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.

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.

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.

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.

Mechanical Response of Porcine Liver Tissue under High Strain Rate Compression.

In automobile accidents, abdominal injuries are often life-threatening yet not apparent at the time of initial injury. The liver is the most commonly injured abdominal organ from this type of trauma. In contrast to current safety tests involving crash dummies, a more detailed, efficient approach to predict the risk of human injuries is computational modelling and simulations. Further, the development of accurate computational human models requires knowledge of the mechanical properties of tissues in various stress states, especially in high-impact scenarios. In this study, a polymeric split-Hopkinson pressure bar (PSHPB) was utilized to apply various high strain rates to porcine liver tissue to investigate its material behavior during high strain rate compression. Liver tissues were subjected to high strain rate impacts at 350, 550, 1000, and 1550 s-1. Tissue directional dependency was also explored by PSHPB testing along three orthogonal directions of liver at a strain rate of 350 s-1. Histology of samples from each of the three directions was performed to examine the structural properties of porcine liver. Porcine liver tissue showed an inelastic and strain rate-sensitive response at high strain rates. The liver tissue was found lacking directional dependency, which could be explained by the isotropic microstructure observed after staining and imaging. Furthermore, finite element analysis (FEA) of the PSHPB tests revealed the stress profile inside liver tissue and served as a validation of PSHPB methodology. The present findings can assist in the development of more accurate computational models of liver tissue at high-rate impact conditions allowing for understanding of subfailure and failure mechanisms.

Closing the Wearable Gap-Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors.

The purpose of this study was to evaluate the use of compressible soft robotic sensors (C-SRS) in determining plantar pressure to infer vertical and shear forces in wearable technology: A ground reaction pressure sock (GRPS). To assess pressure relationships between C-SRS, pressure cells on a BodiTrakTM Vector Plate, and KistlerTM Force Plates, thirteen volunteers performed three repetitions of three different movements: squats, shifting center-of-pressure right to left foot, and shifting toes to heels with C-SRS in both anterior-posterior (A/P) and medial-lateral (M/L) sensor orientations. Pearson correlation coefficient of C-SRS to BodiTrakTM Vector Plate resulted in an average R-value greater than 0.70 in 618/780 (79%) of sensor to cell comparisons. An average R-value greater than 0.90 was seen in C-SRS comparison to KistlerTM Force Plates during shifting right to left. An autoregressive integrated moving average (ARIMA) was conducted to identify and estimate future C-SRS data. No significant differences were seen in sensor orientation. Sensors in the A/P orientation reported a mean R2 value of 0.952 and 0.945 in the M/L sensor orientation, reducing the effectiveness to infer shear forces. Given the high R values, the use of C-SRSs to infer normal pressures appears to make the development of the GRPS feasible.

Data mining the effects of testing conditions and specimen properties on brain biomechanics.

Traumatic brain injury is highly prevalent in the United States. However, despite its frequency and significance, there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques, which are commonly used to determine patterns in large datasets, were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data at various strain rates were collected from published literature and sorted into datasets based on strain rate and tension vs. compression. Self-organizing maps were used to conduct a sensitivity analysis to rank the testing condition parameters by importance. Fuzzy C-means clustering was applied to determine if there were any patterns in the data. The parameter rankings and clustering for each dataset varied, indicating that the strain rate and type of deformation influence the role of these parameters in the datasets.

Molecular dynamics simulations showing 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) membrane mechanoporation damage under different strain paths.

Continuum finite element material models used for traumatic brain injury lack local injury parameters necessitating nanoscale mechanical injury mechanisms be incorporated. One such mechanism is membrane mechanoporation, which can occur during physical insults and can be devastating to cells, depending on the level of disruption. The current study investigates the strain state dependence of phospholipid bilayer mechanoporation and failure. Using molecular dynamics, a simplified membrane, consisting of 72 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) phospholipids, was subjected to equibiaxial, 2:1 non-equibiaxial, 4:1 non-equibiaxial, strip biaxial, and uniaxial tensile deformations at a von Mises strain rate of 5.45 × 108 s-1, resulting in velocities in the range of 1 to 4.6 m·s-1. A water bridge forming through both phospholipid bilayer leaflets was used to determine structural failure. The stress magnitude, failure strain, headgroup clustering, and damage responses were found to be strain state-dependent. The strain state order of detrimentality in descending order was equibiaxial, 2:1 non-equibiaxial, 4:1 non-equibiaxial, strip biaxial, and uniaxial. The phospholipid bilayer failed at von Mises strains of .46, .47, .53, .77, and 1.67 during these respective strain path simulations. Additionally, a Membrane Failure Limit Diagram (MFLD) was created using the pore nucleation, growth, and failure strains to demonstrate safe and unsafe membrane deformation regions. This MFLD allowed representative equations to be derived to predict membrane failure from in-plane strains. These results provide the basis to implement a more accurate mechano-physiological internal state variable continuum model that captures lower length scale damage and will aid in developing higher fidelity injury models.

