Drug-induced respiratory depression: an integrated model of drug effects on the hypercapnic and hypoxic drive

Drug-induced respiratory depression is a common side effect of the agents used in anesthesia practice to provide analgesia and sedation. Depression of the ventilatory drive in the spontaneously breathing patient can lead to severe cardiorespiratory events and it is considered a primary cause of morbidity. Reliable predictions of respiratory inhibition in the clinical setting would therefore provide a valuable means to improve the safety of drug delivery. Although multiple studies investigated the regulation of breathing in man both in the presence and absence of ventilatory depressant drugs, a unified description of respiratory pharmacodynamics is not available. This study proposes a mathematical model of human metabolism and cardiorespiratory regulation integrating several isolated physiological and pharmacological aspects of acute drug-induced ventilatory depression into a single theoretical framework. The description of respiratory regulation has a parsimonious yet comprehensive structure with substantial predictive capability. Simulations relative to the synergistic interaction of the hypercarbic and hypoxic respiratory drive and the global effect of drugs on the control of breathing are in good agreement with published experimental data. Besides providing clinically relevant predictions of respiratory depression, the model can also serve as a test bed to investigate issues of drug tolerability and dose finding/control under non-steady-state conditions.

On the modeling of drug induced respiratory depression in the non-steady-state

The ability of anesthetic agents to provide adequate analgesia and sedation is limited by the ventilatory depression associated with overdosing in spontaneously breathing patients. Therefore, quantitation of drug induced ventilatory depression is a pharmacokinetic-pharmacodynamic problem relevant to the practice of anesthesia. Although several studies describe the effect of respiratory depressant drugs on isolated endpoints, an integrated description of drug induced respiratory depression with parameters identifiable from clinically available data is not available. This study proposes a physiological model of CO2 disposition, ventilatory regulation, and the effects of anesthetic agents on the control of breathing. The predictive performance of the model is evaluated through simulations aimed at reproducing experimental observations of drug induced hypercarbia and hypoventilation associated with intravenous administration of a fast-onset, highly potent anesthetic mu agonist (including previously unpublished experimental data determined after administration of 1 mg alfentanil bolus). The proposed model structure has substantial descriptive capability and can provide clinically relevant predictions of respiratory inhibition in the non-steady-state to enhance safety of drug delivery in the anesthetic practice.

The influence of colour on oculomotor behaviour during image perception

The aim of this study was to investigate how oculomotor behaviour depends on the availability of colour information in pictorial stimuli. Forty study participants viewed complex images in colour or grey-scale, while their eye movements were recorded. We found two major effects of colour. First, although colour increases the complexity of an image, fixations on colour images were shorter than on their grey-scale versions. This suggests that colour enhances discriminability and thus affects low-level perceptual processing. Second, colour decreases the similarity of spatial fixation patterns between participants. The role of colour on visual attention seems to be more important than previously assumed, in theoretical as well as methodological terms.

Ophthalmologic techniques to assess the severity of hyperviscosity syndrome and the effect of plasmapheresis in patients with Waldenström's macroglobulinemia

BACKGROUND: The aim of this study is to determine the serum immunoglobulin (Ig) M and serum viscosity (SV) levels at which retinal changes associated with hyperviscosity syndrome (HVS) as a result of Waldenström's macroglobulinemia (WM) occur. In addition, the effect of plasmapheresis on HVS-related retinopathy was tested. PATIENTS AND METHODS: A total of 46 patients with WM received indirect ophthalmoscopy, laser Doppler retinal blood flow measurements, serum IgM, and SV determinations. A total of 9 patients with HVS were studied before and after plasmapheresis. RESULTS: Mean IgM and SV levels of patients with the earliest retinal changes were 5442 mg/dL and 3.1 cp, respectively. Plasmapheresis improved retinopathy, decreased serum IgM (46.5 +/- 18%; P = .0009), SV (44.7 +/- 17.3%; P = .002), retinal venous diameter (15.3 +/- 5.8%; P = .0001), and increased venous blood speed by +55.2 +/- 22.5% (P = .0004). CONCLUSION: Examination of the retina is useful in identifying the symptomatic threshold of plasma viscosity levels in patients with HVS and can be used to gauge the effectiveness of plasmapheresis treatment.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: an Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

