Neurodevelopmental and Mental Health Outcomes in a National Clinical Sample of Youth With Sex Chromosome Trisomies Compared With Matched Controls.

Objective: To compare the prevalence of neurodevelopmental and mental health diagnoses in a national sample of youth with sex chromosome trisomies (SCTs) with matched controls.

Methods: Patients in PEDSnet and a diagnosis code mapping to 47,XXY/Klinefelter syndrome (n = 1171), 47,XYY/Double Y syndrome (n = 243), or 47,XXX/Trisomy X syndrome (n = 262) were matched with controls using propensity scores. Generalized estimating equations computed odds ratios (OR) with 95% confidence intervals (CI) for the prevalence of diagnoses within the neurodevelopmental and mental health composites, psychotropic medication prescriptions, and encounters with behavioral health and therapy providers.

Design and validation of cell-based potency assays for frataxin supplementation treatments.

Friedreich's ataxia (FRDA) is a multisystem, autosomal recessive disorder caused by mutations in the frataxin () gene. As FRDA is considered an FXN deficiency disorder, numerous therapeutic approaches in development or clinical trials aim to supplement FXN or restore endogenous expression. These include gene therapy, protein supplementation, genome editing or upregulation of transcription.

Friedreich ataxia: what can we learn from non-GAA repeat mutations?

Friedreich ataxia (FRDA) is a slowly progressive neurological disease resulting from decreased levels of the protein frataxin, a small mitochondrial protein that facilitates the synthesis of iron-sulfur clusters in the mitochondrion. It is caused by GAA (guanine-adenine-adenine) repeat expansions in the gene in 96% of patients, with 4% of patients carrying other mutations (missense, nonsense, deletion) in the gene. Compound heterozygote patients with one expanded GAA allele and a non-GAA repeat mutation can have subtle differences in phenotype from typical FRDA, including, in patients with selected missense mutations, both more severe features and less severe features in the same patient.

A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting.

Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.

Evolution in the Surgical Management of Gastric Cancer Peritoneal Metastases.

Despite therapeutic treatments and the growing utilization of multimodal therapies, gastric cancer (GC) remains a highly aggressive malignancy with high mortality worldwide. Much of the complexity in treating GC is due to the high incidence of peritoneal metastasis (PM), with mean overall survival typically ranging from 4 to 10 months. With current systemic therapy, targeted therapies, and immunotherapies continuing to remain ineffective for GC/PM, there has been a significant growing interest in intraperitoneal (IP) therapies for the treatment of GC/PM.