Recovery of NIS expression in thyroid cancer cells by overexpression of Pax8 gene

BACKGROUND: Recovery of iodide uptake in thyroid cancer cells by means of obtaining the functional expression of the sodium/iodide symporter (NIS) represents an innovative strategy for the treatment of poorly differentiated thyroid cancer. However, the NIS gene expression alone is not always sufficient to restore radioiodine concentration ability in these tumour cells. METHODS: In this study, the anaplastic thyroid carcinoma ARO cells were stably transfected with a Pax8 gene expression vector. A quantitative RT-PCR was performed to assess the thyroid specific gene expression in selected clones. The presence of NIS protein was detected by Western blot and localized by immunofluorescence. A iodide uptake assay was also performed to verify the functional effect of NIS induction and differentiation switch. RESULTS: The clones overexpressing Pax8 showed the re-activation of several thyroid specific genes including NIS, Pendrin, Thyroglobulin, TPO and TTF1. In ARO-Pax8 clones NIS protein was also localized both in cell cytoplasm and membrane. Thus, the ability to uptake the radioiodine was partially restored, associated to a high rate of efflux. In addition, ARO cells expressing Pax8 presented a lower rate of cell growth. CONCLUSION: These finding demonstrate that induction of Pax8 expression may determine a re-differentiation of thyroid cancer cells, including a partial recovery of iodide uptake, fundamental requisite for a radioiodine-based therapeutic approach for thyroid tumours

Scheduling jobs in the cloud using on-demand and reserved instances

Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy — larger or faster instances? on-demand or reserved instances? etc.— and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker’s perspective. We propose, CoH, a family of Cloud-based, online, Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies