Mechanistic Model Research Articles

Exploring the Regimes of Particle Behavior upon Impact via the Discrete Element Method.

Discrete element method simulations are conducted to probe the various regimes of post-impact behavior of particles with solid surfaces. The impacting particles are described as spherical agglomerates consisting of smaller constituent (or primary) particles held together via surface adhesion. Under the influence of a wide range of impact velocities and particle surface energies, five distinct behavioral regimes-rebounding, vibration, fragmentation, pancaking, and shattering-are identified, and force transmission patterns are linked to post-impact behavior. In the rebounding regime, the coefficient of restitution decreases linearly as impact velocity increases and the particle agglomerate experiences compaction. In the fragmentation regime, rebound velocity generally decreases with increasing fragment size. The rebound velocity of fragments decreases with time except for the smallest fragments, which can increase in velocity due to collisions with other fragments of high velocity. Particle breakage in the pancaking regime does not follow common mechanistic models of breakage.

An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.

RETRACTED: Optimizing energy and quantifying dispatchable potential of holistic HVAC systems

The increasing demand for flexible resources in power systems, coupled with the evolution of smart buildings, underscores the significance of optimizing energy use and quantifying dispatchable potential in heating, ventilation, and air conditioning systems. This work develops an energy optimization framework for these systems, accounting for external factors like solar radiation, temperature, and humidity. The goal is to minimize the energy consumption of holistic systems and devise optimal control strategies for each subsystem. The framework includes a mechanistic model that accurately describes the performance of cooling towers, a three-damper control strategy for air-side economizers, and three operational modes for air handling units. Furthermore, the relationship between the fresh air ratio and the supply/return air duct pressure drop obtained through analysis simplifies the optimization problem and reduces the difficulty of solving it. Subsequently, the framework utilizes indoor temperature and humidity ranges to ascertain optimal control strategies for both maximal and minimal energy consumption in the system. Simulation results for a hypothetical building reveal that dispatchable potential of the system can account for up to 13.055% of maximal energy consumption.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

Improved size effect law (SEL) considering size-dependent fracture process zone (FPZ) for quasi-brittle materials

Establishing a mechanistic model to simultaneously capture the descending size effect (DSE) and the change from DSE to the ascending size effect (ASE) remains a challenging issue for quasi-brittle materials. A size-dependent fracture process zone (FPZ) model is proposed and verified by experimental and simulative results. Subsequently, the size effect law (SEL) of unnotched specimens is improved by considering the size-dependent FPZ model, which is termed the improved SEL in this paper. Parametric studies are conducted for a better understanding of the improved SEL. It is found that the underlying mechanism of the DSE and the change from DSE to ASE is the competition between the relative rates of FPZ and specimen size change with respect to specimen size. The ASE is obtained when the relative rate of FPZ change is higher than that of specimen size. Conversely, the DSE is observed. No size effect exists when the relative rate of FPZ change is equal to that of specimen size. Experimental and simulative results are adopted to validate the improved SEL of unnotched specimens. The validation of the improved SEL indicates that the improved SEL not only accurately predicts the DSE, but also correctly captures the change from DSE to ASE. In addition, the size effect of nominal strength with different maximum aggregate size is well captured by the improved SEL. The fitting parameters of the improved SEL lie in the given interval. By comparing the determination coefficients R2 of the SEL, unified size effect law (USEL), and improved SEL, it is found that the R2 of the improved SEL is higher than that of the SEL for the DSE and is close to that of the IUSEL for the change from DSE to ASE. Therefore, it is recommended that engineers and researchers use the improved SEL to describe the DSE and the change from DSE to ASE of unnotched specimens.

