Individualised prediction of resilience and vulnerability to sleep loss using EEG features.

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

Assessment of the unified model of performance: accuracy of group-average and individualised alertness predictions.

To be effective as a key component of fatigue-management systems, biomathematical models that predict alertness impairment as a function of time of day, sleep history, and caffeine consumption must demonstrate the ability to make accurate predictions across a range of sleep-loss and caffeine schedules. Here, we assessed the ability of the previously reported unified model of performance (UMP) to predict alertness impairment at the group-average and individualised levels in a comprehensive set of 12 studies, including 22 sleep and caffeine conditions, for a total of 301 unique subjects. Given sleep and caffeine schedules, the UMP predicted alertness impairment based on the psychomotor vigilance test (PVT) for the duration of the schedule. To quantify prediction performance, we computed the root mean square error (RMSE) between model predictions and PVT data, and the fraction of measured PVTs that fell within the models' prediction intervals (PIs). For the group-average model predictions, the overall RMSE was 43 ms (range 15-74 ms) and the fraction of PVTs within the PIs was 80% (range 41%-100%). At the individualised level, the UMP could predict alertness for 81% of the subjects, with an overall average RMSE of 64 ms (range 32-147 ms) and fraction of PVTs within the PIs conservatively estimated as 71% (range 41%-100%). Altogether, these results suggest that, for the group-average model and 81% of the individualised models, in three out of four PVT measurements we cannot distinguish between study data and model predictions.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss.

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

Metabolic responses in blood-stage malaria parasites associated with increased and decreased sensitivity to PfATP4 inhibitors.

BACKGROUND: Spiroindolone and pyrazoleamide antimalarial compounds target Plasmodium falciparum P-type ATPase (PfATP4) and induce disruption of intracellular Na+ homeostasis. Recently, a PfATP4 mutation was discovered that confers resistance to a pyrazoleamide while increasing sensitivity to a spiroindolone. Transcriptomic and metabolic adaptations that underlie this seemingly contradictory response of P. falciparum to sublethal concentrations of each compound were examined to understand the different cellular accommodation to PfATP4 disruptions. METHODS: A genetically engineered P. falciparum Dd2 strain (Dd2A211V) carrying an Ala211Val (A211V) mutation in PfATP4 was used to identify metabolic adaptations associated with the mutation that results in decreased sensitivity to PA21A092 (a pyrazoleamide) and increased sensitivity to KAE609 (a spiroindolone). First, sublethal doses of PA21A092 and KAE609 causing substantial reduction (30-70%) in Dd2A211V parasite replication were identified. Then, at this sublethal dose of PA21A092 (or KAE609), metabolomic and transcriptomic data were collected during the first intraerythrocytic developmental cycle. Finally, the time-resolved data were integrated with a whole-genome metabolic network model of P. falciparum to characterize antimalarial-induced physiological adaptations. RESULTS: Sublethal treatment with PA21A092 caused significant (p < 0.001) alterations in the abundances of 91 Plasmodium gene transcripts, whereas only 21 transcripts were significantly altered due to sublethal treatment with KAE609. In the metabolomic data, a substantial alteration (≥ fourfold) in the abundances of carbohydrate metabolites in the presence of either compound was found. The estimated rates of macromolecule syntheses between the two antimalarial-treated conditions were also comparable, except for the rate of lipid synthesis. A closer examination of parasite metabolism in the presence of either compound indicated statistically significant differences in enzymatic activities associated with synthesis of phosphatidylcholine, phosphatidylserine, and phosphatidylinositol. CONCLUSION: The results of this study suggest that malaria parasites activate protein kinases via phospholipid-dependent signalling in response to the ionic perturbation induced by the Na+ homeostasis disruptor PA21A092. Therefore, targeted disruption of phospholipid signalling in PA21A092-resistant parasites could be a means to block the emergence of resistance to PA21A092.

Efficacy of two intermittent cooling strategies during prolonged work-rest intervals in the heat with personal protective gear compared with a control condition.

