Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer.

Within ovarian cancer research, patient-derived xenograft (PDX) models recapitulate histologic features and genomic aberrations found in original tumors. However, conflicting data from published studies have demonstrated significant transcriptional differences between PDXs and original tumors, challenging the fidelity of these models. We employed a quantitative mass spectrometry-based proteomic approach coupled with generation of patient-specific databases using RNA-seq data to investigate the proteogenomic landscape of serially-passaged PDX models established from two patients with distinct subtypes of ovarian cancer.

Automated Characterization of Abdominal MRI Exams Using Deep Learning.

Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models.

Dopaminergic Axon Tracts Within a Hyaluronic Acid Hydrogel Encasement to Restore the Nigrostriatal Pathway.

Parkinson's disease is characterized by motor deficits emerging from insufficient dopamine in the striatum after degeneration of dopaminergic neurons and their long-projecting axons comprising the nigrostriatal pathway. To address this, a tissue-engineered nigrostriatal pathway (TE-NSP) featuring a tubular hydrogel with a collagen/laminin core that encases aggregated dopaminergic neurons and their axonal tracts is developed. This engineered microtissue can be implanted to replace neurons and axons with fidelity to the lost pathway and thus may provide dopamine according to feedback from host circuitry.