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Abstract Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.

Abstract Background Plasma p-tau217 has emerged as the most promising blood-based marker (BBM) for the detection of Alzheimer Disease (AD) pathology, yet few studies have evaluated plasma p-tau217 performance in memory clinic settings. We examined the performance of plasma p-tau217 for the detection of AD using a high-sensitivity immunoassay in individuals undergoing diagnostic lumbar puncture (LP). Methods Paired plasma and cerebrospinal fluid (CSF) samples were analysed from the TIMC-BRAiN cohort. Amyloid (Aβ) and Tau (T) pathology were classified based on established cut-offs for CSF Aβ42 and CSF p-tau181 respectively. High-sensitivity electrochemiluminescence (ECL) immunoassays were performed on paired plasma/CSF samples for p-tau217, p-tau181, Glial Fibrillary Acidic Protein (GFAP), Neurofilament Light (NfL) and total tau (t-tau). Biomarker performance was evaluated using Receiver-Operating Curve (ROC) and Area-Under-the-Curve (AUC) analysis. Results Of 108 participants (age: 69 ± 6.5 years; 54.6% female) with paired samples obtained at time of LP, 64.8% (n = 70/108) had Aβ pathology detected (35 with Mild Cognitive Impairment and 35 with mild dementia). Plasma p-tau217 was over three-fold higher in Aβ + (12.4 pg/mL; 7.3—19.2 pg/mL) vs. Aβ- participants (3.7 pg/mL; 2.8—4.1 pg/mL; Mann–Whitney U = 230, p

Observations of fish behavior can provide insights into habitat preferences and use. Black sea bass, Centropristis striata, are a territorial temperate reef finfish species known for their high commercial and recreational value and association with structured habitat. We used underwater action cameras to record video of black sea bass to assess territorial (agonistic, ambush, displacement) and occupancy (station-keeping) behaviors on shelf and bag style oyster aquaculture cages at a shellfish farm, and on boulders at a natural rock reef near Milford, Connecticut in Long Island Sound, Northwest Atlantic. Black sea bass at a variety of life stages were highly associated with cages, including young-of-the-year, and age 1+ fish. The high abundance of black sea bass observed on cages relative to boulders suggests this species has an affinity for the vertical structure created by aquaculture gear. When behaviors were normalized to the total fish sightings, black sea bass showed no significant difference in frequency of behaviors between habitats, indicating that per-fish rates of behavior were similar on cages and boulders. Demonstration of territorial and occupancy behaviors by black sea bass on, and around cages suggests that aquaculture gear provides structured habitat and ecosystem services for this species similar to natural reefs.These results suggest that essential fish habitat descriptions of manmade structures used by black sea bass could be broadened to include aquaculture gear. Our study provides novel information on behavioral interactions of black sea bass with oyster cages that may support aquaculture permitting and consultation processes.

Abstract Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

Abstract Background In 2021, whilst societies were emerging from major social restrictions during the SARS-CoV-2 pandemic, the UK government instigated an Events Research Programme to examine the risk of COVID-19 transmission from attendance at cultural events and explore ways to enable people to attend a range of events whilst minimising risk of transmission. We aimed to measure any impact on risk of COVID-19 transmission from attendance at events held at or close to commercially viable capacity using routinely collected data. Methods Data were obtained on attendees at Phase 3 Events Research Programme events, for which some infection risk mitigation measures were in place (i.e. evidence of vaccination or a negative lateral flow test). Attendance data were linked with COVID-19 test result data from the UK Test and Trace system. Using a self-controlled case series design, we measured the within person incidence rate ratio for testing positive for COVID-19, comparing the rate in days 3 to 9 following event attendance (high risk period) with days 1 and 2 and 10–16 (baseline period). Rate ratios were adjusted for estimates of underlying regional COVID-19 prevalence to account for population level fluctuations in infection risk, and events were grouped into broadly similar types. Results From attendance data available for 188,851 attendees, 3357 people tested positive for COVID-19 during the observation period. After accounting for total testing trends over the period, incidence rate ratios and 95% confidence intervals for positive tests were 1.16 (0.53–2.57) for indoor seated events, 1.12 (0.95–1.30) for mainly outdoor seated events, 0.65 (0.51–0.83) for mainly outdoor partially seated events, and 1.70 (1.52–1.89) for mainly outdoor unseated multi-day events. Conclusions For the majority of event types studied in the third phase of the UK Events Research Programme, we found no evidence of an increased risk of COVID-19 transmission associated with event attendance. However, we found a 70% increased risk of infection associated with attendance at mainly outdoor unseated multi-day events. We have also demonstrated a novel use for self-controlled case series methodology in monitoring infection risk associated with event attendance.

The gold standard for diagnosing malaria remains microscopic examination; however, its application is frequently impeded by the lack of a standardized framework that guarantees uniformity and quality, particularly in scenarios with limited resources and high volume. This study suggests a novel and highly effective automated diagnostic approach that employs deep-learning object detectors to improve the accuracy and efficiency of malaria-infected cell detection and Plasmodium species classification to overcome these challenges. Plasmodium parasites were detected within thin blood stain images using the YOLOv4 and YOLOv5 models, which were optimized for this purpose. YOLOv5 obtains a slightly higher accuracy on the source dataset (mAP@ $0.5=96$ %) than YOLOv4 (mAP@ $0.5=89$ %), but YOLOv4 exhibits superior robustness and generalization across diverse datasets, as demonstrated by its performance on an independent validation set (mAP@ $0.5=90$ %). This robustness emphasizes the dependability of YOLOv4 for deployment in a variety of clinical settings. Furthermore, an automated process was implemented to produce bound single-cell images from YOLOv4’s localization outputs, thereby eradicating the necessity for conventional and time-consuming segmentation methods. The DenseNet-121 model, which was optimized for species identification, obtained an impressive overall accuracy of 95.5% in the subsequent classification stage, indicating excellent generalization across all malaria species. Accurate classification of Plasmodium species on microscopically thin blood films is essential for guiding appropriate therapy and preventing unnecessary anti-malarial treatments, which can lead to adverse effects and contribute to drug resistance. This research contributes to the field of automated malaria diagnosis by offering a comprehensive framework that substantially improves clinical decision-making, particularly in resource-limited environments.