A communication and information technology infrastructure for real time monitoring and management of type 1 diabetes patients

This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

A Communication Platform for Tele-monitoring and Tele-management of Type 1 Diabetes

Type 1 diabetes mellitus is a chronic disease characterized by blood glucose levels out of normal range due to inability of insulin production. This dysfunction leads to many short- and long-term complications. In this paper, a system for tele-monitoring and tele-management of Type 1 diabetes patients is proposed, aiming at reducing the risk of diabetes complications and improving quality of life. The system integrates Wireless Personal Area Networks (WPAN), mobile infrastructure, and Internet technology along with commercially available and novel glucose measurement devices, advanced modeling techniques, and tools for the intelligent processing of the available diabetes patients information. The integration of the above technologies enables intensive monitoring of blood glucose levels, treatment optimisation, continuous medical care, and improvement of quality of life for Type 1 diabetes patients, without restrictions in everyday life activities.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Development of a multifunctional platform for extra- and intracellular ionic activities recording

We present the development of a multifunctional platform equipped with an array of silicon nitride micropipettes with dimensions allowing the implementation of extra- and intracellular operations. Micropipettes with outer diameter that ranges from 6 mum down to 300 nm and with walls thicknesses of 500 down to 150 nm are presented. The generic technology developed to fabricate these micropipettes has a number of advantages, including the ability to be implemented as ion-selective electrodes for (A) intracellular and (B) extracellular recordings and as (C) local drug microdispensers.

Pathogenesis of AF: impact on intracardiac signals

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and is responsible for the highest number of rhythm-related disorders and cardioembolic strokes worldwide. Intracardiac signal analysis during the onset of paroxysmal AF led to the discovery of pulmonary vein as a triggering source of AF, which has led to the development of pulmonary vein ablation--an established curative therapy for drug-resistant AF. Complex, multicomponent and rapid electrical activity widely involving the atrial substrate characterizes persistent/permanent AF. Widespread nature of the problem and complexity of signals in persistent AF reduce the success rate of ablation therapy. Although signal processing applied to extraction of relevant features from these complex electrograms has helped to improve the efficacy of ablation therapy in persistent/permanent AF, improved understanding of complex signals should help to identify sources of AF and further increase the success rate of ablation therapy.

Skin and mucosa care in systemic sclerosis - patients' and family caregivers' experiences and expectations of a specific education programme: a qualitative study

BACKGROUND Skin and mucosal manifestations such as skin thickening, pruritus, reduced microvascular circulation, digital lesions, appearance-related changes, and dryness of the eyes and mucosa are common in systemic sclerosis (SSc). A specific skin and mucosa care education programme for patients and their family caregivers should increase their self-efficacy and improve coping strategies. AIMS The aims of this qualitative study were to explore the participants' experiences of both everyday life with skin and mucosal manifestations and the programme itself, while identifying unmet needs for programme development. METHODS Narrative interviews were conducted with eight SSc patients and two family caregivers of individuals with SSc. Using qualitative content analysis techniques, the transcribed interviews were systematically summarized and categories inductively developed. RESULTS The findings illustrated participants' experiences of skin and mucosal symptoms and revealed them to be experts in finding the right therapy mix alone (before diagnosis) and also in collaboration with health professionals (after diagnosis). Participants emphasized that the programme gave them useful education on skin and mucosa care. They described how they had to cope alone with the lack of information on pathophysiology, people's reactions, and the impact on their family and working lives. Nevertheless, participants said that they maintained a positive attitude by not dwelling on future disabilities. CONCLUSIONS Patients and family caregivers benefited from the individualized and SSc-specific education on skin and mucosa care. Future improvements to the programme should focus on imparting understandable information on SSc pathophysiology, dealing with disfigurement and seeking reliable disease information, as well as facilitating peer support.