Hand injuries in a human caused by a South American porcupine (ouriço-cacheiro)

Human injuries caused by South American porcupines (in Portuguese, ouriço-cacheiro) are rare. This study reports severe hand injuries provoked by the body spines of the animal in a human and discusses the circumstances involved in the accident, with emphasis on environmental factors

Factors associated with uninvestigated dyspepsia in students at 4 Latin American schools of medicine: A multicenter study

Introduction and aims: Dyspepsia is a multifactorial disease that can involve alcohol, tobacco, or nonsteroidal anti-inflammatory drug use, as well as lifestyle, diet, socioeconomic elements, or psychologic factors. The aim of the present article was to establish the frequency of uninvestigated dyspepsia and determine its associated factors in students at 4 Latin American schools of medicine. Materials and methods: A cross-sectional, analytic study was conducted, in which a survey made up of closed-ended questions was applied at just one point in time. The association between the variables was then analyzed. A new questionnaire for the diagnosis of dyspepsia was one of the tests utilized to diagnose uninvestigated dyspepsia. Generalized linear models were used for the bivariate and multivariate analyses, employing the Poisson model with the log link function, obtaining crude prevalence ratios, adjusted prevalence ratios, and their 95% confidence intervals. Results: Of the 1,241 individuals surveyed, 54% (841) were females and the median age was 21 years (range: 19-23 years). Prevalence of uninvestigated dyspepsia was 46%. The factors that had a direct association with dyspepsia were: depression, difficulty sleeping, and coffee consumption. On the contrary, eating regularly in a boarding house and the mate sex had an inverse association. Conclusions: Uninvestigated dyspepsia frequency was high in students at 4 Latin American schools. Depression, difficulty sleeping, and steady coffee drinking were factors directly associated with dyspepsia, whereas male sex and eating out at regular hours were factors with a reverse association. Therefore, we recommend that universities implement early detection programs for this highly preventable pathology. Published by Masson Doyma Mexico S.A. on behalf of Asociacion Mexicana de Gastroenterologia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Introducción y objetivos: La dispepsia es una enfermedad de naturaleza multifactorial, ya que podrían intervenir distintos factores, como el consumo de alcohol, tabaco o antiinflamatorios no esteroideos, el estilo de vida, factores dietéticos, socioeconómicos y psicológicos. El objetivo es establecer la frecuencia y determinar los factores asociados con dispepsia no investigada en estudiantes de 4 facultades de medicina de Latinoamérica. Materiales y métodos: Estudio transversal analítico, aplicando una encuesta cerrada en un solo periodo, analizando posteriormente la asociación entre las variables. Se usó el test A new questionnaire for the diagnosis of dyspepsia para el diagnóstico de dispepsia no investigada, entre otras pruebas. Para el análisis bivariado y multivariado se utilizaron los modelos lineales generalizados, usando la familia Poisson con función de enlace log, obteniendo razones de prevalencia crudas, ajustadas y sus intervalos de confianza al 95%. Resultados: De los 1,241 encuestados, el 54% (841) fueron del sexo femenino y la mediana de edad fue de 21 a˜nos (rango 19-23 a˜nos). La prevalencia de dispepsia no investigada fue del 46%. Los factores que tuvieron una asociación directa a la dispepsia fueron: depresión, problemas para conciliar el sue˜no y el consumo de café. Por el contrario, comer regularmente en una pensión y el sexo masculino tuvieron una asociación inversa. Conclusiones: La frecuencia de dispepsia no investigada fue alta en estudiantes de 4 escuelas latinoamericanas. La depresión, la dificultad para dormir y el consumo constante de café fueron factores que se asociaron directamente con la dispepsia, mientras que el sexo masculino y el comer regularmente en una pensión fueron factores con una asociación inversa. Por lo tanto, recomendamos que las universidades implementen programas de detección temprana para esta patología altamente prevenible

Transcription of toll-like receptors 2, 3, 4 and 9, FoxP3 and Th17 cytokines in a susceptible experimental model of canine Leishmania infantum infection

Canine leishmaniosis (CanL) due to Leishmania infantum is a chronic zoonotic systemic disease resulting from complex interactions between protozoa and the canine immune system. Toll-like receptors (TLRs) are essential components of the innate immune system and facilitate the early detection of many infections. However, the role of TLRs in CanL remains unknown and information describing TLR transcription during infection is extremely scarce. The aim of this research project was to investigate the impact of L. infantum infection on canine TLR transcription using a susceptible model. The objectives of this study were to evaluate transcription of TLRs 2, 3, 4 and 9 by means of quantitative reverse transcription polymerase chain reaction (qRT-PCR) in skin, spleen, lymph node and liver in the presence or absence of experimental L. infantum infection in Beagle dogs. These findings were compared with clinical and serological data, parasite densities in infected tissues and transcription of IL-17, IL-22 and FoxP3 in different tissues in non-infected dogs (n = 10), and at six months (n = 24) and 15 months (n = 7) post infection. Results revealed significant down regulation of transcription with disease progression in lymph node samples for TLR3, TLR4, TLR9, IL-17, IL-22 and FoxP3. In spleen samples, significant down regulation of transcription was seen in TLR4 and IL-22 when both infected groups were compared with controls. In liver samples, down regulation of transcription was evident with disease progression for IL-22. In the skin, upregulation was seen only for TLR9 and FoxP3 in the early stages of infection. Subtle changes or down regulation in TLR transcription, Th17 cytokines and FoxP3 are indicative of the silent establishment of infection that Leishmania is renowned for. These observations provide new insights about TLR transcription, Th17 cytokines and Foxp3 in the liver, spleen, lymph node and skin in CanL and highlight possible markers of disease susceptibility in this model

