a case of angioinvasive cutaneous anaplastic large cell lymphoma completely regressed after low dose systemic methotrexate

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Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Microbiological and serological monitoring in hooded crow (Corvus corone cornix) in the Region Lombardia, Italy

The health status of 276 hooded crows (Corvus corone cornix) from various provinces of Lombardy was monitored for three years. Bacteriological examination detected E. coli (76%), Campylobacter jejuni (17%), Salmonella typhimurium (11.6%), Yersinia spp. (6.5%), Clamydophila abortus and C. psittaci (2.6%); from six birds showing severe prostration Pasteurella multocida was isolated. Virological and serological tests were negative for Avian Influenza virus (AIV), West Nile virus (WNV) and only three samples were positive for Newcastle disease virus (NDV) but only at serology (titre 1:16)

Delivery in pregnant women infected with SARS-CoV-2: A fast review

Background: Few case reports and clinical series exist on pregnant women infected with SARS-CoV-2 who delivered. Objective: To review the available information on mode of delivery, vertical/peripartum transmission, and neonatal outcome in pregnant women infected with SARS-CoV-2. Search strategy: Combination of the following key words: COVID-19, SARS-CoV-2, and pregnancy in Embase and PubMed databases. Selection criteria: Papers reporting cases of women infected with SARS-CoV-2 who delivered. Data collection and analysis: The following was extracted: author; country; number of women; study design; gestational age at delivery; selected clinical maternal data; mode of delivery; selected neonatal outcomes. Main results: In the 13 studies included, vaginal delivery was reported in 6 cases (9.4%; 95% CI, 3.5\u201319.3). Indication for cesarean delivery was worsening of maternal conditions in 31 cases (48.4%; 95% CI, 35.8\u201361.3). Two newborns testing positive for SARS-CoV-2 by real-time RT-PCR assay were reported. In three neonates, SARS-CoV-2 IgG and IgM levels were elevated but the RT-PCR test was negative. Conclusions: The rate of vertical or peripartum transmission of SARS-CoV-2 is low, if any, for cesarean delivery; no data are available for vaginal delivery. Low frequency of spontaneous preterm birth and general favorable immediate neonatal outcome are reassuring

Haemorrhagic enteritis seroconversion in turkey breeders: field observations

Seroconversion to viral haemorrhagic enteritis (HE) was studied in seven flocks of turkey breeders (17.974 birds in total), after 20 weeks of the onset of egg production. They showed no clinical signs, and mortality rate was normal. However, the infection caused a drop in egg production lasting about five weeks (-2.32 eggs laid during this period), but had no effect on hatching parameters

Predictive value of hematological and phenotypical parameters on postchemotherapy leukocyte recovery

Background: Grade IV chemotherapy toxicity is defined as absolute neutrophil count &lt;500/μL. The nadir is considered as the lowest neutrophil number following chemotherapy, and generally is not expected before the 7th day from the start of chemotherapy. The usual prophylactic dose of rHu-G-CSF (Filgrastim) is 300 μg/day, starting 24-48 h after chemotherapy until hematological recovery. However, individual patient response is largely variable, so that rHu-G-CSF doses can be different. The aim of this study was to verify if peripheral blood automated flow cytochemistry and flow cytometry analysis may be helpful in predicting the individual response and saving rHu-G-CSF. Methods: During Grade IV neutropenia, blood counts from 30 cancer patients were analyzed daily by ADVIA 120 automated flow cytochemistry analyzer and by Facscalibur flow cytometer till the nadir. "Large unstained cells" (LUCs), myeloperoxidase index (MPXI), blasts, and various cell subpopulations in the peripheral blood were studied. At nadir rHu-G-CSF was started and 81 chemotherapy cycles were analyzed. Cycles were stratified according to their number and to two dose-levels of rHuG-CSF needed to recovery (300-600 vs. 900-1200 μg) and analyzed in relation to mean values of MPXI and mean absolute number of LUCs in the nadir phase. The linear regressions of LUCs % over time in relation to two dose-levels of rHu-G-CSF and uni-multivariate analysis of lymphocyte subpopulations, CD34+ cells, MPXI, and blasts were also performed. Results: In the nadir phase, the increase of MPXI above the upper limit of normality (&gt;10; median 27.7), characterized a slow hematological recovery. MPXI levels were directly related to the cycle number and inversely related to the absolute number of LUCs and CD34 +/CD45+ cells. A faster hematological recovery was associated with a higher LUC increase per day (0.56% vs. 0.25%), higher blast (median 36.7/μL vs. 19.5/μL) and CD34+/CD45+ cell (median 2.2/μL vs. 0.82/μL) counts. Conclusions: Our study showed that some biological indicators such as MPXI, LUCs, blasts, and CD34 +/CD45+ cells may be of clinical relevance in predicting individual hematological response to rHu-G-CSF. Special attention should be paid when nadir MPXI exceeds the upper limit of normality because the hematological recovery may be delayed. © 2009 Clinical Cytometry Society

