Background: Specific Learning Disorders (SLD) therefore represent chronic, not temporary disorders with varying degrees of expression throughout life. The beginning of imaging, anatomy and genetics studies have made it possible to investigate the brain organization of individuals suffering from SLD (Deheane, 2009).
Objectives: The purpose of this paper is to describe a treatment method for reading and writing disorders through an intervention based on the integration of a sublexical method and a neuropsychological approach, with assistive technologies in the study of a single case.
Methods: The protocol is based on the modularization theory (Karmiloff-Smith, 1990). The data presented in this paper with a A-B-A basic experimental drawing.
Results: This study confirms the degree of effectiveness of the treatments based on the automated identification of syllables and words together with the integrated enhancement of neuropsychological aspects such as visual attention and phonological loop (Benso, 2008), although in the follow-up condition only some abilities maintain the progress achieved.
Conclusions: As previously mentioned, the SLD represents a chronic disorder, consequently the treatment does not solve the root cause of the problem, but can grant a use of the process decidedly more instrumental to everyday life.
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http://dx.doi.org/10.3233/NRE-151270 | DOI Listing |
Sci Rep
December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093, Lublin, Poland.
Using Fourier Transform Infrared spectroscopy (FTIR), it is possible to show chemical composition of materials and / or profile chemical changes occurring in tissues, cells, and body fluids during onset and progression of diseases. For diagnostic application, the use of blood would be the most appropriate in biospectroscopy studies since, (i) it is easily accessible and, (ii) enables frequent analyses of biochemical changes occurring in pathological states. At present, different studies have investigated potential of serum, plasma and sputum being alternative biofluids for lung cancer detection using FTIR.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
Department of Psychology, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, 475000, Malaysia.
This study evaluates the ability of large language models (LLMs) to deliver criterion-based grading and examines the impact of prompt engineering with detailed criteria on grading. Using well-established human benchmarks and quantitative analyses, we found that even free LLMs achieve criterion-based grading with a detailed understanding of the criteria, underscoring the importance of domain-specific understanding over model complexity. These findings highlight the potential of LLMs to deliver scalable educational feedback.
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