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Ruth White,1,2 Chris Hayes,1 Allison W Boyes,2,3 Simon Chiu,3 Christine L Paul2,3 1Hunter Integrated Pain Service, Hunter New England Health, Newcastle, NSW, Australia; 2School of Medicine and Public Health, University of Newcastle, NSW, Australia; 3Hunter Medical Research Institute, Newcastle, NSW, Australia Background: General practitioners’ (GPs) views about deprescribing prescription opioid analgesics (POAs) may influence the care provided for patients experiencing chronic noncancer pain (CNCP). There are limited data addressing GPs’ beliefs about deprescribing, including their decisions to deprescribe different types of POAs.Aim: To determine the proportion of GPs who hold attitudes congruent with local pain stewardship, describe their deprescribing decisions, and determine whether type of POA influences deprescribing.Design and setting: In 2016, a cross-sectional survey of all GPs (n=1,570) in one mixed urban and regional primary health network (PHN) in Australia was undertaken.Methods: A mailed self-report questionnaire assessed agreement with local guidelines for treating CNCP; influences on deprescribing POAs and likelihood of deprescribing in a hypothetical case involving either oral codeine or oxycodone.Results: A response rate of 46% was achieved. Approximately half (54%) of GPs agreed POAs should be reserved for people with acute, cancer pain or palliative care and a third (32%) did not agree that a medication focus has limited benefits for peoples’ long-term quality of life and function. Most (77%) GPs were less likely to deprescribe when effective alternate treatments were lacking, while various patient factors (eg, fear of weaning) were reported to decrease the likelihood of deprescribing for 25% of GPs. A significantly higher proportion of GPs reported being very likely to deprescribe codeine compared to the equivalent opioid dose of oxycodone for a hypothetical patient.Conclusions: Many GPs in the PHN hold attitudes at odds with local guidance that opioids are a nonsuperior treatment for CNCP. Attitudinal barriers to deprescribing include: a lack of consistent approach to deprescribing opioids as a class of drugs, perceived lack of effective treatment alternatives and patient fear of deprescribing. Therefore, the next step in this target population is to appropriately train and support GPs in how to apply the evidence in practice and how to support patients appropriately. Keywords: prescriptions, attitude, general practice, opioid, chronic pain, analgesics

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.

Dendritic cells (DCs) are critical players at the intersection of innate and adaptive immunity, making them ideal candidates for anticancer vaccine development. DC-based immunotherapies typically involve isolating patient-derived DCs, pulsing them with tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs), and utilizing maturation cocktails to ensure their effective activation. These matured DCs are then reinfused to elicit tumor-specific T-cell responses. While this approach has demonstrated the ability to generate potent immune responses, its clinical efficacy has been limited due to the immunosuppressive tumor microenvironment. Recent efforts have focused on enhancing the immunogenicity of DC-based vaccines, particularly through combination therapies with T cell-targeting immunotherapies. This Trial Watch summarizes recent advances in DC-based cancer treatments, including the development of new preclinical and clinical strategies, and discusses the future potential of DC-based vaccines in the evolving landscape of immuno-oncology.

Animal models are an important tool in the research of chronic autoimmune diseases, like systemic lupus erythematosus (SLE). MRL-Faslpr mice are one of different lupus models that develop spontaneously an SLE-like disease with autoantibodies and immune complex deposition that leads into damage of different organs. In contrast to human SLE, both sexes of MRL-Faslpr mice develop a similar autoimmune disease. Due to the sex bias in human and the delayed disease progression in male MRL-Faslpr mice, the majority of studies have been performed in female mice. To determine the suitability of male MRL-Faslpr mice for SLE research, especially with regard to the 3 R-principle and animal welfare, analyses of phenotype, inflammation and damage with focus on kidney and spleen were performed in mice of both sexes. Female mice developed lymphadenopathy and skin lesions earlier as males. At an age of 3.5 month, more immune cells infiltrated kidney and spleen in females compared to males. At the age of 5 months, however, substantially less sex-specific differences were detected. Since other studies have shown differences between both sexes on other manifestations like autoimmune pancreatitis and Sjögren syndrome in MRL-Faslpr mice, the use of male mice as part of 3 R-principle and animal welfare must be carefully considered.

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.