Mass-spectrometric studies of new 6-nitroquipazines—serotonin transporter inhibitors

Six synthesized 6-nitroquipazine derivatives were examined by electron ionization (EI) and electrospray ionization (ESI) mass spectrometry in positive and negative ion mode. The compounds exhibit high affinity for the serotonin transporter (SERT) and belong to a new class of SERT inhibitors. The EI mass spectra registered in negative ion mode showed prominent molecular ions for all the compounds studied. All EI mass spectra and all ESI mass spectra showed similar fragmentation pathways of molecular ions, but the pathways differed between EI and ESI. The differences were explained with the aid of theoretical evaluation of the stability of the respective radical ions (EI MS) and protonated ions (ESI MS)

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation