Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Modeling the impact of thoracic pressure on intracranial pressure.

A potential contribution to the progression of Spaceflight Associated Neuro-ocular Syndrome is the thoracic-to-spinal dural sac transmural pressure relationship. In this study, we utilize a lumped-parameter computational model of human cerebrospinal fluid (CSF) systems to investigate mechanisms of CSF redistribution. We present two analyses to illustrate potential mechanisms for CSF pressure alterations similar to those observed in microgravity conditions. Our numerical evidence suggests that the compliant relationship between thoracic and CSF compartments is insufficient to solely explain the observed decrease in CSF pressure with respect to the supine position. Our analyses suggest that the interaction between thoracic pressure and the cardiovascular system, particularly the central veins, has greater influence on CSF pressure. These results indicate that future studies should focus on the holistic system, with the impact of cardiovascular changes to the CSF pressure emphasized over the sequestration of fluid in the spine.

Cumulus: A federated EHR-based learning system powered by FHIR and AI.

Objective: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Impact of mobile phone usage on dynamic postural control among South Indian college students.

BACKGROUND: Smartphones are a technical marvel that rapidly evolved to play an important role in our lives. One downside to smartphone use is that it significantly worsens posture. It is believed that using a smartphone while walking increases the risk of cognitive decline and the loss of dynamic balance needed to perform functional tasks. OBJECTIVE: The objective of the study was to determine the impact of smartphone usage on dynamic postural control among South Indian college students. METHODS: The study was carried out in a private college with 400 invited students through online social media platforms. The four-square step test and SAS-SV were used to determine the impact of smartphone usage on dynamic postural control. The students were informed about the study process. A total of 250 participants were included based on the inclusion criteria. RESULTS: There was a high percentage of agreement on smartphone usage on dynamic postural control measured with SAS-SV, revealing statistical significance with a mean value of 41.532 and SD of 10.010886. The four-square step test with a mean value of 22.5 and SD of 1.8995878 also proved significant impact. A positive correlation was found between smartphone usage and dynamic postural control, which was analysed using Pearson's correlation coefficient of 0.90130. CONCLUSION: A significant correlation was noted between mobile usage and dynamic balance. Smartphones can have a negative impact on dynamic balance by distracting users from their surroundings and increase the risk of falls.

Role and Clinical Significance of Monocarboxylate Transporter 8 (MCT8) During Pregnancy.

The review aims to summarize the available research focusing on the importance of monocarboxylate transporter (MCT8) in thyroid hormone trafficking across the placenta and fetal development. A systematic search was carried out in PubMed; studies available in English related to "monocarboxylate transporter", "adverse pregnancy", "fetal development," and "thyroid hormone" were identified and assessed. The references within the resulting articles were manually searched. MCT8 is a highly active and selective thyroid hormone transporter that facilitates the cellular uptake of triiodothyronine (T3), thyroxine (T4), reverse triiodothyronine (rT3), and diiodothyronine (T2) in different tissues. MCT8 is expressed in the placenta from the first trimester onwards, allowing the transport of thyroid hormone from mother to fetus. Mutations in MCT8 cause an X-linked disorder known as Allan-Herndon-Dudley syndrome (AHDS), characterized by severe psychomotor impairment and peripheral thyrotoxicosis. Hence, any maternal thyroid dysfunction may cause severe consequences for the fetus and newborn. Further research regarding MCT8 gene expression, polymorphic variation, and adverse pregnancy outcomes must be done to establish that MCT8 is a novel prognostic marker for the early detection of pregnancy-related complications.

Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning.

Background: The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance. Methods: We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center. Results: Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches. Conclusion: From these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer-associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.

Increase in serum nerve growth factor but not intervertebral disc degeneration following whole-body vibration in rats.