Average causal effects are averages of (heterogeneous) individual treatment effects (ITEs) taken over the entire target population. The estimation of average causal effects has been studied in depth, but averages are insufficient for more individualised decision-making where ITEs are more appropriate. However, estimating ITEs for every population member is challenging, particularly when estimation must be based on observational data rather than data from randomised experiments. One potential problem with observational data arises when there are large differences between the sample distributions of the input features of the treated and control units. This problem is known as covariate shift. It can lead to model misspecification the harmful effects of which can be severe for ITE estimation because point estimation is highly sensitive to regions of the common support of the input space in which the number of treated or control units is very small. Moreover, common solutions are often based on reweighing schemes involving propensity scores which were originally designed for average effects and not ITEs. In this paper, we propose Debiasing Generative Trees, a novel data augmentation method based on generative trees that debiases and undersmooths causal estimators trained on augmented data. It encourages higher modelling complexity that reduces misspecification and improves estimation of ITEs. We show empirically that our proposed approach yields models of higher complexity and more accurate predictions of ITEs, and is competitive with traditional methods for estimating average treatment effects. Our results confirm that reweighing methods can struggle with ITE estimation and that the choice of model class can significantly impact prediction performance.

Abstract Background Despite the proven efficacy of Community Health Volunteers (CHVs) in promoting primary healthcare in low- and middle-income countries (LMICs), they are not adequately financed and compensated. The latter contributes to the challenge of high attrition rates observed in many settings, highlighting an urgent need for innovative compensation strategies for CHVs amid budget constraints experienced by healthcare systems. This study sought to identify strategies for implementing Income-Generating Activities (IGAs) for CHVs in Kilifi County in Kenya to improve their livelihoods, increase motivation, and reduce attrition. Methods An exploratory qualitative research study design was used, which consisted of Focus group discussions with CHVs involved in health promotion and data collection activities in a local setting. Further, key informant in-depth interviews were conducted among local stakeholder representatives and Ministry of Health officials. Data were recorded, transcribed and thematically analysed using MAXQDA 20.4 software. Data coding, analysis and presentation were guided by the Okumus’ (2003) Strategy Implementation framework. Results A need for stable income was identified as the driving factor for CHVs seeking IGAs, as their health volunteer work is non-remunerative. Factors that considered the local context, such as government regulations, knowledge and experience, culture, and market viability, informed their preferred IGA strategy. Individual savings through table-banking, seeking funding support through loans from government funding agencies (e.g., Uwezo Fund, Women Enterprise Fund, Youth Fund), and grants from corporate organizations, politicians, and other donors were proposed as viable options for raising capital for IGAs. Formal registration of IGAs with Government regulatory agencies, developing a guiding constitution, empowering CHVs with entrepreneurial and leadership skills, project and group diversity management, and connecting them to support agencies were the control measures proposed to support implementation and enhance the sustainability of IGAs. Group-owned and managed IGAs were preferred over individual IGAs. Conclusion CHVs are in need of IGAs. They proposed implementation strategies informed by local context. Agencies seeking to support CHVs’ livelihoods should, therefore, engage with and be guided by the input from CHVs and local stakeholders.

Abstract Periodontal disease is a significant burden for oral health, causing progressive and irreversible damage to the support structure of the tooth. This complex structure, the periodontium, is composed of interconnected soft and mineralised tissues, posing a challenge for regenerative approaches. Materials combining silicon and lithium are widely studied in periodontal regeneration, as they stimulate bone repair via silicic acid release while providing regenerative stimuli through lithium activation of the Wnt/β-catenin pathway. Yet, existing materials for combined lithium and silicon release have limited control over ion release amounts and kinetics. Porous silicon can provide controlled silicic acid release, inducing osteogenesis to support bone regeneration. Prelithiation, a strategy developed for battery technology, can introduce large, controllable amounts of lithium within porous silicon, but yields a highly reactive material, unsuitable for biomedicine. This work debuts a strategy to lithiate porous silicon nanowires (LipSiNs) which generates a biocompatible and bioresorbable material. LipSiNs incorporate lithium to between 1% and 40% of silicon content, releasing lithium and silicic acid in a tailorable fashion from days to weeks. LipSiNs combine osteogenic, cementogenic and Wnt/β-catenin stimuli to regenerate bone, cementum and periodontal ligament fibres in a murine periodontal defect.

Abstract Aortic aneurysms, which may dissect or rupture acutely and be lethal, can be a part of multisystem disorders that have a heritable basis. We report four patients with deficiency of selenocysteine-containing proteins due to selenocysteine Insertion Sequence Binding Protein 2 (SECISBP2) mutations who show early-onset, progressive, aneurysmal dilatation of the ascending aorta due to cystic medial necrosis. Zebrafish and male mice with global or vascular smooth muscle cell (VSMC)-targeted disruption of Secisbp2 respectively show similar aortopathy. Aortas from patients and animal models exhibit raised cellular reactive oxygen species, oxidative DNA damage and VSMC apoptosis. Antioxidant exposure or chelation of iron prevents oxidative damage in patient’s cells and aortopathy in the zebrafish model. Our observations suggest a key role for oxidative stress and cell death, including via ferroptosis, in mediating aortic degeneration.