Opinion — On a New Mechamistic Model Toward the Catalytic Reactions: From Hydrogen Combustion to Fischer-Tropsch Reaction

Mechanism research in catalytic chemistry is both fascinating and confusing, particularly when it comes to solid-state catalysts, the nature of catalytic behaviours has been unidentified so far. For a mechanistic model to be acceptable, it should have an ability to explain all unique aspects of a given catalytic reaction and provide an illuminating explanation to a widely range of catalytic reaction. In our recent reports, a new mechanistic model was suggested for catalytic CO<sub>2</sub> reduction reaction on Cu metal and hydrogen evolution reaction on various transition metals, which provides a reasonable interpretation to both catalytic reactions (from the diversity of product distribution and catalytic behaviour of various metals). Here, it is expected to extend this new mechanistic model to a wider range of catalytic reactions over various catalysts. Such as hydrogen combustion with Cu metal adding, oxidation of SO<sub>2</sub> by O<sub>2</sub> to give SO<sub>3</sub> with NO adding, conversion of CO and NO into CO<sub>2</sub> and N<sub>2</sub> with Ru metal adding, and hydrogeneration of propylene with Pt metal adding. Importantly, this model seems also to pertain to the mechanism of the Fischer-Tropsch (T-F) reaction, i.e. the conversion of CO and H<sub>2</sub> to hydrocarbons, principally a mixture of linear alkanes (including methane) and alkenes, by passage over various heterogeneous transition-metal catalysts (Fe, Co, Ni et.al.).

Quantitative assessment of microstructural damage for interior crack initiation and high cycle fatigue life modeling

Axially loaded cyclic tests of a nickel-based alloy were conducted at 550 °C, 630 °C and 650 °C and stress ratios of −1, −0.3 and 0.5 up to high cycle fatigue regime. Multiscale characterizations and quantitative analyses of microstructural damage were carried out to illustrate the physics and mechanics of interior microstructure induced fatigue strength degradation and cracking. Results showed that the fatigue strength increased with the decreases of twin spacing, the size and aspect ratio of carbide, and γ′ phase size. Twin collapse due to slip band-twin interaction was found for the first time and confirmed as a form of crack initiation process, followed by short crack growth forming facets with significant contribution from shear stress. A new mechanistic fatigue life model of interior microstructure induced cracking was established with sound agreement with experimental data. All these combined into a design-manufacturing integrated approach which are capable of ensuring the safe operation and maintenance of service structures in long-life regime.

A.3 Beta-amyloid is a cytokine and Alzheimer’s is an autoimmune disease

Background: Despite the limited successes of recent amyloid-targetting biologics, the need for a new pathogenesis mechanistic model of Alzheimer’s disease (AD) is a continuing priority, to facilitate improved rational drug design. Methods: To devise a new AD model, we performed an extensive, comprehensive series of in silico, in vitro, and in vivo studies explicitly evaluating the atomistic–molecular mechanisms of cytokine-mediated and amyloid-beta (Aβ)-mediated neurotoxicities in AD. Results: A new model of AD has been devised: In response to pathogen-/damage-associated molecular pattern-stimulating events (e.g., infection, trauma, ischaemia), Aβ is released as an early responder cytokine triggering an innate immunity cascade in which Aβ exhibits immunomodulatory/antimicrobial duality. However, Aβ’s antimicrobial properties result in a misdirected cytotoxic attack upon “self” neurons, arising from the electrophysiological similarities between neurons and bacteria in terms of transmembrane potential and anionic charges on outer membrane macromolecules. The subsequent breakdown products (amyloid-ganglioside complexes) released from the damaged neurons diffuse to adjacent neurons eliciting further release of Aβ, leading to a chronic, self-perpetuating disease cycle. In short, AD occurs because Aβ cannot differentiate neurons from bacteria. Conclusions: An innovative new model of AD has been devised, recognizing Aβ as a physiologically oligomerizing cytokine and conceptualizing AD as brain-centric autoimmune disorder of innate immunity.

The Ocular Glymphatic System-Current Understanding and Future Perspectives.

The ocular glymphatic system subserves the bidirectional polarized fluid transport in the optic nerve, whereby cerebrospinal fluid from the brain is directed along periarterial spaces towards the eye, and fluid from the retina is directed along perivenous spaces following upon its axonal transport across the glial lamina. Fluid homeostasis and waste removal are vital for retinal function, making the ocular glymphatic fluid pathway a potential route for targeted manipulation to combat blinding ocular diseases such as age-related macular degeneration, diabetic retinopathy, and glaucoma. Several lines of work investigating the bidirectional ocular glymphatic transport with varying methodologies have developed diverging mechanistic models, which has created some confusion about how ocular glymphatic transport should be defined. In this review, we provide a comprehensive summary of the current understanding of the ocular glymphatic system, aiming to address misconceptions and foster a cohesive understanding of the topic.

