scholarly journals The role of molecular imaging in modern drug development

2014 ◽  
Vol 19 (7) ◽  
pp. 936-948 ◽  
Author(s):  
Lídia Cunha ◽  
Krisztián Szigeti ◽  
Domokos Mathé ◽  
Luís F. Metello
2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S71-S71
Author(s):  
Anissa Abi-Dargham ◽  
Lawrence Kegeles ◽  
Mark Slifstein

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 943-P
Author(s):  
LAI-SAN THAM ◽  
JEANNE GEISER ◽  
CHENG CAI TANG ◽  
KAREN SCHNECK ◽  
DAVID COX ◽  
...  

2015 ◽  
Vol 15 (13) ◽  
pp. 1073-1094 ◽  
Author(s):  
Rubel Chakravarty ◽  
Sudipta Chakraborty ◽  
Ashutosh Dash

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Miao-Miao Zhao ◽  
Wei-Li Yang ◽  
Fang-Yuan Yang ◽  
Li Zhang ◽  
Wei-Jin Huang ◽  
...  

AbstractTo discover new drugs to combat COVID-19, an understanding of the molecular basis of SARS-CoV-2 infection is urgently needed. Here, for the first time, we report the crucial role of cathepsin L (CTSL) in patients with COVID-19. The circulating level of CTSL was elevated after SARS-CoV-2 infection and was positively correlated with disease course and severity. Correspondingly, SARS-CoV-2 pseudovirus infection increased CTSL expression in human cells in vitro and human ACE2 transgenic mice in vivo, while CTSL overexpression, in turn, enhanced pseudovirus infection in human cells. CTSL functionally cleaved the SARS-CoV-2 spike protein and enhanced virus entry, as evidenced by CTSL overexpression and knockdown in vitro and application of CTSL inhibitor drugs in vivo. Furthermore, amantadine, a licensed anti-influenza drug, significantly inhibited CTSL activity after SARS-CoV-2 pseudovirus infection and prevented infection both in vitro and in vivo. Therefore, CTSL is a promising target for new anti-COVID-19 drug development.


Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.


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