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Biology subjects

Kelsey, K.

Publications and source records attributed to Kelsey, K..

2 recordsLinked to original sources

The effect of light vs dark coat color on thermal status in Labrador Retriever dogs.

Although dark coat color in dogs has been theorized as a risk factor for thermal stress, there is little evidence in the scientific literature to support that position. We utilized 16 non-conditioned Labradors (8 black and 8 yellow) in a three-phase test to examine effects of coat color on thermal status of the dog. Rectal, gastrointestinal (GI), surface temperature, and respiration rate measured in breaths per minute (bpm), were collected prior to (Baseline -- phase 1) and immediately after a controlled 30-minute walk in an open-air environment on a sunny day (Sunlight -- phase 2). Follow up measurements were taken 15 minutes after walking (Cool down - phase 3) to determine post-exposure return to baseline. No effect of coat color was measured for rectal, gastrointestinal or surface temperature, or respiration (P > 0.05) in dogs following their 30-minute walk. Temperatures increased similarly across both coat colors (rectal 1.88 {degrees}C and 1.83 {degrees}C; GI 1.89 {degrees}C and 1.94 {degrees}C; eye 1.89 {degrees}C and 1.94 {degrees}C; abdominal 2.93 {degrees}C and 2.35 {degrees}C) for black and yellow dogs respectively during the sunlight phase (P > 0.05). All temperatures and respiration rates decreased similarly across coat colors for rectal (0.9{degrees}C and 1.0{degrees}C) and GI (1.5 {degrees}C and 1.3{degrees}C) for black and yellow dogs respectively (P > 0.05). Similarly, sex did not impact thermal status across rectal, gastrointestinal or surface temperature or respiration rates measured (P > 0.05). These data contradict the commonly held theory that dogs with darker coat color may experience a greater thermal change when exposed to direct sunlight compared to dogs with a lighter coat color.

systems biology

A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features

Improving the consistency and reproducibility of bladder cancer prognoses necessitates the development of accurate, predictive prognostic models. Current methods of determining the prognosis of bladder cancer patients rely on manual decision-making, including factors with high intra- and inter-observer variability, such as tumor grade. To advance the long-term prediction of bladder cancer prognoses, we developed and tested a computational model to predict the 10-year overall survival outcome using population-based bladder cancer data, without considering tumor grade classification. The resulted predictive model demonstrated promising performance using a combination of clinical and molecular features, and was also strongly related to patient overall survival in Cox models. Our study suggests that machine learning methods can provide reliable long-term prognoses for bladder cancer patients, without relying on the less consistent tumor grade. If validated in clinical trials, this automated approach could guide and improve personalized management and treatment for bladder cancer patients.

bioinformatics