Digital biomarker-based individualized prognosis for people at risk of dementia

Background: Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker-based prognostic models and focused on generalizability and robustness of the models. Method: We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi-site, 40-month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results: Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion: Digital biomarker prognostic models can be a useful tool to assist large-scale population screening for the early detection of cognitive impairment and patient monitoring over time

General Form of the Cross Energy Parameter of Equations of State

Phase equilibrium calculations with cubic equations of state are sensitive to mixing and combining rules employed. In this work, we present a suitable general form of the combining rule for the cross-energy parameter, often considered to be the key property in phase equilibrium calculations. The proposed rule is derived from molecular considerations, namely, the London−Mie theory. The typically used geometric mean (GM) and other combining rules can be deducted from this expression from different values of the parameter n, which is the attractive tail of the Mie potential. We show that using this n parameter as the variable instead of the commonly employed kij offers useful insight into the behavior of cubic equations of state for a large number of asymmetric systems including gas/alkanes, polymer solutions and blends, and alcohol/alkane and gas/solid systems

Finite Size Effects in Networks of Coupled Neurons

We use extensive computer simulations to study synchronization phenomena in networks of biological neurons. Each individual neuron is modeled using the leaky integrate-and-fire (LIF) scheme, while many neurons are coupled nonlocally in a network. In this system chimera states develop, which are complex states consisting of coexisting synchronous and asynchronous network areas. We study the influence of the network size on the properties and the form of chimera states. We show that for constant coupling strength, the number of the synchronous/asynchronous domains depends quantitatively on the coupling ratio. This dependence allows to extract synchronization properties in large ensembles of neurons after extrapolating from simulations of small networks. Since computer simulations of even small neuron networks are highly demanding in memory and CPU time, this property is particularly important in view of the large number of neurons involved in any cognitive function. In total, the number of neurons in the human brain is of the order of 1010, and each of them is connected with an average of 103 other neurons. © Springer Nature Switzerland AG 2020

In Silico Structural Analysis Predicting the Pathogenicity of PLP1 Mutations in Multiple Sclerosis

The X chromosome gene PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus–Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients

Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers

Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder’s underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided

Machine Learning Analysis of Alzheimer’s Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions

Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer’s disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer’s disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer’s disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer’s disease