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Advancing medication compounding: Use of a pharmaceutical 3D printer to auto-fill minoxidil capsules for dispensing to patients in a community pharmacy.
Int J Pharm
January 2025
Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FABRX Artificial Intelligence, Carretera de Escairón, 14, Currelos (O Saviñao) CP 27543, Spain; FABRX Ltd., Henwood House, Henwood, Ashford, Kent TN24 8DH, UK; Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK. Electronic address:
Compounding medications in pharmacies is a common practice for patients with prescriptions that are not available commercially, but it is a laborious and error-prone task. The incorporation of emerging technologies to prepare personalised medication, such as 3D printing, has been delayed in smaller pharmacies due to concerns about potential workflow disruptions and learning curves associated with novel technologies. This study examines the use in a community pharmacy of a pharmaceutical 3D printer to auto-fill capsules and blisters using semisolid extrusion, incorporating an integrated quality control system.
View Article and Find Full Text PDFEffect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.
Eur Radiol
January 2025
Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
Objectives: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.
Materials And Methods: Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection.
How Outcome Prediction Could Aid Clinical Practice.
Br J Hosp Med (Lond)
January 2025
Department of Surgery & Cancer, Imperial College London, London, UK.
Predictive algorithms have myriad potential clinical decision-making implications from prognostic counselling to improving clinical trial efficiency. Large observational (or "real world") cohorts are a common data source for the development and evaluation of such tools. There is significant optimism regarding the benefits and use cases for risk-based care, but there is a notable disparity between the volume of clinical prediction models published and implementation into healthcare systems that drive and realise patient benefit.
View Article and Find Full Text PDFTesting for myelin oligodendrocyte glycoprotein antibodies: Who, what, where, when, why, and how.
Mult Scler
January 2025
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Testing for myelin oligodendrocyte glycoprotein immunoglobulin G antibodies (MOG-IgG) is essential to the diagnosis of MOG antibody-associated disease (MOGAD). Due to its central role in the evaluation of suspected inflammatory demyelinating disease, the last 5 years has been marked by an abundance of research into MOG-IgG testing ranging from appropriate patient selection, to assay performance, to utility of serum titers as well as cerebrospinal fluid (CSF) testing. In this review, we synthesize current knowledge pertaining to the "who, what, where, when, why, and how" of MOG-IgG testing, with the aim of facilitating accurate MOGAD diagnosis in clinical practice.
View Article and Find Full Text PDFCOVID-19: Lessons Learned from Molecular and Clinical Research.
Int J Mol Sci
January 2025
IRCAD (Interdisciplinary Research Center of Autoimmune Diseases), Università del Piemonte Orientale (UPO), 28100 Novara, Italy.
SARS-CoV-2 virus, the etiological agent of the novel coronavirus disease 19 (COVID-19), was first identified in late 2019, following the sudden appearance of a cluster of pneumonia cases of unknown origin in China [...
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