in two focus groups. Four overarching themes were identified, pointing to the importance of (1) information tailored to the different phases of recovery, (2) significant others’ wellbeing and involvement, (3) a therapeutic relationship and low-threshold access to healthcare professionals and (4) to participate in decision-making. Eighteen patient-centred process quality of care indicators within nine aspects of care were formulated. The overarching themes are reflected in patient-centred quality indicators, which present a broadened and complementary view of existing clinical quality indicators for burn care. Evaluating these patient-centred quality indicators may increase quality of care and refine patient-centred care.

Abstract Background Lynch syndrome (LS) is under-diagnosed. UK National Institute for Health and Care Excellence guidelines recommend multistep molecular testing of all colorectal cancers (CRCs) to screen for LS. However, the complexity of the pathway has resulted in limited improvement in diagnosis. Methods One-step multiplex PCR was used to generate sequencing-ready amplicons from 14 microsatellite instability (MSI) markers and 22 BRAF, KRAS, and NRAS mutation hotspots. MSI and BRAF/RAS variants were detected using amplicon-sequencing and automated analysis. The assay was clinically validated and deployed into service in northern England, followed by regional and local audits to assess its impact. Results MSI analysis achieved 99.1% sensitivity and 99.2% specificity and was reproducible (r = 0.995). Mutation hotspot analysis had 100% sensitivity, 99.9% specificity, and was reproducible (r = 0.998). Assay-use in service in 2022–2023 increased CRC testing (97.2% (2466/2536) versus 28.6% (601/2104)), halved turnaround times, and identified more CRC patients at-risk of LS (5.5% (139/2536) versus 2.9% (61/2104)) compared to 2019–2020 when a multi-test pathway was used. Conclusion A novel amplicon-sequencing assay of CRCs, including all biomarkers for LS screening and anti-EGFR therapy, achieved >95% testing rate. Adoption of this low cost, scalable, and fully automatable test will complement on-going, national initiatives to improve LS screening.

There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson-plus and the UK National PSP Research Network (PROSPECT-MR). Resting-state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large-scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between-network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients demographics, structural imaging, and clinical scores using five-fold cross-validation. In PSP and CBS, between-network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between-network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics.

Objective The objective of this study is to identify the top 10 research priorities on reducing and stopping psychiatric medication that reflect the perspectives and unmet needs of three key stakeholder groups (people with lived experience, family members/carers/supporters and healthcare professionals).Methods A priority-setting partnership was conducted using the James Lind Alliance’s seven-step process. This involved (1) creating an international Steering Group of key stakeholder representatives and (2) identifying potential partners; (3) gathering stakeholders’ uncertainties about reducing and stopping psychiatric medication using an online survey and summarising the survey responses; (4) checking the summary questions against existing evidence and verifying uncertainties; (5) shortlisting the questions using a second online survey; (6) determining the top 10 research questions through a prioritisation workshop; and (7) disseminating the results.Results A total of 3635 questions were collected in the initial survey from 884 respondents of which 32 questions were verified as uncertainties. These questions were then ranked in a second online survey by 526 respondents and the findings discussed in a final prioritisation workshop by 30 participants to produce the final top 10 list of research questions. These questions cover a range of areas including the most effective ways of safely reducing/stopping psychiatric medication and providing support to individuals undergoing the discontinuation process, as well as the best ways to educate healthcare professionals on this topic.Conclusion The top 10 list of research priorities was produced through extensive engagement with key stakeholders and highlights important uncertainties and gaps in the existing evidence base that need to be addressed by future research.

