Organ donation and utilization in the United States: 1998-2007.

Organ transplantation remains the only life-saving therapy for many patients with organ failure. Despite the work of the Organ Donation and Transplant Collaboratives, and the marked increases in deceased donors early in the effort, deceased donors only rose by 67 from 2006 and the number of living donors declined during the same time period. There continue to be increases in the use of organs from donors after cardiac death (DCD) and expanded criteria donors (ECD). This year has seen a major change in the way organs are offered with increased patient safety measures in those organ offers made by OPOs using DonorNet. Unfortunately, the goals of 75% conversion rates, 3.75 organs transplanted per donor, 10% of all donors from DCD sources and 20% growth of transplant center volume have yet to be reached across all donation service areas (DSAs) and transplant centers; however, there are DSAs that have not only met, but exceeded, these goals. Changes in organ preservation techniques took place this year, partly due to expanding organ acceptance criteria and increasing numbers of ECDs and DCDs. Finally, the national transplant environment has changed in response to increased regulatory oversight and new requirements for donation and transplant provider organizations.

Pediatric transplantation in the United States, 1997-2006.

This article represents the sixth annual review of the current state of pediatric transplantation in the United States from the Scientific Registry of Transplant Recipients (SRTR). It presents updated trends, discussion of analyses presented during the year by the SRTR to the committees of the Organ Procurement and Transplantation Network (OPTN) and discussion of important issues currently facing pediatric organ transplantation. Unless otherwise stated, the statistics in this article are drawn from the reference tables of the 2007 OPTN/SRTR Annual Report. In this article, pediatric patients are defined as candidates, recipients or donors aged 17 years or less. Data for both graft and patient survival are reported as unadjusted survival, unless otherwise stated (adjusted patient and graft survival are available in the reference tables). Short-term survival (3 month and 1 year) reflects outcomes for transplants performed in 2004 and 2005; 3-year survival reflects transplants from 2002 to 2005; and 5-year survival reports on transplants performed from 2000 to 2005. Details on the methods of analysis employed may be found in the reference tables themselves or in the technical notes of the 2007 OTPN/SRTR Annual Report, both available online at http://www.ustransplant.org.

Pharmacometric Modeling of Liver Metastases' Diameter, Volume, and Density and Their Relation to Clinical Outcome in Imatinib-Treated Patients With Gastrointestinal Stromal Tumors.

Three-dimensional and density-based tumor metrics have been suggested to better discriminate tumor response to treatment than unidimensional metrics, particularly for tumors exhibiting nonuniform size changes. In the developed pharmacometric modeling framework based on data from 77 imatinib-treated gastrointestinal patients, the time-courses of liver metastases' maximum transaxial diameters, software-calculated actual volumes (Vactual ) and calculated ellipsoidal volumes were characterized by logistic growth models, in which imatinib induced a linear dose-dependent size reduction. An indirect response model best described the reduction in density. Substantial interindividual variability in the drug effect of all response assessments and additional interlesion variability in the drug effect on density were identified. The predictive ability of longitudinal tumor unidimensional and three-dimensional size and density on overall survival (OS) and progression-free survival (PFS) were compared using parametric time-to-event models. Death hazard increased with increasing Vactual . This framework may guide early clinical interventions based on three-dimensional tumor responses to enhance benefits for patients with gastrointestinal stromal tumors (GIST).

Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals.

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the r-r interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.

Inflammasome activity is essential for one kidney/deoxycorticosterone acetate/salt-induced hypertension in mice.

