Publications by authors named "J N Connelly"

The incidence of ulnar collateral ligament injuries has increased over the past decade. As a result, the rate of ulnar collateral ligament reconstruction has increased dramatically at all levels of competition in overhead athletes. Currently, there is no consensus on milestones during rehabilitation or a largely agreed-upon structured throwing program after ulnar collateral ligament injuries.

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Objectives: Long-term kidney outcomes after extracorporeal membrane oxygenation (ECMO) are little quantified and understood. We aimed to describe the frequency of kidney dysfunction screening during follow-up and the prevalence of long-term kidney disease.

Design: Retrospective cohort of pediatric ECMO patients with estimated glomerular filtration rate (eGFR) (mL/min/1.

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Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co-sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups.

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Epigenetic clocks provide powerful tools for estimating health and lifespan but their ability to predict brain degeneration and neuronal damage during the aging process is unknown. In this study, we use GrimAge, an epigenetic clock correlated to several blood plasma proteins, to longitudinally investigate brain cellular microstructure in axonal white matter from a cohort of healthy aging individuals. A specific focus was made on white matter hyperintensities, a visible neurological manifestation of small vessel disease, and the axonal pathways throughout each individual's brain affected by their unique white matter hyperintensity location and volume.

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A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Pre- and post-contrast T-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans.

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