Summary: Background: The World Health Organisation (WHO) has identified a range of symptomatic manifestations to aid in the clinical diagnosis of post-COVID conditions, herein referred to as post-acute COVID-19 symptoms. We conducted an international network cohort study to estimate the burden of these symptoms in North American, European, and Asian populations. Methods: A federated analysis was conducted including 10 databases from the United Kingdom, Netherlands, Norway, Estonia, Spain, France, South Korea, and the United States, between September 1st 2020 and latest data availability (which varied from December 31st 2021 to February 28th 2023), covering primary and secondary care, nationwide registries, and claims data, all mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We defined two cohorts for the main analyses: a SARS-CoV-2 infection cohort [positive polymerase chain reaction (PCR) or rapid lateral flow test (LFT) result or clinical COVID-19 diagnosis] and a general population cohort. Individuals with less than 365 days of prior history or 120 days of follow-up were excluded. We estimated incidence rates (IRs) of the 25 WHO-proposed post-acute COVID-19 symptoms, considering symptoms that occurred ≥90 and ≤365 days after index date, excluding individuals with the respective symptoms 180 days prior to the index event. Stratified analyses were conducted by age and sex. Incidence rate ratios (IRRs) were calculated comparing rates in the infected cohort versus the general population. Results from the different databases were combined using random-effects meta-analyses. Findings: 3,019,408 individuals were included in the infection cohort. 1,585,160 of them were female and 1,434,248 of them male. 929,351,505 individuals were included in the general population group. 461,195,036 of them were female and 466,022,004 of them male. The 1-year IR of any post-acute COVID-19 symptom in the COVID-19 infection cohort varied significantly across databases, from 4.4 (95% CI 3.8–5.1) per 100 person-years to 103.9 (95% CI 103.2–104.7). The five most common symptoms were joint pain (from 1.6 (95% CI 1.3–1.9) to 14.3 (95% CI 14.1–14.6)), abdominal pain (from 0.3 (95% CI 0.1–0.5) to 9.9 (95% CI 9.7–10.1)), gastrointestinal issues (from 0.6 (95% CI 0.4–0.9) to 13.3 (95% CI 13.1–13.6)), cough (from 0.3 (95% CI 0.2–0.5) to 9.1 (95% CI 8.9–9.3)), and anxiety (from 0.8 (95% CI 0.6–1.2) to 11.4 (95% CI 11.2–11.6)); whereas muscle spasms (from 0.01 (95% CI 0.008–0.2) to 1.7 (95% CI 1.6–1.8)), pins and needles (from 0.05 (95% CI 0.03–0.0.9) to 1.5 (95% CI 1.4–1.6)), memory issues (from 0.03 (95% CI 0.02–0.06) to 0.8 (95% CI 0.7–0.8)), cognitive dysfunction (from 0.007 (95% CI 0.004–0.01) to 0.6 (95% CI 0.4–0.8)), and altered smell and/or taste (from 0.04 (95% CI 0.03–0.04) to 0.7 (95% CI 0.6–0.8)) were least common. Incidence rates of any post-acute COVID-19 symptoms generally increased with age, with certain symptoms peaking in middle-aged adults (anxiety, depressive disorders, headache, altered smell and taste) and others in pre-school children (gastrointestinal issues and cough). Females had higher incidence rates for most symptoms. Based on the random-effects model, the infected cohort had a higher incidence of any post-acute COVID-19 symptom than the general population, with a meta-analytic incidence rate ratio (meta-IRR) of 1.4 (1–2). A similar pattern was seen for all individual symptoms. The highest meta-IRRs were depressive disorder, 2.6 (1.7–3.9); anxiety, 2.3 (1.4–3.8); allergy, 2.1 (1.7–2.8) and sleep disorders, 2.1 (1.5–2.6). The meta-IRR for altered smell and/or taste was 1.9 (1.3–2.8). Interpretation: Post-acute COVID-19 symptoms, as listed by the WHO, were commonly observed following COVID-19 infection. However, even after standardising research methods, there was significant heterogeneity in the incidence rates from different healthcare settings and geographical locations. This is the first international study of the epidemiology of post-acute COVID-19 symptoms using the WHO-listed symptoms. Its findings contibute to understand the epidemiology of this condition from a multinational approach. Limitations of this study include the lack of consensus of the post-acute COVID-19 definition, as well as the difficulty to capture the impact on daily life of the post-acute COVID-19 symptoms in the available datasets. Funding: This work has been funded by the European Health Data Evidence Network (EHDEN) through an Evidence Generation Fund Grant and by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC).