#894 Dynamic multi-omics and mechanistic modelling approach provides deeper insight into kidney fibrosis progression

Abstract Background and Aims Chronic kidney disease (CKD) affects more than 10% of the world's population and causes millions of deaths annually. Organ fibrosis is the main driver of CKD, leading to excessive accumulation of extracellular matrix (ECM). The presence of myofibroblasts correlates with disease progression. The pro-fibrotic responses of these cells, such as ECM production, are driven by the master regulator TGF-β. Despite progress in understanding the disease, there are no treatments available, and current diagnostic indicators neither facilitate early detection of CKD nor correlate with actual renal damage [1–4] Using human kidney-derived PDGFRβ+ mesenchymal cells we aim to provide time-resolved mechanistic insight into CKD progression. Method Our goal is to understand and model how these cells contribute to fibrosis by studying their kinetic response to TGF-β. Therefore, we use multiple omics measurements: transcriptomics, secretomics and phospho-/proteomics coupled with quantitative imaging of the ECM. For each time point, we quantified differential changes in omics and estimated the activities of transcription factors, kinases and phosphatases [5]. Furthermore, we used causal reasoning to draw mechanistic relationships between omics layers within and across time points. Results For validation, we focused on a set of transcription factors that also showed importance in the network context that considers several data modalities. Using knock-down experiments, we show the relevance of these transcription factors for the ECM deposition as well as the fibrotic response to TGF-β. Conclusion Taken together, by applying mechanistic modeling and the integration of multiple datasets, we are contributing to a better understanding of the factors and mechanisms driving kidney fibrosis. Furthermore, we will publish this dataset and thus provide a valuable resource for researchers in the field of CKD.

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.

Towards a mechanistic model of oxidase deactivation in a bubble column

Despite the many advantages of biocatalysis, enzyme stability remains an issue. This work investigates the long-term kinetic stability of a water-forming NAD(P)H-oxidase (NOX2), an enzyme of potential industrial interest for NAD(P)H regeneration, in a bubble column sparged with air, which enables reasonable oxygen transfer but without drastic enzyme deactivation. Experiments have been performed with the particular goal of explaining the observed two-stage deactivation trend. We show evidence supporting the hypothesis that the first stage is related to the adsorption of enzymes to the interface, followed by a subsequent deactivation stage at the interface.The characterization of NOX deactivation in the bubble column setup, complemented by additional’quiescent’ experiments, has enabled the development of a first principles model as the first step towards a complete mechanistic model of oxidase deactivation at gas–liquid interfaces.

Evaluation and Optimization of a Microcavity Plate-Based Human Hepatocyte Spheroid Model for Predicting Clearance of Slowly Metabolized Drug Candidates.

In vitro clearance assays are routinely conducted in drug discovery to predict in vivo clearance, but low metabolic turnover compounds are often difficult to evaluate. Hepatocyte spheroids can be cultured for days, achieving higher drug turnover, but have been hindered by limitations on cell number per well. Corning Elplasia microcavity 96-well microplates enable the culture of 79 hepatocyte spheroids per well. In this study, microcavity spheroid properties (size, hepatocyte function, longevity, culturing techniques) were assessed and optimized for clearance assays, which were then compared with microsomes, hepatocyte suspensions, two-dimensional-plated hepatocytes, and macrowell spheroids cultured as one per well. Higher enzyme activity coupled with greater hepatocyte concentrations in microcavity spheroids enabled measurable turnover of all 17 test compounds, unlike the other models that exhibited less drug turnover. Microcavity spheroids also predicted intrinsic clearance (CLint) and blood clearance (CLb) within threefold for 53% [9/17; average absolute fold error (AAFE), 3.9] and 82% (14/17; AAFE, 2.6) of compounds using a linear regression correction model, respectively. An alternate method incorporating mechanistic modeling that accounts for mass transport (permeability and diffusion) within spheroids demonstrated improved predictivity for CLint (12/17; AAFE, 4.0) and CLb (14/17; AAFE, 2.1) without the need for empirical scaling factors. The estimated fraction of drug metabolized by cytochrome P450 3A4 (fm,CYP3A4) using 3 μM itraconazole was within 25% of observed values for 6 of 8 compounds, with 5 of 8 compounds within 10%. In sum, spheroid cultures in microcavity plates permit the ability to test and predict clearance as well as fm,CYP3A4 of low metabolic turnover compounds and represent a valuable complement to conventional in vitro clearance assays. SIGNIFICANCE STATEMENT: Culturing multiple spheroids in ultralow attachment microcavities permits accurate quantitation of metabolically stable compounds in substrate depletion assays, overcoming limitations with singly cultured spheroids. In turn, this permits robust estimates of intrinsic clearance, which is improved with the consideration of mass transport within the spheroid. Incubations with 3 μM itraconazole enabled assessments of CYP3A4 involvement in hepatic clearance.

