Modeling autosomal dominant Alzheimer's disease with machine learning

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions

Mastering the canonical loop of serine protease inhibitors: Enhancing potency by optimising the internal hydrogen bond network

Background Canonical serine protease inhibitors commonly bind to their targets through a rigid loop stabilised by an internal hydrogen bond network and disulfide bond(s). The smallest of these is sunflower trypsin inhibitor (SFTI-1), a potent and broad-range protease inhibitor. Recently, we re-engineered the contact β-sheet of SFTI-1 to produce a selective inhibitor of kallikrein-related peptidase 4 (KLK4), a protease associated with prostate cancer progression. However, modifications in the binding loop to achieve specificity may compromise structural rigidity and prevent re-engineered inhibitors from reaching optimal binding affinity. Methodology/Principal Findings In this study, the effect of amino acid substitutions on the internal hydrogen bonding network of SFTI were investigated using an in silico screen of inhibitor variants in complex with KLK4 or trypsin. Substitutions favouring internal hydrogen bond formation directly correlated with increased potency of inhibition in vitro. This produced a second generation inhibitor (SFTI-FCQR Asn14) which displayed both a 125-fold increased capacity to inhibit KLK4 (Ki = 0.0386±0.0060 nM) and enhanced selectivity over off-target serine proteases. Further, SFTI-FCQR Asn14 was stable in cell culture and bioavailable in mice when administered by intraperitoneal perfusion. Conclusion/Significance These findings highlight the importance of conserving structural rigidity of the binding loop in addition to optimising protease/inhibitor contacts when re-engineering canonical serine protease inhibitors

Provision of Palliative Care Services for Cancer Patients in the Community in Africa

World Health Organization (WHO) (The protocol for the WHO study on the effectiveness of community-based programmes for NCD prevention and control (No. NMH/NPH/NCP/03.09). World Health Organization, Geneva, 2003) predicts that by 2030, non-communicable diseases will be the leading cause of death in Sub-Saharan Africa. In 2012, there were 645,000 new cancer cases and 456,000 cancer-related deaths in Africa. The need for palliative care in the region is expected to keep increasing. Palliative care is a relatively new field in Africa, and only about 5% of patients who need it can access it. Governments are investing more in cure and prevention, and very few have integrated palliative care into mainstream policies or created stand-alone policies. WHO (1990) recommends a public health approach as the best in establishing and integrating palliative care. Sub-Saharan Africa is currently using the community-based models of providing care which involves a lot of community participation and provision of home-based care. Many challenges still exist in service provision such as inability to reach rural populations who need the care most, unavailability of opioids and lack of financial support from governments