D
David Killock
Researcher at King's College London
Publications - 260
Citations - 767
David Killock is an academic researcher from King's College London. The author has contributed to research in topics: Medicine & MEDLINE. The author has an hindex of 10, co-authored 232 publications receiving 547 citations. Previous affiliations of David Killock include National Institutes of Health.
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Journal ArticleDOI
The cytoplasmic domains of TNFα-converting enzyme (TACE/ADAM17) and L-selectin are regulated differently by p38 MAPK and PKC to promote ectodomain shedding
David Killock,Aleksandar Ivetic +1 more
TL;DR: It was showed that TNFalpha-induced shedding of L-selectin in monocytes was strikingly similar to cantharidin- induced shedding and suggested that this newly characterized mechanism could be physiologically relevant in inflammatory cells.
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In Vitro and in Vivo Characterization of Molecular Interactions between Calmodulin, Ezrin/Radixin/Moesin, and L-selectin.
David Killock,Maddy Parsons,Marouan Zarrouk,Simon Ameer-Beg,Anne J. Ridley,Dorian O. Haskard,Marketa Zvelebil,Aleksandar Ivetic +7 more
TL;DR: It was found that recombinant purified CaM and ERM bound non-competitively to the same tail of L-selectin, which highlights a novel intracellular event that occurs as a consequence of L -selectin clustering, which could participate in transducing signals that promote the transition from rolling to arrest.
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AI outperforms radiologists in mammographic screening.
TL;DR: Artificial intelligence has the potential to alleviate pressures on services in the context of a worldwide shortage of radiologists and increase the accuracy, efficacy and efficiency of screening, as well as reduce patient wait times and stress.
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Skin cancer: T-VEC oncolytic viral therapy shows promise in melanoma.
TL;DR: A joint review panel have recommended that the FDA approve T-VEC for patients with unresectable and metastatic melanoma; thus, final FDA approval later this year is widely anticipate.
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CNS cancer: molecular classification of glioma.
TL;DR: Incorporating the authors' molecular classification schema into a patient’s diagnosis will more-accurately predict their prognosis and help determine how they should be treated, Eckel-Passow concludes.