458 resultados para Cardiac disease


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In the nineteenth century, when female travel narratives of miss(adventure) were still read as excursions rather than expeditions, it was common for women travellers to preface their writing with an apology or admission of guilt—a type of disclaimer that excused the author for engaging in such inappropriate activity and bothering the reader with their trivial endeavours. Susan Gilman’s Undress Me in the Temple of Heaven offers no such thing. Instead Gilman begins her memoir with a confession about its lack of lies, half-truth and spin. ‘This is a true story,’ she writes, ‘recounted as accurate as possible and corroborated by notes I took at the time and by others who were present.’

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Chlamydia trachomatis is an obligate intracellular bacterial pathogen that infects the genital and ocular mucosa of humans, causing infections that can lead to pelvic inflammatory disease, infertility, and blinding trachoma. C. pneumoniae is a respiratory pathogen that is the cause of 12–15% of community-acquired pneumonia. Both chlamydial species were believed to be restricted to the epithelia of the genital, ocular, and respiratory mucosa; however, increasing evidence suggests that both these pathogens can be isolated from peripheral blood of both healthy individuals and patients with inflammatory conditions such as coronary artery disease and asthma. Chlamydia can also be isolated from brain tissues of patients with degenerative neurological disorders such as Alzheimer’s disease and multiple sclerosis, and also from certain lymphomas. An increasing number of in vitro studies suggest that some chlamydial species can infect immune cells, at least at low levels. These infections may alter immune cell function in a way that promotes chlamydial persistence in the host and contributes to the progression of several chronic inflammatory diseases. In this paper, we review the evidence for the growth of Chlamydia in immune cells, particularly monocytes/macrophages and dendritic cells, and describe how infection may affect the function of these cells.

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Obestatin is a 23 amino acid, ghrelin gene-derived peptide hormone produced in the stomach and a range of other tissues throughout the body. While it was initially reported that obestatin opposed the actions of ghrelin with regards to appetite and food intake, it is now clear that obestatin is not an endogenous ghrelin antagonist of ghrelin, but it is a multi-functional peptide hormone in its own right. In this review we will discuss the controversies associated with the discovery of obestatin and explore emerging central and peripheral roles of obestatin, roles in adipogenesis, pancreatic homeostasis and cancer.

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Previous studies exploring the incidence and readmission rates of cardiac patients admitted to a coronary care unit (CCU) with type 2 diabetes [1] have been undertaken by the first author. Interviews of these patients regarding their experiences in managing their everyday conditions [2] provided the basis for developing the initial cardiac–diabetes self-management programme (CDSMP) [3]. Findings from each of these previous studies highlighted the complexity of self-management for patients with both conditions and contributed to the creation of a new self-management programme, the CDSMP, based on Bandura’s (2004) self-efficacy theory [4]. From patient and staff feedback received for the CDSMP [3], it became evident that further revision of the programme was needed to improve self-management levels of patients and possibility of incorporating methods of information technology (IT). Little is known about the applicability of different methods of technology for delivering self-management programmes for patients with chronic diseases such as those with type 2 diabetes and cardiac conditions. Although there is some evidence supporting the benefits and the great potential of using IT in supporting self-management programmes, it is not strong, and further research on the use of IT in such programmes is recommended [5–7]. Therefore, this study was designed to pilot test feasibility of the CDSMP incorporating telephone and text-messaging as follow-up approaches.

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This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

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