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Introduction Adolescence represents a critical developmental period, with changes in emotional regulation capacities influencing physical and mental health. With less than 6% of Canadian youth currently meeting the 24-hour movement guidelines for physical activity, sleep and sedentary behaviour, there is an urgent need to understand the potential association between movement behaviours, physical literacy, emotional regulation and mental health during adolescence. Additionally, there is a need to better understand these associations among equity-deserving groups. We developed the Adolescents’ Daily Lives (ADL) project to identify how, when, under what contexts and to whom to promote healthy engagement in movement behaviours to optimise youth mental health.Methods and analysis For the ADL project, we will employ a 14-day intensive longitudinal design to investigate the associations between physical literacy, movement behaviours, emotion regulation and mental health among a diverse sample of 120 adolescents (ages 13–17 years) living in the Greater Victoria Area, British Columbia, Canada. A comprehensive baseline survey and movement competence test, assessing physical and mental well-being, 24-hour movement behaviours (ie, physical activity, sleep and sedentary behaviours) and physical literacy, will be accompanied by daily diary surveys and accelerometer-based movement tracking (ie, Fitbit Inspire 3) to assess daily fluctuations in movement behaviour, emotional regulation and mood. Multivariate analyses, including multilevel modelling, multilevel structural equation modelling and Bayesian hierarchical continuous-time SEM, will be used to model the repeated measures data and understand the simultaneous variations in daily movement behaviours, emotion regulation and mental health.Ethics and dissemination The ADL project received ethical approval from the University of Victoria Behavioural Research Ethics Board (protocol #22-0262). Study participation is voluntary, and data collection will be anonymised to protect participant privacy and confidentiality. Research findings will be shared through academic publications and conference proceedings. Through knowledge mobilisation resources, cocreated with the youth community advisory board, relevant findings will be shared directly with the wider community of adolescents.

Abstract Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer‐related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistry‐based biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p‐cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (χ2 = 53.183, p

BackgroundSmall bowel adenocarcinoma (SBA) is a rare malignancy of the small intestine associated with late stage diagnosis and poor survival outcome. High expression of immune cells and immune checkpoint biomarkers especially programmed cell death ligand-1 (PD-L1) have been shown to significantly impact disease progression. We have analysed the expression of a subset of immune cell and immune checkpoint biomarkers in a cohort of SBA patients and assessed their impact on progression-free survival (PFS) and overall survival (OS).Methods25 patient samples in the form of formalin fixed, paraffin embedded (FFPE) tissue were obtained in tissue microarray (TMAs) format. Automated immunohistochemistry (IHC) staining was performed using validated antibodies for CD3, CD4, CD8, CD68, PD-L1, ICOS, IDO1 and LAG3. Slides were scanned digitally and assessed in QuPath, an open source image analysis software, for biomarker density and percentage positivity. Survival analyses were carried out using the Kaplan Meier method.ResultsVarying expressions of biomarkers were recorded. High expressions of CD3, CD4 and IDO1 were significant for PFS (p = 0.043, 0.020 and 0.018 respectively). High expression of ICOS was significant for both PFS (p = 0.040) and OS (p = 0.041), while high PD-L1 expression in tumour cells was significant for OS (p = 0.033). High correlation was observed between PD-L1 and IDO1 expressions (Pearson correlation co-efficient = 1) and subsequently high IDO1 expression in tumour cells was found to be significant for PFS (p = 0.006) and OS (p = 0.034).ConclusionsHigh levels of immune cells and immune checkpoint proteins have a significant impact on patient survival in SBA. These data could provide an insight into the immunotherapeutic management of patients with SBA.

Abstract Colorectal cancer (CRC) remains a leading cause of cancer mortality. Here, we define the colonic epithelial expression of cathelicidin (LL‐37) in CRC. Cathelicidin exerts pleotropic effects including anti‐microbial and immunoregulatory functions. Genetic knockout of cathelicidin led to increased size and number of colorectal tumours in the azoxymethane‐induced murine model of CRC. We aimed to translate this to human disease. The expression of LL‐37 in a large (n = 650) fully characterised cohort of treatment‐naïve primary human colorectal tumours and 50 matched normal mucosa samples with associated clinical and pathological data (patient age, gender, tumour site, tumour stage [UICC], presence or absence of extra‐mural vascular invasion, tumour differentiation, mismatch repair protein status, and survival to 18 years) was assessed by immunohistochemistry. The biological consequences of LL‐37 expression on the epithelial barrier and immune cell phenotype were assessed using targeted quantitative PCR gene expression of epithelial permeability (CLDN2, CLDN4, OCLN, CDH1, and TJP1) and cytokine (IL‐1β, IL‐18, IL‐33, IL‐10, IL‐22, and IL‐27) genes in a human colon organoid model, and CD3+, CD4+, and CD8+ lymphocyte phenotyping by immunohistochemistry, respectively. Our data reveal that loss of cathelicidin is associated with human CRC progression, with a switch in expression intensity an early feature of CRC. LL‐37 expression intensity is associated with CD8+ T cell infiltrate, influenced by tumour characteristics including mismatch repair protein status. There was no effect on epithelial barrier gene expression. These data offer novel insights into the contribution of LL‐37 to the pathogenesis of CRC and as a therapeutic molecule.

