Experimental models of sepsis and their clinical relevance

Sepsis remains a major cause of morbidity and mortality mainly because of sepsis-induced multiple organ dysfunction. In contrast to preclinical studies, most clinical trials of promising new treatment strategies for sepsis have failed to demonstrate efficacy. Although many reasons could account for this discrepancy, the misinterpretation of preclinical data obtained from experimental studies and especially the use of animal models that do not adequately mimic human sepsis may have been contributing factors. In this review, the potentials and limitations of various animal models of sepsis are discussed to clarify to which extent these findings are relevant to human sepsis. Such models include intravascular infusion of endotoxin or live bacteria, bacterial peritonitis, cecal ligation and perforation, soft tissue infection, pneumonia or meningitis models using different animal species including rats, mice, rabbits, dogs, pigs, sheep, and nonhuman primates. Despite several limitations, animal models remain essential in the development of all new therapies for sepsis and septic shock because they provide fundamental information about the pharmacokinetics, toxicity, and mechanism of drug action that cannot be replaced by other methods. New therapeutic agents should be studied in infection models, even after the initiation of the septic process. Furthermore, debility conditions need to be reproduced to avoid the exclusive use of healthy animals, which often do not represent the human septic patient.

Virtual plants - integrating architectural and physiological models

Recent advances in computer technology have made it possible to create virtual plants by simulating the details of structural development of individual plants. Software has been developed that processes plant models expressed in a special purpose mini-language based on the Lindenmayer system formalism. These models can be extended from their architectural basis to capture plant physiology by integrating them with crop models, which estimate biomass production as a consequence of environmental inputs. Through this process, virtual plants will gain the ability to react to broad environmental conditions, while crop models will gain a visualisation component. This integration requires the resolution of the fundamentally different time scales underlying the approaches. Architectural models are usually based on physiological time; each time step encompasses the same amount of development in the plant, without regard to the passage of real time. In contrast, physiological models are based in real time; the amount of development in a time step is dependent on environmental conditions during the period. This paper provides a background on the plant modelling language, then describes how widely-used concepts of thermal time can be implemented to resolve these time scale differences. The process is illustrated using a case study. (C) 1997 Elsevier Science Ltd.

A defense of backwards in time causation models in quantum mechanics

This paper offers a defense of backwards in time causation models in quantum mechanics. Particular attention is given to Cramer's transactional account, which is shown to have the threefold virtue of solving the Bell problem, explaining the complex conjugate aspect of the quantum mechanical formalism, and explaining various quantum mysteries such as Schrodinger's cat. The question is therefore asked, why has this model not received more attention from physicists and philosophers? One objection given by physicists in assessing Cramer's theory was that it is not testable. This paper seeks to answer this concern by utilizing an argument that backwards causation models entail a fork theory of causal direction. From the backwards causation model together with the fork theory one can deduce empirical predictions. Finally, the objection that this strategy is questionable because of its appeal to philosophy is deflected.

Risk Estimates of Dengue in Travelers to Dengue Endemic Areas Using Mathematical Models

Dengue has emerged as a frequent problem in international travelers. The risk depends on destination, duration, and season of travel. However, data to quantify the true risk for travelers to acquire dengue are lacking. We used mathematical models to estimate the risk of nonimmune persons to acquire dengue when traveling to Singapore. From the force of infection, we calculated the risk of dengue dependent on duration of stay and season of arrival. Our data highlight that the risk for nonimmune travelers to acquire dengue in Singapore is substantial but varies greatly with seasons and epidemic cycles. For instance, for a traveler who stays in Singapore for 1 week during the high dengue season in 2005, the risk of acquiring dengue was 0.17%, but it was only 0.00423% during the low season in a nonepidemic year such as 2002. Risk estimates based on mathematical modeling will help the travel medicine provider give better evidence-based advice for travelers to dengue endemic countries.