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information. Plain language summary Whole-body magnetic resonance imaging demonstrates the entire body and can detect the spread of tumour, without the burden of ionising radiation. Recently, the STREAMLINE study reported that whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. However, whole-body magnetic resonance imaging is complex to report. Machine learning is a type of artificial intelligence whereby a computer learns from being given previous data to undertake a task, using techniques such as classification forests, convolutional neural networks, and multi-atlas approaches. Our aim was to develop a machine-learning method to automatically detect lesions on whole-body magnetic resonance imaging to support radiologists by potentially improving their ability to correctly detect disease and reduce the reading time of whole-body magnetic resonance imaging scans in patients with cancer. Firstly, whole-body magnetic resonance imaging scans from 51 healthy volunteers were used to develop machine-learning methods to automatically detect normal organs. Secondly, machine-learning methods were trained to detect cancer lesions, using 271 whole-body magnetic resonance imaging scans from a previous study. Finally, the refined machine-learning technique was tested in 188 patient scans from a previous study, to see if the technique could improve radiology reporting by increasing accuracy and speed in detecting disease. We designed a system to test the accuracy of radiologists reading whole-body magnetic resonance imaging with or without machine-learning support in a near-real clinical National Health Service setting. Twenty-five independent radiologists (18 experienced in reading whole-body magnetic resonance imaging and 7 radiologists inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging scans to read with or without machine-learning support. We found that machine-learning support resulted in similar accuracy for detecting disease and was slightly more efficient in the reading time than for radiological reads without machine-learning support. Differences in interpretation between experienced readers were considered moderate in both cases. Overall, the study was an ambitious attempt to undertake a highly complex machine-learning task, to detect cancer on whole-body magnetic resonance imaging. Many important steps have been taken but the current machine-learning algorithm did not result in a significant improvement in the radiologist’s accuracy for disease detection, although it may have slightly reduced the time taken to read the study. Future work is advocated to further develop machine-learning tools to improve the accuracy of tumour detection. Scientific summary Background Whole-body magnetic resonance imaging (WB-MRI) has been developed in the last decade and it has been proposed as an alternative to multimodality cancer staging pathways. The STREAMLINE study has demonstrated that staging of lung and colon cancer via WB-MRI was of similar accuracy to standard care staging pathways but resulted in fewer tests being required, and a reduction in staging time and cost. However, this technique has not widely translated into clinical practice, being limited to a few expert centres. A barrier to translation may be the complexity of interpretation by inexperienced readers. A machine-learning (ML) algorithm for automated detection of cancer lesions, to assist radiologists, may allow clinical translation of WB-MRI for the benefit of patient care. Objective The objective of Phases 1 and 2 was to develop a ML method for automatic detection of cancer lesions on WB-MRI. The primary objective of Phase 3 was to compare the diagnostic test accuracy of WB-MRI in patients being staged for cancer with and without ML support, when read by independent, experienced readers. The reference standard was the consensus reference panel from the STREAMLINE study, which had recorded the sites of disease in the STREAMLINE patients using all available clinical information for a 12-month follow-up from time of enrolment. The planned secondary objectives of this study were: to compare the reading time of WB-MRI scans, with and without ML support; to determine the interobserver variability of WB-MRI diagnosis by different radiologists, with and without ML support; to evaluate the diagnostic accuracy of WB-MRI read by non-experienced readers, with and without ML support; to evaluate different combinations of acquired MRI sequences; all the above with and without ML support (not achieved); to determine any difference in costs related to radiology reading time by means of a simple cost-effectiveness analysis (not achieved). Design This was an observational study (study limited to working with data), using different patient cohorts, and methodologies, being evaluated in series during three consecutive phases. Phase 1: Previously acquired WB-MRI scans in 51 healthy volunteers were stitched into imaging volumes. The normal