Longevity of subtidal mussel beds (Mytilus edulis) in eutrophic coastal areas

Mussel populations (Mytilus edulis) around the coasts of Europe and the North Atlantic are often ephemeral and have notably experienced a large decline in abundance. Since 1993, annual blue mussel stock surveys have been carried out in the Limfjorden, Denmark. We used the stock survey data combined with electronic monitoring fishing data and Mechanistic Models for the Limfjorden, providing environmental data to investigate the impact of various stressors. Multiple factors were found to affect the longevity of subtidal mussel beds in Limfjorden. Predation by starfish, fishing activities, shell length of the mussels, amplitude in summer temperature and consecutive days of oxygen depletion decreased the longevity of the beds. Conversely, increased biomass, multiple cohorts and increasing water depth demonstrate stabilising effects. Water column stratification had both a negative and positive impact on bed longevity depending on the duration. These analyses can help inform environmental, conservation, and fisheries managers on the long-term trends of population dynamics and gain a deeper understanding of what factors can affect mussel bed longevity in the context of declining stocks.

Range of trait variation in prey determines evolutionary contributions to predator growth rates.

Evolutionary and ecological dynamics can occur on similar timescales and thus influence each other. While it has been shown that the relative contribution of ecological and evolutionary change to population dynamics can vary, it still remains unknown what influences these differences. Here, we test whether prey populations with increased variation in their defense and competitiveness traits will have a stronger impact of evolution for predator growth rates. We controlled trait variation by pairing distinct clonal lineages of the green alga Chlamydomonas reinhardtii with known traits as prey with the rotifer Brachionus calyciforus as predator and compared those results with a mechanistic model matching the empirical system. We measured the impact of evolution (shift in prey clonal frequency) and ecology (shift in prey population density) for predator growth rate and its dependency on trait variation using an approach based on a two-way ANOVA. Our experimental results indicated that higher trait variation, i.e., a greater distance in trait space, increased the relative contribution of prey evolution to predator growth rate over 3-4 predator generations, which was also observed in model simulations spanning longer time periods. In our model, we also observed clone-specific results, where a more competitive undefended prey resulted in a higher evolutionary contribution, independent of the trait distance. Our results suggest that trait combinations and total prey trait variation combine to influence the contribution of evolution to predator population dynamics, and that trait variation can be used to identify and better predict the role of eco-evolutionary dynamics in predator-prey systems.

Constructed wetlands as nature-based solutions in managing per-and poly-fluoroalkyl substances (PFAS): Evidence, mechanisms, and modelling

Per- and poly-fluoroalkyl substances (PFAS) have emerged as newly regulated micropollutants, characterised by extreme recalcitrance and environmental toxicity. Constructed wetlands (CWs), as a nature-based solution, have gained widespread application in sustainable water and wastewater treatment and offer multiple environmental and societal benefits. Despite CWs potential, knowledge gaps persist in their PFAS removal capacities, associated mechanisms, and modelling of PFAS fate. This study carried out a systematic literature review, supplemented by unpublished experimental data, demonstrating the promise of CWs for PFAS removal from the influents of varying sources and characteristics. Median removal performances of 64, 46, and 0 % were observed in five free water surface (FWS), four horizontal subsurface flow (HF), and 18 vertical flow (VF) wetlands, respectively. PFAS adsorption by the substrate or plant root/rhizosphere was deemed as a key removal mechanism. Nevertheless, the available dataset resulted unsuitable for a quantitative analysis. Data-driven models, including multiple regression models and machine learning-based Artificial Neural Networks (ANN), were employed to predict PFAS removal. These models showed better predictive performance compared to various mechanistic models, which include two adsorption isotherms. The results affirmed that artificial intelligence is an efficient tool for modelling the removal of emerging contaminants with limited knowledge of chemical properties. In summary, this study consolidated evidence supporting the use of CWs for mitigating new legacy PFAS contaminants. Further research, especially long-term monitoring of full-scale CWs treating real wastewater, is crucial to obtain additional data for model development and validation.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

