Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Desmin: molecular interactions and putative functions of the muscle intermediate filament protein

Desmin is the intermediate filament (IF) protein occurring exclusively in muscle and endothelial cells. There are other IF proteins in muscle such as nestin, peripherin, and vimentin, besides the ubiquitous lamins, but they are not unique to muscle. Desmin was purified in 1977, the desmin gene was characterized in 1989, and knock-out animals were generated in 1996. Several isoforms have been described. Desmin IFs are present throughout smooth, cardiac and skeletal muscle cells, but can be more concentrated in some particular structures, such as dense bodies, around the nuclei, around the Z-line or in costameres. Desmin is up-regulated in muscle-derived cellular adaptations, including conductive fibers in the heart, electric organs, some myopathies, and experimental treatments with drugs that induce muscle degeneration, like phorbol esters. Many molecules have been reported to associate with desmin, such as other IF proteins (including members of the membrane dystroglycan complex), nebulin, the actin and tubulin binding protein plectin, the molecular motor dynein, the gene regulatory protein MyoD, DNA, the chaperone alphaB-crystallin, and proteases such as calpain and caspase. Desmin has an important medical role, since it is used as a marker of tumors' origin. More recently, several myopathies have been described, with accumulation of desmin deposits. Yet, after almost 30 years since its identification, the function of desmin is still unclear. Suggested functions include myofibrillogenesis, mechanical support for the muscle, mitochondrial localization, gene expression regulation, and intracellular signaling. This review focuses on the biochemical interactions of desmin, with a discussion of its putative functions.

Disintegrins: integrin selective ligands which activate integrin-coupled signaling and modulate leukocyte functions

Extracellular matrix proteins and cell adhesion receptors (integrins) play essential roles in the regulation of cell adhesion and migration. Interactions of integrins with the extracellular matrix proteins lead to phosphorylation of several intracellular proteins such as focal adhesion kinase, activating different signaling pathways responsible for the regulation of a variety of cell functions, including cytoskeleton mobilization. Once leukocytes are guided to sites of infection, inflammation, or antigen presentation, integrins can participate in the initiation, maintenance, or termination of the immune and inflammatory responses. The modulation of neutrophil activation through integrin-mediated pathways is important in the homeostatic control of the resolution of inflammatory states. In addition, during recirculation, T lymphocyte movement through distinct microenvironments is mediated by integrins, which are critical for cell cycle, differentiation and gene expression. Disintegrins are a family of low-molecular weight, cysteine-rich peptides first identified in snake venom, usually containing an RGD (Arg-Gly-Asp) motif, which confers the ability to selectively bind to integrins, inhibiting integrin-related functions in different cell systems. In this review we show that, depending on the cell type and the microenvironment, disintegrins are able to antagonize the effects of integrins or to act agonistically by activating integrin-mediated signaling. Disintegrins have proven useful as tools to improve the understanding of the molecular events regulated by integrin signaling in leukocytes and prototypes in order to design therapies able to interfere with integrin-mediated effects.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Fuzzy real option analysis in patent related decision making and patent valuation

The shift towards a knowledge-based economy has inevitably prompted the evolution of patent exploitation. Nowadays, patent is more than just a prevention tool for a company to block its competitors from developing rival technologies, but lies at the very heart of its strategy for value creation and is therefore strategically exploited for economic pro t and competitive advantage. Along with the evolution of patent exploitation, the demand for reliable and systematic patent valuation has also reached an unprecedented level. However, most of the quantitative approaches in use to assess patent could arguably fall into four categories and they are based solely on the conventional discounted cash flow analysis, whose usability and reliability in the context of patent valuation are greatly limited by five practical issues: the market illiquidity, the poor data availability, discriminatory cash-flow estimations, and its incapability to account for changing risk and managerial flexibility. This dissertation attempts to overcome these impeding barriers by rationalizing the use of two techniques, namely fuzzy set theory (aiming at the first three issues) and real option analysis (aiming at the last two). It commences with an investigation into the nature of the uncertainties inherent in patent cash flow estimation and claims that two levels of uncertainties must be properly accounted for. Further investigation reveals that both levels of uncertainties fall under the categorization of subjective uncertainty, which differs from objective uncertainty originating from inherent randomness in that uncertainties labelled as subjective are highly related to the behavioural aspects of decision making and are usually witnessed whenever human judgement, evaluation or reasoning is crucial to the system under consideration and there exists a lack of complete knowledge on its variables. Having clarified their nature, the application of fuzzy set theory in modelling patent-related uncertain quantities is effortlessly justified. The application of real option analysis to patent valuation is prompted by the fact that both patent application process and the subsequent patent exploitation (or commercialization) are subject to a wide range of decisions at multiple successive stages. In other words, both patent applicants and patentees are faced with a large variety of courses of action as to how their patent applications and granted patents can be managed. Since they have the right to run their projects actively, this flexibility has value and thus must be properly accounted for. Accordingly, an explicit identification of the types of managerial flexibility inherent in patent-related decision making problems and in patent valuation, and a discussion on how they could be interpreted in terms of real options are provided in this dissertation. Additionally, the use of the proposed techniques in practical applications is demonstrated by three fuzzy real option analysis based models. In particular, the pay-of method and the extended fuzzy Black-Scholes model are employed to investigate the profitability of a patent application project for a new process for the preparation of a gypsum-fibre composite and to justify the subsequent patent commercialization decision, respectively; a fuzzy binomial model is designed to reveal the economic potential of a patent licensing opportunity.