organs and skeleton were segmented by a trained radiologist. We compared and tested several state-of-the-art medical image segmentation methods to train an algorithm to automatically detect and segment the organs. This included a multi-atlas (MA) approach, classification forests (CFs) and convolutional neural networks (CNNs) methods. For the CFs method, we used 50 trees with a maximum tree depth of 30 in the segmentation. The stopping criterion for growing trees was if either the objective function (information gain) could not be further improved or the number of training samples in a leaf fell below a threshold of four samples. For the CNNs method, we employed a dual pathway (2 resolutions), 11-layer deep CNN, where the last 2 layers correspond to fully connected layers, which combine the features extracted on the 2 resolution pathways. We adopted 50–70 feature maps (different kernels) for each layer. The network architecture was fully convolutional and there were no max-pooling layers, which we found to increase segmentation accuracy. The CNN architecture was a balance between model capacity, training efficiency, and memory demands. The third algorithm was based on a MA label propagation approach. MA segmentation uses a set of atlases (images with corresponding segmentations) that represent the interparticipant variability of the anatomy to be segmented. Each atlas was registered to the new image to be segmented using a deformable image registration. The MA approach accounts for anatomical shape variability and is more robust than single-atlas propagation methods, in that at any errors associated with propagation were averaged out when combining multiple atlases. The approach employed here makes use of efficient 3D–3D intensity-based image registration with free-form deformations as the transformation model and correlation coefficient as the similarity measure. Majority voting is used to derive the final tissue label at each voxel. Phase 2: Available WB-MRI scans from the STREAMLINE study (n = 438) were allocated by the study statistician to Phase 2 (model training) and clinical validation (hold-out set of 193 cases). The visible lesions in cases allocated to Phase 2 were segmented by trained radiologists, using the defined consensus reference standard for the sites of disease. Using 226 evaluable and annotated cases, we trained a model for lesion detection and localisation rather than attempting accurate automated segmentation. We found that lesion detection in WB-MRI was suboptimal with the CNN alone. An optimal ‘two-stage’ ML method was developed and tested. In the first stage of the ‘two-stage’ method, the information from Phase 1 was used to identify the position of organs and bones and in stage two, the lesions were detected. Stage two could be modular with respect to the anatomical location in which the suspected lesion could be found. The architecture and configuration of the used CNN were modified to achieve optimal performance. Phase 3: The final two-stage ML algorithm from Phase 2 was applied to the 193 cases that were held out for Phase 3, generating probability heatmap volumes. WB-MRI scans were reported by 25 independent radiologists (18 radiologists experienced in reading WB-MRI and 7 radiologists inexperienced in WB-MRI). All radiologists took part in the 1st and 2nd round reads which incorporated inter-rater reads, and 8 participated in the 3rd round read for intrarater reads. All reads were undertaken in an NHS radiology reporting room, although using a separate cloud-based picture archiving and communication system (PACS). Based on experience level, 10–16 cases were randomly allocated by the study statistician to the reading lists for inexperienced or experienced radiologists. Each radiologist read both lung and colon cases with and without ML as a paired cohort. There were at least 4 weeks interval between each reading round for individual radiologists. Cases allocated to round 1 would be a random mixture of cases with and without ML support and then in round 2 the cases would be reversed such that each case was read once with and once without ML support. The number of cases with and without ML output was balanced in each reading round. A scribe recorded reading time. Results Phase 1 results We found that CNNs outperformed CFs and the MA algorithm when T2w volumes were used as input to the algorithms and when using pooled overlap-evaluation metrics [Dice similarity coefficient (DSC), recall (RE), precision (PR)] to assess the accuracy of segmentation. When the performance of the algorithms was assessed, with pooled surface distance metrics [average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD)], it was the MA algorithm that performed best. Single misinterpreted voxels in CFs and CNNs can greatly elevate ASD, RMSSD, and HD; these metrics are particularly sensitive to outliers. We then assessed the pooled metrics performance of CFs and CNNs when using all imaging combinations [T2w + T1w + diffusion-weighted imaging (DWI)] as input, arguing that maximisation of training information to the algorithms might improve the performance of segmentation. We found that the performance of CFs was improved, however not significantly, when using all imaging combinations as input for training. The opposite was observed for CNNs. The findings for the pooled metrics analysis, described above, were corroborated by a ‘per-organ’ quantitative analysis of the commonly used DSC, to assess the performance of our segmentation algorithms. This analysis confirmed that for all individual anatomical structures (except for the bladder), the algorithm that returned the greatest DSC was CNNs with T2w images only used as input. As our morphological T2w and T1w scans were acquired using breath-holds and the DWI sequence was acquired with free breathing, we found that there was significant displacement between soft tissues in anatomical areas adjacent to the diaphragm between these types of scans. As the employed affine registration method could not fully compensate for nonlinear motions caused by breathing, we assumed that misregistration could be the reason why the performance of CNNs, despite performing better than the other two algorithms when using T2w volumes as input only, was degraded when using all imaging combinations as input for training. A more robust, nonlinear registration method could improve the accuracy of CNNs and further improve the performance of CFs. The performance of our methods cannot be directly compared to similar methods in the literature because there is no previous work describing automatic, simultaneous segmentation of healthy organs and bones in multiparametric WB-MRI. We believe, however, that our methods may compare favourably to other ML methods for detection and segmentation in medical imaging because our classifiers are inherently multilabel and effective training was achieved when using a relatively small number of data sets, something that is very important in the clinical setting. However, we still need to address the performance limitations of our algorithms when segmenting organs with big variability in appearance (e.g. the gallbladder or the pancreas). Phase 2 results We tested different ML methods for lesion detection. We found that using CNNs was not optimal for lesion detection on WB-MRI. This may have been due to the small fraction of lesion volume occupying the scanned space, when compared to the WB volume. It may also have been due to the complexity of intensities in background tissue and the lesion with weak boundaries causing challenges for the CNNs. We, therefore, adapted our process to become an optimal two-stage process, with an initial stage for detection of organs as per the Phase 1 technique followed by a CNN to detect lesions. Stage two could be modular with respect to the anatomical location where the suspected lesion can be found. The architecture and configuration of the used CNN could be modified to achieve optimal performance for lesion detection. Phase 3 results All radiology reads were completed between November 2019 and March 2020. Among the 193 cases allocated to Phase 3, 188 WB-MRI scans were evaluable (117 colon and 71 lung cancer) and 50/188 cases had metastases. Per-patient specificity for detection of metastases within experienced readers was 86.2% (WB-MRI + ML) and 87.7% [WB-MRI + standard deviation (SD)], [difference −1.5%, 95% confidence interval (CI) −6.4%, 3.5%; p = 0.387]. Per-patient sensitivity produced results of 66.0% (WB-MRI + ML) and 70.0% (WB-MRI + SD) (difference −4.0%, 95% CI −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (WB-MRI + ML) and 60.0% (WB-MRI + SD). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to the low number of lesions in each site, hampering interpretation. Reading time was lowered under ML by 6.2% (95% CI −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% CI 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using ML in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% CI 0.47 to 0.81 (WB-MRI + ML) and Cohen’s κ = 0.66, 95% CI 0.47 to 0.81 (WB-MRI + SD). Conclusion Phase 1 In Phase 1, we developed and evaluated three state-of-the-art algorithms that automatically segment healthy organs and bones in WB-MRI with accuracy comparable to the one achieved manually by clinical experts, using relatively sparse training data. An algorithm based on CNNs and trained using T2w-only images as input performs favourably when compared to CFs or a MA algorithm, trained with either T2w-only images or a combination of imaging inputs (T2w + T1w + DWI). Using multimodal MRI data as input for training did not improve the segmentation performance in this work, but it is anticipated to improve the segmentation performance if more effective WB registration between the various imaging modalities can be performed. This investigation was the first step towards developing robust algorithms for the automatic detection and segmentation of benign and malignant lesions in WB-MRI