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Dibyen Majumdar

Researcher at University of Illinois at Chicago

Publications -  67
Citations -  2238

Dibyen Majumdar is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Optimal design & Melanoma. The author has an hindex of 25, co-authored 67 publications receiving 2033 citations. Previous affiliations of Dibyen Majumdar include Indian Statistical Institute.

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Bacterial redox protein azurin, tumor suppressor protein p53, and regression of cancer.

TL;DR: The use of a purified bacterial redox protein, azurin, that enters human cancer (melanoma UISO-Mel-2) cells and induces apoptosis is reported and has been shown to allow regression of human U ISO- Mel-2 tumors xenotransplanted in nude mice and may potentially be used in cancer treatment.
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Bacterial cupredoxin azurin as an inducer of apoptosis and regression in human breast cancer

TL;DR: In conclusion, azurin blocks breast cancer cell proliferation and induces apoptosis through the mitochondrial pathway both in vitro and in vivo, thereby suggesting a potential chemotherapeutic application of this bacterial cupredoxin for the treatment of breast cancer.
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A first-in-class, first-in-human, phase i trial of p28, a non-HDM2-mediated peptide inhibitor of p53 ubiquitination in patients with advanced solid tumours

TL;DR: Evidence of anti-tumour activity indicates a highly favourable therapeutic index and demonstrates proof of concept for this new class of non-HDM2-mediated peptide inhibitors of p53 ubiquitination.
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Micropthalmia transcription factor: a new prognostic marker in intermediate-thickness cutaneous malignant melanoma.

TL;DR: The data suggest that Mitf may be a new molecular prognostic marker in patients with intermediate-thickness melanoma and findings persisted in multivariate analysis.
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Optimal designs for comparing test treatments with controls

TL;DR: In this article, existing knowledge on optimal designs for comparing test treatments with controls under 0-, 1-, and 2-way elimination of heterogeneity models is presented. But the results are motivated through numerical examples.