Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

Trajectories of frailty with aging:Coordinated analysis of five longitudinal studies

BACKGROUND AND OBJECTIVES: There is an urgent need to better understand frailty and its predisposing factors. Although numerous cross-sectional studies have identified various risk and protective factors of frailty, there is a limited understanding of longitudinal frailty progression. Furthermore, discrepancies in the methodologies of these studies hamper comparability of results. Here, we use a coordinated analytical approach in 5 independent cohorts to evaluate longitudinal trajectories of frailty and the effect of 3 previously identified critical risk factors: sex, age, and education. RESEARCH DESIGN AND METHODS: We derived a frailty index (FI) for 5 cohorts based on the accumulation of deficits approach. Four linear and quadratic growth curve models were fit in each cohort independently. Models were adjusted for sex/gender, age, years of education, and a sex/gender-by-age interaction term. RESULTS: Models describing linear progression of frailty best fit the data. Annual increases in FI ranged from 0.002 in the Invecchiare in Chianti cohort to 0.009 in the Longitudinal Aging Study Amsterdam (LASA). Women had consistently higher levels of frailty than men in all cohorts, ranging from an increase in the mean FI in women from 0.014 in the Health and Retirement Study cohort to 0.046 in the LASA cohort. However, the associations between sex/gender and rate of frailty progression were mixed. There was significant heterogeneity in within-person trajectories of frailty about the mean curves. DISCUSSION AND IMPLICATIONS: Our findings of linear longitudinal increases in frailty highlight important avenues for future research. Specifically, we encourage further research to identify potential effect modifiers or groups that would benefit from targeted or personalized interventions

The Physiology of Vasodilatation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68237/2/10.1177_000331976101200602.pd

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bia

Protein–lipid charge interactions control the folding of outer membrane proteins into asymmetric membranes

Biological membranes consist of two leaflets of phospholipid molecules that form a bilayer, each leaflet comprising a distinct lipid composition. This asymmetry is created and maintained in vivo by dedicated biochemical pathways, but difficulties in creating stable asymmetric membranes in vitro have restricted our understanding of how bilayer asymmetry modulates the folding, stability and function of membrane proteins. In this study, we used cyclodextrin-mediated lipid exchange to generate liposomes with asymmetric bilayers and characterize the stability and folding kinetics of two bacterial outer membrane proteins (OMPs), OmpA and BamA. We found that excess negative charge in the outer leaflet of a liposome impedes their insertion and folding, while excess negative charge in the inner leaflet accelerates their folding relative to symmetric liposomes with the same membrane composition. Using molecular dynamics, mutational analysis and bioinformatics, we identified a positively charged patch critical for folding and stability. These results rationalize the well-known ‘positive-outside’ rule of OMPs and suggest insights into the mechanisms that drive OMP folding and assembly in vitro and in vivo

Variation in sire genetics is an irrelevant determinant of digestibility in supplemented crossbred sheep

The efficiency with which sheep produce meat and/or wool relies on a combination of available high quality nutrition and good genetics, hence the constant quest for sheep breed combinations that best utilise feeds to the maximum. High digestibility and nutrient retention of feed on offer are important indices of protein and energy available for wool fibre synthesis or muscle accretion in sheep. Both wool fibre number and diameter are strongly genetically determined. To our knowledge, it has not yet been established if sire genetics alone influences the digestibility and nutrient retention of dietary energy and protein in supplemented crossbred sheep. Therefore, the objective of this study was to evaluate the influence of sire genetics, dietary protein supplement type, level of feeding, sex and their second order interactions on metabolisable energy and nitrogen digestibility in first cross progeny of Merino dams sired by 5 ram breeds. Weaner sheep ( 40) sired by 5 ram breeds (Poll Dorset, Coopworth, Texel, East Friesian and White Suffolk), were balanced for liveweight (30 kg) and body condition score (3.0) on the average, before being randomly assigned to two supplementary feeds ( canola or lupins) and fed at two levels (1 % or 2.0% BW). The feeding trial lasted for six weeks including an initial adjustment period of 3 weeks and the final 1 wk of faecal and urinary collection. All treatment groups received a daily allocation of an isocaloric and isonitrogenous diet comprising 0.5 kg of barley, 0.1 kg molasses-treated straw and 0.001 kg vitamin-mineral mix at 10:00 h. Each sheep had ad libitum access to clean, drinking water. DM intake and output, body weight, and change in wool fibre diameter ( difference in wool microns at the beginning and end of the feeding trial) were measured. DM digestibilities were measured and data subjected to a general linear models procedure (PROC GLM) of the Statistical Analysis System® (SAS Institute, 2007) and significance established using orthogonal contrasts and Tukey pairwise comparisons. The model included sire breed, supplement, feeding level, sex as main effects and their second order interactions. Regardless of sire genetics, feeding level or gender, sheep supplemented with canola consumed 4.5% more feed (DMI 163.5 vs. 149.2 g/day), voided 17% more faeces (51.08 vs. 35.97 g/day), digested 8.5% more ME (52.23 vs. 44.23%) and had 4% heavier liveweights (40 vs. 36.9kg) than those supplemented with lupins. Feeding supplements at 1 % of body weight triggered higher ME (49.9% vs. 46.5%) and N (64.9% vs. 63.2%) digestibility responses than feeding at 2%. There was a tendency for females to eat more than males (161.8 vs. 149.6 g/day DMI]) and N digestibility was 2% higher in males (65%) than females (63%). Sire genetics x level of feeding interactions significantly influenced ME and N digestibility (P<0.05) whereby Coopworth-sired sheep supplemented at 1 % of their body weight recorded the highest ME and N digestibility of S4% and 67% compared to 42% and 62% respectively, than their counterparts fed at 2% of body weight. There was a highly significant (P<0.01) effect of type of supplement x level of feeding interaction on wool fibre diameter because sheep fed canola supplements at 1 % of body weight bad finer wool (22.1 microns) than their 2%-fed counterparts (25.4 microns). The post-ruminal delivery of nutrients in sheep supplemented with canola was more efficient than in lupin-supplemented sheep, hence their higher energy and protein digestibility and retention. Furthermore, variation due to sire genetics alone was insufficient in accounting for differences in digestibility and wool fibre diameter, but significantly interacted with type of supplement; level of feeding and sex .. Finally, sire breed variation in digestibility is unlikely to be a useful predictor of genetic merit for wool fibre diameter in first cross sheep

Enabling precision coordinate metrology for universal optical testing and alignment applications

Optical designs for the next generation space science instruments call for unconventional, aspheric, and freeform (FF), prescriptions with tight tolerances. These advanced surfaces enable superior-performance, compact, and lower cost systems but are more challenging to characterize and, hence, to fabricate and integrate. A method was developed to characterize a wide range of optical surfaces, without requiring custom-made correctors, and to align them to each other for a high-performance optical system. A precision coordinate measuring machine, equipped with a non-contact, chromatic confocal probe, was used to measure numerous optics including large convex conics, high-sloped aspherics, several FF surfaces, and grazing-incidence x-ray optics. The resulting data were successfully reduced using custom-developed, advanced surface fitting analysis tool, to determine the optic's alignment relative to the global and local coordinate systems, surface departure from design, and the as-built optical prescription. This information guided the modeling and the alignment of the corresponding as-built optical systems, including a flight system composed of a three-mirror anastigmat. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]