BACKGROUND: Low back pain is a leading cause of disability and is frequently associated with whole-body vibration exposure in industrial workers and military personnel. While the pathophysiological mechanisms by which whole-body vibration causes low back pain have been studied in vivo, there is little data to inform low back pain diagnosis. Using a rat model of repetitive whole-body vibration followed by recovery, our objective was to determine the effects of vibration frequency on hind paw withdrawal threshold, circulating nerve growth factor concentration, and intervertebral disc degeneration. METHODS: Male Sprague-Dawley rats were vibrated for 30 min at an 8 Hz or 11 Hz frequency every other day for two weeks and then recovered (no vibration) for one week. Von Frey was used to determine hind paw mechanical sensitivity every two days. Serum nerve growth factor concentration was determined every four days. At the three-week endpoint, intervertebral discs were graded histologically for degeneration. FINDINGS: The nerve growth factor concentration increased threefold in the 8 Hz group and twofold in the 11 Hz group. The nerve growth factor concentration did not return to baseline by the end of the one-week recovery period for the 8 Hz group. Nerve growth factor serum concentration did not coincide with intervertebral disc degeneration, as no differences in degeneration were observed among groups. Mechanical sensitivity generally decreased over time for all groups, suggesting a habituation (desensitization) effect. INTERPRETATION: This study demonstrates the potential of nerve growth factor as a diagnostic biomarker for low back pain due to whole-body vibration.

A novel DPH5-related diphthamide-deficiency syndrome causing embryonic lethality or profound neurodevelopmental disorder.

PURPOSE: Diphthamide is a post-translationally modified histidine essential for messenger RNA translation and ribosomal protein synthesis. We present evidence for DPH5 as a novel cause of embryonic lethality and profound neurodevelopmental delays (NDDs). METHODS: Molecular testing was performed using exome or genome sequencing. A targeted Dph5 knockin mouse (C57BL/6Ncrl-Dph5em1Mbp/Mmucd) was created for a DPH5 p.His260Arg homozygous variant identified in 1 family. Adenosine diphosphate-ribosylation assays in DPH5-knockout human and yeast cells and in silico modeling were performed for the identified DPH5 potential pathogenic variants. RESULTS: DPH5 variants p.His260Arg (homozygous), p.Asn110Ser and p.Arg207Ter (heterozygous), and p.Asn174LysfsTer10 (homozygous) were identified in 3 unrelated families with distinct overlapping craniofacial features, profound NDDs, multisystem abnormalities, and miscarriages. Dph5 p.His260Arg homozygous knockin was embryonically lethal with only 1 subviable mouse exhibiting impaired growth, craniofacial dysmorphology, and multisystem dysfunction recapitulating the human phenotype. Adenosine diphosphate-ribosylation assays showed absent to decreased function in DPH5-knockout human and yeast cells. In silico modeling of the variants showed altered DPH5 structure and disruption of its interaction with eEF2. CONCLUSION: We provide strong clinical, biochemical, and functional evidence for DPH5 as a novel cause of embryonic lethality or profound NDDs with multisystem involvement and expand diphthamide-deficiency syndromes and ribosomopathies.

Ophthalmic manifestations in Costello syndrome caused by Ras pathway dysregulation during development.

BACKGROUND: Costello syndrome (CS) is a multisystem developmental disorder caused by germline pathogenic variants in HRAS resulting in dysregulation of the Ras pathway. A systematic characterization of ophthalmic manifestations provides a unique opportunity to understand the role of Ras signal transduction in ocular development and guide optimal ophthalmic care in CS individuals. METHODS: Visual function, ocular features and genotype/phenotype correlations were evaluated in CS individuals harboring HRAS pathogenic variants, by cross-sectional and retrospective studies, and were recruited through the Costello Syndrome Family Network (CSFN) between 2007 and 2020. RESULTS: Fifty-six molecularly diagnosed CS individuals including 34 females and 22 males, ages ranging from 0.5 to 37 years were enrolled. The most common ophthalmic manifestations in the cross-sectional study were lack of stereopsis (96%), refractive errors (83%), strabismus (72%), nystagmus (69%), optic nerve hypoplasia or pallor (55%) and ptosis (13.7%) with higher prevalence than in the retrospective data (refractive errors (41%), strabismus (44%), nystagmus (26%), optic nerve hypoplasia or pallor (7%) and ptosis (11%)). Visual acuities were found to ranged from 20/25 to 20/800 and contrast sensitivity from 1.6% to 44%. HRAS pathogenic variants included p.G12S (84%), p.G13C (7%), p.G12A (5.4%), p.G12C (1.8%) and p.A146V (1.8%). CONCLUSION: Majority of individuals with CS have refractive errors, strabismus, nystagmus, absent stereopsis, and optic nerve abnormalities suggesting that HRAS and the Ras pathway play a vital role in visual system development. Ptosis, refractive errors and strabismus are amenable to treatment and early ophthalmic evaluation is crucial to prevent long-term vision impairment and improve overall quality of life in CS.

