The open medical informatics journal最新文献

Although antiretroviral drug resistance is common in treated HIV infected individuals, it is not a consistent indicator of HIV morbidity and mortality. To the contrary, HIV resistance-associated mutations may lead to changes in viral fitness that are beneficial to infected individuals. Using a bioinformatics-based model to assess the effects of numerous drug resistance mutations, we determined that the D30N mutation in HIV-1 protease had the largest decrease in replication capacity among known protease resistance mutations. To test this in silico result in an in vivo environment, we constructed several drug-resistant mutant HIV-1 strains and compared their relative fitness utilizing the SCID-hu mouse model. We found HIV-1 containing the D30N mutation had a significant defect in vivo, showing impaired replication kinetics and a decreased ability to deplete CD4+ thymocytes, compared to the wild-type or virus without the D30N mutation. In comparison, virus containing the M184V mutation in reverse transcriptase, which shows decreased replication capacity in vitro, did not have an effect on viral fitness in vivo. Thus, in this study we have verified an in silico bioinformatics result with a biological assessment to identify a unique mutation in HIV-1 that has a significant fitness defect in vivo.

Insulin sensitivity (SI) estimation has numerous uses in medical and clinical situations. However, highresolution tests that are useful for clinical diagnosis and monitoring are often too intensive, long and costly for regular use. Simpler tests that mitigate these issues are not accurate enough for many clinical diagnostic or monitoring scenarios. The gap between these tests presents an opportunity for new approaches. The quick dynamic insulin sensitivity test (DISTq) utilises the model-based DIST test protocol and a series of population estimates to eliminate the need for insulin or C-peptide assays to enable a high resolution, low-intensity, real-time evaluation of SI. The method predicts patient specific insulin responses to the DIST test protocol with enough accuracy to yield a useful clinical insulin sensitivity metric for monitoring of diabetes therapy. The DISTq method replicated the findings of the fully sampled DIST test without the use of insulin or C-peptide assays. Correlations of the resulting SI values was R=0.91. The method was also compared to the euglycaemic hyperinsulinaemic clamp (EIC) in an in-silico Monte-Carlo analysis and showed a good ability to re-evaluate SI(EIC) (R=0.89), compared to the fully sampled DIST (R=0.98) Population-derived parameter estimates using a-posteriori population-based functions derived from DIST test data enables the simulation of insulin profiles that are sufficiently accurate to estimate SI to a relatively high precision. Thus, costly insulin and C-peptide assays are not necessary to obtain an accurate, but inexpensive, real-time estimate of insulin sensitivity. This estimate has enough resolution for SI prediction and monitoring of response to therapy. In borderline cases, re-evaluation of stored (frozen) blood samples for insulin and C-peptide would enable greater accuracy where necessary, enabling a hierarchy of tests in an economical fashion.

Objectives: This paper reports an evaluation of the properties of a generic electronic health record information model that were actually required and used when importing an existing clinical application into a generic EHR repository.

Method: A generic EHR repository and system were developed as part of the EU Projects Synapses and SynEx. A Web application to support the management of anticoagulation therapy was developed to interface to the EHR system, and deployed within a north London hospital with five years of cumulative clinical data from the previous existing anticoagulation management application. This offered the opportunity to critique those parts of the generic EHR that were actually needed to represent the legacy data.

Results: The anticoagulation records from 3,226 patients were imported and represented using over 900,000 Record Components (i.e. each patient's record contained on average 289 nodes), of which around two thirds were Element Items (i.e. value-containing leaf nodes), the remainder being container nodes (i.e. headings and sub-headings). Each node is capable of incorporating a rich set of context properties, but in reality it was found that many properties were not used at all, and some infrequently (e.g. only around 0.5% of Record Components had ever been revised).