Temporal distribution of dissolved trace metal in the coastal waters of Southwestern Bay of Bengal, India.

The objective of the present study was to characterize the concentrations of selected dissolved trace metals in the coastal waters (500 m from shore) of Kalpakkam, Tamil Nadu, India. The order of dissolved concentration of these metals was found to be as follows: Co (cobalt) < Cd (cadmium) < Cr (chromium) < Mn (manganese) < Cu (copper) < Ni (nickel) < Pb (lead) < Zn (zinc). The levels of these trace metals were found to be relatively low as compared to the reported values for other Indian coastal waters, which indicates negligible pollution at this location. Cadmium was the only metal found to increase its concentration during the monsoon period, suggesting its allochthonous input. Factor analysis indicated that chromium, nickel, zinc, cobalt, copper, manganese, and lead were of common origin, and external inputs through land runoff had nominal or little impact, typifying in-situ regeneration and remineralization linkage with their temporal variation. However, levels of zinc, cobalt, and copper remained relatively high during the summer period, and abrupt increases in their concentration during December (monsoon season) may be due to their dual (autochthonous as well as allochthonous) input.

Studies on some trace and minor elements in blood. A survey of the Kalpakkam (India) population. Part I: Standardization of analytical methods using ICP-MS and AAS.

Blood is one of the widely used specimens for biological trace element research because of its biological significance and ease of sampling. We have conducted a study of the blood of the Kalpakkam township population for trace and minor elements. For this purpose, analytical methods have been developed and standardized in our laboratory for the elemental analysis of blood plasma and red cells. Inductively coupled plasma-mass spectrometry (ICP-MS), a relatively new technique, has been applied for the analysis of trace elements. Details regarding spectral interference and matrix interference encountered in the analysis of blood and the methods of correcting them have been discussed. Flame atomic absorption spectrometry (AAS)/atomic emission spectrometry (AES) has been applied for the determination of minor elements. Precision and accuracy of these methods have also been discussed.

Studies on some trace and minor elements in blood. A survey of the Kalpakkam (India) population. Part III: Studies on dietary intake and its correlation to blood levels.

In our studies on elemental levels in blood of the Kalpakkam population, it was found that the reference values for many elements were normal, but some deficiency with respect to Se was noticed. As a followup study, the dietary ingredients of the local population were analyzed for trace and minor elements to assess the dietary intake of these elements. Details of the analytical methods developed using the technique of inductively coupled plasma-mass spectrometry (ICP-MS) and atomic absorption spectrometry (AAS) have been described. The dietary intake of many of these trace and minor elements were found to be quite adequate according to the recommended dietary allowance (RDA) levels prescribed, except for Se and Zn. The dietary intake of Se was found to be in the range 20-50 micrograms/d (as opposed to the RDA of 50-200 micrograms/d), whereas the intake of Zn was found to be in the range 8-10 mg/d (as opposed to the RDA of 15 mg/d). Although the deficiency of Se intake was reflected in the blood, that of Zn was not, probably owing to the high level of homeostasis for this element. Fish and egg were found to be rich sources of Se, followed by cereals and pulses, which were found to be the major sources of Zn.

Studies on some trace and minor elements in blood. A survey of the Kalpakkam (India) population. Part II: Reference values for plasma and red cell, and correlation with coronary risk index.

Since data on the trace element levels in Indian population are lacking, we chose to conduct a survey of the Kalpakkam township population. People in the age group 40-55 were included in this study. Reference values for trace and minor elements of the blood of the Kalpakkam population were arrived at by carrying out the analysis of plasma and red cells of healthy subjects of the Kalpakkam population. Although the "reference values" for many elements were found to be normal and comparable to values available in the literature, slight deficiency with respect to Se was noticed. Subjects with high coronary risk index were also included in the study to assess the possible correlation of elemental and lipid profile. A study of box plots showed that the elements Se, Mg, Na, K, and Fe show significant differences between "high risk" coronary risk index (CRI > 5) and "no risk" (CRI < 4.5). In the plasma, the levels of Mg, Na, and K were found to be less in the high-risk group. In red cells, the amount of Se, Fe, and K were found to be significantly less in the "high-risk" group as compared to the "no-risk" group.