4 resultados para luteinizing hormone receptor


Relevância:

90.00% 90.00%

Publicador:

Resumo:

Background: Women with germline BRCA1 mutations have a high lifetime risk of breast cancer, with the only available risk-reduction strategies being risk-reducing surgery or chemoprevention. These women predominantly develop triple-negative breast cancers; hence, it is unlikely that selective estrogen receptor modulators (serms) will reduce the risk of developing cancer, as these have not been shown to reduce the incidence of estrogen receptor–negative breast cancers. Preclinical data from our laboratory suggest that exposure to estrogen and its metabolites is capable of causing dna double-strand breaks (dsbs) and thus driving genomic instability, an early hallmark of BRCA1-related breast cancer. Therefore, an approach that lowers circulating estrogen levels and reduces estrogen metabolite exposure may prove a successful chemopreventive strategy.

Aims: To provide proof of concept of the hypothesis that the combination of luteinizing-hormone releasing-hormone agonists (lhrha) and aromatase inhibitors (ais) can suppress circulating levels of estrogen and its metabolites in BRCA1 mutation carriers, thus reducing estrogen metabolite levels in breast cells, reducing dna dsbs, and potentially reducing the incidence of breast cancer.

Methods: 12 Premenopausal BRCA1 mutation carriers will undergo baseline ultrasound-guided breast core biopsy and plasma and urine sampling. Half the women will be treated for 3 months with combination goserelin (lhrha) plus anastrazole (ai), and the remainder with tamoxifen (serm) before repeat tissue, plasma, and urine sampling. After a 1-month washout period, groups will cross over for a further 3 months treatment before final biologic sample collection. Tissue, plasma, and urine samples will be examined using a combination of immunohistochemistry, comet assays, and ultrahigh performance liquid chromatography tandem mass spectrometry to assess the impact of lhrha plus ai compared with serm on levels of dna damage, estrogens, and genotoxic estrogen metabolites. Quality of life will also be assessed during the study.

Results: This trial is currently ongoing.

Relevância:

80.00% 80.00%

Publicador:

Resumo:

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

B-type natriuretic peptide (BNP) is a prognostic and diagnostic marker for heart failure (HF). An anti-inflammatory, cardio-protective role for BNP was proposed. In cardiovascular diseases including pressure overload-induced HF, perivascular inflammation and cardiac fibrosis are, in part, mediated by monocyte chemoattractant protein (MCP)1-driven monocyte migration. We aimed to determine the role of BNP in monocyte motility to MCP1. A functional BNP receptor, natriuretic peptide receptor-A (NPRA) was identified in human monocytes. BNP treatment inhibited MCP1-induced THP1 (monocytic leukemia cells) and primary monocyte chemotaxis (70 and 50 %, respectively). BNP did not interfere with MCP1 receptor expression or with calcium. BNP inhibited activation of the cytoskeletal protein RhoA in MCP1-stimulated THP1 (70 %). Finally, BNP failed to inhibit MCP1-directed motility of monocytes from patients with hypertension (n = 10) and HF (n = 6) suggesting attenuation of this anti-inflammatory mechanism in chronic heart disease. We provide novel evidence for a direct role of BNP/NPRA in opposing human monocyte migration and support a role for BNP as a cardio-protective hormone up-regulated as part of an adaptive compensatory response to combat excess inflammation.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Crystallization and determination of the high resolution three-dimensional structure of the β2-adrenergic receptor in 2007 was followed by structure elucidation of a number of other receptors, including those for neurotensin and glucagon. These major advances foster the understanding of structure-activity relationship of these receptors and structure-based rational design of new ligands having more predictable activity. At present, structure determination of gut hormone receptors in complex with their ligands (natural, synthetic) and interacting signalling proteins, for example, G-proteins, arrestins, represents a challenge which promises to revolutionize gut hormone endocrinonology.