Abstract Background Limited studies examine the immune landscape in Esophageal Adenocarcinoma (EAC). We aim to identify novel associations, which may inform immunotherapy treatment stratification. Methods Three hundred twenty-nine EAC cases were available in Tissue Microarrays (TMA) format. A discovery cohort of 166 EAC cases were stained immunohistochemically for range of adaptive immune (CD3, CD4, CD8 and CD45RO) and immune checkpoint biomarkers (ICOS, IDO-1, PD-L1, PD-1). A validation cohort of 163 EAC cases was also accessed. A digital pathology analysis approach was used to quantify biomarker density. Results CD3, CD4, CD8, CD45RO, ICOS and PD-1 were individually predictive of better overall survival (OS) (Log rank p =

Abstract Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.

Clinical trials for MET inhibitors have demonstrated limited success for their use in colon cancer (CC). However, clinical efficacy may be obscured by a lack of standardisation in MET assessment for patient stratification. In this study, we aimed to determine the molecular context in which MET is deregulated in CC using a series of genomic and proteomic tests to define MET expression and identify patient subgroups that should be considered in future studies with MET‐targeted agents. To this aim, orthogonal expression analysis of MET was conducted in a population‐representative cohort of stage II/III CC patients (n = 240) diagnosed in Northern Ireland from 2004 to 2008. Targeted sequencing was used to determine the relative incidence of MET R970C and MET T992I mutations within the cohort. MET amplification was assessed using dual‐colour dual‐hapten brightfield in situ hybridisation (DDISH). Expression of transcribed MET and c‐MET protein within the cohort was assessed using digital image analysis on MET RNA in situ hybridisation (ISH) and c‐MET immunohistochemistry (IHC) stained slides. We found that less than 2% of the stage II/III CC patient population assessed demonstrated a genetic MET aberration. Determination of a high MET RNA‐ISH/low c‐MET IHC protein subgroup was found to be associated with poor 5‐year cancer‐specific outcomes compared to patients with concordant MET RNA‐ISH and c‐MET IHC protein expression (HR 2.12 [95%CI: 1.27–3.68]). The MET RNA‐ISH/c‐MET IHC protein biomarker paradigm identified in this study demonstrates that subtyping of MET expression may be required to identify MET‐addicted malignancies in CC patients who will truly benefit from MET inhibition.

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. This generally requires consultation of documentation for multiple coding libraries, as well as trial and error to ensure that the techniques used on the images are appropriate. Herein we introduce HistoClean; a user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit.HistoClean is an application that aims to help bridge the knowledge gap between pathologists, biomedical scientists and computer scientists by providing transparent image augmentation and pre-processing techniques which can be applied without prior coding knowledge.In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. This study demonstrates how HistoClean can be used to improve a standard deep learning workflow via classical image augmentation and pre-processing techniques, even with a relatively simple convolutional neural network architecture. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

Multiplex immunofluorescence is a powerful tool for the simultaneous detection of tissue‐based biomarkers, revolutionising traditional immunohistochemistry. The Opal methodology allows up to eight biomarkers to be measured concomitantly without cross‐reactivity, permitting identification of different cell populations within the tumour microenvironment. In this study, we aimed to validate a multiplex immunofluorescence workflow in two complementary multiplex panels and evaluate the tumour immune microenvironment in colorectal cancer (CRC) formalin‐fixed paraffin‐embedded tissue. We stained CRC and tonsil samples using Opal multiplex immunofluorescence on a Leica BOND RX immunostainer. We then acquired images on an Akoya Vectra Polaris and performed multispectral unmixing using inform. Antibody panels were validated on tissue microarray sections containing cores from six normal tissue types, using qupath for image analysis. Comparisons between chromogenic immunohistochemistry and multiplex immunofluorescence on consecutive sections from the same tissue microarray showed significant correlation (rs > 0.9, P‐value

Abstract Background The cohesin complex plays a major role in folding the human genome into 3D structural domains. Mutations in members of the cohesin complex are known early drivers of myelodysplastic syndromes (MDS) and acute myeloid leukaemia (AML), with STAG2 the most frequently mutated complex member. Methods Here we use functional genomics (RNA-seq, ChIP-seq and HiChIP) to investigate the impact of chronic STAG2 loss on three-dimensional genome structure and transcriptional programming in a clinically relevant model of chronic STAG2 loss. Results The chronic loss of STAG2 led to loss of smaller loop domains and the maintenance/formation of large domains that, in turn, led to altered genome compartmentalisation. These changes in genome structure resulted in altered gene expression, including deregulation of the HOXA locus and the MAPK signalling pathway, resulting in increased sensitivity to MEK inhibition. Conclusions The altered genomic architecture driven by the chronic loss of STAG2 results in altered gene expression that may contribute to leukaemogenesis and may be therapeutically targeted.

Robots are increasingly entering the social sphere and taking on more sophisticated roles. One application for which robots are already being deployed is in civilian security tasks, in which robots augment security and police forces. In this domain, robots will encounter individuals in crisis who may pose a threat to themselves, others, or personal property. In such interactions with human police and security officers, a key goal is to de-escalate the situation to resolve the interaction. This paper considers the task of utilizing mobile robots in de-escalation tasks, using the mechanisms developed for de-escalation in human–human interactions. What strategies should a robot follow in order to leverage existing de-escalation approaches? Given these strategies, what sensing and interaction capabilities should a robot be capable of in order to engage in de-escalation tasks with humans? First, we discuss the current understanding of de-escalation with individuals in crisis and present a working model of the de-escalation process and strategies. Next, we review the capabilities that an autonomous agent should demonstrate to be able to apply such strategies in robot-mediated crisis de-escalation. Finally, we explore data-driven approaches to training robots in de-escalation and the next steps in moving the field forward.