Influence of stromal neural crest progenitor cells on neuroblastoma radioresistance.

Purpose: A substantial proportion of children with high risk Neuroblastoma die within the first 5 years post-diagnosis despite the complex treatment applied. In the recent years, tumor environment has been revealed as key factor for cancer treatment efficacy. In this sense, non-tumorigenic Neural Crest progenitor cells from high risk patients, have been described as part of Neuroblastoma stroma, promoting tumor growth and contributing to mesenchyme formation.

Potential Prognostic Protein Biomarkers in Tears From Noninfectious Uveitis Patients Under Biologic Treatment as a Prelude to Personalized Medicine.

Purpose: Adalimumab (ADA) is a systemic biological treatment option approved for the treatment of noninfectious uveitis (NIU); however, up to 40% of patients do not respond to the drug, either in a primary or secondary manner. Here, we evaluated the proteomic profile of patients with NIU who fail to ADA to identify proteins implicated in intraocular inflammation, as well as potential biomarkers for treatment response and novel therapeutic targets.

Methods: Cross-sectional observational study of patients with NIU under ADA treatment for six or more months.

ZenoSWATH DIA proteomics and clustering analysis of the effect of cysteamine at the cellular level in cystinotic fibroblasts.

Cysteamine, an aminothiol, is the only available treatment for cystinosis, an incurable metabolic recessive disease characterized by detrimental symptoms at the renal, ocular, and muscular levels. Cystinosis is due to mutations in the CTNS gene encoding for the lysosomal symporter cystinosine. Cysteamine treatment only delays the symptoms, presents undesirable side effects and the patients depend on it for life.

The CrowdGleason dataset: Learning the Gleason grade from crowds and experts.

Background: Currently, prostate cancer (PCa) diagnosis relies on the human analysis of prostate biopsy Whole Slide Images (WSIs) using the Gleason score. Since this process is error-prone and time-consuming, recent advances in machine learning have promoted the use of automated systems to assist pathologists. Unfortunately, labeled datasets for training and validation are scarce due to the need for expert pathologists to provide ground-truth labels.