Transitioning From Nusinersen to Risdiplam for Spinal Muscular Atrophy in Clinical Practice: A Single-Center Experience.

Background: Nusinersen and risdiplam are U.S. Food and Drug Administration (FDA)-approved treatments for spinal muscular atrophy (SMA).

Psychometric evaluation of the PROMIS parent proxy mobility item bank for use in Duchenne muscular dystrophy.

Aim: To evaluate the psychometric properties and measurement quality of the Patient-Reported Outcomes Measurement Information System Parent Proxy (PROMIS PP) Mobility item bank (v1.0, 23 items) for children with Duchenne muscular dystrophy (DMD), through Rasch statistical analysis.

Method: De-identified PROMIS PP Mobility items were completed by the caregivers of male patients with DMD, aged 4 to 12 years, as part of standard clinical care at the Nationwide Children's Hospital clinic; data were mined retrospectively from electronic health records.

Prospective observational study of FKRP-related limb-girdle muscular dystrophy R9: A GRASP consortium study.

Objective: Limb-girdle muscular dystrophy R9 (LGMDR9, formerly known as LGMD2I), caused by variants in the fukutin-related protein (FKRP) gene leads to progressive muscle weakness of the shoulder and pelvic limb-girdles and loss of motor function over time. Clinical management and future trial design are improved by determining which standardized clinical outcome assessments (COA) of function are most appropriate to capture disease presentation and progression, informing endpoint selection and enrollment criteria. The purpose of our study was to evaluate the cross-sectional validity and reliability of clinical outcome assessments in patients with FKRP-related LGMDR9 participating in the Genetic Resolution and Assessments Solving Phenotypes in LGMD (GRASP) natural history study.

A systematic review on artificial intelligence approaches for smart health devices.

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients' health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information.