Multiple Intussusceptions Associated with Polycythemia in an Anabolic Steroid Abuser , A Case Report and Literature Review

Intussusceptions are generally associated with mechanical lead points or localized inflammation that function as foci for intestinal telescoping. We present the case of a patient whose abuse of anabolic steroids resulted in the development of multiple simultaneous intussusceptions. Our patient had no additional identifiable risk factors for intussusception. Consistent with previous reports, corticosteroid induced polycythemia and its consequent hyperviscosity led to intravascular sludging and mesenteric ischemia with associated bowel wall thickening. The localized intestinal induration then served as mechanical foci for intussusception. Due to the illicit nature of anabolic androgenic steroid (AAS) abuse, the physiologic effects of supraphysiologic doses are sparsely reported and poorly understood. The scope of AAS abuse and its consequences are likely under‑reported and under‑recognized within the medical community. Our case presented a unique diagnostic and therapeutic challenge with which we aim to increasing awareness and clinical suspicion for AAS among healthcare personnel.Keywords: Anabolic, Steroid, Intussusception, Androgens, Polycythemia, Hormones, Anabolic androgenic steroi

Radiographic features of liver allograft rejection

The radiographic features of 19 transplanted patients with failure of the liver allograft were evaluated. These features were: poor filling, stretching, attenuation of intrahepatic biliary ducts documented by T-tube cholangiogram, attenuation of branches of the hepatic artery seen on angiogram as well as a decrease of blood flow through the liver seen on angiogram and nuclear medicine dynamic scintigram. These findings were secondary to swelling of the transplanted liver and were not specific for rejection; they may also be present in hepatic infarction or infection

ARBEITSBEREICH WISSENSBASIERTE SYSTEME TEAM PROGRAMMING IN GOLOG UNDER PARTIAL OBSERVABILITY

Abstract. We present and explore the agent programming language TEAMGOLOG, which is a novel approach to programming a team of cooperative agents under partial observability. Every agent is associated with a partial control program in Golog, which is completed by the TEAMGOLOG interpreter in an optimal way by assuming a decision-theoretic semantics. The approach is based on the key concepts of a synchronization state and a communication state, which allow the agents to passively resp. actively coordinate their behavior, while keeping their belief states, observations, and activities invisible to the other agents. We show the practical usefulness of the TEAMGOLOG approach in a rescue simulated domain. We describe the algorithms behind the TEAMGOLOG interpreter and provide a prototype implementation. We also show through experimental results that the TEAMGOLOG approach outperforms a standard greedy one in the rescue simulated domain

Autonomous Detection of the Loss of a Wing for Underwater Gliders

Over the past five years, two of the Slocum underwater gliders operated by the UK National Oceanography Centre have lost a wing mid-mission without the pilot being aware of the problem until the point of vehicle retrieval. In this study, the steady-state data collected by gliders during the two deployments has been analysed to develop a fault detection system. From the data analysis, it is clear that the loss of the wing was a sudden event for both gliders. The main changes to the system dynamics associated with the event are an increase in the positive buoyancy of the glider and the occurrence of a roll angle on the side of the lost wing, with significant difference between dives and climbs. Hence, a simple effective system for the detection of the wing loss has been designed using the roll angle. Since sensors are known to fail and the roll sensor is non-critical to the operation of the glider, a back-up diagnostics system has been developed based on the dynamic model of the vehicle, capturing the change in buoyancy. Both systems are able to correctly detect the loss of the wing and notify pilots, who can re-plan missions to safely recover the vehicle

Investigating the causes of patient anxiety at induction of anaesthesia: A mixed methods study

Aim: To investigate patient anxiety at anaesthetic induction and whether this is affected by anaesthetic room interventions. Methods: A mixed methods study was carried out: pre-induction interventions were directly observed. Patient anxiety was assessed quantitatively with cardiovascular changes, the visual analogue scale and the state-trait anxiety inventory. Interviews allowed qualitative assessment. Results: Patient-reported anxiety did not correlate with cardiovascular changes. Anaesthetic room interventions were not predictive of anxiety. Postoperative interviews identified five sources of anxiety, mostly related to preparation for surgery. Staff responses to anxiety were also highlighted. Discussion: Patient-reported anxiety and its biological response are not correlated. Pre-induction interventions do not contribute to anxiety. Anxiety levels at induction are similar to or lower than earlier in the preoperative period. Conclusions: On induction of anaesthesia, patients have little control over their situation but are actively reassured and distracted by theatre staff. Our data suggest staff are good at this. More could still be done to reduce preoperative sources of anxiety

