Behavioural aspects of smoking (both passive and active) and alcohol consumption on the risk of myocardial infarction

Objectives: To investigate the effect of alcohol consumption and of passive and active smoking on the risk of myocardial infarction (MI). Methods: Data on 429 cases with MI and 434 controls was obtained through an interviewer-led questionnaire as part of the Maltese Acute Myocardial Infarction (MAMI) Study. Regular alcohol drinkers were defined as subjects having at least one drink per week for one year and binge drinkers as having six or more drinks on one occasion this last year. Current smokers were excluded from the analysis of passive smoking. Odds ratios (AdjOR) were adjusted for age, gender, smoking/drinking alcohol, hypertension, diabetes, hypercholesterolaemia and BMI. Results: Regular alcohol drinkers were protected against MI [AdjOR 0.6 (95%CI 0.4-0.8)]. The risk of MI associated with binge drinking varies with the frequency, reaching an AdjOR of 5.8 (95%CI 1.2-27.1) in daily binge drinkers. The AdjOR for current smokers was 3.1 (95%CI 2.0-4.9) and for ex-smokers 1.6 (95%CI 1.1-2.4). Passive smoking also increased the risk of MI [AdjOR 3.0 (95%CI 1.7-5.4)]. Passive smoke exposure in a home setting had a greater deleterious effect [AdjOR 2.8 (95%CI 1.6-4.7)] than exposure in a public setting [AdjOR 1.4 (95%CI 0.9-2.2)]. While periods of 1 hour or longer of passive smoke exposure were found to be deleterious in both the investigated settings, exposure for less than 1 hour was only a risk factor in a home setting. Conclusion: The effect of alcohol consumption on the risk for MI varies from protective to extremely deleterious depending on the frequency of drinking. Daily binge drinking is associated with a high risk of MI. Smoking, even passive smoking, is a risk factor of MI. The effect of passive smoking on the risk of MI is greater in a home than in a public setting

Automatic semantic annotation using unsupervised information extraction and integration

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.peer-reviewe

Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV-1 infection

Cells release diverse types of extracellular vesicles (EVs), which transfer complex signals to surrounding cells. Specific markers to distinguish different EVs (e.g. exosomes, ectosomes, enveloped viruses like HIV) are still lacking. We have developed a proteomic profiling approach for characterizing EV subtype composition and applied it to human Jurkat T cells. We generated an interactive database to define groups of proteins with similar profiles, suggesting release in similar EVs. Biochemical validation confirmed the presence of preferred partners of commonly used exosome markers in EVs: CD81/ADAM10/ITGB1, and CD63/syntenin. We then compared EVs from control and HIV-1-infected cells. HIV infection altered EV profiles of several cellular proteins, including MOV10 and SPN, which became incorporated into HIV virions, and SERINC3, which was re-routed to non-viral EVs in a Nef-dependent manner. Furthermore, we found that SERINC3 controls the surface composition of EVs. Our workflow provides an unbiased approach for identifying candidate markers and potential regulators of EV subtypes. It can be widely applied to in vitro experimental systems for investigating physiological or pathological modifications of EV release

Severe akathisia as a side effect of metoclopramide

Case description A case of severe metoclopramide-induced akathisia in a breast cancer patient being treated with chemotherapy is presented, eventually culminating in hospital admission. In retrospect, this adverse effect was not recognized for several weeks because the prescription had not been properly recorded in the chart, the patient initially denied using the drug, and extensive psychological adjustment difficulties were also present. Conclusion Movement disorders as an adverse effect of metoclopramide have been described on a regular basis over the past decades. Case reports such as this confirm there is under-recognition of adverse effects and emphasize the need to take a comprehensive medication history and recognize well known side effects of medications such as metoclopramide

Mathematical modeling of the metastatic process

Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on understanding tumor growth laws and has grown to include the competition between healthy and normal tissue, carcinogenesis, therapy and metastasis. It is the latter topic, metastasis, on which we will focus this short review, specifically discussing various computational and mathematical models of different portions of the metastatic process, including: the emergence of the metastatic phenotype, the timing and size distribution of metastases, the factors that influence the dormancy of micrometastases and patterns of spread from a given primary tumor.Comment: 24 pages, 6 figures, Revie

Allometric Scaling of the Active Hematopoietic Stem Cell Pool across Mammals

BACKGROUND: Many biological processes are characterized by allometric relations of the type Y = Y (0) M(b) between an observable Y and body mass M, which pervade at multiple levels of organization. In what regards the hematopoietic stem cell pool, there is experimental evidence that the size of the hematopoietic stem cell pool is conserved in mammals. However, demands for blood cell formation vary across mammals and thus the size of the active stem cell compartment could vary across species. METHODOLOGY/PRINCIPLE FINDINGS: Here we investigate the allometric scaling of the hematopoietic system in a large group of mammalian species using reticulocyte counts as a marker of the active stem cell pool. Our model predicts that the total number of active stem cells, in an adult mammal, scales with body mass with the exponent ¾. CONCLUSION/SIGNIFICANCE: The scaling predicted here provides an intuitive justification of the Hayflick hypothesis and supports the current view of a small active stem cell pool supported by a large, quiescent reserve. The present scaling shows excellent agreement with the available (indirect) data for smaller mammals. The small size of the active stem cell pool enhances the role of stochastic effects in the overall dynamics of the hematopoietic system

A Simple Mathematical Model Based on the Cancer Stem Cell Hypothesis Suggests Kinetic Commonalities in Solid Tumor Growth

Background: The Cancer Stem Cell (CSC) hypothesis has gained credibility within the cancer research community. According to this hypothesis, a small subpopulation of cells within cancerous tissues exhibits stem-cell-like characteristics and is responsible for the maintenance and proliferation of cancer. Methodologies/Principal Findings: We present a simple compartmental pseudo-chemical mathematical model for tumor growth, based on the CSC hypothesis, and derived using a ‘‘chemical reaction’ ’ approach. We defined three cell subpopulations: CSCs, transit progenitor cells, and differentiated cells. Each event related to cell division, differentiation, or death is then modeled as a chemical reaction. The resulting set of ordinary differential equations was numerically integrated to describe the time evolution of each cell subpopulation and the overall tumor growth. The parameter space was explored to identify combinations of parameter values that produce biologically feasible and consistent scenarios. Conclusions/Significance: Certain kinetic relationships apparently must be satisfied to sustain solid tumor growth and to maintain an approximate constant fraction of CSCs in the tumor lower than 0.01 (as experimentally observed): (a) the rate of symmetrical and asymmetrical CSC renewal must be in the same order of magnitude; (b) the intrinsic rate of renewal and differentiation of progenitor cells must be half an order of magnitude higher than the corresponding intrinsic rates for cancer stem cells; (c) the rates of apoptosis of the CSC, transit amplifying progenitor (P) cells, and terminally differentiate

Imatinib Mesylate Reduces Endoplasmic Reticulum Stress and Induces Remission of Diabetes in db/db Mice

OBJECTIVE—Imatinib has been reported to induce regression of type 2 diabetes in chronic leukemia patients. However, the mechanism of diabetes amelioration by imatinib is unknown, and it is uncertain whether imatinib has effects on type 2 diabetes itself without other confounding diseases like leukemia. We studied the effect of imatinib on diabetes in db/db mice and investigated possible mechanism's underlying improved glycemic control by imatinib