Purpose: The aim of the study was to explore aspects of learning, from a lifelong perspective, in individuals with Alström syndrome (AS). AS is an autosomal recessive disorder causing early blindness, progressive sensorineural hearing loss, cardiomyopathy, endocrine disorders, metabolic dysfunction, and abbreviated lifespan.
Method: Eleven individuals with AS participated. The study had a qualitative explorative design, giving voice to the participants' perspectives on their situation. Data were collected using semi-structured interviews, which were subjected to conventional (inductive) qualitative content analysis.
Results: The analysis revealed in the participants a quest for independence and an image of themselves as capable people willing to learn, but in constant need of support to continue learning throughout their lives to be as independent as possible.
Conclusion: Based on the levels of functioning, i.e. personal resources, revealed in the interviews, supervisors, caregivers, and teachers are encouraged to allow people with AS to be their own advocates, as they know best how, what, and with whom they learn, and what type of sensory material - tactile, auditory, visual, or a combination - is most helpful.
Implications For Rehabilitation: Individuals with AS strive for independence, and to be independent they need to continue to learn throughout their lives. Individuals with AS know best how they learn, and should be asked what modalities are the most effective for them. The tactile modality for learning will continue throughout life and should be emphasized early in the individual's education and rehabilitation.
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http://dx.doi.org/10.3109/09638288.2015.1055381 | DOI Listing |
J Nurs Res
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Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
Background: Critical illness not only threatens the life of the patient but also may profoundly impact the lives of their loved ones. For teenagers with a critically ill parent, these impacts may have significant, developmentally impactful effects. A descriptive understanding of these effects may advance scholarly understanding of the challenges these teenagers face.
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School of Dentistry, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.
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January 2025
HealthPartners Institute, Neuroscience Research, HealthPartners Neuroscience Center, Saint Paul, MN, United States.
Introduction: Intranasal (IN) deferoxamine (DFO) has emerged over the past decade as a promising therapeutic in preclinical experiments across neurodegenerative and neurovascular diseases. As an antioxidant iron chelator, its mechanisms are multimodal, involving the binding of brain iron and the consequent engagement of several pathways to counter pathogenesis across multiple diseases. We and other research groups have shown that IN DFO rescues cognitive impairment in several rodent models of Alzheimer Disease (AD).
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View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
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