Publications by authors named "H Rick Casey"
Article Synopsis
- This study investigates how well standing balance conditions and digital sway measurements can distinguish between people with Friedreich's ataxia (FRDA) and healthy controls (HC).
- Twenty-eight individuals with FRDA and twenty healthy participants performed various standing tests while their postural sway was measured using a wearable sensor.
- Findings showed that specific sway measures reliably differentiated FRDA patients from healthy individuals, with some correlations found between these measures and clinical scores related to balance.
Background: Maintaining balance is crucial for independence and quality of life. Loss of balance is a hallmark of spinocerebellar ataxia (SCA).
Objective: The aim of this study was to identify which standing balance conditions and digital measures of body sway were most discriminative, reliable, and valid for quantifying balance in SCA.
Chimerism testing supports the study of engraftment and measurable residual disease (MRD) in patients after allogeneic hematopoietic stem cell transplant. In chimerism MRD, relapse can be predicted by increasing mixed chimerism (IMC), recipient increase ≥0.1% in peripheral blood, and proliferating recipient cells as a surrogate of tumor activity.
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- * The study assessed how early life events, such as maternal difficulties during pregnancy and participation in school sports, influence AAO, severity, and progression using a social determinant of health approach.
- * Findings revealed that maternal difficulties and sports participation were linked to an earlier AAO by over 13 years, while higher education levels led to slower disease progression and less severity, highlighting the impact of non-genetic factors on SCA6 outcomes.
Biothreat detection has continued to gain attention. Samples suspected to fall into any of the CDC's biothreat categories require identification by processes that require specialized expertise and facilities. Recent developments in analytical instrumentation and machine learning algorithms offer rapid and accurate classification of Gram-positive and Gram-negative bacterial species.
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