Background: While cognitive deficits in memory and processing speed have been well-documented in individuals with multiple sclerosis (MS), language is largely considered to be intact. Verbal fluency deficits observed in MS are often attributed to impaired processing speed and executive functions rather than language ability. The current study evaluates the contribution of various cognitive factors to verbal fluency including language ability, oral-motor speed, processing speed, and executive functions.
Methods: We analyzed pre-existing data from seventy-four (74) individuals with MS who completed a battery of neuropsychological tests designed to assess individual ability for various cognitive factors. We conducted linear multiple regression analyses with letter and category verbal fluency as outcome variables and performance on other cognitive domains (e.g., processing speed, executive functioning) as predictors.
Results: Both vocabulary and processing speed predicted letter fluency while only vocabulary predicted category fluency. These findings suggest that the observed verbal fluency deficits in MS may reflect both impaired language ability and processing speed.
Conclusion: We propose that further research on language ability in MS is needed to determine if comprehensive neuropsychological test batteries for persons with MS should include tests of language ability to fully understand the cognitive profile of any given patient. Given the importance of language ability, it may be necessary to conduct a more thorough assessment of language in individuals with MS who experience a deficit in this domain.
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http://dx.doi.org/10.1016/j.msard.2021.102846 | DOI Listing |
In 2021, a year before ChatGPT took the world by storm amid the excitement about generative artificial intelligence (AI), AlphaFold 2 cracked the 50-year-old protein-folding problem, predicting three-dimensional (3D) structures for more than 200 million proteins from their amino acid sequences. This accomplishment was a precursor to an unprecedented burgeoning of large language models (LLMs) in the life sciences. That was just the beginning.
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July 2024
Department of Pulmonary Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Rev Med Suisse
January 2025
Unité d'éducation thérapeutique du patient, Centre collaborateur OMS, Service de médecine de premier recours, Département de médecine de premier recours, Hôpitaux universitaires de Genève, 1211 Genève 14.
Migrant and allophone people often face linguistic, cultural and structural barriers, with limited access to healthcare. To address this issue, the Therapeutic Patient Education Unit has created at the University Hospitals of Geneva a new therapeutic programme specifically for these people living with obesity. It includes educational workshops tailored to their language skills, health literacy and migratory background.
View Article and Find Full Text PDFArch Clin Neuropsychol
January 2025
École des Sciences de la Réadaptation, Faculté de médecine, Université Laval, 1050, avenue de la Médecine, bureau 4211 Université Laval Québec, QC, Canada.
Objective: Anomia is defined by difficulty in retrieving content words like nouns and verbs from long-term memory, independent of any impairments related to articulatory movements or motor speech execution. The tools for measuring picture naming, the conventional method for assessing anomia, are very limited in Turkey. The aim of this study was to adapt the Test de Dénomination de Québec-60 images/Quebec picture-naming test-60 pictures (TDQ-60), a color picture-naming test for adults and the elderly into Turkish, establish its validity, and develop normative data adapted to the Turkish population to address this gap.
View Article and Find Full Text PDFSci Rep
January 2025
Office for the Advancement of Educational Information, Chengdu Normal University, Chengdu, 610000, China.
In the training of teacher students, simulated teaching is a key method for enhancing teaching skills. However, traditional evaluations of simulated teaching typically rely on direct teacher involvement and guidance, increasing teachers' workload and limiting the opportunities for teacher students to practice independently. This paper introduces a Retrieval-Augmented Generation (RAG) framework constructed using various open-source tools (such as FastChat for model inference and Whisper for speech-to-text) combined with a local large language model (LLM) for audio analysis of simulated teaching.
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