Publications by authors named "S H Adler"
Inclusive research is needed to understand how contemplative practices are used by people of diverse identities. Metta meditation-also known as loving-kindness meditation-may be particularly relevant for people committed to equity and justice because of the social nature of the practice. Using community-based participatory research and an intersectional framework, we assessed how people in a diverse meditation community teach and practice metta meditation.
View Article and Find Full Text PDFObjectives: Gastroesophageal reflux disease (GERD) is frequent in systemic sclerosis (SSc) and could predict progression of interstitial lung disease (ILD). We aimed to analyse (1) the prevalence of GERD among SSc-ILD patients, (2) its association with disease characteristics and (3) predictive factors for ILD progression in SSc-ILD patients with GERD.
Methods: SSc patients from the EUSTAR database with ILD were included.
Two observational studies were conducted to support an initiative to qualify translational kidney safety biomarkers as clinical drug development tools that identify tubular injury prior to changes in estimated glomerular filtration rate (eGFR). Normal healthy volunteers provided three morning spot urine collections over 4 weeks. Patients undergoing surgical resection and intrathoracic cisplatin for malignant pleural mesothelioma provided urine samples pre- and postoperatively at 4, 8, and 12 hours and daily for 6 days.
View Article and Find Full Text PDFObjective: Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection.
View Article and Find Full Text PDFArticle Synopsis
- Hippocampal sclerosis (HS) is a major cause of temporal lobe epilepsy (TLE) but can be hard to detect on MRI, leading to surgical delays, so researchers created open-source software to improve diagnosis.
- The study involved 365 participants, using the software HippUnfold to analyze MRI scans and develop a logistic regression model that accurately identifies and localizes HS.
- The classifier showed high accuracy in detecting HS in both initial and independent patient cohorts, proving effective for individual assessments by comparing patient data with normative growth patterns.