scans for staging of cancer patients. Phase 2 There were many challenges in training the algorithm for lesion detection, with a very heterogeneous data set, acquired at 16 sites on different machines. The low number of metastatic lesions were scattered through many anatomic sites. Identification of lesions of relatively small volume in relation to the large volume of the WB-MRI required a two-stage approach for lesion detection, with initial organ identification followed by lesion detection. Phase 3 We undertook a robust diagnostic test accuracy study comparing WB-MRI with ML support (index text) with standard WB-MRI, without ML support (comparator test), with paired reads separated by a wash-out period. Although there was no clear statistical difference with or without ML support in terms of diagnostic test accuracy, either in experienced or inexperienced hands, we found that ML reads were likely to be a little shorter in reading time. Implications for health care Machine learning support for image interpretation is a rapidly expanding area of research. With continually increasing demand for diagnostic imaging and a radiology workforce crisis in the NHS, as well as globally, ML techniques may offer support to allow complex imaging modalities, such as WB-MRI, to be ready for translation into clinical care for the benefit of patients. Phase 1 demonstrated that with relatively sparse data, we could develop very successful organ segmentation tools in highly complex data sets and this has many potential future roles. In this study, we used this as a first step prior to lesion detection and this generic method could be applied to a wider range of disease areas, including other tumour types. We found that ML support slightly shorted the reading time, although with no improvement in diagnostic accuracy. However, we have shown that lesion detection using ML is achievable on heterogeneous and complex multiparametric WB-MRI scans. With further model training, we believe that ML support is feasible in the future, with the potential to improve translation of WB-MRI more widely. In addition, many of the techniques that have been developed in the course of the study have the potential to be applied to other areas of diagnostic imaging. Recommendations for future research Future research should investigate: Further improvement in lesion detection accuracy, with evaluation of failure cases for improved model training. Evaluate lesion detection technique on other cancers, notably breast, prostate and myeloma. Further developments in understanding the marrow composition in healthy aging and in metastatic cancer. Developing further understanding and methods to overcome registration challenges between breath-hold and non-breath-hold MRI sequences in order to improve ML tissue characterisation. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Summary: Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (

Background: Fathers' mental health, parenting practices and co-parenting relationships have a powerful effect on child development. The paucity of research on the parenting strengths and support needs of fathers of children across childhood and adolescence compels the current study addressing the parenting experiences of a large sample of fathers of birth to 18-year-old children. Objective: The study aims address: (1) differences in the views of mothers and fathers regarding their parenting practices, sense of parenting efficacy, mental health and the co-parenting relationship; (2) investigation of factors affecting fathers' mental health; (3) exploration of fathers' views about the co-parenting relationship; and (4) examination of influences on fathers' parenting. Methods: The study involves secondary analysis of data collected from the first wave of the Australian "Parenting Today in Victoria" study, conducted in 2016 (N = 2600 mothers and fathers) involving 1044 fathers (40% of the sample) recruited through random dialling of landline and mobile numbers to produce a representative sample of parents of children 0-18 years. Results: Results demonstrate a positive picture of fathering at a population level -- most reported (a) high levels of parenting self-efficacy, (b) using positive parenting strategies, (c) talking to their children directly when their children experience problems, and (d) feeling supported by their parenting partner in their role as fathers. Nevertheless, important areas of need are identified. Fathers' reports of mental health challenges were associated with reduced parenting sense of efficacy and reduced opportunities for positive father-child interactions. While most fathers reported good partner support, this was often in contrast to mothers' views. Conclusions: These results from one of the largest surveys of fathers of its kind provide credible insights into the experiences and support needs of fathers, with clear implications for policy makers and service providers responsible for designing and delivering supports for fathers.

