860 resultados para Hormone profile


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BACKGROUND:
It is compulsory that domestic football/soccer teams in UEFA competitions organise players' pre-participation medicals. Although screening guidelines have been established, these remain controversial. The findings of medical examinations can have lasting consequences for athletes and doctors. No previous studies have reported UEFA pre-participation screening results in semi-professional footballers. This study aims to further knowledge regarding 'normal' data in this population.

METHOD:
Retrospective audit and analysis of records of pre-season medicals for all male first-team players at one semi-professional Northern Ireland Premiership team between 2009-2012. Medicals were conducted by the club doctor following the UEFA proforma. Height, weight, blood pressure (BP), full blood count (FBC), dipstick urinalysis and resting electrocardiogram (ECG) were conducted by an independent nurse. Only one ECG must be documented during a player's career; other tests are repeated yearly.

RESULTS:
89 medicals from 47 players (6 goalkeepers, 11 defenders, 22 midfielders and 8 attackers; mean age 25.0 years (SD 4.86)) were reviewed. Mean height of the players was 179.3 cm (SD 5.90) with a mean weight of 77.6 kg (SD 10.5). Of 89 urine dipsticks, 7 were positive for protein; all 7 were normal on repeat testing following 48 hours of rest. Of 40 ECGs (mean ventricular rate 61.2 bpm (SD 11.6)), one was referred to cardiology (right bundle branch block; prolonged Q-T interval). No players were excluded from participation.

CONCLUSIONS:
This study provides important information about 'normal' values in a population of semi-professional footballers. Urinalysis showing protein is not uncommon but is likely to be normal on repeat testing.

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Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones released from intestinal enteroendocrine (EE) cells and have well-established glucose-lowering actions. Lactic acid bacteria (LAB) colonise the human intestine, but it is unknown whether LAB and EE cells interact. Acute co-culture of LAB with EE cells showed that certain LAB strains elicit GLP-1 and GIP secretion (13-194-fold) and upregulate their gene expression. LAB-induced incretin hormone secretion did not appear to involve nutrient mechanisms, nor was there any evidence of cytolysis. Instead PCR array studies implicated signalling agents of the toll-like receptor system, e.g. adaptor protein MyD88 was decreased 23-fold and cell surface antigen CD14 was increased 17-fold. Mechanistic studies found that blockade of MyD88 triggered significant GLP-1 secretion. Furthermore, blocking of CD14 completely attenuated LAB-induced secretion. A recent clinical trial clearly shows that LAB have potential for alleviating type 2 diabetes, and further characterisation of this bioactivity is warranted.

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Erbil (Hawler in Kurdish), is the capital and the largest city of Iraqi Kurdistan. Having been continuously inhabited for about 6000 years, the city has recently been regarded by UNESCO World Heritage as one of the world’s oldest urban settlements. The city is witnessing remarkable urban growth and rapid spatial expansion compounded by a dramatic increase in population due to emigration from the countryside and rural areas over the last three decades. Following the changing geopolitical landscape of post-war Iraq, urban changes and socio-political transformation are largely driven by Erbil’s growing autonomous status as the capital of northern region of Kurdistan since 2003. This paper explores the layers of historical, spatial and social developments of the contemporary urban context of Kurdistan in general and of Erbil in particular as a reflection of the changing status of the city, as well as the polarization of Iraq and the emergence of neoliberal urbanism. The tension between the global and modern from one side and traditional and authentic from another is ever present and evident in everyday challenges in the planning of the city. In large part, Erbil’s built fabric embodies the dichotomy of identity and contests between its past and future, in which the present remains a transition between two disconnected realities.

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Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

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Extrusion is one of the major methods for processing polymeric materials and the thermal homogeneity of the process output is a major concern for manufacture of high quality extruded products. Therefore, accurate process thermal monitoring and control are important for product quality control. However, most industrial extruders use single point thermocouples for the temperature monitoring/control although their measurements are highly affected by the barrel metal wall temperature. Currently, no industrially established thermal profile measurement technique is available. Furthermore, it has been shown that the melt temperature changes considerably with the die radial position and hence point/bulk measurements are not sufficient for monitoring and control of the temperature across the melt flow. The majority of process thermal control methods are based on linear models which are not capable of dealing with process nonlinearities. In this work, the die melt temperature profile of a single screw extruder was monitored by a thermocouple mesh technique. The data obtained was used to develop a novel approach of modelling the extruder die melt temperature profile under dynamic conditions (i.e. for predicting the die melt temperature profile in real-time). These newly proposed models were in good agreement with the measured unseen data. They were then used to explore the effects of process settings, material and screw geometry on the die melt temperature profile. The results showed that the process thermal homogeneity was affected in a complex manner by changing the process settings, screw geometry and material.