The Molecular Clockwork of the Fire Ant Solenopsis invicta

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

Combining Deep Facial and Ambient Features for First Impression Estimation

14th European Conference on Computer Vision (ECCV) -- OCT 08-16, 2016 -- Amsterdam, NETHERLANDSFirst impressions influence the behavior of people towards a newly encountered person or a human-like agent. Apart from the physical characteristics of the encountered face, the emotional expressions displayed on it, as well as ambient information affect these impressions. In this work, we propose an approach to predict the first impressions people will have for a given video depicting a face within a context. We employ pre-trained Deep Convolutional Neural Networks to extract facial expressions, as well as ambient information. After video modeling, visual features that represent facial expression and scene are combined and fed to a Kernel Extreme Learning Machine regressor. The proposed system is evaluated on the ChaLearn Challenge Dataset on First Impression Recognition, where the classification target is the Big Five personality trait labels for each video. Our system achieved an accuracy of 90.94% on the sequestered test set, 0.36% points below the top system in the competition

Role of C/EBPβ Transcription Factor in Adult Hippocampal Neurogenesis

[Background]: The dentate gyrus of the hippocampus is one of the regions in which neurogenesis takes place in the adult brain. We have previously demonstrated that CCAAT/enhancer binding protein β (C/EBPβ) is expressed in the granular layer of the dentate gyrus of the adult mouse hippocampus. Taking into account the important role of C/EBPβ in the consolidation of long term memory, the fact that newborn neurons in the hippocampus contribute to learning and memory processes, and the role of this transcription factor, previously demonstrated by our group, in regulating neuronal differentiation, we speculated that this transcription factor could regulate stem/progenitor cells in this region of the brain. [Methodologu/Principal Findings]: Here, we show, using C/EBPβ knockout mice, that C/EBPβ expression is observed in the subset of newborn cells that proliferate in the hippocampus of the adult brain. Mice lacking C/EBPβ present reduced survival of newborn cells in the hippocampus, a decrease in the number of these cells that differentiate into neurons and a diminished number of cells that are proliferating in the subgranular zone of the dentate gyrus. These results were further confirmed in vitro. Neurosphere cultures from adult mice deficient in C/EBPβ present less proliferation and neuronal differentiation than neurospheres derived from wild type mice. [Conclusions/Significance]: In summary, using in vivo and in vitro strategies, we have identified C/EBPβ as a key player in the proliferation and survival of the new neurons produced in the adult mouse hippocampus. Our results support a novel role of C/EBPβ in the processes of adult hippocampal neurogenesis, providing new insights into the mechanisms that control neurogenesis in this region of the brain.This work was supported by a postdoctoral fellowship of the Consejo Superior de Investigaciones Cientificas (M.C.-C.) Grant Sponsor: Ministerio de Investigación y Ciencia; Grant numbers: SAF2007-62811 and SAF2010-16365. CIBERNED is funded by the Instituto de Salud Carlos III.Peer reviewe

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

P-loop mutations and novel therapeutic approaches for imatinib failures in chronic myeloid leukemia

Imatinib was the first BCR-ABL-targeted agent approved for the treatment of patients with chronic myeloid leukemia (CML) and confers significant benefit for most patients; however, a substantial number of patients are either initially refractory or develop resistance. Point mutations within the ABL kinase domain of the BCR-ABL fusion protein are a major underlying cause of resistance. Of the known imatinib-resistant mutations, the most frequently occurring involve the ATP-binding loop (P-loop). In vitro evidence has suggested that these mutations are more oncogenic with respect to other mutations and wild type BCR-ABL. Dasatinib and nilotinib have been approved for second-line treatment of patients with CML who demonstrate resistance (or intolerance) to imatinib. Both agents have marked activity in patients resistant to imatinib; however, they have differential activity against certain mutations, including those of the P-loop. Data from clinical trials suggest that dasatinib may be more effective vs. nilotinib for treating patients harboring P-loop mutations. Other mutations that are differentially sensitive to the second-line tyrosine kinase inhibitors (TKIs) include F317L and F359I/V, which are more sensitive to nilotinib and dasatinib, respectively. P-loop status in patients with CML and the potency of TKIs against P-loop mutations are key determinants for prognosis and response to treatment. This communication reviews the clinical importance of P-loop mutations and the efficacy of the currently available TKIs against them

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future