Background: Psoriasis, a chronic inflammatory disease affecting 2–3% of the population, is characterised by epidermal hyperplasia, a sustained pro-inflammatory immune response and is primarily a T-cell driven disease. Previous work determined that Connexin26 is upregulated in psoriatic tissue. This study extends these findings. Methods: Biopsies spanning psoriatic plaque (PP) and non-involved tissue (PN) were compared to normal controls (NN). RNA was isolated and subject to real-time PCR to determine gene expression profiles, including GJB2/CX26, GJB6/CX30 and GJA1/CX43. Protein expression was assessed by immunohistochemistry. Keratinocytes and fibroblasts were isolated and used in 3D organotypic models. The pro-inflammatory status of fibroblasts and 3D cultures was assessed via ELISA and RnD cytokine arrays in the presence or absence of the connexin channel blocker Gap27. Results: Connexin26 expression is dramatically enhanced at both transcriptional and translational level in PP and PN tissue compared to NN (>100x). In contrast, CX43 gene expression is not affected, but the protein is post-translationally modified and accumulates in psoriatic tissue. Fibroblasts isolated from psoriatic patients had a higher inflammatory index than normal fibroblasts and drove normal keratinocytes to adopt a “psoriatic phenotype” in a 3D-organotypic model. Exposure of normal fibroblasts to the pro-inflammatory mediator peptidoglycan, isolated from Staphylococcus aureus enhanced cytokine release, an event protected by Gap27. Conclusion: dysregulation of the connexin26:43 expression profile in psoriatic tissue contributes to an imbalance of cellular events. Inhibition of connexin signalling reduces pro-inflammatory events and may hold therapeutic benefit.
Many supervised activity recognition systems require a fully labelled time-series for accurate classification. However, gathering these labels is a difficult and often unrealistic task, especially over long-time frames or outside of laboratory conditions. A potential solution is through diary studies, allowing for a user-trained activity recognition system. Requests will be presented on the user's smart device and while this approach will be significantly less intrusive than current methods, frequent or inappropriately timed requests could reduce user acceptance. This paper proposes to further reduce user intrusion by making a prediction about the next user request and analyzing the classifiers confidence in this prediction. Two methods are presented, and with careful selection of the confidence threshold, they resulted in up to a 35% reduction in user requests with a minimal reduction in accuracy.
UNSTRUCTURED The increased use of sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the elderly population in remote and rural areas who may struggle with lengthy commutes to clinics. However, the usability of such systems can often discourage patients from adopting these services. To increase the adoption rates of wearable technology, the usability factors influencing continued device usage in the elderly population are examined. This work focuses on evaluating the usability of a popular activity tracker, the Xiaomi Mi Band 3, amongst the over 65 population. The study consisted of 65 elders wearing the wearable sensor for 7 days while only doffing the device for charging. The study was conducted across 4 different regions; Northern Ireland, Ireland, Sweden, and Finland, to diminish any geographical differences in usability perception. At the end of the week, a customised usability questionnaire was taken by the participants to gain insights into their experience. The aim of the study was to identify influencing factors on whether an elder would continue using a wearable device. This paper concludes that comfort and accuracy are the two main influencing factors in sustaining wearable device usage. The study formed part of The Smart sENsor Devices fOr rehabilitation and Connected health (SENDoc) project, which assessed the usability of sensors for remote rehabilitation of elders in the Northern Periphery of Europe.
With smart-devices becoming increasingly more commonplace, methods of capturing an individual's activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition.
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