The Communicative Effectiveness Index (CETI) is an informant rating scale designed to measure changes in functional communication in people with aphasia (PWA) from the carer's perspective. It offers a comprehensive view of aphasia's impact on everyday communication situations, aiding clinicians in designing personalized intervention plans. The aim of this study was to translate and adapt the CETI into Greek (CETI-GR) and validate its psychometric properties. The CETI-GR was translated into Greek using back-translation. A pilot and a content validity study ensured its acceptability. The study involved 30 people with aphasia and 30 carers. The CETI-GR's psychometric properties were evaluated, including internal consistency, test-retest reliability, inter-rater reliability, and validity measures. The CETI-GR demonstrated excellent internal consistency (Cronbach's α > 0.95) and excellent inter-rater reliability (ICC ≥ 0.93). Excellent consistency was revealed when testing the CETI responses given only by PWA (α = 0.91) versus their carers (α = 0.97). Test-retest reliability was high (ICC = 0.88). Significant correlations between the CETI-GR and measures of language severity, functional communication, and quality of life supported convergent validity. The CETI-GR is a reliable tool for assessing functional communication in chronic aphasia. Its Greek adaptation enhances aphasia rehabilitation, enabling person-centered care and improving the quality of life for people with aphasia and carers.
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http://dx.doi.org/10.3390/brainsci14070689 | DOI Listing |
Annu Rev Biomed Eng
January 2025
2Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA; email:
Regulation of the brain's neuroimmune system is central to development, normal function, and disease. Neuronal communication to microglia, the primary immune cells of the brain, is well known to involve purinergic signaling mediated via ATP secretion and the cytokine fractalkine. Recent evidence shows that neurons release multiple cytokines beyond fractalkine, yet these are less studied and poorly understood.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston.
Purpose: The Daily Phonotrauma Index (DPI) can quantify pathophysiological mechanisms associated with daily voice use in individuals with phonotraumatic vocal hyperfunction (PVH). Since DPI was developed based on weeklong ambulatory voice monitoring, this study investigated if DPI can achieve comparable performance using (a) short laboratory speech tasks and (b) fewer than 7 days of ambulatory data.
Method: An ambulatory voice monitoring system recorded the vocal function/behavior of 134 females with PVH and vocally healthy matched controls in two different conditions.
PLoS Comput Biol
January 2025
Electrical and Computer Engineering Department, Concordia University, Montreal, Canada.
Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
Exosomes are natural membrane-enclosed nanovesicles (30-150 nm) involved in cell-cell communication. Recently, they have garnered considerable interest as nanocarriers for the controlled transfer of therapeutic agents to cells. Here, exosomes were derived from bone marrow mesenchymal stem cells using three different isolation methods.
View Article and Find Full Text PDFPLoS One
January 2025
College of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China.
In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions.
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