Nicola L Barclay,1 Theresa Burkard,1 Edward Burn,1 Antonella Delmestri,1 Andrea Miquel Dominguez,2 Asieh Golozar,3 Carlos Guarner-Argente,4 Francesc Xavier Avilés-Jurado,5 Wai Yi Man,1 Àlvar Roselló Serrano,6 Andreas Weinberger Rosen,7 Eng Hooi Tan,1 Ilona Tietzova,8 Daniel Prieto Alhambra,1,9 Danielle Newby1 On behalf of the OPTIMA Consortium1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom; 2Otorrinolaringology department, Hospital Joan XXIII de Tarragona, Tarragona, Spain; 3Odysseus Data Services, Cambridge, MA, USA; 4Gastroenterology Department, Hospital de la Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Sant Quintí, Barcelona, Spain; 5Head Neck Tumors Unit, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain; 6Institut Català d’Oncologia, Hospital Universitari Dr Josep Trueta, Girona, Spain; 7Centre for Surgical Science, Department of Surgery, Zealand University Hospital, Koege, Denmark; 8First Department of Tuberculosis and Respiratory Diseases, First Faculty of Medicine, Charles University, Prague, Czech Republic; 9Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, the NetherlandsCorrespondence: Daniel Prieto Alhambra, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, United Kingdom, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: The COVID-19 pandemic profoundly affected healthcare systems and patients. There is a need to comprehend the collateral effects of the pandemic on non-communicable diseases. We examined the impact of the pandemic on short-term survival for common solid tumours, including breast, colorectal, head and neck, liver, lung, oesophageal, pancreatic, prostate, and stomach cancer in the UK.Methods: This was a population-based cohort study of electronic health records from the UK primary care Clinical Practice Research Datalink GOLD database. In sum, 12,259,744 eligible patients aged ≥ 18 years with ≥ 1 year’s history identified from January 2000 to December 2022 were included. We estimated age-standardised incidence and short-term (one- and two-year) survival for several common cancers from 2000 to 2019 (in five-year strata) and compared these to 2020– 2022 using the Kaplan–Meier method.Results: Incidence decreased for most cancers in 2020 and recovered to different extents in 2021– 2022. Short-term survival improved for most cancers between 2000 and 2019, but then declined, albeit minimally, for those diagnosed in 2020– 2022. This was most pronounced for colorectal cancer, with one-year survival falling from 78.8% (95% CI 78%– 79.6%) in 2015– 2019 to 77% (95% CI 75.6– 78.3%) for those diagnosed in 2020– 2022.Conclusion: Short-term survival for many cancers was impacted, albeit minimally, by the pandemic in the UK, with reductions in survivorship from colorectal cancer equivalent to returning to the mortality seen in the first decade of the 2000s. While data on longer-term survival are needed to fully comprehend the impact of COVID-19 on cancer care, our findings illustrate the need for an urgent and substantial commitment from the UK National Health Service to address the existing backlog in cancer screening and diagnostic procedures to improve cancer care and mortality.Keywords: cancer, COVID-19, colorectal cancer, incidence, pandemic, survival

Abstract There are 90 independent genome-wide significant genetic risk variants for Parkinson’s disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.

Abstract Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are distinct cancer syndromes caused, respectively, by mono- and bi-allelic germline mismatch repair (MMR) variants. LS predisposes to mainly gastrointestinal and genitourinary cancers in adulthood. CMMRD predisposes to brain, haematological, and LS-spectrum cancers from childhood. Two suspected LS patients with first cancer diagnosis aged 27 or 38 years were found to be homozygous for an MMR (likely) pathogenic variant, MSH6 c.3226C>T (p.(Arg1076Cys)), or variant of uncertain significance (VUS), MLH1 c.306G>A (p.(Glu102=)). MLH1 c.306G>A was shown to cause leaky exon 3 skipping. The apparent genotype-phenotype conflict was resolved by detection of constitutional microsatellite instability in both patients, a hallmark feature of CMMRD. A hypomorphic effect of these and other variants found in additional late onset CMMRD cases, identified by literature review, likely explains a LS-like phenotype. CMMRD testing in carriers of compound heterozygous or homozygous MMR VUS may find similar cases and novel hypomorphic variants. Individualised management of mono- and bi-allelic carriers of hypomorphic MMR variants is needed until we better characterise the associated phenotypes.