Summary: Background: There is wide variation in practices regarding routine bathing/washing of babies in neonatal intensive care units (NICUs). Evidence is lacking as to the benefit of routine antiseptic washes for reducing infection. We aimed to compare the antiseptic tolerance of Coagulase Negative Staphylococci (CoNS) within two UK NICUs with very different approaches to skin washing. Methods: We compared antiseptic susceptibility of CoNS isolated from skin swabs of neonates admitted to the Norfolk and Norwich University Hospital (NNUH) NICU in December 2017–March 2018 with those isolated in the Bradford Royal Infirmary (BRI) NICU in January–March 2020. The NNUH does not practise routine whole-body washing whereas BRI practises daily whole-body washing from post-menstrual age 27 weeks using Octenisan wash lotion (0.3% octenidine; 1 minute contact time before washing off with sterile water). A total of 78 CoNS isolates from BRI and 863 from the NNUH were tested for susceptibility against the antiseptics octenidine (OCT) and chlorhexidine (CHX). Results: Isolates from the BRI with practice of routine washing did not show increased antiseptic tolerance to OCT or CHX. Isolates from the NNUH which does not practise routine whole-body washing and rarely uses octenidine, were comparatively less susceptible to both CHX and OCT antiseptics. Conclusions: Daily whole-body skin washing with OCT does not appear to select for CoNS isolates that are antiseptic tolerant towards OCT and CHX. There remains considerable uncertainty about the impact of different antiseptic regimes on neonatal skin microbiota, the benefit of routine washing, and the development of antiseptic tolerance in the NICU.

As a consequence of the cultural turn, the cultural approach focused on the spatial processes in which culture is involved. Hence a widening and deepening of cultural geography. (i) Culture is partly shaped by the modes of transmission responsible for its passing down from one generation to the next. They stress more or less know-hows and knowledge (i.e. intelligence) and emotivity and imagination (i.e. imaginaries). (ii) Culture is equally shaped by the debates and opinions it induces, the genesis of identities, the dynamics of imitation or distinction and the process and conflicts of civilization or decline they initiate. (iii) Culture is combined with other processes when it deals with matter or life, produces artefacts, deals with nature, is applied to the human body, analyses the role of senses and shapes the landscape. (iv) It contributes to transform social life into a show and its setting into a stage. In its present form, cultural geography completes by a socio-cultural approach the older

Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts “anatomical knowledge” by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid–boundary distance of 1.16 mm (95% CI: −4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid–boundary distances of −0.27 mm (95% CI: −3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications.

A20, the central inhibitor of NFκB, has multiple anti-inflammatory properties, making it an interesting target in kidney autoimmune disease and transplant biology. It has been shown to be able to inhibit inflammatory functions in macrophages, dendritic cells, T cells, and B cells in various ways, leading to less tissue damage and better graft outcomes. In this review, we will discuss the current literature regarding A20 in kidney transplantation and autoimmunity. Future investigations on animal models and in existing immunosuppressive therapies are needed to establish A20 as a therapeutic target in kidney transplantation and autoimmunity. Cell-based therapies, modified viruses or RNA-based therapies could provide a way for A20 to be utilized as a promising mediator of inflammation and tissue damage.

Objective This study aims to evaluate whether the first wave of the COVID-19 pandemic resulted in a deterioration in the quality of care for socially and/or clinically vulnerable stroke and ST-segment elevation myocardial infarction (STEMI) patients.Design Two cohorts of STEMI and stroke patients in the Aquitaine neurocardiovascular registry.Setting Six emergency medical services, 30 emergency units, 14 hospitalisation units and 11 catheterisation laboratories in the Aquitaine region in France.Participants This study involved 9218 patients (6436 stroke and 2782 STEMI patients) in the neurocardiovascular registry from January 2019 to August 2020.Primary outcome measures Care management times in both cohorts: first medical contact-to-procedure time for the STEMI cohort and emergency unit admission-to-imaging time for the stroke cohort. Associations between social (deprivation index) and clinical (age >65 years, neurocardiovascular history) vulnerabilities and care management times were analysed using multivariate linear mixed models, with an interaction on the time period (pre-wave, per-wave and post-first COVID-19 wave).Results The first medical contact procedure time was longer for elderly (p

Summary: Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Background: The aim of the study was to characterize the different ways in which, based on certain physical manifestations that an individual suddenly experiences, people judge the possibility that these manifestations indicate the onset of a heart attack. Methods: One hundred ninety-four French adults--plus six physicians--were presented with a set of realistic vignettes composed by orthogonally crossing the levels of four factors: the type of pain felt, and the presence or absence of nausea, excess sweating, and of difficulty breathing. Results: Four qualitatively different reactions were found among the lay people. The majority reaction (54%) was close to the physicians’ reaction. It consisted of suspecting a heart attack as soon as intense pain occurs in the chest or back. The second reaction (25%) retained from the first one only the idea that a heart attack should be suspected if the pain is localized to the chest. The third reaction (14%) reflected some people’s uncertainty in the face of disturbing manifestations that they find difficult to interpret. The fourth reaction (7%) was that no set of symptoms could mean, for them, the onset of a heart attack. Conclusion: Only about half of the participants appeared to be able to consider unpleasant physical manifestations as a whole and integrate that information into an overall warning judgment that can lead into prompt life-saving actions. We recommend that judgment training on warning symptoms and signs be performed, especially for high-risk patients, in the offices of primary care providers and specialists.

BackgroundSegmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation.MethodsWe mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed “virtual dissection,” was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study.ResultsIn independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%–97.7%) and 93.5% in external (IQR: 91.9%–94.7%) test data, with good agreement with experts (r = 0.99; p

Background: Different dementia support roles exist but evidence is lacking on which aspects are best, for whom, and in what circumstances, and on their associated costs and benefits. Phase 1 of the Dementia PersonAlised Care Team programme (D-PACT) developed a post-diagnostic primary care-based intervention for people with dementia and their carers and assessed the feasibility of a trial. Aim: Phase 2 of the programme aims to 1) refine the programme theory on how, when, and for whom the intervention works; and 2) evaluate its value and impact. Design & setting: A realist longitudinal mixed-methods evaluation will be conducted in urban, rural, and coastal areas across South West and North West England where low-income or ethnic minority populations (for example, South Asian) are represented. Design was informed by patient, public, and professional stakeholder input and phase 1 findings. Method: High-volume qualitative and quantitative data will be collected longitudinally from people with dementia, carers, and practitioners. Analyses will comprise the following: 1) realist longitudinal case studies; 2) conversation analysis of recorded interactions; 3) statistical analyses of outcome and experience questionnaires; 4a) health economic analysis examining costs of delivery; and 4b) realist economic analysis of high-cost events and ‘near misses’. All findings will be synthesised using a joint display table, evidence appraisal tool, triangulation, and stakeholder co-analysis. Conclusion: The realist evaluation will describe how, why, and for whom the intervention does or does not lead to change over time. It will also demonstrate how a non-randomised design can be more appropriate for complex interventions with similar questions or populations.

IntroductionThis study examined the effects of Libifem® on exercise performance and body composition in females 25–45 years old.MethodsParticipants were randomized to three equal groups to consume: 600 mg Libifem®/day, 300 mg Libifem®/day or a placebo for 8 weeks. Participants completed a whole-body exercise program three times a week for 8 weeks. At baseline, week 4 and week 8, muscle strength and endurance, functional threshold power, body composition, and sex hormones were measured. At week 8, all three groups increased leg press 1RM compared to baseline.ResultsA significant difference between group treatment effect was seen for leg press at week 8 (p = 0.045), with the 600 mg Libifem® group significantly increasing their leg press 1RM compared to placebo (p = 0.014). The 600 mg Libifem® group significantly reduced their total fat mass (0.96 kg loss) from baseline compared to placebo group (0.09 kg gain). There was no significant difference in fat mass for the 300 mg Libifem® group (0.23 kg loss). The 600 mg Libifem® group had a significant increase in lean mass compared to both the 300 mg and placebo groups (p = 0.011 and 0.009, respectively).DiscussionOverall, there were significant and dose-related changes in body composition and ergogenic parameters, comparable with previous findings in males. Clinical Trial RegistrationThis trial was registered with the Australian and New Zealand Clinical Trials registry [ACTRN12618001538235].

Abstract Background Undiagnosed type 2 diabetes is common and can lead to unrecognized health complications. Given that earlier detection can reduce the damage to vital organs, it is important for all persons to be able to make the connection between certain new manifestations in their bodies and the possibility of diabetes. This study examined the extent to which people use the behavioral changes they observe in others (or in themselves), as well as relevant family history, to judge the possibility of the onset of diabetes. Methods One hundred and fifty-six adults living in France examined a set of realistic vignettes describing a person with (or without) signs suggestive of diabetes (e.g., increased thirst, family antecedents) and judged the possibility of the disease in each case. Results Overall, 36% of participants focused on reported symptoms when judging the possibility of diabetes, 37% focused on family history, and 29% were not able to use the information or tended systematically to minimize the possibility of diabetes. Conclusions People in France and probably around the world need a greater awareness not only of the factors putting them at risk of diabetes, but also of the specific signs and symptoms suggesting that they might be developing it.

Abstract Immunotherapy has transformed cancer treatments; however, a large fraction of patients encounter resistance. Such resistance is mediated by complex factors, often involving interactions between multiple genes. Thus, it is crucially important to identify genetic interactions between genes that are significantly mutated in cancer patients and those involved in immune responses, ideally the ones that currently have chemical compounds for direct targeting. To systematically interrogate such genetic interactions that mediate cancer cells’ response to T cell killing, we designed an asymmetric dual perturbation library targeting the matched combinations between significantly mutated tumor suppressors and immune resistance genes. We performed a combinatorial double knockout screen on 1159 gene pairs and identified those where joint loss-of-function renders altered cellular response to T cell cytotoxicity. We also performed comparative transcriptomics-based analyses on tumor and normal samples from TCGA and GTEx cohorts, mutational profiling analyses, and survival analyses to further characterize the significance of identified hits in clinical patients. Interactions between significantly mutated tumor suppressors and potentially druggable immune resistance genes may offer insights on potential new concepts of how to target clinically relevant cancer mutations with currently available agents. This study also provides a technology platform and an asymmetric double knockout library for interrogating genetic interactions between cancer mutations and immune resistance pathways under various settings.

