Ochroconis gallopava bronchitis mimicking haemoptysis in a patient with bronchiectasis.

Ochroconis gallopava is an anamorphic mould characterized by slow growth rate and production of a maroon pigment, which has been isolated worldwide from soil, thermal springs, decaying vegetation, and chicken litter. It has been reported to cause localized, mostly pulmonary, and systemic infection in severely immunocompromised patients. We describe the case of a 76-year-old woman known for ulcerative colitis-related bronchiectasis treated with low dose oral steroids, who developed a fungal bronchitis with dark, bloody-like, sputum which was initially misinterpreted as haemoptysis. A filamentary mould grew on sputum culture, and was identified by DNA analysis as Ochroconis gallopava. We observed a significant clinical improvement after 6 weeks of itraconazole therapy

Alcohol, microbiome, life style influence alcohol and non-alcoholic organ damage

This paper is based upon the "8th Charles Lieber's Satellite Symposium" organized by Manuela G. Neuman at the Research Society on Alcoholism Annual Meeting, on June 25, 2016 at New Orleans, Louisiana, USA. The integrative symposium investigated different aspects of alcohol-induced liver disease (ALD) as well as non alcohol -induced liver disease (NAFLD) and possible repair. We revealed the basic aspects of alcohol metabolism that may be responsible for the development of liver disease as well as the factors that determine the amount, frequency and which type of alcohol misuse leads to liver and gastrointestinal diseases. We aimed to (1) describe the immuno-pathology of ALD, (2) examine the role of genetics in the development of alcoholic hepatitis (ASH) and NAFLD, (3) propose diagnostic markers of ASH and non-alcoholic steatohepatitis (NASH), (4) examine age and ethnic differences as well as analyze the validity of some models, (5) develop common research tools and biomarkers to study alcohol-induced effects, 6) examine the role of alcohol in oral health and colon and gastrointestinal cancer and (7) focus on factors that aggravate the severity of organ-damage. The present review includes pre-clinical, translational and clinical research that characterizes ALD and NAFLD. Strong clinical and experimental evidence lead to recognition of the key toxic role of alcohol in the pathogenesis of ALD with simple fatty infiltrations and chronic alcoholic hepatitis with hepatic fibrosis or cirrhosis. These latter stages may also be associated with a number of cellular and histological changes, including the presence of Mallory's hyaline, megamitochondria, or perivenular.and perisinusoidal fibrosis. Genetic polymorphisms of ethanol metabolizing enzymes and cytochrome p450 (CYP) 2E1 activation may change the severity of ASH and NASH. Other risk factors such as its co-morbidities with chronic viral hepatitis in the presence or absence of human deficiency virus were discussed. Dysregulation of metabolism, as a result of ethanol exposure, in the intestine leads to colon carcinogenesis. The hepatotoxic effects of ethanol undermine the contribution of malnutrition to the liver injury. Dietary interventions such as micro and macronutrients, as well as changes to the microbiota have been suggested. The clinical aspects of NASH, as part of the metabolic syndrome in the aging population, have been presented. The symposium addressed mechanisms and biomarkers of alcohol induced damage to different organs, as well as the role of the microbiome in this dialog. The microbiota regulates and acts as a key element in harmonizing immune responses at intestinal mucosal surfaces. It is known that microbiota is an inducer of proinflammatory T helper 17 cells and regulatory T cells in the intestine. The signals at the sites of inflammation mediate recruitment and differentiation in order to remove inflammatory inducers and promote tissue homeostasis restoration. The change in the intestinal microbiota also influences the change in obesity and regresses the liver steatosis. Evidence on the positive role of moderate alcohol consumption on heart and metabolic diseases as well on reducing steatosis have been looked up. Moreover nutrition as a therapeutic intervention in alcoholic liver disease has been discussed. In addition to the original data, we searched the literature (2008-2016) for the latest publication on the described subjects. In order to obtain the updated data we used the usual engines (Pub Med and Google Scholar). The intention of the eighth symposia was to advance the international profile of the biological research on alcoholism. We also wish to further our mission of leading the forum to progress the science and practice of translational research in alcoholism. (C) 2017 Elsevier Inc. All rights reserved.Peer reviewe

Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB

The multiple faces of self-assembled lipidic systems

Lipids, the building blocks of cells, common to every living organisms, have the propensity to self-assemble into well-defined structures over short and long-range spatial scales. The driving forces have their roots mainly in the hydrophobic effect and electrostatic interactions. Membranes in lamellar phase are ubiquitous in cellular compartments and can phase-separate upon mixing lipids in different liquid-crystalline states. Hexagonal phases and especially cubic phases can be synthesized and observed in vivo as well. Membrane often closes up into a vesicle whose shape is determined by the interplay of curvature, area difference elasticity and line tension energies, and can adopt the form of a sphere, a tube, a prolate, a starfish and many more. Complexes made of lipids and polyelectrolytes or inorganic materials exhibit a rich diversity of structural morphologies due to additional interactions which become increasingly hard to track without the aid of suitable computer models. From the plasma membrane of archaebacteria to gene delivery, self-assembled lipidic systems have left their mark in cell biology and nanobiotechnology; however, the underlying physics is yet to be fully unraveled

Location patterns of urban industry in Shanghai and implications for sustainability

China’s economy has undergone rapid transition and industrial restructuring. The term “urban industry” describes a particular type of industry within Chinese cities experiencing restructuring. Given the high percentage of industrial firms that have either closed or relocated from city centres to the urban fringe and beyond, emergent global cities such as Shanghai, are implementing strategies for local economic and urban development, which involve urban industrial upgrading numerous firms in the city centre and urban fringe. This study aims to analyze the location patterns of seven urban industrial sectors within the Shanghai urban region using 2008 micro-geography data. To avoid Modifiable Areal Unit Problem (MAUP) issue, four distance-based measures including nearest neighbourhood analysis, Kernel density estimation, K-function and co-location quotient have been extensively applied to analyze and compare the concentration and co-location between the seven sectors. The results reveal disparate patterns varying with distance and interesting co-location as well. The results are as follows: the city centre and the urban fringe have the highest intensity of urban industrial firms, but the zones with 20–30 km from the city centre is a watershed for most categories; the degree of concentration varies with distance, weaker at shorter distance, increasing up to the maximum distance of 30 km and then decreasing until 50 km; for all urban industries, there are three types of patterns, mixture of clustered, random and dispersed distribution at a varied range of distances. Consequently, this paper argues that the location pattern of urban industry reflects the stage-specific industrial restructuring and spatial transformation, conditioned by sustainability objectives