Neural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis.

Background: The modified Rodnan skin score (mRSS), used to measure dermal thickness in patients with systemic sclerosis (SSc), is agnostic to inflammation and vasculopathy. Previously, we demonstrated the potential of neural network-based digital pathology applied to stained skin biopsies from SSc patients as a quantitative outcome. We leveraged deep learning and histologic analyses of clinical trial biopsies to decipher SSc skin features 'seen' by artificial intelligence (AI).

A machine learning approach for quantifying age-related histological changes in the mouse kidney.

The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples.

A machine learning approach for quantifying age-related histological changes in the mouse kidney.

The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples.

Fine Spike Timing in Hippocampal-Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains.

Spatial working memory (SWM) is a central cognitive process during which the hippocampus and prefrontal cortex (PFC) encode and maintain spatial information for subsequent decision-making. This occurs in the context of ongoing computations relating to spatial position, recall of long-term memory, attention, among many others. To establish how intermittently presented information is integrated with ongoing computations we recorded single units, simultaneously in hippocampus and PFC, in control rats and those with a brain malformation during performance of an SWM task.

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

Background: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability.

No Gastrointestinal Dysmotility in Transgenic Mouse Models of Migraine.

Objective: To determine whether transgenic mouse models of migraine exhibit upper gastrointestinal dysmotility comparable to those observed in migraine patients.

Background: There is considerable evidence supporting the comorbidity of gastrointestinal dysmotility and migraine. Gastrointestinal motility, however, has never been investigated in transgenic mouse models of migraine.

Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks.

With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning to analyze histologic images of periodic acid-Schiff-stained renal sections from a cohort of mice with different genotypes. We demonstrate that DNN-based machine learning has strong generalization performance on multiple histologic image processing tasks.