Canine Best disease as a translational model.

In this review, we summarize the findings of several pre-clinical studies in the canine BEST1 disease model. To this end, client-owned and purpose bred dogs that were compound heterozygotes or homozygotes, respectively, for two or one of 3 different mutations in BEST1 were evaluated by ophthalmic examination, cSLO/sdOCT imaging, and retinal immunohistochemistry to characterize the clinical and microanatomic features of the disease. Subsequently AAV-mediated gene therapy was done to transfer the BEST1 transgene to the RPE under control of a hVMD2 promoter.

Canine models of inherited retinal diseases: from neglect to well-recognized translational value.

Large animal models of inherited retinal diseases, particularly dogs, have been extensively used over the past decades to study disease natural history and evaluate therapeutic interventions. Our group of investigators at the University of Pennsylvania, School of Veterinary Medicine, has played a pivotal role in characterizing several of these animal models, documenting the natural history of their diseases, developing gene therapies, and conducting proof-of-concept studies. Additionally, we have assessed the potential toxicity of these therapies for human clinical trials, contributing to the regulatory approval of voretigene neparvovec-rzyl (Luxturna) by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of patients with confirmed biallelic mutation-associated retinal dystrophy.

Digital Twin for Volumetric Thermal Error Compensation of Large Machine Tools.

Machine tool accuracy is greatly influenced by geometric and thermal errors that cause positioning deviations within its working volume. Conventionally, these two error sources are treated separately, with distinct procedures employed for their characterization and correction. This research proposes a unified volumetric error compensation approach in terms of a calibration procedure and error compensation model, which considers geometric and thermal errors as a single error source that exhibits temporal variation primarily due to changes in the machine's thermal state.

Model based fitting of pattern reversal visually evoked potentials provides a reliable characterization of waveform components.

Objective: To introduce a novel approach to analyzing pattern reversal visual evoked potentials (prVEPs) using a difference-of-gammas model-based fitting method.

Methods: prVEP was recorded from uninjured youth ages 11-19 years during pre- or postseason sports evaluation. A difference-of-gammas model fit was used to extract the amplitude, peak time, and peak width of each of four gamma components.