Nitric oxide synthesis and biological functions of nitric oxide released from ruthenium compounds

During three decades, an enormous number of studies have demonstrated the critical role of nitric oxide (NO) as a second messenger engaged in the activation of many systems including vascular smooth muscle relaxation. The underlying cellular mechanisms involved in vasodilatation are essentially due to soluble guanylyl-cyclase (sGC) modulation in the cytoplasm of vascular smooth cells. sGC activation culminates in cyclic GMP (cGMP) production, which in turn leads to protein kinase G (PKG) activation. NO binds to the sGC heme moiety, thereby activating this enzyme. Activation of the NO-sGC-cGMP-PKG pathway entails Ca2+ signaling reduction and vasodilatation. Endothelium dysfunction leads to decreased production or bioavailability of endogenous NO that could contribute to vascular diseases. Nitrosyl ruthenium complexes have been studied as a new class of NO donors with potential therapeutic use in order to supply the NO deficiency. In this context, this article shall provide a brief review of the effects exerted by the NO that is enzymatically produced via endothelial NO-synthase (eNOS) activation and by the NO released from NO donor compounds in the vascular smooth muscle cells on both conduit and resistance arteries, as well as veins. In addition, the involvement of the nitrite molecule as an endogenous NO reservoir engaged in vasodilatation will be described.

Silencing HIF-1α reduces the adhesion and secretion functions of acute leukemia hBMSCs

Hypoxia inducible factor-1α (HIF-1α) is an important transcription factor, which plays a critical role in the formation of solid tumor and its microenviroment. The objective of the present study was to evaluate the expression and function of HIF-1α in human leukemia bone marrow stromal cells (BMSCs) and to identify the downstream targets of HIF-1α. HIF-1α expression was detected at both the RNA and protein levels using real-time PCR and immunohistochemistry, respectively. Vascular endothelial growth factor (VEGF) and stromal cell-derived factor-1α (SDF-1α) were detected in stromal cells by enzyme-linked immunosorbent assay. HIF-1α was blocked by constructing the lentiviral RNAi vector system and infecting the BMSCs. The Jurkat cell/BMSC co-cultured system was constructed by putting the two cells into the same suitable cultured media and conditions. Cell adhesion and secretion functions of stromal cells were evaluated after transfection with the lentiviral RNAi vector of HIF-1α. Increased HIF-1α mRNA and protein was detected in the nucleus of the acute myeloblastic and acute lymphoblastic leukemia compared with normal BMSCs. The lentiviral RANi vector for HIF-1α was successfully constructed and was applied to block the expression of HIF-1α. When HIF-1α of BMSCs was blocked, the expression of VEGF and SDF-1 secreted by stromal cells were decreased. When HIF-1α was blocked, the co-cultured Jurkat cell’s adhesion and migration functions were also decreased. Taken together, these results suggest that HIF-1α acts as an important transcription factor and can significantly affect the secretion and adhesion functions of leukemia BMSCs.

Acute effect of alcohol intake on sine-wave Cartesian and polar contrast sensitivity functions

The aim of this study was to assess contrast sensitivity for angular frequency stimuli as well as for sine-wave gratings in adults under the effect of acute ingestion of alcohol. We measured the contrast sensitivity function (CSF) for gratings of 0.25, 1.25, 2.5, 4, 10, and 20 cycles per degree of visual angle (cpd) as well as for angular frequency stimuli of 1, 2, 4, 24, 48, and 96 cycles/360°. Twenty adults free of ocular diseases, with normal or corrected-to-normal visual acuity, and no history of alcoholism were enrolled in two experimental groups: 1) no alcohol intake (control group) and 2) alcohol ingestion (experimental group). The average concentration of alcohol in the experimental group was set to about 0.08%. We used a paradigm involving a forced-choice method. Maximum sensitivity to contrast for sine-wave gratings in the two groups occurred at 4 cpd sine-wave gratings and at 24 and 48 cycles/360° for angular frequency stimuli. Significant changes in contrast sensitivity were observed after alcohol intake compared with the control condition at spatial frequency of 4 cpd and 1, 24, and 48 cycles/360° for angular frequency stimuli. Alcohol intake seems to affect the processing of sine-wave gratings at maximum sensitivity and at the low and high frequency ends for angular frequency stimuli, both under photopic luminance conditions.

Modelling Energy Consumption of a Switch using Fuzzy-Rule Classifier

The energy consumption of IT equipments is becoming an issue of increasing importance. In particular, network equipments such as routers and switches are major contributors to the energy consumption of internet. Therefore it is important to understand how the relationship between input parameters such as bandwidth, number of active ports, traffic-load, hibernation-mode and their impact on energy consumption of a switch. In this paper, the energy consumption of a switch is analyzed in extensive experiments. A fuzzy rule-based model of energy consumption of a switch is proposed based on the result of experiments. The model can be used to predict the energy saving when deploying new switches by controlling the parameters to achieve desired energy consumption and subsequent performance. Furthermore, the model can also be used for further researches on energy saving techniques such as energy-efficient routing protocol, dynamic link shutdown, etc.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.