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Taylor, C. O.

Publications and source records attributed to Taylor, C. O..

3 recordsLinked to original sources

Identification of molecular and clinical ALS subgroups based on TDP-43 loss of function molecular markers from population-based patient-derived iPS motor neurons

BackgroundAmyotrophic lateral sclerosis (ALS) is a uniformly fatal neurodegenerative disease characterized by progressive cortical and spinal motor neuron loss, with most patients surviving only 2-5 years post-diagnosis. While approximately 10% of cases are familial (fALS), the remaining 90% are sporadic (sALS) with unknown genetic drivers. Importantly, clinical presentations are heterogeneous in both sporadic and familial ALS, underscoring the complexity of the disease. A pathological hallmark of ALS is the mislocalization of RNA-binding protein TDP-43 from the nucleus to the cytoplasm. This mislocalization produces both loss of function consequences, such as widespread RNA processing and splicing defects, as well as potential toxic gain of function effects associated with cytoplasmic aggregation. ResultsIn this study, we used RT-PCR data from induced pluripotent stem cell-derived motor neurons derived from 180 sALS and C9orf72 fALS patients from the Answer ALS collection to identify biological subgroups based on TDP-43 loss-of-function signatures. Spectral embedding revealed four distinct molecular clusters, including one subgroup genetically similar to controls and another with the most dysregulated mRNA expression, suggesting differing disease severity. Linear mixed models were then used to assess the longitudinal trajectory of over 90 clinical measures, and the between-cluster interaction effects were evaluated. Conclusions36 clinical outcomes showed significant differences across clusters, supporting the presence of biologically and clinically distinct ALS subtypes based on the TDP-43 associated pathogenic cascade. These findings demonstrate a critical role of RNA profiling in uncovering biologically meaningful subtypes of ALS, potentially allowing for more precise prognostic tools and the development of future personalized therapeutic approaches.

neuroscience↗

Deep Learning Detection of Subtle Torsional Eye Movements: Preliminary Results

The control of torsional eye position is a key component of ocular motor function. Ocular torsion can be affected by pathologies that involve ocular motor pathways, spanning from the vestibular labyrinth of the inner ears to various regions of the brainstem and cerebellum. Timely and accurate diagnosis enables efficient interventions and management of each case which are crucial for patients with dizziness, vertical double vision, or imbalance. Such detailed evaluation of eye movements may not be possible in all frontline clinical settings, particularly for detecting torsional abnormalities. These abnormalities are often more challenging to identify at the bedside compared to horizontal or vertical eye movements. To address these challenges, we used a dataset of torsional eye movements recorded with video-oculography (VOG) to develop deep learning models for detecting ocular torsion. Our models achieve 0.9308 AUROC and 86.79 % accuracy, leveraging ocular features particularly pertinent to tracking torsional eye position.

neuroscience↗

Genomic Considerations for FHIR; eMERGE Implementation Lessons

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Networks Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), to represent clinical genomics results. These new standards improve the utility of HL7 FHIR as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

genomics↗