Psoriatic arthritis (PsA) presents various clinical manifestations, including skin lesions, peripheral arthritis, axial involvement, enthesitis, nail involvement, dactylitis, and uveitis. In addition, it causes a high incidence of lifestyle-related diseases and an increase in cerebrovascular and cardiovascular events. As the pathology of PsA has been clarified, molecular-targeted drugs targeting tumor necrosis factor-α, interleukin (IL)-17A, IL-17A/F, IL-17 receptor, IL-12/23(p40), IL-23p19, Cytotoxic T-lymphocyte Antigen-4 (CTLA-4), Janus kinase, and phosphodiesterase-4 have been developed and are widely used in clinical practice. PsA is clinically and molecularly heterogeneous, and it is necessary to improve various clinical symptoms with limited treatment options simultaneously; therefore, rheumatologists sometimes encounter difficult situations in clinical practice. Hence, the development of precision medicine may improve treatment outcomes. Recently, the strategic use of molecular-targeted drugs based on the stratification of patients with PsA by peripheral blood lymphocyte phenotyping and serum cytokine concentrations has been reported to possibly lead to a higher therapeutic response. A randomized controlled trial was initiated to verify the efficacy of this treatment strategy. However, to make precision medicine in PsA feasible, shifting from conventional clinical trials to clinical trials based on biomarker profiles and accumulating further data are necessary.
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http://dx.doi.org/10.1177/1759720X241311462 | DOI Listing |
Alzheimers Dement
December 2024
Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau - Autonomous University of Barcelona, Barcelona, Catalonia, Spain.
Background: Alzheimer's and related disorders (ADRD) represent a range of neurodegenerative conditions characterized by abnormal protein deposits in the brain. Despite advances, there is a need for enhanced diagnostic and treatment approaches that acknowledge the diversity of ADRD. This project introduces the Alzheimer's and Related Disorders Multicenter Archive (ARMA), a collaborative platform with an advanced Electronic Data Capture (EDC) system linked to Electronic Medical Records (EMR) designed to refine ADRD diagnosis and natural history understanding, thus informing precision medicine.
View Article and Find Full Text PDFBackground: Early-onset Alzheimer's disease (EOAD) associated with amyloid precursor protein (APP) duplications or presenilin (PSEN) variants increases risk of seizures. Targeting epileptiform activity with antiseizure medicine (ASM) administration to AD patients may beneficially attenuate cognitive decline (Vossel et al, JAMA Neurology 2021). However, whether mechanistically distinct ASMs differentially suppress seizures in discrete EOAD models is understudied (Lehmann et al, Neurochem Res 2021).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Alzheimer's disease (AD) presents challenges with its complex neurodegenerative mechanisms, leading to a high failure rate in clinical trials. While drug repositioning offers a cost-effective solution, the lack of a subtype-driven strategy hinders success. Previously, we defined genetic subtypes and their prioritized genes for each genetic subtype (Sahelijo et al.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
NYU Langone Health, New York, NY, USA.
Background: Large language models (LLMs) provide powerful natural language processing capabilities in medical and clinical tasks. Evaluating LLM performance is crucial due to potential false results. In this study, we assessed ChatGPT and Llama2, two state-of-the-art LLMs, in extracting information from clinical notes, focusing on cognitive tests, specifically the Mini Mental State Exam (MMSE) and Cognitive Dementia Rating (CDR).
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