Predicting Space Radiation Single Ion Exposure in Rodents: A Machine Learning Approach.

This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for 4He, 16O, 28Si, 48Ti, or 56Fe up to 150 mGy, based on Attentional Set-shifting (ATSET) experimental tests. The ATSET assay consists of a series of cognitive performance tasks on irradiated male Wistar rats. The GCR ion doses represent the expected cumulative radiation astronauts may receive during a Mars mission on an individual ion basis. The primary objective is to synthesize and assess predictive models on a per-subject level through Machine Learning (ML) classifiers. The raw cognitive performance data from individual rodent subjects are used as features to train the models and to explore the capabilities of three different ML techniques for elucidating a range of correlations between received radiation on rodents and their performance outcomes. The analysis employs scores of selected input features and different normalization approaches which yield varying degrees of model performance. The current study shows that support vector machine, Gaussian naive Bayes, and random forest models are capable of predicting individual ion exposure using ATSET scores where corresponding Matthews correlation coefficients and F1 scores reflect model performance exceeding random chance. The study suggests a decremental effect on cognitive performance in rodents due to ≤150 mGy of single ion exposure, inasmuch as the models can discriminate between 0 mGy and any exposure level in the performance score feature space. A number of observations about the utility and limitations in specific normalization routines and evaluation scores are examined as well as best practices for ML with imbalanced datasets observed.

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation.

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent's susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4He in SD and 28Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut's impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions.

A finite element (FE)-guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body's response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC15) trends were predicted from Kriging models across varied impact velocities (10-45 mph; 16.1-72.4 km/h), locations (near side, far side, front, and rear), and angles (-45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC15 increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC15 under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously. This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10-45 mph), locations, and angles (-45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.

In Silico Finite Element Analysis of the Foot Ankle Complex Biomechanics: A Literature Review.

Computational approaches, especially finite element analysis (FEA), have been rapidly growing in both academia and industry during the last few decades. FEA serves as a powerful and efficient approach for simulating real-life experiments, including industrial product development, machine design, and biomedical research, particularly in biomechanics and biomaterials. Accordingly, FEA has been a "go-to" high biofidelic software tool to simulate and quantify the biomechanics of the foot-ankle complex, as well as to predict the risk of foot and ankle injuries, which are one of the most common musculoskeletal injuries among physically active individuals. This paper provides a review of the in silico FEA of the foot-ankle complex. First, a brief history of computational modeling methods and finite element (FE) simulations for foot-ankle models is introduced. Second, a general approach to build an FE foot and ankle model is presented, including a detailed procedure to accurately construct, calibrate, verify, and validate an FE model in its appropriate simulation environment. Third, current applications, as well as future improvements of the foot and ankle FE models, especially in the biomedical field, are discussed. Finally, a conclusion is made on the efficiency and development of FEA as a computational approach in investigating the biomechanics of the foot-ankle complex. Overall, this review integrates insightful information for biomedical engineers, medical professionals, and researchers to conduct more accurate research on the foot-ankle FE models in the future.

Microstructure and nanomechanical properties of the exoskeleton of an ironclad beetle (Zopherus haldemani).

This study examined natural composite structures within the remarkably strong exoskeleton of the southwestern ironclad beetle (Z. haldemani). Structural and nanomechanical analyses revealed that the exoskeleton's extraordinary resistance to external forces is provided by its exceptional thickness and multi-layered structure, in which each layer performed a distinct function. In detail, the epicuticle, the outmost layer, comprised 3%-5% of the overall thickness with reduced Young's moduli of 2.2-3.2 GPa, in which polygonal-shaped walls (2-3µm in diameter) were observed on the surface. The next layer, the exocuticle, consisted of 17%-20% of the total thickness and exhibited the greatest Young's moduli (∼15 GPa) and hardness (∼800 MPa) values. As such, this layer provided the bulk of the mechanical strength for the exoskeleton. While the endocuticle spanned 70%-75% of the total thickness, it contained lower moduli (∼8-10 GPa) and hardness (∼400 MPa) values than the exocuticle. Instead, this layer may provide flexibility through its specifically organized chitin fiber layers, known as Bouligand structures. Nanoindentation testing further reiterated that the various fibrous layer orientations resulted in different elastic moduli throughout the endocuticle's cross-section. Additionally, this exoskeleton prevented delamination within the composite materials by overlapping approximately 5%-19% of each fibrous stack with neighboring layers. Finally, the innermost layer, the epidermis contributing 5%-7 % of the total thickness, contains attachment sites for muscle and soft tissue that connect the exoskeleton to the beetle. As such, it is the softest region with reduced Young's modulus of ∼2-3 GPa and hardness values of ∼290 MPa. These findings can be applied to the development of innovative, fiber-reinforced composite materials.