Abstract Background Colorectal cancers (CRCs) in the Lynch syndromes have been assumed to emerge through an accelerated adenoma-carcinoma pathway. In this model adenomas with deficient mismatch repair have an increased probability of acquiring additional cancer driver mutation(s) resulting in more rapid progression to malignancy. If this model was accurate, the success of colonoscopy in preventing CRC would be a function of the intervals between colonoscopies and mean sojourn time of detectable adenomas. Contrary to expectations, colonoscopy did not decrease incidence of CRC in the Lynch syndromes and shorter colonoscopy intervals have not been effective in reducing CRC incidence. The prospective Lynch Syndrome Database (PLSD) was designed to examine these issues in carriers of pathogenic variants of the mis-match repair (path_MMR) genes. Materials and methods We examined the CRC and colorectal adenoma incidences in 3,574 path_MLH1, path_MSH2, path_MSH6 and path_PMS2 carriers subjected to regular colonoscopy with polypectomy, and considered the results based on sojourn times and stochastic probability paradigms. Results Most of the path_MMR carriers in each genetic group had no adenomas. There was no association between incidences of CRC and the presence of adenomas. There was no CRC observed in path_PMS2 carriers. Conclusions Colonoscopy prevented CRC in path_PMS2 carriers but not in the others. Our findings are consistent with colonoscopy surveillance blocking the adenoma-carcinoma pathway by removing identified adenomas which might otherwise become CRCs. However, in the other carriers most CRCs likely arised from dMMR cells in the crypts that have an increased mutation rate with increased stochastic chaotic probabilities for mutations. Therefore, this mechanism, that may be associated with no or only a short sojourn time of MSI tumours as adenomas, could explain the findings in our previous and current reports.

Ostracod taxa from shallow freshwater lakes are sensitive to a range of limnological factors including temperature, hydrological habitat, lake level, and the distribution of aquatic plants. Ostracod assemblages preserved in Quaternary lake sediments can be used to reconstruct limnological change and are therefore potentially valuable palaeoenvironmental proxies. However, lack of autecological information about some taxa may limit the validity of such reconstructions. We use fossil ostracod assemblages recovered from radiocarbon-dated late Holocene sediments from Wallywash Great Pond, a small, shallow freshwater lake in southwestern Jamaica, to reconstruct limnological change over the past ∼ 1800 years. We circumvent ongoing taxonomic and ecological uncertainties associated with the identification of fossil ostracod taxa by drawing on observations of the ecology of ostracods found living in Jamaican water bodies. By combining this information with limnological data from the extant lake, and with sedimentological and isotopic data from the lake sediments, we show that a published interpretation of ostracod assemblages for the late Quaternary of Wallywash Great Pond is simplistic, at least for the late Holocene section of the sediment record. We conclude that changes in ostracod assemblages are linked to variations in the input of undersaturated groundwater to the northern part of the lake from which the core was recovered. These variations, which were driven by changes in the precipitation / evaporation ratio (effective moisture), also controlled sedimentation, with reduced effective moisture and a decline in undersaturated groundwater input favouring marl precipitation, whereas organic sediments are linked to increased effective moisture and enhanced groundwater input. Our findings suggest that the dramatic shifts in ostracod assemblages at this site are a complex response to changes in hydrology, sedimentology, and carbonate saturation rather than being a simple indicator of lake-level change. Combining ostracod assemblage data with the results of other palaeolimnological analyses also allows more detailed reconstructions to be made for this lake, and such a multiproxy approach is recommended for similar lakes elsewhere.