A fuzzy Reed-Frost model for epidemic spreading

In this paper, we present a fuzzy approach to the Reed-Frost model for epidemic spreading taking into account uncertainties in the diagnostic of the infection. The heterogeneities in the infected group is based on the clinical signals of the individuals (symptoms, laboratorial exams, medical findings, etc.), which are incorporated into the dynamic of the epidemic. The infectivity level is time-varying and the classification of the individuals is performed through fuzzy relations. Simulations considering a real problem with data of the viral epidemic in a children daycare are performed and the results are compared with a stochastic Reed-Frost generalization.

Assessment of immediate conservative breast surgery reconstruction: A classification system of defects revisited and an algorithm for selecting the appropriate technique

Background: Although various techniques have been used for breast conservation surgery reconstruction, there are few studies describing a logical approach to reconstruction of these defects. The objectives of this study were to establish a classification system for partial breast defects and to develop a reconstructive algorithm. Methods: The authors reviewed a 7-year experience with 209 immediate breast conservation surgery reconstructions. Mean follow-up was 31 months. Type I defects include tissue resection in smaller breasts (bra size A/B), including type IA, which involves minimal defects that do not cause distortion; type III, which involves moderate defects that cause moderate distortion; and type IC, which involves large defects that cause significant deformities. Type II includes tissue resection in medium-sized breasts with or without ptosis (bra size C), and type III includes tissue resection in large breasts with ptosis (bra size D). Results: Eighteen percent of patients presented type I, where a lateral thoracodorsal flap and a latissimus dorsi flap were performed in 68 percent. Forty-five percent presented type II defects, where bilateral mastopexy was performed in 52 percent. Thirty-seven percent of patients presented type III distortion, where bilateral reduction mammaplasty was performed in 67 percent. Thirty-five percent of patients presented complications, and most were minor. Conclusions: An algorithm based on breast size in relation to tumor location and extension of resection can be followed to determine the best approach to reconstruction. The authors` results have demonstrated that the complications were similar to those in other clinical series. Success depends on patient selection, coordinated planning with the oncologic surgeon, and careful intraoperative management.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Individual risk prediction of nodal and distant metastasis for patients with typical bronchial carcinoid tumors

Objective: Bronchial typical carcinoid tumors are tow-grade malignancies. However, metastases are diagnosed in some patients. Predicting the individual risk of these metastases to determine patients eligible for a radical lymphadenectomy and patients to be followed-up because of distant metastasis risk is relevant. Our objective was to screen for predictive criteria of bronchial typical carcinoid tumor aggressiveness based on a logistic regression model using clinical, pathological and biomolecular data. Methods: A multicenter retrospective cohort study, including 330 consecutive patients operated on for bronchial typical carcinoid tumors and followed-up during a period more than 10 years in two university hospitals was performed. Selected data to predict the individual risk for both nodal and distant metastasis were: age, gender, TNM staging, tumor diameter and location (central/peripheral), tumor immunostaining index of p53 and Ki67, Bcl2 and the extracellular density of neoformed microvessels and of collagen/elastic extracellular fibers. Results: Nodal and distant metastasis incidence was 11% and 5%, respectively. Univariate analysis identified all the studied biomarkers as related to nodal metastasis. Multivariate analysis identified a predictive variable for nodal metastasis: neo angiogenesis, quantified by the neoformed pathological microvessels density. Distant metastasis was related to mate gender. Discussion: Predictive models based on clinical and biomolecular data could be used to predict individual risk for metastasis. Patients under a high individual risk for lymph node metastasis should be considered as candidates to mediastinal lymphadenectomy. Those under a high risk of distant metastasis should be followed-up as having an aggressive disease. Conclusion: Individual risk prediction of bronchial typical carcinoid tumor metastasis for patients operated on can be calculated in function of biomolecular data. Prediction models can detect high-risk patients and help surgeons to identify patients requiring radical lymphadenectomy and help oncologists to identify those as having an aggressive disease requiring prolonged follow-up. (C) 2008 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissues in vitro

This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsies in vitro with the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changes in vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.