Abstract Plasmodium knowlesi infections in Malaysia are a new threat to public health and to the national efforts on malaria elimination. In the Kapit division of Sarawak, Malaysian Borneo, two divergent P. knowlesi subpopulations (termed Cluster 1 and Cluster 2) infect humans and are associated with long-tailed macaque and pig-tailed macaque hosts, respectively. It has been suggested that forest-associated activities and environmental modifications trigger the increasing number of knowlesi malaria cases. Since there is a steady increase of P. knowlesi infections over the past decades in Sarawak, particularly in the Kapit division, we aimed to identify hotspots of knowlesi malaria cases and their association with forest activities at a geographical scale using the Geographic Information System (GIS) tool. A total of 1064 P. knowlesi infections from 2014 to 2019 in the Kapit and Song districts of the Kapit division were studied. Overall demographic data showed that males and those aged between 18 and 64 years old were the most frequently infected (64%), and 35% of infections involved farming activities. Thirty-nine percent of Cluster 1 infections were mainly related to farming surrounding residential areas while 40% of Cluster 2 infections were associated with activities in the deep forest. Average Nearest Neighbour (ANN) analysis showed that humans infected with both P. knowlesi subpopulations exhibited a clustering distribution pattern of infection. The Kernel Density Analysis (KDA) indicated that the hotspot of infections surrounding Kapit and Song towns were classified as high-risk areas for zoonotic malaria transmission. This study provides useful information for staff of the Sarawak State Vector-Borne Disease Control Programme in their efforts to control and prevent zoonotic malaria.

Objectives To assess the sensitivity and specificity of the 2019 EULAR/American College of Rheumatology (ACR) classification criteria for systemic lupus erythematosus (SLE) in outpatients at an academic tertiary care centre and to compare them to the 1997 ACR and the 2012 Systemic Lupus International Collaborating Clinics criteria.Methods Prospective and retrospective observational cohort study.Results 3377 patients were included: 606 with SLE, 1015 with non-SLE autoimmune-mediated rheumatic diseases (ARD) and 1756 with non-ARD diseases (hepatocellular carcinoma, primary biliary cirrhosis, autoimmune hepatitis). The 2019 criteria were more sensitive than the 1997 criteria (87.0% vs 81.8%), but less specific (98.1% vs 99.5% in the entire cohort and 96.5% vs 98.8% in patients with non-SLE ARD), resulting in Youden Indexes for patients with SLE/non-SLE ARD of 0.835 and 0.806, respectively. The most sensitive items were history of antinuclear antibody (ANA) positivity and detection of anti-double-stranded deoxyribonucleic acid (dsDNA) antibodies. These were also the least specific items. The most specific items were class III/IV lupus nephritis and the combination of low C3 and low C4 complement levels, followed by class II/V lupus nephritis, either low C3 or low C4 complement levels, delirium and psychosis, when these were not attributable to non-SLE causes.Conclusions In this cohort from an independent academic medical centre, the sensitivity and specificity of the 2019 lupus classification criteria were confirmed. Overall agreement of the 1997 and the 2019 criteria was very good.

In April 2023, the superBIT telescope was lifted to the Earth’s stratosphere by a helium-filled super-pressure balloon to acquire astronomical imaging from above (99.5% of) the Earth’s atmosphere. It was launched from New Zealand and then, for 40 days, circumnavigated the globe five times at a latitude 40 to 50 degrees south. Attached to the telescope were four “drs” (Data Recovery System) capsules containing 5 TB solid state data storage, plus a gnss receiver, Iridium transmitter, and parachute. Data from the telescope were copied to these, and two were dropped over Argentina. They drifted 61 km horizontally while they descended 32 km, but we predicted their descent vectors within 2.4 km: in this location, the discrepancy appears irreducible below ∼2 km because of high speed, gusty winds and local topography. The capsules then reported their own locations within a few metres. We recovered the capsules and successfully retrieved all of superBIT’s data despite the telescope itself being later destroyed on landing.

Abstract Extracellular vesicles (EVs) are nanoparticles found in all biological fluids, capable of transporting biological material around the body. Extensive research into the physiological role of EVs has led to the development of the Minimal Information for Studies of Extracellular Vesicles (MISEV) framework in 2018. This framework guides the standardisation of protocols in the EV field. To date, the focus has been on EVs of human origin. As comparative medicine progresses, there has been a drive to study similarities between diseases in humans and animals. To successfully research EVs in felines, we must validate the application of the MISEV guidelines in this group. EVs were isolated from the plasma of healthy humans and felines. EV characterisation was carried out according to the MISEV guidelines. Human and feline plasma showed a similar concentration of EVs, comparable expression of known EV markers and analogous particle to protein ratios. Mass spectrometry analyses showed that the proteomic signature of EVs from humans and felines were similar. Asymmetrical flow field flow fractionation, showed two distinct subpopulations of EVs isolated from human plasma, whereas only one subpopulation was isolated from feline plasma. Metabolomic profiling showed similar profiles for humans and felines. In conclusion, isolation, and characterisation of EVs from humans and felines show that MISEV2018 guidelines may also be applied to felines. Potential comparative medicine studies of EVs may provide a model for studying naturally occurring diseases in both humans and felines.

The real-world object localization application needs a low-latency and power efficient computing system. Here, Moro et al. demonstrate a neuromorphic in-memory event driven system, inspired by the barn owl’s neuroanatomy, which is orders of magnitude more energy efficient than microcontrollers.

Here the authors show that Omicron neutralizing antibody titers decline over time in mice immunized with a wild-type (WT) lipid nanoparticle (LNP)-mRNA vaccine and are robustly increased by WT or Omicron LNP-mRNA and that Omicron boosters elicit higher BA.1-neutralizing titer than WT boosters.

VACTERL Syndrome affects multiple body systems and can include various anomalies of the Vertebral column, Anus and/or rectum, heart (Cardiac), Tracheo-Esophagus, kidneys (Renal), and Limbs. Patients with VACTERL syndrome are at increased risk of having a congenital duodenal obstruction that may be extrinsic in the form of an annular pancreas or intrinsic in the form of duodenal atresia, stenosis or web. Simultaneous presentation of both the annular pancreas and duodenal web is a rare clinical entity and typically presents in neonates. However, late presentation of annular pancreas combined with a duodenal web is exceedingly uncommon. We present a case of late diagnosis of annular pancreas with duodenal web resulting in an entrapped ingested foreign body.

