Pronounced impairment of activities of daily living in posterior cortical atrophy

Introduction : The impact of several dementia syndromes on activities of daily living (ADLs) has been well documented, but no study has yet investigated functional ability in posterior cortical atrophy (PCA). The primarily visual nature of deficits in this condition is likely to have a pronounced impact on ADLs. Objective : The aim of this study was to profile functional change in PCA and identify predictors of change. Method : Twenty-nine PCA patients and 25 patients with typical Alzheimer’s disease (AD) and their caregivers were included in this cross-sectional study. ADLs were assessed using the Disability Assessment for Dementia (DAD), administered to caregivers, assessing basic ADLs (e.g., eating, dressing) and instrumental ADLs (e.g., managing finances, meal preparation). The predictive utility of cognitive domains (Addenbrooke’s Cognitive Examination), behavioural impairment (Cambridge Behavioural Inventory-Revised) and demographic variables on ADL ability was also examined. Results : PCA patients showed significantly reduced total ADL scores compared to AD patients (medium effect size, d = –0.7; p 0.05). A model combining patient mood, disinhibition, apathy, symptom duration, and memory and attention/orientation scores explained the variance of scores in functional decline (61.2%), but the key factor predicting ADL scores was attention/orientation (p = 0.048). Conclusion : This study shows the profound impact of PCA on ADLs and factors underpinning patients’ disability. Attention/orientation deficits were found to correlate and contribute to variance in ADL scores. Future work to develop tailored interventions to manage ADL impairment in PCA should take these findings into account

Combining metabolic modelling with machine learning accurately predicts yeast growth rate

New metabolic engineering techniques hold great potential for a range of bio-industrial applications. However, their practical use is hindered by the huge number of possible modifications, especially in eukaryotic organisms. To address this challenge, we present a methodology combining genome-scale metabolic modelling and machine learning to precisely predict cellular phenotypes starting from gene expression readouts. Our methodology enables the identification of candidate genetic manipulations that maximise a desired output--potentially reducing the number of in vitro experiments otherwise required. We apply and validate this methodology to a screen of 1,143 Saccharomyces cerevisiae knockout strains. Within the proposed framework, we compare different combinations of feature selection and supervised machine/deep learning approaches to identify the most effective model