A Preliminary Study Assessing a Transfer Learning Approach to Intestinal Image Analysis to Help Determine Treatment Response in Canine Protein-Losing Enteropathy.

Dogs with protein-losing enteropathy (PLE) caused by inflammatory enteritis, intestinal lymphangiectasia, or both, have a guarded prognosis, with death occurring as a result of the disease in approximately 50% of cases. Although dietary therapy alone is significantly associated with a positive outcome, there is limited ability to differentiate between food-responsive (FR) PLE and immunosuppressant-responsive (IR) PLE at diagnosis in dogs. Our objective was to determine if a transfer learning computational approach to image classification on duodenal biopsy specimens collected at diagnosis was able to differentiate FR-PLE from IR-PLE. This was a retrospective study using paraffin-embedded formalin-fixed duodenal biopsy specimens collected during upper gastrointestinal tract endoscopy as part of the diagnostic investigations from 17 client-owned dogs with PLE due to inflammatory enteritis at a referral teaching hospital that were subsequently classified based on treatment response into FR-PLE (n = 7) or IR-PLE (n = 10) after 4 months of follow-up. A machine-based algorithm was used on lower magnification and higher resolution images of endoscopic duodenal biopsy specimens. Using the pre-trained Convolutional Neural Network model with a 70/30 training/test ratio for images, the model was able to differentiate endoscopic duodenal biopsy images from dogs with FR-PLE and IR-PLE with an accuracy of 83.78%. Our study represents an important first step toward the use of machine learning in improving the decision-making process for clinicians with regard to the initial treatment of canine PLE.

The mutational landscape of the adult healthy parous and nulliparous human breast.

The accumulation of somatic mutations in healthy human tissues has been extensively characterized, but the mutational landscape of the healthy breast is still poorly understood. Our analysis of whole-genome sequencing shows that in line with other healthy organs, the healthy breast during the reproduction years accumulates mutations with age, with the rate of accumulation in the epithelium of 15.24 ± 5 mutations/year. Both epithelial and stromal compartments contain mutations in breast-specific driver genes, indicative of subsequent positive selection. Parity- and age-associated differences are evident in the mammary epithelium, partly explaining the observed difference in breast cancer risk amongst women of different childbearing age. Parity is associated with an age-dependent increase in the clone size of mutated epithelial cells, suggesting that older first-time mothers have a higher probability of accumulating oncogenic events in the epithelium compared to younger mothers or nulliparous women. In conclusion, we describe the reference genome of the healthy female human breast during reproductive years and provide evidence of how parity affects the genomic landscape of the mammary gland.