Oyster aquaculture gear may augment natural seafloor by providing structured habitat for economically important fish species. Underwater video census was used to assess fish abundance and community composition on eastern oyster (Crassostrea virginica) shelf and bag style aquaculture off-bottom cages and within natural rock reef habitat. During 2018, underwater cameras were mounted on four study cages at a dense shellfish farm of 40-100 commercial cages (dense cage farm), on four single cages interspersed on low relief seafloor (sparse cage farm), and amongst four boulders on a rock reef (rock reef), within an embayment off Milford, Connecticut, which is part of Long Island Sound, in the United States. In 2019, cameras were similarly deployed on two study cages per farm at each of three shellfish farms off Milford, Norwalk and Westport, CT. Video was recorded hourly in eight-minute segments from 7 am to 7 pm. Data loggers monitored seawater temperature, light intensity, and current speed. Fish abundance (MaxN) was calculated for all videos. We compared fish abundance and community composition between May and September, on cage and boulder habitat, on cages at dense and sparse farms and on cages across farm locations. In 2018, abundance of black sea bass (Centropristis striata), scup (Stenotomus chrysops) and tautog (Tautoga onitis) was significantly higher on cages than boulders, regardless of cage number. Cunner (Tautogolabrus adspersus) abundance was significantly higher on boulders than cages when cage sites were grouped. However pairwise comparisons indicated that cunner were significantly higher at the sparse cage farm versus the rock reef but cunner abundance on the dense cage farm and rock reef sites was not significantly different. Abundance of black sea bass, scup, tautog, and cunner were not significantly different across 2019 farm locations. Young-of-the-year fish occurred episodically at all sites during both years, with black sea bass and scup most abundant. Fish abundance corresponded to seasonal changes in seawater temperature and was highest at the warmest temperatures. Light intensity and current speed explained less variation in fish abundance relative to temperature, and relationships were inconsistent across habitats and farm locations Our results suggest that multi-tiered oyster aquaculture cages contribute structure to seafloor environments that provide habitat for temperate reef fish similar to natural rock reefs.

Objective: We sought to determine predictors, incidence, and interventions required for patients who developed barotrauma. Pneumothorax, subcutaneous emphysema, and pneumomediastinum have all been reported as complications related to COVID-19-positive patients requiring invasive mechanical ventilation. Methods: In this retrospective study, clinical and imaging data from COVID-19 patients were collected and reviewed by two independent intensivists between January 4, 2020 and January 10, 2020. Data were used to identify COVID-19-positive patients requiring invasive mechanical ventilation and the incidence of barotrauma. Two separate cohorts were created as non-injured (no barotrauma) and injured (barotrauma present). We then sought to identify the risk factors for barotrauma in the non-injured cohort on Days 0, 7, 10, and 14 after intubation and day of injury in the injured cohort. Results: Of the 264 patients with COVID-19, 55.8% were African American. The non-injured group was older (60 ± 15 versus 49 ± 16, p = 0.006), with male predominance in the injured group versus non-injured group (75% versus 55%). A total of 16 (6.5%) patients developed one or more complications of barotrauma, defined as subcutaneous emphysema, pneumothorax, or pneumomediastinum. Length of stay was longer for the injured group versus non-injured group (47 versus 25 days). Plateau pressure ( p = 0.024), fraction of inspired oxygen ( p

Summary: Background: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755–0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642–0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867–0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Despite effective vaccines against SARS-CoV-2, therapeutic options such as anti-virals and neutralizing antibodies are critical in treating disease, especially given the breakthrough infections of emerging VOCs. Here, Peng et al. generate two potent monoclonal antibodies and a bispecific antibody with two antigenrecognition variable regions targeting SARS-CoV-2 spike, provide CryoEM structures and show in vitro and in vivo efficacy of a humanized antibody against wildtype virus and delta variant.

Introduction Engagement of general practitioners (GPs) is essential to improving rates of diagnosis and treatment of viral hepatitis, as traditional tertiary hospital-based models of care often face challenges. Aim We investigated Australian general practitioners’ perceptions of a community-based model of care for patients with viral hepatitis. Methods We conducted a mixed methods study using survey questionnaires (23 GPs) and an open-ended interview guide (10 GPs) at primary care clinics in Brisbane, Queensland, Australia. Quantitative data were analysed using descriptive statistics in Stata 15.1 and qualitative data were analysed using thematic analysis following an inductive approach. Results Patient loss to follow up and high cost of diagnostic tests were identified as the biggest challenges to providing care for patients with viral hepatitis. Most GPs interviewed believe a more community-based model of care with support to GPs may improve uptake of care and increase both GP and patient engagement. General practitioners felt a community-based model of care could also improve viral hepatitis testing and follow-up rates. Discussion For Australian GPs, improving patient retention and diagnostics access were key pressure points in the viral hepatitis care pathway. A broader and more profound capacity-building allows community-based viral hepatitis care to be sustainable.