A remote anomaly detection system for Slocum underwater gliders

Marine Autonomous Systems (MAS) operating at sea beyond visual line of sight need to be self-reliant, as any malfunction could lead to loss or pose a risk to other sea users. In the absence of fully automated on-board control and fault detection tools, MAS are piloted and monitored by experts, resulting in high operational costs and limiting the scale of observational fleets that can be deployed simultaneously. Hence, an effective anomaly detection system is fundamental to increase fleet capacity and reliability. In this study, an on-line, remote fault detection system is developed for underwater gliders. Two alternative methods are analysed using time series data: feedforward deep neural networks estimating the glider’s vertical velocity and an autoencoder. The systems are trained using field data from four baseline deployments of Slocum gliders and tested on six deployments of vehicles suffering from adverse behaviour. The methods are able to successfully detect a range of anomalies in the near real time data streams, whilst being able to generalise to different glider configurations. The autoencoder’s error in reconstructing the original signals is the clearest indicator of anomalies. Thus, the autoencoder is a prime candidate to be included into an all-encompassing condition monitoring system for MAS

Systematic Synthesis of Passive Fault-Tolerant Augmented Neural Lyapunov Control Laws for Nonlinear Systems

Performance and closed-loop stability of control systems can be jeopardised by actuator faults. Actuator redundancy in combination with appropriate control laws can increase the resiliency of a system to both loss of efficiency or jamming. Passive Fault-Tolerant Control (FTC) systems aim at designing a unique control law with guaranteed stability in both nominal and faulty scenarios. In this work, a novel machine learning-based method is devised to systematically synthesise control laws for systems affected by actuator faults, whilst formally certifying the closed-loop stability. The learning architecture trains two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). In parallel, a Satisfiability Modulo Theory solver is employed to certify that the obtained CLF formally guarantees the Lyapunov conditions. The method is showcased for two scenarios, one encompassing the stabilisation of an inverted pendulum with redundant actuators, whilst the other covers the control of an Autonomous Underwater Vehicle. The framework is shown capable of synthesising both linear and nonlinear control laws with minimal hyperparameter tuning and within limited computational resources

Effect of pancreatic and/or renal transplantation on diabetic autonomic neuropathy

Thirty-nine Type 1 (insulin-dependent) diabetic patients were studied prospectively after simultaneous pancreas and kidney (n=26) and kidney grafting alone (n=13) by measuring heart rate variation during various manoeuvers and answering a standardized questionnaire every 6 to 12 months post-transplant. While age, duration of diabetes, and serum creatinine (168.1±35.4 vs 132.7±17.7 mgrmol/l) were comparable, haemoglobin A1 levels were significantly lower (6.6±0.2 vs 8.5±0.3%; p<0.01) and the mean observation time longer (35±2 vs 25±3 months; p<0.05) in the pancreas recipients when compared with kidney transplanted patients. Heart rate variation during deep breathing, lying/standing and Valsalva manoeuver were very similar in both groups initially and did not improve during follow-up. However, there was a significant reduction in heart rate in the pancreas recipient group. Autonomic symptoms of the gastrointestinal and thermoregulatory system improved more in the pancreas grafted subjects, while hypoglycaemia unawareness deteriorated in the kidney recipients. This study suggests that long-term normoglycaemia by successful pancreatic grafting is able to halt the progression of autonomic dysfunction

Machine learning for automatic prediction of the quality of electrophysiological recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters

A Marine Growth Detection System for Underwater Gliders

Marine growth has been observed to cause a drop in the horizontal and vertical velocities of underwater gliders, thus making them unresponsive and needing immediate recovery. Currently, no strategies exist to correctly identify the onset of marine growth for gliders and only limited data sets of biofouled hulls exist. Here, a field test has been conducted to first investigate the impact of marine growth on the dynamics and power consumption of underwater gliders and then design an anomaly detection system for high levels of biofouling. A Slocum glider was deployed first for eight days with drag stimulators to imitate severe biofouling; then, the vehicle was redeployed with no additions to the hull for further 20 days. The mimicked biofouling caused a speed reduction due to a significant increase in drag. Additionally, the lower speed causes the steady-state flight stage to last longer and the rudder to become less responsive; hence, marine growth results in a shortening of deployment duration through an increase in power consumption. As actual biofouling due to p. pollicipes occurred during the second deployment, it is possible to develop and test a system that successfully detects and identifies high levels of marine growth on the glider, blending model- and data-based solutions using steady-state flight data. The system will greatly help pilots replan missions to safely recover the vehicle if significant biofouling is detected