Early Young Adult Death Underscores the Need for Adolescent and Young Adult Transition Programs in Neurofibromatosis Type 1.

There are few centers with combined pediatric and adult neurofibromatosis 1 practices and transition of care programming. Using an electronic health records-based approach, we found an early death peak in the fourth decade of life largely owing to malignancy, underscoring the need for integrated neurological training and practice across the lifespan.

Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles.

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, rather than assuming that the cohort is homogenous.

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods.

Objective: Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.

Pathogenic variants in have distinct -regulatory effects on enhancers and silencers in photoreceptors.

Dozens of variants in the gene for the homeodomain transcription factor (TF) cone-rod homeobox () are linked with human blinding diseases that vary in their severity and age of onset. How different variants in this single TF alter its function in ways that lead to a range of phenotypes is unclear. We characterized the effects of human disease-causing variants on CRX -regulatory function by deploying massively parallel reporter assays (MPRAs) in mouse retina explants carrying knock-ins of two variants, one in the DNA-binding domain (p.

Leveraging GPT-4 for Identifying Cancer Phenotypes in Electronic Health Records: A Performance Comparison between GPT-4, GPT-3.5-turbo, Flan-T5 and spaCy's Rule-based & Machine Learning-based methods.

Objective: Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.

Pathogenic variants in Crx have distinct cis-regulatory effects on enhancers and silencers in photoreceptors.

Dozens of variants in the photoreceptor-specific transcription factor (TF) CRX are linked with human blinding diseases that vary in their severity and age of onset. It is unclear how different variants in this single TF alter its function in ways that lead to a range of phenotypes. We examined the effects of human disease-causing variants on CRX -regulatory function by deploying massively parallel reporter assays (MPRAs) in live mouse retinas carrying knock-ins of two variants, one in the DNA binding domain (p.

Machine Learning for Benchmarking Adolescent Idiopathic Scoliosis Surgery Outcomes.

Study Design: Retrospective cohort.

Objective: The aim of this study was to design a risk-stratified benchmarking tool for adolescent idiopathic scoliosis (AIS) surgeries.

Summary Of Background Data: Machine learning (ML) is an emerging method for prediction modeling in orthopedic surgery.

Extraction of clinical phenotypes for Alzheimer's disease dementia from clinical notes using natural language processing.

Objectives: There is much interest in utilizing clinical data for developing prediction models for Alzheimer's disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.

Selective sweep for an enhancer involucrin allele identifies skin barrier adaptation out of Africa.

The genetic modules that contribute to human evolution are poorly understood. Here we investigate positive selection in the Epidermal Differentiation Complex locus for skin barrier adaptation in diverse HapMap human populations (CEU, JPT/CHB, and YRI). Using Composite of Multiple Signals and iSAFE, we identify selective sweeps for LCE1A-SMCP and involucrin (IVL) haplotypes associated with human migration out-of-Africa, reaching near fixation in European populations.

High-Throughput Analysis of Retinal Cis-Regulatory Networks by Massively Parallel Reporter Assays.

Inherited retinal degenerations are diverse and debilitating blinding diseases. Genetic tests and exome sequencing have identified mutations in many protein-coding genes associated with such diseases, but causal sequence variants remain to be found in many retinopathy cases. Since 99% of our genome does not code for protein but contains cis-regulatory elements (CREs) that regulate the expression of essential genes, CRE variants might hold the answer for some of these cases.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq).

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome.

The Molecular Revolution in Cutaneous Biology: EDC and Locus Control.

The epidermal differentiation complex (EDC) locus consists of a cluster of genes important for the terminal differentiation of the epidermis. While early studies identified the functional importance of individual EDC genes, the recognition of the EDC genes as a cluster with its shared biology, homology, and physical linkage was pivotal to later studies that investigated the transcriptional regulation of the locus. Evolutionary conservation of the EDC and the transcriptional activation during epidermal differentiation suggested a cis-regulatory mechanism via conserved noncoding elements or enhancers.

Regulation of the dynamic chromatin architecture of the epidermal differentiation complex is mediated by a c-Jun/AP-1-modulated enhancer.

The epidermal differentiation complex (EDC) locus comprises a syntenic and linear cluster of genes whose concomitant expression is a hallmark feature of differentiation in the developing skin epidermis. Many of the EDC proteins are cross-linked together to form the cornified envelope, an essential and discrete unit of the mammalian skin barrier. The mechanism underlying coordinate transcriptional activation of the EDC is unknown.