ABSTRACT
BACKGROUND: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. METHODS: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. RESULTS: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). CONCLUSION: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.
Subject(s)
Alzheimer Disease , Cognitive Dysfunction , Semantics , Speech , Task Performance and Analysis , Verbal Behavior , Aged , Aged, 80 and over , Alzheimer Disease/diagnosis , Alzheimer Disease/psychology , Area Under Curve , Cognitive Dysfunction/diagnosis , Cognitive Dysfunction/psychology , Electronic Data Processing , Female , Geriatric Assessment/methods , Humans , Male , Neuropsychological TestsABSTRACT
Contact with Leishmania leads to a decreases in mononuclear phagocyte adherence to connective tissue. In this work, we studied the early stages of bond formation between VLA4 and fibronectin, measured the kinetics of membrane alignment and the monocyte cytoplasm spreading area over a fibronectin-coated surface, and studied the expression of high affinity integrin epitope in uninfected and Leishmania-infected human monocytes. Our results show that the initial VLA4-mediated interaction of Leishmania-infected monocyte with a fibronectin-coated surface is preserved, however, the later stage, leukocyte spreading over the substrate is abrogated in Leishmania-infected cells. The median of spreading area was 72 [55-89] µm(2) for uninfected and 41 [34-51] µm(2) for Leishmania-infected monocyte. This cytoplasm spread was inhibited using an anti-VLA4 blocking antibody. After the initial contact with the fibronectrin-coated surface, uninfected monocyte quickly spread the cytoplasm at a 15 µm(2) s(-1) ratio whilst Leishmania-infected monocytes only made small contacts at a 5.5 µm(2) s(-1) ratio. The expression of high affinity epitope by VLA4 (from 39 ± 21% to 14 ± 3%); and LFA1 (from 37 ± 32% to 18 ± 16%) molecules was reduced in Leishmania-infected monocytes. These changes in phagocyte function may be important for parasite dissemination and distribution of lesions in leishmaniasis.
Subject(s)
Fibronectins/metabolism , Integrin beta1/metabolism , Leishmania/metabolism , Leishmaniasis/metabolism , Leishmaniasis/parasitology , Monocytes/metabolism , Monocytes/parasitology , Cell Adhesion/physiology , Humans , Integrin alpha4beta1/metabolism , Kinetics , Leukocytes/metabolism , Leukocytes/parasitologyABSTRACT
BACKGROUND: To evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD). METHODS: Healthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their "power" to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy. RESULTS: The classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility. CONCLUSION: Automatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.