VP50.10: Predicting fetal and maternal complications with sFlt1/PlGF ratio in hypertensive disorders of pregnancy

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Predição da estrutura de proteínas off-lattice usando evolução diferencial multiobjetivo adaptativa

Protein Structure Prediction (PSP) can be considered one of the most challenging problems in Bioinformatics nowadays. When a protein is in its conformation state, the free energy is minimized. Evaluation of protein conformation is generally performed based on two values of the estimated free energy, i.e., those provided by intra and intermolecular interactions among atoms. Some recent experimental studies show that these interactions are in conflit, justifying the use of multiobjective optimization approaches to solve PSP. In this case, the energy optimization is performed separately, different from the mono-objective optimization which considers the sum of free energy. Differential Evolution (DE) is a technique based on Evolutionary Computation and represents an interesting alternative to solve multiobjective PSP. In this work, an optimizer based on DE is proposed to solve the PSP problem. Due to the great number of parameters, typical for evolutionary algorithms, this work also investigates adaptive parameters strategies. In experiments, a simple approach based on ED is evaluated for PSP. An evolution for this method, which incorporates concepts of the MOEA/D algorithm and parameter adaptation techniques is tested for a set of benchmarks in the multiobjective optimization context. The preliminary results for PSP (for six real proteins) are promising and those obtained for the benchmark set stands the proposed approach as a candidate to the state-of-art for multiobjective optimization.Fundação AraucáriaA Predição da Estrutura das Proteínas, conhecida como PSP (Protein Structure Prediction) pode ser considerada um dos problemas mais desafiadores da Bioinformática atualmente. Quando uma proteína está em seu estado de conformação nativa, a energia livre tende para um valor mínimo. Em geral, a predição da conformação de uma proteína por métodos computacionais é feita pela estimativa de dois valores de energia livre que são provenientes das interações intra e intermoleculares entre os átomos. Alguns estudos recentes indicam que estas interações estão em conflito, justificando o uso de abordagens baseadas em otimização multiobjetivo para a solução do PSP. Neste caso, a otimização destas energias é realizada separadamente, diferente da formulação mono-objetivo que considera a soma das energias. A Evolução Diferencial (ED) é uma técnica baseada em Computação Evolucionária e representa uma alternativa interessante para abordar o PSP. Este trabalho busca desenvolver um otimizador baseado no algoritmo de ED para o problema da Predição da Estrutura de Proteínas multiobjetivo. Este trabalho investiga ainda estratégias baseadas em parâmetros adaptativos para a evolução diferencial. Nicialmente avalia-se uma abordagem simples baseada em ED proposta para a solução do PSP. Uma evolução deste método que incorpora conceitos do algoritmo MOEA/D e adaptação de parâmetros é testada em um conjunto de problemas benchmark de otimização multiobjetivo. Os resultados preliminares obtidos para o PSP (para seis proteínas reais) são promissores e aqueles obtidos para o conjunto benchmark colocam a abordagem proposta como candidata para otimização multiobjetivo