Post-surgical hemifrontal anhidrosis Fernández Rodríguez, J; Cariñena Amigo, A; Sevillano Torrado, C ...
Revista española de anestesiología y reanimación
61, Issue:
10
Journal Article
Pharmacological pseudo-Fuchs Sevillano Torrado, C; Lugo Adán, E; Viso Outeiriño, E
Archivos de la Sociedad Española de Oftalmología (English ed.),
7/2013, Volume:
88, Issue:
7
Journal Article
Peer reviewed
Abstract Case report A case of unilateral iridis hyperpigmentation and uveitis due to travoprost is presented. Discussion Anterior uveitis is a rare side-effect of travoprost. In this case, ...heterochromic iris was also presented, which led us to the wrong diagnosis of a Fuchs heterochromic iridocyclitis. The differential diagnosis along with the associated literature is discussed.
This study aims to analyze the efficacy of wearable and mobile systems to assist people with High-Functioning Autism (HFA) in their emotional self-regulation learning process compared to the proven ...efficacy of this technology with individuals with autism in the low functioning area of the spectrum. For that purpose, we carry out an experiment with a smartwatch system (Taimun-Watch) that had been tested previously with individuals in the low-functioning range. This experiment involves two (N = 2) individuals with HFA and we compare their performance to the obtained in the prior experiment by monitoring their activity and observing their behavior during 7 and 9 labor days, respectively. The results evidence that, although it takes more time to find and customize effective self-regulation strategies in comparison to the low-functioning autism individuals due to their sharper, more complex cognitive abilities and perception, they were able to use the system to recover from stress episodes as well using the system and tolerated suitably the devices in their daily activity.
Background Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, ...multimorbidity and neurological disorders, such as Parkinson’s disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. Methods ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. Discussion We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.
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