ObjectivesTo describe the extent to which caregivers' emotional and communication needs were met during pediatric emergency department (PED) visits. Secondary objectives included describing the association of caregiver emotional needs, satisfaction with care, and comfort in caring for their child's illness at the time of discharge with demographic characteristics, caregiver experiences, and ED visit details.Study designElectronic surveys with medical record review were deployed at ten Canadian PEDs from October 2018 -March 2020. A convenience sample of families with children ResultsThis study recruited 2005 caregivers who self-identified as mothers (74.3%, 1462/1969); mean age was 37.8 years (SD 7.7). 71.7% (1081/1507) of caregivers felt their emotional needs were met. 86.4% (1293/1496) identified communication with the doctor as good/very good and 83.4% (1249/1498) with their child's nurse. Caregiver involvement in their child's care was reported as good/very good 85.6% (1271/1485) of the time. 81.8% (1074/1313) of caregivers felt comfortable in caring for their child at home at the time of discharge. Lower caregiver anxiety scores, caregiver involvement in their child's care, satisfactory updates, and having questions adequately addressed positively impacted caregiver emotional needs and increased caregiver comfort in caring for their child's illness at home.ConclusionApproximately 30% of caregivers presenting to PEDs have unmet emotional needs, over 15% had unmet communication needs, and 15% felt inadequately involved in their child's care. Family caregiver involvement in care and good communication from PED staff are key elements in improving overall patient experience and satisfaction.

Background/ObjectivesCervical squamous cell carcinoma of unknown primary (SCCUP) is a rare entity within head and neck cancer and both treatment regimens as well as identified potential predictors for oncological outcomes vary between published series. In this study, we evaluated oncological outcomes and identified potential prognostic factors for outcome.Patients and methodsThis retrospective monocentric cohort study includes 82 SCCUP patients diagnosed and treated between January 2000 and June 2021. Overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS) and locoregional recurrence-free survival (LRFS) were evaluated. The Cox proportional hazards model was used to analyze the prognostic effect of patient and tumor characteristics on oncological outcomes.ResultsFive year OS, DSS, DFS and LRFS were respectively 53.9%, 72.2%, 68.9% and 67.3%. The p16 status was evaluated in 55 patients with 40% being p16 positive. On univariable analysis, p16 negative SCCUPs had significantly worse survival and recurrence rates in the presence of clinical extranodal extension (cENE) (OS: p=0.0013, DSS: p=0.0099, DFS: p=0.0164, LRFS: p=0.0099) and radiological extranodal extension (rENE) (OS: p=0.0034, DSS: p=0.0137, DFS: p=0.0167, LRFS: p=0.0100). In p16 positive SCCUP patients, rENE had a significantly negative prognostic effect on DFS (p=0.0345) and LRFS (p=0.0367). Total group multivariate analysis identified rENE as an independent negative predictor for all oncological outcomes. The “number of positive lymph nodes” was a second independent predictor for DSS (p=0.0257) and DFS (p=0.0435).ConclusionsWe report favorable oncological outcomes, comparable to previously published results. Although the presence of rENE seems associated with poor oncological outcomes, the differential effect of clinical, radiological and pathological ENE in both p16 positive and negative subgroups remain to be elucidated by further prospective research.

Babies in intensive care are at higher risk for meningitis and sensorineural hearing loss (SNHL). We reviewed the rate of SNHL among definite cases of bacterial/fungal meningitis in our neonatal intensive care unit over a 16-year period (2006–2021). We identified 16 confirmed meningitis cases among 16 070 admissions: 8 of 10 surviving infants with available diagnostic audiology had normal/satisfactory hearing while 2 of 10 had SNHL. Both infants with permanent hearing loss had been born extremely preterm and received potentially ototoxic antimicrobials. Larger studies are needed to clarify whether SNHL occurs mainly due to meningitis itself or to its antimicrobial drug treatment.

Este documento é uma reflexão pessoal sobre o papel da cultura na geografia. Foi já importante nas primeiras fases da geografia humana (geografia clássica e a Nova Geografia dos anos cinquenta), mas o funcionalista predominante abordagens limitou sua expressão. Graças às iniciativas de diferentes grupos de geógrafos, o escopo da disciplina foi ampliado depois dos anos 70: isto transformação foi caracterizada no final da década de 1990 como a virada cultural do disciplina. Foi exemplificada pelo structuracionista e pós-estruturalista desenvolvimentos de um lado, a abordagem cultural do outro. Para mim, o último não se sofre com as mesmas limitações que os formadores.

Book review

The term neuronutrition has been proposed as part of nutritional neuroscience, studying the effects of various dietary components on behavior and cognition. Other researchers underline that neuronutrition includes the use of various nutrients and diets to prevent and treat neurological disorders. The aim of this narrative review was to explore the current understanding of the term neuronutrition as the key concept for brain health, its potential molecular targets, and perspectives of its nutritional approach to the prevention and treatment of Alzheimer’s and Parkinson’s diseases, multiple sclerosis, anxiety, depressive disorders, migraine, and chronic pain. Neuronutrition can be defined as a part of neuroscience that studies the influence of various aspects of nutrition (nutrients, diet, eating behavior, food environment, etc.) on the development of nervous disorders and includes nutrition, clinical dietetics, and neurology. There is evidence that the neuronutritional approach can influence neuroepigenetic modifications, immunological regulation, metabolic control, and behavioral patterns. The main molecular targets in neuronutrition include neuroinflammation, oxidative/nitrosative stress and mitochondrial dysfunction, gut–brain axis disturbance, and neurotransmitter imbalance. To effectively apply neuronutrition for maintaining brain health, a personalized approach is needed, which includes the adaptation of the scientific findings to the genetic, biochemical, psycho-physiological, and environmental features of each individual.