Balanced Mix Design Plus for Mixtures that Contain Recycled Asphalt Pavement

Recent efforts have been made to include mixture performance tests at the mix design stage using so-called “balanced” mix design (BMD). This paper evaluates the use of two mixtures that contain intermediate and high recycled asphalt pavement contents in the context of a BMD approach referred to as balanced mix design plus (BMD+). BMD+ utilizes the dynamic modulus, cyclic fatigue, and stress sweep rutting tests and mechanistic models and has three tiers of design based on the amount of performance testing required. As part of the BMD+ approach, index–volumetrics relationships (IVRs) and performance–volumetrics relationships (PVRs) based on the four-corner concept were developed for the two mixtures and verified using the performance data from seven other volumetric conditions. The developed PVRs were used to perform BMD+ Tier 3 analysis of the two mixtures, whereas the IVRs based on two points at the target air void content of 4% were used to conduct BMD+ Tier 2 analysis. The results indicate that the use of BMD+ Tier 2, which requires performance tests at only two conditions and without pavement simulations, yields longer lasting mixtures compared with Tier 1, which follows the Superpave volumetric mix design method. It was also found that Tier 3 requires more testing and pavement simulations but leads to even further gains in predicted life duration compared with Tier 2.

The PR-10 Protein Pru p 1 is an Endonuclease that Preferentially Cleaves Single-Stranded RNA.

Pathogenesis-related class 10 (PR-10) proteins play a crucial role in plant defense by acting as ribonucleases. The specific mechanism of action and substrate specificity of these proteins have remained largely unexplored so far. In this study, we elucidate the enzymatic activity of Pru p 1, a PR-10 protein from peach. We demonstrate that this protein catalyzes the endonucleolytic backbone cleavage of RNA substrates into short oligonucleotides. Initial cleavage products, identified through kinetic analysis, can bind again, priming them for further degradation. NMR binding site mapping reveals that the large internal cavity of Pru p 1, which is characteristic for PR-10 proteins, serves as an anchoring site for single-stranded ribonucleotide chains. We propose a structure-based mechanistic model that accounts for the observed cleavage patterns and the inhibitory effect of zeatin, a nucleoside analog, on the ribonuclease activity of Pru p 1.

The microbiota-gut-brain-immune interface in the pathogenesis of neuroinflammatory diseases: a narrative review of the emerging literature.

Research is beginning to elucidate the sophisticated mechanisms underlying the microbiota-gut-brain-immune interface, moving from primarily animal models to human studies. Findings support the dynamic relationships between the gut microbiota as an ecosystem (microbiome) within an ecosystem (host) and its intersection with the host immune and nervous systems. Adding this to the effects on epigenetic regulation of gene expression further complicates and strengthens the response. At the heart is inflammation, which manifests in a variety of pathologies including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Multiple Sclerosis (MS). Generally, the research to date is limited and has focused on bacteria, likely due to the simplicity and cost-effectiveness of 16s rRNA sequencing, despite its lower resolution and inability to determine functional ability/alterations. However, this omits all other microbiota including fungi, viruses, and phages, which are emerging as key members of the human microbiome. Much of the research has been done in pre-clinical models and/or in small human studies in more developed parts of the world. The relationships observed are promising but cannot be considered reliable or generalizable at this time. Specifically, causal relationships cannot be determined currently. More research has been done in Alzheimer's disease, followed by Parkinson's disease, and then little in MS. The data for MS is encouraging despite this. While the research is still nascent, the microbiota-gut-brain-immune interface may be a missing link, which has hampered our progress on understanding, let alone preventing, managing, or putting into remission neurodegenerative diseases. Relationships must first be established in humans, as animal models have been shown to poorly translate to complex human physiology and environments, especially when investigating the human gut microbiome and its relationships where animal models are often overly simplistic. Only then can robust research be conducted in humans and using mechanistic model systems.