BACKGROUND AND PURPOSE: Inflammasomes are multimeric complexes that facilitate caspase-1-mediated processing of the pro-inflammatory cytokines IL-1ß and IL-18. Clinical hypertension is associated with renal inflammation and elevated circulating levels of IL-1ß and IL-18. Therefore, we investigated whether hypertension in mice is associated with increased expression and/or activation of the inflammasome in the kidney, and if inhibition of inflammasome activity reduces BP, markers of renal inflammation and fibrosis. EXPERIMENTAL APPROACH: Wild-type and inflammasome-deficient ASC(-/-) mice were uninephrectomized and received deoxycorticosterone acetate and saline to drink (1K/DOCA/salt). Control mice were uninephrectomized but received a placebo pellet and water. BP was measured by tail cuff; renal expression of inflammasome subunits and inflammatory markers was measured by real-time PCR and immunoblotting; macrophage and collagen accumulation was assessed by immunohistochemistry. KEY RESULTS: 1K/DOCA/salt-induced hypertension in mice was associated with increased renal mRNA expression of inflammasome subunits NLRP3, ASC and pro-caspase-1, and the cytokine, pro-IL-1ß, as well as protein levels of active caspase-1 and mature IL-1ß. Following treatment with 1K/DOCA/salt, ASC(-/-) mice displayed blunted pressor responses and were also protected from increases in renal expression of IL-6, IL-17A, CCL2, ICAM-1 and VCAM-1, and accumulation of macrophages and collagen. Finally, treatment with a novel inflammasome inhibitor, MCC950, reversed hypertension in 1K/DOCA/salt-treated mice. CONCLUSIONS AND IMPLICATIONS: Renal inflammation, fibrosis and elevated BP induced by 1K/DOCA/salt treatment are dependent on inflammasome activity, highlighting the inflammasome/IL-1ß pathway as a potential therapeutic target in hypertension.

A fusion-based clinical decision support for disease diagnosis from endoscopic images.

This paper presents an intelligent decision support system designed on a decision fusion framework coupled with a priori knowledge base for abnormality detection from endoscopic images. Sub-decisions are made based on associated component feature sets derived from the endoscopic images and predefined algorithms, and subsequently fused to classify the patient state. Bayesian probability computations are employed to evaluate the accuracies of sub-decisions, which are utilized in estimating the probability of the fused decision. The overall detectability of abnormalities by using the proposed fusion approach is improved in terms of detection of true positive and true negative conditions when compared with corresponding results from individual methods.

Color image segmentation based on fuzzy rule-based reasoning applied to colonoscopic images.

A fuzzy color segmentation approach is developed for the analysis of colonoscopic images. The segmentation is made up of two phases: segmentation through histogram space filtering and region merging using fuzzy rule-base reasoning. The first phase involves using a scale-space filter to analyze the hue, saturation, and intensity (HSI) histograms to determine the number of classes and construct a 3-D class grid. The color image is then segmented based on the class grid. In the second phase, region merging based on applying the fuzzy rule-base is employed to guide the combining process of the segmented regions. For fuzzy reasoning, three criteria are evaluated, namely, the edge strength along the boundary, color similarity, and spatial connectivity of adjoining regions. Experimental testing of the proposed method applied on colonoscopic images was conducted, and the results are encouraging.

Analysis of cardiac signals using spatial filling index and time-frequency domain.

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%.

Comprehensive analysis of cardiac health using heart rate signals.

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc may contain useful information about the nature of disease affecting the heart. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Analysis of heart rate variability (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. The HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially nonstationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper deals with the analysis of eight types of cardiac abnormalities and presents the ranges of linear and nonlinear parameters calculated for them with a confidence level of more than 90%.

Classification of cardiac abnormalities using heart rate signals.

The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%.

A dynamic nonlinear time domain model for reconstruction and compression of cardiovascular signals with application to telemedicine.

A new nonlinear time domain model is proposed in this paper for signals of cardiovascular origin. An equation of the dynamic nonlinear model has been obtained by considering a masking function, which is modulated by a harmonic series with the baseline drift incorporated into the model. Signal reconstruction using model parameters has established the effectiveness of the model for signal compression. Improvement has been effected by using neural networks for reducing the time for optimizing the initial parameters. An improved adaptive optimization step size algorithm has also been implemented. Results show that the technique is able to provide reasonable compression with low error between the original and reconstructed signals. One of the main advantages of the model is its potential of being used for compression of many different types of biosignals transmitted in parallel. Incorporation of the compression model into a telemedicine system has led to considerable saving in transmission time for patient data.

IL-1ß and IL-18: inflammatory markers or mediators of hypertension?