Introduction: Although hyperferritinemia may reflect the inflammatory status of patients with non-alcoholic fatty liver disease (NAFLD), approximately 33% of hyperferritinemia cases reflect real hepatic iron overload. Aim: To evaluate a non-invasive method for assessing mild iron overload in patients with NAFLD using 3T magnetic resonance imaging (MRI) relaxometry, serum hepcidin, and the expression of ferritin subunits. Methods: This cross-sectional study assessed patients with biopsy-proven NAFLD. MRI relaxometry was performed using a 3T scanner in all patients, and the results were compared with iron content determined by liver biopsy. Ferritin, hepcidin, and ferritin subunits were assessed and classified according to ferritin levels and to siderosis identified by liver biopsy. Results: A total of 67 patients with NAFLD were included in the study. MRI revealed mild iron overload in all patients (sensitivity, 73.5%; specificity, 70%). For mild (grade 1) siderosis, the transverse relaxation rate (R2*) threshold was 58.9 s−1 and the mean value was 72.5 s−1 (SD, 33.9), while for grades 2/3 it was 88.2 s−1 (SD, 31.9) (p 30.2 ng/mL (sensitivity, 87%; specificity, 82%). Ferritin H and ferritin L subunits were expressed similarly in patients with NAFLD, regardless of siderosis. There were no significant differences in laboratory test results between the groups, including glucose parameters and liver function tests. Conclusions: MRI relaxometry and serum hepcidin accurately assessed mild iron overload in patients with dysmetabolic iron overload syndrome.

In this article, we elaborate on the construct ChemoKnowings as subject-specific powerful knowings for chemical agency in the Anthropocene era. Related to constructs such as critical chemical literacy, ChemoCapabilities, and eco-reflexive chemical thinking, we unpack the construct as an example of Carlgren’s powerful knowings, which relates Young’s powerful knowledge to the idea and tradition of Bildung. It means powerful knowledge containing embodied and relational (or tacit) dimensions. ChemoKnowings can therefore be described as embodied and relational knowledge in and about chemistry – (critical) chemical knowledge that matters meaningfully to the student, connecting them to themselves and the world, and conferring an ethical compass. By situating the teaching of ChemoKnowings within a vision for chemistry teaching as a part of a world-centered vision for schooling in the Anthropocene, ChemoKnowings are viewed as having the capacity to mobilise an ethico-socio-political action, that is, chemical agency. By focusing on student transformation of content for ChemoKnowings and integrating elements of a theoretical didaktik model for eco-reflexive chemistry education, we develop a vision-oriented didaktik model for ChemoKnowings. More generally, we argue that didaktik models for supporting teachers’ consideration of student transformation of content for powerful subject-knowings are an important part of general subject didaktik. We present in the article vignettes that detail personal accounts for each of the three authors describing examples of chemistry-specific knowings that matter meaningfully to each of us, and which articulate our own embodied ethico-socio-political actions as students, teachers, researchers, and consumers. Inspired by Klafki’s didaktik analysis, we end the article by proposing four areas of questions that the teacher can use in guiding their preparation and transformation of the content they bring into the classroom for promoting students’ ChemoKnowings, and thus Bildung in the 21st century.

Objective This scoping review aimed to bring together and identify digital tools that support people with one or more long-term conditions to maintain physical activity and describe their components and theoretical underpinnings. Methods Searches were conducted in Cumulative Index to Nursing and Allied Health Literature, Medline, EMBASE, IEEE Xplore, PsycINFO, Scopus, Google Scholar and clinical trial databases, for studies published between 2009 and 2019, across a range of long-term conditions. Screening and data extraction was undertaken by two independent reviewers and the Preferred Reporting Items for Scoping Reviews guidelines informed the review's conduct and reporting. Results A total of 38 results were identified from 34 studies, with the majority randomised controlled trials or protocols, with cardiovascular disease, type 2 diabetes mellitus and obesity the most common long-term conditions. Comorbidities were reported in >50% of studies but did not clearly inform intervention development. Most digital tools were web-browser-based ± wearables/trackers, telerehabilitation tools or gaming devices/components. Mobile device applications and combination short message service/activity trackers/wearables were also identified. Most interventions were supported by a facilitator, often for goal setting/feedback and/or monitoring. Physical activity maintenance outcomes were mostly reported at 9 months or 3 months post-intervention, while theoretical underpinnings were commonly social cognitive theory, the transtheoretical model and the theory of planned behaviour. Conclusions This review mapped the literature on a wide range of digital tools and long-term conditions. It identified the increasing use of digital tools, in combination with human support, to help people with long-term conditions, to maintain physical activity, commonly for under a year post-intervention. Clear gaps were the lack of digital tools for multimorbid long-term conditions, longer-term follow-ups, understanding participant's experiences and informs future questions around effectiveness.

Context: Adult social care need is increasing, posing a challenge to public and societal resources. Musculoskeletal disease and chronic pain are also increasing in prevalence, and given their disabling nature, may be contributing to social care demands. However, we do not know what proportion of adults with chronic musculoskeletal pain use social care services. Objective: To describe social care use in adults with chronic pain. Methods: An online cross-sectional survey was sent to participants of a previous mobile health study investigating people with chronic pain. It collected data on care received in the last month, including providers, frequency and duration of care, demographics and medical diagnoses. Descriptive statistics summarised these data, and a multivariable logistic regression model identified factors associated with care receipt. Findings: There were 981 respondents, 95% identified MSK disease as the cause of their pain. Five hundred twenty-seven (54%) reported receiving social care services in the last month, with the majority (338, 74%) receiving this care informally from family and friends. From multivariate analyses, those receiving care were more likely to have a diagnosis of osteoarthritis or fibromyalgia. Limitations: The sample may not be fully representative of the chronic MSK pain population. Implications: Adults with chronic MSK pain have significant social care use, predominantly informal. Understanding the relationship between MSK diseases and social care may allow better design of social care services in this context, as well as opportunities to develop prevention strategies. Future research should focus on identifying whether these findings are replicated in a more representative sample of people with MSK disease.