Conclusions: The process of developing generic EHR information models, arising from research and embodied within new-generation interoperability standards and specifications, has been strongly driven by requirements. These requirements have been gathered primarily by collecting use cases and examples from clinical communities, and been added to successive generations of these models. A priority setting approach has not to date been pursued - all requirements have been received and almost invariably met. This work has shown how little of the resulting model is actually needed to represent useful and usable clinical data. A wider range of such evaluations, looking at different kinds of existing clinical system, is needed to balance the theoretical requirements gathering processes, in order to result in EHR information models of an ideal level of complexity.

We propose a new home health care network for the acquisition and transmission of data from ordinary home health care appliances based on IEEE11073. In this study, we develop a standard protocol for data collection and a simple interface to accommodate different monitoring systems that make use of different data protocols. The system provides for one-way data transmission, thus saving power and conforming to Japanese pharmaceutical law. Our standardized protocol was verified during a 1-year field test involving 20 households in Japan. Data transmission errors between home health care devices and the home gateway were 4.21 per a day with our newly developed standard protocol. Over a 1 year period, we collected and analyzed data from 241,000 separate sources associated with healthy, home-based patients and chronically ill, clinic-based patients, the latter through physician intervention. We evaluate some of the possible applications for collecting daily health care data and introduce some of our findings relating primarily to body weight and blood pressure monitoring for elderly subjects in their own homes.

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the "leave-one-out cross-validation" scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors' correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers' signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.

This brief report describes the development and first year of use of an Internet-based screener for Cannabis users. Two versions of the Check Your Cannabis screener were compared, one linked to an already established harm reduction website for young Cannabis users (as an exercise called "Check how I compare with others," on www.WhatsWithWeed.ca) and the other a standalone version (www.CheckYourCannabis.net). The What's With Weed version attracted ten times more users and had a significantly younger audience as compared to the standalone version, underlining the benefits of targeting a website to a specific audience and linking to websites with already established reputations. Further work is needed to establish any impact on actual Cannabis use from taking the Check Your Cannabis screener.

Myocardial contusion (MC) is a common injury following blunt chest trauma without any specific symptoms.Several techniques such as electrocardiogram, estimation of myocardial band fraction of creatine phosphokinase, chest radiography and the scintiscanning missed efficiency to characterize the MC.Another technique based on transesophageal echocardiography [TEE] allows to visualize the structures of the heart with a good spatial resolution.We postulated that the quantitative texture analysis of regional image texture in two- dimensional [2D] TEE echocardiograms would be an accurate method to differentiate normal from abnormal myocardial wall.This preliminary experimental study demonstrated the feasibility of the proposed technique.

A global study of geometry and material properties of orthopedic screws was performed, considering not only the effect of each single factor (screw pitch, number of threads, fillet angle, etc.) but also their interactions with respect to bone density.The stress patterns resulting from different screw geometries and bone densities were analyzed using finite element techniques, taking into account different levels of osseointegration between the screw and the bone. These numerical models where validated through experimental pull-out tests, where a pull out force of 120 N produced localized failure of the last thread (stresses above 0.42 MPa). The results of the numerical simulations were then summarised using a multi-factorial parametric analysis. This demonstrated the great relevance of the interaction between bone density and screw pitch, showing that the optimal screw pitch can vary by more than 25% for different densities (0.35 g/cm(3) and 0.47 g/cm(3), respectively).The parameters calculated by means of the multi-factorial analysis allow the pull out force to be estimated for different osseointegration levels, different screw geometries and material properties, and for different bone densities. The final objective is to determine the best choice of implant for each individual patient.

We present a numerical model to fit the electroretinogram (ERG), a gross evoked eye visual potential, that originate in the retina through photons absorption by photoreceptors and then involve the contribution form others retinal neurons. We use the ERG measured in a hummingbird, to evaluate the most likely retinal mechanism - cones visual pigments and oil-droplets - that participate in their high dimensional tetra or pentachromatic color hyperspace. The model - a nonlinear fit - appears to be a very useful tool to predict the underlying contribution visual mechanism for a variety of retinal preparation.

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.