Chronic inflammation in the kidneys and vascular wall is a major contributor to hypertension. However, the stimuli and cellular mechanisms responsible for such inflammatory responses remain poorly defined. Inflammasomes are crucial initiators of sterile inflammation in other diseases such as rheumatoid arthritis and gout. These pattern recognition receptors detect host-derived danger-associated molecular patterns (DAMPs), such as microcrystals and reactive oxygen species, and respond by inducing activation of caspase-1. Caspase-1 then processes the cytokines pro-IL-1ß and pro-IL-18 into their active forms thus triggering inflammation. While IL-1ß and IL-18 are known to be elevated in hypertensive patients, no studies have examined whether this occurs downstream of inflammasome activation or whether inhibition of inflammasome and/or IL-1ß/IL-18 signalling prevents hypertension. In this review, we will discuss some known actions of IL-1ß and IL-18 on leukocyte and vessel wall function that could potentially underlie a prohypertensive role for these cytokines. We will describe the major classes of inflammasome-activating DAMPs and present evidence that at least some of these are elevated in the setting of hypertension. Finally, we will provide information on drugs that are currently used to inhibit inflammasome/IL-1ß/IL-18 signalling and how these might ultimately be used as therapeutic agents for the clinical management of hypertension.

Extraction of brain tumor from MR images using one-class support vector machine.

A novel image segmentation approach by exploring one-class support vector machine (SVM) has been developed for the extraction of brain tumor from magnetic resonance (MR) images. Based on one-class SVM, the proposed method has the ability of learning the nonlinear distribution of the image data without prior knowledge, via the automatic procedure of SVM parameters training and an implicit learning kernel. After the learning process, the segmentation task is performed. The proposed technique is applied to 24 clinical MR images of brain tumor for both visual and quantitative evaluations. Experimental results suggest that the proposed query-based approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy.

High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network.

An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

Micromachines in endoscopy.

Conventional endoscopy has reached a plateau in technical development, necessitating the exploration of bold new ideas in order to make further advances. One such idea is a self-navigating, independent, intelligent colonoscopic micro-robot. The design of a vehicle that can negotiate the difficult and hostile terrain of the colon is a complex task. Options include wheeled or tracked vehicles and pneumatically driven devices. The development of navigation and lesion recognition software to drive such a vehicle is also challenging. The various mathematical concepts involved in the development of such software are explored in this article.

Future developments in high-technology abdominal surgery: ultrasound, stereo imaging, robotics.

The surgical world is experiencing a revolution brought about by the proliferation of minimally invasive techniques. These developments have had most impact on abdominal surgery and chest surgery, but there are ramifications affecting other fields as well. One feature of this change is the increasing dependence of surgeons on technology. Developments in video imaging, ultrasound and robotics are required to make complex endoscopic procedures surgeon-friendly, just as the minimally invasive approach has made surgery more patient-friendly. In the future, integration of stereo imaging systems, computers, microrobots and robotic manipulators will result in technically sophisticated but ergonomic operating systems that will allow surgeons to perform endoscopically almost any type of surgery that can be done today.

Extraction of microcalcifications in digital mammograms using regional watershed.

In this report, a novel technique is proposed for computer-aided automatic extraction of microcalcifications in a digital mammogram. First, the microcalcifications are detected by morphological filtering, followed by entropy-based thresholding. Next, the microcalcifications are segmented by computing regional watershed. The proposed automatic technique is designed to serve as a visual aid to radiologists. Its efficacy is demonstrated through experimental results.

Wavelet transform domain data embedding in a medical image.

In this work, we propose a wavelet-based technique for embedding medical data in a medical image. In the spectral domain, the patient data are embedded into the wavelet coefficients of the host image. A diagnostic distortion measure (DDM) has been defined to measure the visible distortions between the original image and the embedded image. The performance of the DDM has been compared with the standard PSNR characteristics. The results show that DDM not only captures the distortions for different quantities of embedded data but also can quantify the differences when the same data are embedded at different subbands. The embedment of data into the mid and high frequency subbands of the host image show lower values of DDM and higher values of PSNR.

Control of hypertension in postoperative patients.

In this paper, adaptive control systems have been developed for the closed-loop control of mean arterial pressure using vasoactive drugs. An adaptive algorithm based on generalized predictive control law has been presented. A adaptive PI controller has been designed. The recursive least-square identification algorithm is used for on-line parameter estimation. The supervisor is added to provide safety and efficacy of control under the consideration of the physical and physiological constraints. Extensive computer simulation in the presence of unpredictable disturbances shows the system is capable of inducing hypertension.