Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer's disease-associated metabolites.

Type 2 diabetes (T2D) is implicated as a risk factor for Alzheimer's disease (AD), the most common form of dementia. In this work, we investigated neuroinflammatory responses of primary neurons to potentially circulating, blood-brain barrier (BBB) permeable metabolites associated with AD, T2D, or both. We identified nine metabolites associated with protective or detrimental properties of AD and T2D in literature (lauric acid, asparagine, fructose, arachidonic acid, aminoadipic acid, sorbitol, retinol, tryptophan, niacinamide) and stimulated primary mouse neuron cultures with each metabolite before quantifying cytokine secretion via Luminex. We employed unsupervised clustering, inferential statistics, and partial least squares discriminant analysis to identify relationships between cytokine concentration and disease-associations of metabolites. We identified MCP-1, a cytokine associated with monocyte recruitment, as differentially abundant between neurons stimulated by metabolites associated with protective and detrimental properties of AD and T2D. We also identified IL-9, a cytokine that promotes mast cell growth, to be differentially associated with T2D. Indeed, cytokines, such as MCP-1 and IL-9, released from neurons in response to BBB-permeable metabolites associated with T2D may contribute to AD development by downstream effects of neuroinflammation.

Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer's disease-associated metabolites.

Type 2 diabetes (T2D) is implicated as a risk factor for Alzheimer's disease (AD), the most common form of dementia. In this work, we investigated neuroinflammatory responses of primary neurons to potentially circulating, blood-brain barrier (BBB) permeable metabolites associated with AD, T2D, or both. We identified nine metabolites associated with protective or detrimental properties of AD and T2D in literature (lauric acid, asparagine, fructose, arachidonic acid, aminoadipic acid, sorbitol, retinol, tryptophan, niacinamide) and stimulated primary mouse neuron cultures with each metabolite before quantifying cytokine secretion via Luminex. We employed unsupervised clustering, inferential statistics, and partial least squares discriminant analysis to identify relationships between cytokine concentration and disease-associations of metabolites. We identified MCP-1, a cytokine associated with monocyte recruitment, as differentially abundant between neurons stimulated by metabolites associated with protective and detrimental properties of AD and T2D. We also identified IL-9, a cytokine that promotes mast cell growth, to be differentially associated with T2D. Indeed, cytokines, such as MCP-1 and IL-9, released from neurons in response to BBB-permeable metabolites associated with T2D may contribute to AD development by downstream effects of neuroinflammation.

Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age.

Brain extracellular matrix (ECM) structure mediates many aspects of neural development and function. Probing structural changes in brain ECM could thus provide insights into mechanisms of neurodevelopment, the loss of neural function in response to injury, and the detrimental effects of pathological aging and neurological disease. We demonstrate the ability to probe changes in brain ECM microstructure using multiple particle tracking (MPT). We performed MPT of colloidally stable polystyrene nanoparticles in organotypic rat brain slices collected from rats aged 14-70 days old. Our analysis revealed an inverse relationship between nanoparticle diffusive ability in the brain extracellular space and age. Additionally, the distribution of effective ECM pore sizes in the cortex shifted to smaller pores throughout development. We used the raw data and features extracted from nanoparticle trajectories to train a boosted decision tree capable of predicting chronological age with high accuracy. Collectively, this work demonstrates the utility of combining MPT with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as chronological age. This will enable further understanding of the roles brain ECM play in development and aging and the specific mechanisms through which injuries cause aberrant neuronal function. Additionally, this approach has the potential to develop machine learning models capable of detecting the presence of injury or indicating the extent of injury based on changes in the brain microenvironment microstructure.

Robotics in Simulated COVID-19 Patient Room for Health Care Worker Effector Tasks: Preliminary, Feasibility Experiments.

The coronavirus disease 2019 (COVID-19) pandemic has strained health care systems and personal protective equipment (PPE) supplies globally. We hypothesized that a collaborative robot system could perform health care worker effector tasks inside a simulated intensive care unit (ICU) patient room, which could theoretically reduce both PPE use and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposures. We planned a prospective proof-of-concept feasibility and design pilot study to test 5 discrete medical tasks in a simulated ICU room of a COVID-19 patient using a collaborative robot: push a button on intravenous pole machine when alert occurs for downstream occlusion, adjust ventilator knob, push button on ICU monitor to silence false alerts, increase oxygen flow on wall-mounted flow meter to allow the patient to walk to the bathroom and back (dial-up and dial-down oxygen flow), and push wall-mounted nurse call button. Feasibility was defined as task completion robotically. A training period of 45 minutes to 1 hour was needed to program the system de novo for each task. In less than 30 days, the team completed 5 simple effector task experiments robotically. Selected collaborative robotic effector tasks appear feasible in a simulated ICU room of the COVID-19 patient. Theoretically, this robotic approach could reduce PPE use and staff SARS-CoV-2 exposure. It requires future validation and health care worker learning similar to other ICU device training.