scholarly journals Morphological subclassification of intrahepatic cholangiocarcinoma: etiological, clinicopathological, and molecular features

2014 ◽  
Vol 27 (8) ◽  
pp. 1163-1173 ◽  
Author(s):  
Jau-Yu Liau ◽  
Jia-Huei Tsai ◽  
Ray-Hwang Yuan ◽  
Chih-Ning Chang ◽  
Hsin-Jung Lee ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Molecular Features

Immunohistochemical and molecular features of cholangiolocellular carcinoma are similar to well-differentiated intrahepatic cholangiocarcinoma

2019 ◽  
Vol 32 (10) ◽  
pp. 1486-1494 ◽  
Author(s):  
Dana Balitzer ◽  
Nancy M. Joseph ◽  
Linda Ferrell ◽  
Nafis Shafizadeh ◽  
Dhanpat Jain ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Cholangiolocellular Carcinoma ◽  
Molecular Features ◽  
Well Differentiated

scholarly journals Driver mutations of intrahepatic cholangiocarcinoma shape clinically relevant genomic clusters with distinct molecular features and therapeutic vulnerabilities

Theranostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 260-276
Author(s):  
Xiang-Yu Wang ◽  
Wen-Wei Zhu ◽  
Zheng Wang ◽  
Jian-Bo Huang ◽  
Sheng-Hao Wang ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Driver Mutations ◽  
Molecular Features ◽  
Genomic Clusters

An umbrella concept for patients with advanced intrahepatic cholangiocarcinoma

2020 ◽  
Author(s):  
T Longerich ◽  
KH Weiss ◽  
C Springfeld ◽  
A Stenzinger ◽  
P Schirmacher
Keyword(s):  
Intrahepatic Cholangiocarcinoma

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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