A mesoscale finite element modeling approach for understanding brain morphology and material heterogeneity effects in chronic traumatic encephalopathy.

Chronic Traumatic Encephalopathy (CTE) affects a significant portion of athletes in contact sports but is difficult to quantify using clinical examinations and modeling approaches. We use an in silico approach to quantify CTE biomechanics using mesoscale Finite Element (FE) analysis that bridges with macroscale whole head FE analysis. The sulci geometry produces complex stress waves that interact with one another to create increased shear stresses at the sulci depth that are significantly larger than in analyses without sulci (from 0.5 to 18.0 kPa). Sulci peak stress concentration regions coincide with experimentally observed CTE sites documented in the literature. HighlightsSulci introduce stress localizations at their depth in the gray matterSulci stress fields interact to produce stress concentration sites in white matterDifferentiating brain tissue properties did not significantly affect peak stresses.

Shear-deformation based continuum-damage constitutive modeling of brain tissue.

Traumatic brain injury (TBI) is a leading cause of death in the United States. Depending on the severity of injury, complications such as memory loss and emotional changes are common. While the exact mechanisms are still unclear, these cognitive deficiencies are thought to arise from microstructural damages to the brain tissue, such as in diffuse-axonal injury where neuron fibers are sheared. Constitutive models can predict such damage at a microstructural level and allow for insight into the mechanisms of injury initiating at lower length scales. In this study, we developed a continuum damage model of brain tissue that is validated by experimental quasi-static stress-strain tests in tension, compression, and shear. The present work shows that damage is most present in the shear stress state, making the tissue suitable for damage modeling via shear interaction terms. Using this model, new insights into microstructural breakdown due to shear stresses and strains can be gained by application to simulations.

Coherent Force Chains in Disordered Granular Materials Emerge from a Percolation of Quasilinear Clusters.

Dense granular materials and other particle aggregates transmit stress in a manner that belies their microstructural disorder. A subset of the particle contact network is strikingly coherent, wherein contacts are aligned nearly linearly and transmit large forces. Important material properties are associated with these force chains, but their origin has remained a puzzle. We classify subnetworks by their linear connectivity, and show the emergence of a percolation transition at a critical linearity at which the network is sparse, coherent, and contains the force chains. The subnetwork at critical linearity closely reflects the macroscopic stress and explains distinctive features of granular mechanics.

Closing the Wearable Gap-Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements.

The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot-ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements-plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)-were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot-ankle movement capture.

A Mechanical Brain Damage Framework Used to Model Abnormal Brain Tau Protein Accumulations of National Football League Players.

A mechanics-based brain damage framework is used to model the abnormal accumulation of hyperphosphorylated p-tau associated with chronic traumatic encephalopathy within the brains of deceased National Football League (NFL) players studied at Boston University and to provide a framework for understanding the damage mechanisms. p-tau damage is formulated as the multiplicative decomposition of three independently evolving damage internal state variables (ISVs): nucleation related to number density, growth related to the average area, and coalescence related to the nearest neighbor distance. The ISVs evolve under different rates for three well known mechanical boundary conditions, which in themselves introduce three different rates making a total of nine scenarios, that we postulate are related to brain damage progression: (1) monotonic overloads, (2) cyclic fatigue which corresponds to repetitive impacts, and (3) creep which is correlated to damage accumulation over time. Different NFL player positions are described to capture the different types of damage progression. Skill position players, such as quarterbacks, are expected to exhibit a greater p-tau protein accumulation during low cycle fatigue (higher amplitude impacts with a lesser number), and linemen who exhibit a greater p-tau protein accumulation during high cycle fatigue (lower amplitude impacts with a greater number of impacts). This mechanics-based damage framework presents a foundation for developing a multiscale model for traumatic brain injury that combines mechanics with biology.