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

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

Biliary stenting alone versus biliary stenting plus sphincterotomy for the treatment of post-laparoscopic cholecystectomy biliary leaks: a prospective randomized study

Objective Although various endoscopic techniques have been proved effective in treating post-cholecystectomy biliary leaks, the choice of the best method remains controversial. The aim of this prospective study was to compare the efficacy and safety of biliary stenting alone with biliary stenting plus sphincterotomy for the treatment of post-cholecystectomy biliary leaks. Methods Patients with post-laparoscopic cholecystectomy leaks were randomized into two groups. The first group included 24 patients who were treated with a 7 Fr biliary stent alone, and the second group included 28 patients who underwent an endoscopic sphincterotomy followed by insertion of a 10 Fir biliary stent. Results Endoscopic therapy was successful in all patients (100%). Clinical improvement was observed after 2-6 days. Patients remained hospitalized for 4-12 days. Stents were removed after 6.7 (6-8) weeks. The overall complication rate was 4.16% for the first group and 10.71% for the second (P=0.615). No complications were recorded during the follow-up period. Conclusions Endoscopic therapy of biliary leaks with a small-diameter biliary stent alone is as effective and safe as endoscopic sphincterotomy followed by insertion of a large-diameter stent

Runtime interval optimization and dependable performance for application-level checkpointing

As aggressive integration paves the way for performance enhancement of many-core chips and technology nodes go below deca-nanometer dimensions, system-wide failure rates are becoming noticeable. Inevitably, system designers need to properly account for such failures. Checkpoint/Restart (C/R) can be deployed to prolong dependable operation of such systems. However, it introduces additional overheads that lead to performance variability. We present a versatile dependability manager (DepMan) that orchestrates a many-core application-level C/R scheme, while being able to follow time-varying error rates. DepMan also contains a dedicated module that ensures on-the-fly performance dependability for the executing application. We evaluate the performance of our scheme using an error injection module both on the experimental Intel Single-Chip Cloud Computer (SCC) and on a commercial Intel i7 general purpose computer. Runtime checkpoint interval optimization adapts to a variety of failure rates without extra performance or energy costs. The inevitable timing overhead of C/R is reclaimed systematically with Dynamic Voltage and Frequency Scaling (DVFS), so that dependable application performance is ensured

Abnormal Pattern Detection in Wireless Capsule Endoscopy Images Using Nonlinear Analysis in RGB Color Space

In recent years, an innovative method has been developed for the non-invasive observation of the gastrointestinal tract (GT), namely Wireless Capsule Endoscopy (WCE). WCE especially enables a detailed inspection of the entire small bowel and identification of its clinical lesions. However, the foremost disadvantage of this technological breakthrough is the time consuming task of reviewing the vast amount of images produced. To address this, a novel technique for distinguishing pathogenic endoscopic images related to ulcer, the most common disease of GT, is presented here. Towards this direction, the Bidimensional Ensemble Empirical Mode Decomposition was applied to RGB color images of the small bowel acquired by a WCE system in order to extract their Intrinsic Mode Functions (IMFs). The IMFs reveal differences in structure from their finest to their coarsest scale, providing a new analysis domain. Additionally, lacunarity analysis was employed as a method to quantify and extract the texture patterns of the ulcer regions and the normal mucosa, respectively, in order to discriminate the abnormal from the normal images. Experimental results demonstrated promising classification accuracy (> 95%), exhibiting a high potential towards WCE-based analysis