Tissue detection of natural killer cells in colorectal adenocarcinoma

BACKGROUND: Natural killer (NK) cells represent a first line of defence against a developing cancer; however, their exact role in colorectal cancer remains undetermined. The aim of the present study was to evaluate the expression of CD16 and CD57 [immunohistochemical markers of natural NK cells] in colorectal adenocarcinoma. METHODS: Presence of NK cells was investigated in 82 colorectal adenocarcinomas. Immunohistochemical analysis was performed, using 2 monoclonal antibodies (anti-Fc Gamma Receptor II, CD16 and an equivalent to Leu-7, specific for CD-57). The number of immunopositive cells (%) was evaluated by image analysis. The cases were characterized according to: patient gender and age, tumor location, size, grade, bowel wall invasion, lymph node metastases and Dukes' stage. RESULTS: NK cells were detected in 79/82 cases at the primary tumor site, 27/33 metastatic lymph nodes and 3/4 hepatic metastases; they were detected in levels similar to those reported in the literature, but their presence was not correlated to the clinical or pathological characteristics of the series, except for a negative association with the patients' age (p = 0.031). CONCLUSIONS: Our data do not support an association of NK cell tissue presence with clinical or pathological variables of colorectal adenocarcinoma, except for a negative association with the patients' age; this might possibly be attributed to decreased adhesion molecule expression in older ages

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study

Background: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. Methods: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. Findings: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. Interpretation: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. Funding: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre

Motion Planning for Socially Competent Robot Navigation

Crowded human environments such as pedestrian scenes constitute challenging domains for mobile robot navigation, for a variety of reasons including the heterogeneity of pedestrians’ decision-making mechanisms, the lack of channels of explicit communication among them and the lack of universal rules or social conventions regulating traffic. Despite these complications, humans exhibit socially competent navigation through coordination, realized with implicit communication via a variety of modalities such as path shape and body posture. Sophisticated mechanisms of inference and decision-making allow them to understand subtle communication signals and encode them into their own actions. Although the problem of planning socially competent robot navigation has received significant attention over the past three decades, state-of-the-art approaches tend to explicitly focus on reproducing selected social norms or directly imitating observed human behaviors, while often lack of extensive and thorough validation procedures, thus raising questions about their generalization and reproducibility. This thesis introduces a family of planning algorithms, inspired by studies on human navigation. Our algorithms are designed to produce socially competent robot navigation behaviors by leveraging the existing mechanisms of implicit coordination in humans. We model multi-agent motion coordination through a series of data structures, based on mathematical abstractions from low-dimensional topology and physics, that capture fundamental properties of multi-agent collision avoidance. These models enable a robot to anticipate the effects of its actions on the inference and decision-making processes of nearby agents and allow for the generation of motion that is compliant with the unfolding evolution of the scene and consistent with the robot’s intentions. The introduced planning algorithms are supported by extensive simulated and experimental validation. Key findings include: (1) evidence extracted from a series of simulated studies, suggesting that the outlined planning architecture indeed results in effective coordination within groups of non-communicating agents in a variety of simulated scenarios; (2) evidence extracted from an online, video-based user study with more than 180 participants, indicating that humans perceive the motion generated by our framework as intent-expressive; (3) evidence extracted from an experimental study, conducted in a controlled lab environment with 105 human participants, suggesting that humans follow low-acceleration paths when navigating next to a robot running our framework