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Biomedical subjects

M Langaas

Publications and source records attributed to M Langaas.

2 recordsLinked to original sources

Identification of novel growth factor-responsive genes in neuroendocrine gastrointestinal tumour cells.

Targeting growth-regulatory pathways is a promising approach in cancer treatment. A prerequisite to the development of such therapies is characterisation of tumour growth regulation in the particular tumour cell type of interest. In order to gain insight into molecular mechanisms underlying proliferative responses in neuroendocrine (NE) gastrointestinal (GI) tumours, we investigated gene expression in human carcinoid BON cells after exposure to gastrin, hepatocyte growth factor (HGF), pituitary adenylate cyclase-activating polypeptide or epidermal growth factor. We particularly focused on gastrin- and HGF-induced gene expression, and identified 95 gastrin- and 101 HGF-responsive genes. The majority of these genes are known mediators of processes central in tumour biology, and a number of them have been associated with poor prognosis and metastasis in cancer patients. Furthermore, we identified 12 genes that were regulated by all four factors, indicating that they may be universally regulated during NE GI tumour cell proliferation. Our findings provide useful hypotheses for further studies aimed to search for new therapeutic targets as well as tumour markers in NE GI tumours.

Carcinoid Tumor↗

Simulated likelihood methods for complex double-platform line transect surveys.

The conventional line transect approach of estimating effective search width from the perpendicular distance distribution is inappropriate in certain types of surveys, e.g., when an unknown fraction of the animals on the track line is detected, the animals can be observed only at discrete points in time, there are errors in positional measurements, and covariate heterogeneity exists in detectability. For such situations a hazard probability framework for independent observer surveys is developed. The likelihood of the data, including observed positions of both initial and subsequent observations of animals, is established under the assumption of no measurement errors. To account for measurement errors and possibly other complexities, this likelihood is modified by a function estimated from extensive simulations. This general method of simulated likelihood is explained and the methodology applied to data from a double-platform survey of minke whales in the northeastern Atlantic in 1995.

Animals↗