Novel artificial eye service evaluation using patient reported outcome measures

Background This service evaluation explores patient reported outcomes from patients provided with high definition ocular prostheses (artificial eyes). Methods Validated patient questionnaires (FACE-Q, DAS24 and HADS) were utilised to evaluate patient experiences of their new ocular prosthesis. 10 patients were included in the service evaluation, which was conducted between December 2018 and September 2019. Descriptive analysis of the mean and 95% CI was undertaken for all questionnaires. Statistical analysis was performed using SPSS 21 Principal Component Analysis (PCA) for FACE-Q questionnaires. Correlations were significant when factor loading is at α > 0.4. Results A questionnaire response rate of 80% was achieved (n = 8). PCA analysis showed the number of variables tested could be reduced. Two principal components (PC1 and PC2) had very good to excellent internal consistency between variables with factor loading (α = 0.7–0.9). PC1 contained questionnaires 1–7, all of which were highly correlated. PC2 contained question number 8 with a factor loading of α = 0.8. This indicates good reliability, validity and responsiveness. Conclusions We hope to demonstrate the importance of service evaluations with respect to rapidly evolving technological advances in medical devices, pharmaceuticals and imaging modalities. Further feasibility and full clinical studies are required to confirm the positive results of the novel artificial eye service we have evaluated with respect to the traditional approach

A cross-over, randomised feasibility study of digitally printed versus hand-painted artificial eyes in adults: PERSONAL-EYE-S - a study protocol

ackground/objectives: Around 11,500 artificial eyes are required yearly for new and existing patients. Artificial eyes have been manufactured and hand-painted at the National Artificial Eye Service (NAES) since 1948, in conjunction with approximately 30 local artificial eye services throughout the country. With the current scale of demand, services are under significant pressure. Manufacturing delays as well as necessary repainting to obtain adequate colour matching, may severely impact a patient’s rehabilitation pathway to a normal home, social and work life. However, advances in technology mean alternatives are now possible. The aim of this study is to establish the feasibility of conducting a large-scale study of the effectiveness and cost-effectiveness of digitally printed artificial eyes compared to hand-painted eyes. Methods: A cross-over, randomised feasibility study evaluating a digitally-printed artificial eye with a hand-painted eye, in patients aged ≥18 years with a current artificial eye. Participants will be identified in clinic, via ophthalmology clinic databases and two charity websites. Qualitative interviews will be conducted in the later phases of the study and focus on opinions on trial procedures, the different artificial eyes, delivery times, and patient satisfaction. Discussion: Findings will inform the feasibility, and design, of a larger fully powered randomised controlled trial. The long-term aim is to create a more life-like artificial eye in order to improve patients’ initial rehabilitation pathway, long term quality of life, and service experience. This will allow the transition of research findings into benefit to patients locally in the short term and National Health Service wide in the medium to long term

Soft-bound synaptic plasticity increases storage capacity

Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems