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Purpose: To evaluate the effect of osilodrostat and hypercortisolism control on blood pressure (BP) and glycemic control in patients with Cushing's disease.
Methods: Pooled analysis of two Phase III osilodrostat studies (LINC 3 and LINC 4), both comprising a 48-week core phase and an optional open-label extension. Changes from baseline in systolic and diastolic BP (SBP and DBP), fasting plasma glucose (FPG), and glycated hemoglobin (HbA) were evaluated during osilodrostat treatment in patients with/without hypertension or diabetes at baseline.
Use of the Adaptive Behaviour Dementia Questionnaire in a Down Syndrome Specialty Clinic.
J Integr Neurosci
January 2025
Down Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, MA 02114, USA.
Objective: To study the use of a dementia screening tool in our clinic cohort of adults with Down syndrome.
Study Design: A retrospective chart review of patients with Down syndrome was conducted to follow the use of the Adaptive Behaviour Dementia Questionnaire (ABDQ) in a dementia screening protocol. The ABDQ results for patients aged 40 years and older at a Down syndrome specialty clinic program were assessed.
Deep Learning Approaches for the Prediction of Protein Functional Sites.
Molecules
January 2025
Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.
View Article and Find Full Text PDFPaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs).
Biomolecules
January 2025
School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.
Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, especially when homology transfer cannot provide information about a protein's structure, is a significant step toward addressing the protein structure prediction challenge. Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins.
View Article and Find Full Text PDFA Refined Nomenclature System to Better Discriminate Endo- and Exo-Type Fructanases and Glucanases.
Biomolecules
December 2024
Laboratory of Molecular Plant Biology and KU Leuven Plant Institute, Kasteelpark Arenberg 31, 3001 Leuven, Belgium.
Distinguishing between endo- and exo-type enzymes within the glycoside hydrolase (GH) classification presents significant challenges. Traditional methods, often based on endpoint activity measurements, do not capture the full range of products generated, leading to inconsistencies in classification. Not all exo-acting fructanases and glucanases produce monosaccharides (like fructose or glucose), while endo-acting enzymes do not solely produce higher-degree polymerization oligosaccharides.
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