Publications by authors named "Lane Fitzsimmons"
Background: The mechanistic pathways that give rise to the extreme symptoms exhibited by rare disease patients are complex, heterogeneous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional series of interrelated symptoms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population.
View Article and Find Full Text PDFGermline variants in the NSD1 gene are responsible for Sotos syndrome, while somatic variants promote neoplastic cell transformation. Our previous studies revealed three alternative RNA isoforms of present in fibroblast cell lines (FBs): the canonical full transcript and 2 alternative transcripts, termed AT2 (NSD1 Δ5Δ7) and AT3 ( Δ19-23 at the 5' end). The precise molecular pathways affected by each specific isoform of are uncharacterized to date.
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- The study explores complex biological mechanisms behind extreme symptoms in rare disease patients, emphasizing the need for treatments targeting underlying causes rather than just symptoms.
- It focuses on seizures as a common symptom in patients with ultrarare disorders and analyzes genotype and phenotype data from the UK Biobank to uncover related biological pathways.
- The researchers present case studies of undiagnosed patients with seizures and discuss how their findings can provide insights into the molecular mechanisms of rare diseases, highlighting the importance of large-scale data analysis in understanding these conditions.
Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations.
View Article and Find Full Text PDFCharacterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations.
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- The review assesses the use of machine learning models for diagnostic purposes using text data, emphasizing the importance of diverse study populations in medical informatics.
- Out of 2,260 papers reviewed, 78 were included; the most common model used was neural networks, and the majority of studies were conducted on predominantly White patient populations.
- The discussion highlights the need for comprehensive demographic data to avoid potential biases in machine learning algorithms as the reliance on these technologies in clinical settings increases.