In vivo detection of laryngeal cancer by hyperspectral imaging combined with deep learning methods (Conference Presentation)

Author(s):  
Dennis Eggert ◽  
Marcel Bengs ◽  
Stephan Westermann ◽  
Nils Gessert ◽  
Andreas O. Gerstner ◽  
...  
Author(s):  
Dennis Eggert ◽  
Marcel Bengs ◽  
Stephan Westermann ◽  
Nils Gessert ◽  
Andreas O.H. Gerstner ◽  
...  

Author(s):  
Marcel Bengs ◽  
Stephan Westermann ◽  
Nils Gessert ◽  
Dennis Eggert ◽  
Andreas O. H. Gerstner ◽  
...  

2019 ◽  
Vol 158 ◽  
pp. 258-270 ◽  
Author(s):  
Jun-Li Xu ◽  
Alexia Gobrecht ◽  
Daphné Héran ◽  
Nathalie Gorretta ◽  
Marie Coque ◽  
...  

2000 ◽  
Vol 110 (3) ◽  
pp. 368-373 ◽  
Author(s):  
Wolfgang Delank ◽  
Barbara Khanavkar ◽  
John A. Nakhosteen ◽  
Wolfgang Stoll

2021 ◽  
Author(s):  
Constantin Schneider ◽  
Andrew Buchanan ◽  
Bruck Taddese ◽  
Charlotte M. Deane

AbstractAntibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in-vivo and in-vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. DLAB also outperforms baseline methods at identifying binding antibodies against specific antigens in a series of case studies. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.


2016 ◽  
Vol 250 ◽  
pp. 106-113 ◽  
Author(s):  
Hideo Chihara ◽  
Naoya Oishi ◽  
Akira Ishii ◽  
Toshihiro Munemitsu ◽  
Daisuke Arai ◽  
...  

2016 ◽  
Author(s):  
Yingjie Qu ◽  
Wenqi Ren ◽  
Songde Liu ◽  
Peng Liu ◽  
Lan Xie ◽  
...  

RSC Advances ◽  
2020 ◽  
Vol 10 (68) ◽  
pp. 41936-41945
Author(s):  
Tianying Yan ◽  
Long Duan ◽  
Xiaopan Chen ◽  
Pan Gao ◽  
Wei Xu

Hyperspectral imaging provides an effective way to identify the geographical origin of Radix Glycyrrhizae to assess its quality.


1991 ◽  
Vol 65 (04) ◽  
pp. 432-437 ◽  
Author(s):  
A W J Stuttle ◽  
M J Powling ◽  
J M Ritter ◽  
R M Hardisty

SummaryThe anti-platelet monoclonal antibody P256 is currently undergoing development for in vivo detection of thrombus. We have examined the actions of P256 and two fragments on human platelet function. P256, and its divalent fragment, caused aggregation at concentrations of 10−9−3 × 10−8 M. A monovalent fragment of P256 did not cause aggregation at concentrations up to 10−7 M. P256–induced platelet aggregation was dependent upon extracellular calcium ions as assessed by quin2 fluorescence. Indomethacin partially inhibited platelet aggregation and completely inhibited intracellular calcium mobilisation. Apyrase caused partial inhibition of aggregation. Aggregation induced by the divalent fragment was dependent upon fibrinogen and was inhibited by prostacyclin. Aggregation induced by the whole antibody was only partially dependent upon fibrinogen, but was also inhibited by prostacyclin. P256 whole antibody was shown, by flow cytometry, to induce fibrinogen binding to indomethacin treated platelets. Monovalent P256 was shown to be a specific antagonist for aggregation induced by the divalent forms. In–111–labelled monovalent fragment bound to gel-filtered platelets in a saturable and displaceable manner. Monovalent P256 represents a safer form for in vivo applications


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