INTRODUCTION: Personal protective equipment (PPE) inhibits heat dissipation and elevates heat strain. Impaired cooling with PPE warrants investigation into practical strategies to improve work capacity and mitigate exertional heat illness. PURPOSE: Examine physiological and subjective effects of forearm immersion (FC), fan mist (MC), and passive cooling (PC) following three intermittent treadmill bouts while wearing PPE. METHODS: Twelve males (27 ± 6 years; 57.6 ± 6.2 ml/kg/min; 78.3 ± 8.1 kg; 183.1 ± 7.2 cm) performed three 50-min (10 min of 40%, 70%, 40%, 60%, 50% vVO2max) treadmill bouts in the heat (36 °C, 30% relative humidity). Thirty minutes of cooling followed each bout, using one of the three strategies per trial. Rectal temperature (Tcore), skin temperature (Tsk), heart rate (HR), heart rate recovery (HRR), rating of perceived exertion (RPE), thirst, thermal sensation (TS), and fatigue were obtained. Repeated-measures analysis of variance (condition x time) detected differences between interventions. RESULTS: Final Tcore was similar between trials (P > .05). Cooling rates were larger in FC and MC vs PC following bout one (P < .05). HRR was greatest in FC following bouts two (P = .013) and three (P < .001). Tsk, fluid consumption, and sweat rate were similar between all trials (P > .05). TS and fatigue during bout three were lower in MC, despite similar Tcore and HR. CONCLUSION: Utilizing FC and MC during intermittent work in the heat with PPE yields some thermoregulatory and cardiovascular benefit, but military health and safety personnel should explore new and novel strategies to mitigate risk and maximize performance under hot conditions while wearing PPE.

Rate- and Region-Dependent Mechanical Properties of Göttingen Minipig Brain Tissue in Simple Shear and Unconfined Compression.

Traumatic brain injury (TBI), particularly from explosive blasts, is a major cause of casualties in modern military conflicts. Computational models are an important tool in understanding the underlying biomechanics of TBI but are highly dependent on the mechanical properties of soft tissue to produce accurate results. Reported material properties of brain tissue can vary by several orders of magnitude between studies, and no published set of material parameters exists for porcine brain tissue at strain rates relevant to blast. In this work, brain tissue from the brainstem, cerebellum, and cerebrum of freshly euthanized adolescent male Göttingen minipigs was tested in simple shear and unconfined compression at strain rates ranging from quasi-static (QS) to 300 s-1. Brain tissue showed significant strain rate stiffening in both shear and compression. Minimal differences were seen between different regions of the brain. Both hyperelastic and hyper-viscoelastic constitutive models were fit to experimental stress, considering data from either a single loading mode (unidirectional) or two loading modes together (bidirectional). The unidirectional hyper-viscoelastic models with an Ogden hyperelastic representation and a one-term Prony series best captured the response of brain tissue in all regions and rates. The bidirectional models were generally able to capture the response of the tissue in high-rate shear and all compression modes, but not the QS shear. Our constitutive models describe the first set of material parameters for porcine brain tissue relevant to loading modes and rates seen in blast injury.

Effect of stride length on the running biomechanics of healthy women of different statures.

BACKGROUND: Tibial stress fracture is a debilitating musculoskeletal injury that diminishes the physical performance of individuals who engage in high-volume running, including Service members during basic combat training (BCT) and recreational athletes. While several studies have shown that reducing stride length decreases musculoskeletal loads and the potential risk of tibial injury, we do not know whether stride-length reduction affects individuals of varying stature differently. METHODS: We investigated the effects of reducing the running stride length on the biomechanics of the lower extremity of young, healthy women of different statures. Using individualized musculoskeletal and finite-element models of women of short (N = 6), medium (N = 7), and tall (N = 7) statures, we computed the joint kinematics and kinetics at the lower extremity and tibial strain for each participant as they ran on a treadmill at 3.0 m/s with their preferred stride length and with a stride length reduced by 10%. Using a probabilistic model, we estimated the stress-fracture risk for running regimens representative of U.S. Army Soldiers during BCT and recreational athletes training for a marathon. RESULTS: When study participants reduced their stride length by 10%, the joint kinetics, kinematics, tibial strain, and stress-fracture risk were not significantly different among the three stature groups. Compared to the preferred stride length, a 10% reduction in stride length significantly decreased peak hip (p = 0.002) and knee (p < 0.001) flexion angles during the stance phase. In addition, it significantly decreased the peak hip adduction (p = 0.013), hip internal rotation (p = 0.004), knee extension (p = 0.012), and ankle plantar flexion (p = 0.026) moments, as well as the hip, knee, and ankle joint reaction forces (p < 0.001) and tibial strain (p < 0.001). Finally, for the simulated regimens, reducing the stride length decreased the relative risk of stress fracture by as much as 96%. CONCLUSIONS: Our results show that reducing stride length by 10% decreases musculoskeletal loads, tibial strain, and stress-fracture risk, regardless of stature. We also observed large between-subject variability, which supports the development of individualized training strategies to decrease the incidence of stress fracture.

Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning.

The main limitation in developing deep neural network (DNN) models to predict bioactivity properties of chemicals is the lack of sufficient assay data to train the network's classification layers. Focusing on feedforward DNNs that use atom- and bond-based structural fingerprints as input, we examined whether layers of a fully trained DNN based on large amounts of data to predict one property could be used to develop DNNs to predict other related or unrelated properties based on limited amounts of data. Hence, we assessed if and under what conditions the dense layers of a pre-trained DNN could be transferred and used for the development of another DNN associated with limited training data. We carried out a quantitative study employing more than 400 pairs of assay datasets, where we used fully trained layers from a large dataset to augment the training of a small dataset. We found that the higher the correlation r between two assay datasets, the more efficient the transfer learning is in reducing prediction errors associated with the smaller dataset DNN predictions. The reduction in mean squared prediction errors ranged from 10 to 20% for every 0.1 increase in r2 between the datasets, with the bulk of the error reductions associated with transfers of the first dense layer. Transfer of other dense layers did not result in additional benefits, suggesting that deeper, dense layers conveyed more specialized and assay-specific information. Importantly, depending on the dataset correlation, training sample size could be reduced by up to tenfold without any loss of prediction accuracy.

A Strain Rate-Dependent Constitutive Model for Göttingen Minipig Cerebral Arteries.

Computational simulations of traumatic brain injury (TBI) are commonly used to advance understanding of the injury-pathology relationship, tissue damage thresholds, and design of protective equipment such as helmets. Both human and animal TBI models have developed substantially over recent decades, partially due to the inclusion of more detailed brain geometry and representation of tissues like cerebral blood vessels. Explicit incorporation of vessels dramatically affects local strain and enables researchers to investigate TBI-induced damage to the vasculature. While some studies have indicated that cerebral arteries are rate-dependent, no published experimentally based, rate-sensitive constitutive models of cerebral arteries exist. In this work, we characterize the mechanical properties of axially failed porcine arteries, both quasi-statically (0.01 s-1) and at high rate (>100 s-1), and propose a rate-sensitive model to fit the data. We find that the quasi-static and high-rate stress-stretch curves become significantly different (p < 0.05) above a stretch of 1.23. We additionally find a significant change in both failure stretch and stress as a result of strain rate. The stress-stretch curve is then modeled as a Holzapfel-Gasser-Ogden material, with a Prony series added to capture the effects of viscoelasticity. Ultimately, this paper demonstrates that rate dependence should be considered in the material properties of cerebral arteries undergoing high strain-rate deformations and provides a ready-to-use model for finite element implementation.

2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.

BACKGROUND: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. METHODS: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS: For the 4 simulated conditions, the 2B-Alert Web-proposed interventions increased mean alertness by 36% to 94% and decreased peak alertness impairment by 31% to 71% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. CONCLUSIONS: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules.

Metabolic Survival Adaptations of Plasmodium falciparum Exposed to Sublethal Doses of Fosmidomycin.

The malaria parasite Plasmodium falciparum contains the apicoplast organelle that synthesizes isoprenoids, which are metabolites necessary for posttranslational modification of Plasmodium proteins. We used fosmidomycin, an antibiotic that inhibits isoprenoid biosynthesis, to identify mechanisms that underlie the development of the parasite's adaptation to the drug at sublethal concentrations. We first determined a concentration of fosmidomycin that reduced parasite growth by ∼50% over one intraerythrocytic developmental cycle (IDC). At this dose, we maintained synchronous parasite cultures for one full IDC and collected metabolomic and transcriptomic data at multiple time points to capture global and stage-specific alterations. We integrated the data with a genome-scale metabolic model of P. falciparum to characterize the metabolic adaptations of the parasite in response to fosmidomycin treatment. Our simulations showed that, in treated parasites, the synthesis of purine-based nucleotides increased, whereas the synthesis of phosphatidylcholine during the trophozoite and schizont stages decreased. Specifically, the increased polyamine synthesis led to increased nucleotide synthesis, while the reduced methyl-group cycling led to reduced phospholipid synthesis and methyltransferase activities. These results indicate that fosmidomycin-treated parasites compensate for the loss of prenylation modifications by directly altering processes that affect nucleotide synthesis and ribosomal biogenesis to control the rate of RNA translation during the IDC. This also suggests that combination therapies with antibiotics that target the compensatory response of the parasite, such as nucleotide synthesis or ribosomal biogenesis, may be more effective than treating the parasite with fosmidomycin alone.

Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells.

BACKGROUND: Cultured human red blood cells (RBCs) provide a powerful ex vivo assay platform to study blood-stage malaria infection and propagation. In recent years, high-resolution metabolomic methods have quantified hundreds of metabolites from parasite-infected RBC cultures under a variety of perturbations. In this context, the corresponding control samples of the uninfected culture systems can also be used to examine the effects of these perturbations on RBC metabolism itself and their dependence on blood donors (inter-study variations). METHODS: Time-course datasets from five independent studies were generated and analysed, maintaining uninfected RBCs (uRBC) at 2% haematocrit for 48 h under conditions originally designed for parasite cultures. Using identical experimental protocols, quadruplicate samples were collected at six time points, and global metabolomics were employed on the pellet fraction of the uRBC cultures. In total, ~ 500 metabolites were examined across each dataset to quantify inter-study variability in RBC metabolism, and metabolic network modelling augmented the analyses to characterize the metabolic state and fluxes of the RBCs. RESULTS: To minimize inter-study variations unrelated to RBC metabolism, an internal standard metabolite (phosphatidylethanolamine C18:0/20:4) was identified with minimal variation in abundance over time and across all the samples of each dataset to normalize the data. Although the bulk of the normalized data showed a high degree of inter-study consistency, changes and variations in metabolite levels from individual donors were noted. Thus, a total of 24 metabolites were associated with significant variation in the 48-h culture time window, with the largest variations involving metabolites in glycolysis and synthesis of glutathione. Metabolic network analysis was used to identify the production of superoxide radicals in cultured RBCs as countered by the activity of glutathione oxidoreductase and synthesis of reducing equivalents via the pentose phosphate pathway. Peptide degradation occurred at a rate that is comparable with central carbon fluxes, consistent with active degradation of methaemoglobin, processes also commonly associated with storage lesions in RBCs. CONCLUSIONS: The bulk of the data showed high inter-study consistency. The collected data, quantification of an expected abundance variation of RBC metabolites, and characterization of a subset of highly variable metabolites in the RBCs will help in identifying non-specific changes in metabolic abundances that may obscure accurate metabolomic profiling of Plasmodium falciparum and other blood-borne pathogens.

Injury Length and Arteriole Constriction Shape Clot Growth and Blood-Flow Acceleration in a Mouse Model of Thrombosis.

OBJECTIVE: Quantitative relationships between the extent of injury and thrombus formation in vivo are not well understood. Moreover, it has not been investigated how increased injury severity translates to blood-flow modulation. Here, we investigated interconnections between injury length, clot growth, and blood flow in a mouse model of laser-induced thrombosis. Approach and Results: Using intravital microscopy, we analyzed 59 clotting events collected from the cremaster arteriole of 14 adult mice. We regarded injury length as a measure of injury severity. The injury caused transient constriction upstream and downstream of the injury site resulting in a 50% reduction in arteriole diameter. The amount of platelet accumulation and fibrin formation did not depend on arteriole diameter or deformation but displayed an exponentially increasing dependence on injury length. The height of the platelet clot depended linearly on injury length and the arteriole diameter. Upstream arteriolar constriction correlated with delayed upstream velocity increase, which, in turn, determined downstream velocity. Before clot formation, flow velocity positively correlated with the arteriole diameter. After the onset of thrombus growth, flow velocity at the injury site negatively correlated with the arteriole diameter and with the size of the above-clot lumen. CONCLUSIONS: Injury severity increased platelet accumulation and fibrin formation in a persistently steep fashion and, together with arteriole diameter, defined clot height. Arterial constriction and clot formation were characterized by a dynamic change in the blood flow, associated with increased flow velocity.

The importance of modeling the human cerebral vasculature in blunt trauma.

BACKGROUND: Multiple studies describing human head finite element (FE) models have established the importance of including the major cerebral vasculature to improve the accuracy of the model predictions. However, a more detailed network of cerebral vasculature, including the major veins and arteries as well as their branch vessels, can further enhance the model-predicted biomechanical responses and help identify correlates to observed blunt-induced brain injury. METHODS: We used an anatomically accurate three-dimensional geometry of a 50th percentile U.S. male head that included the skin, eyes, sinuses, spine, skull, brain, meninges, and a detailed network of cerebral vasculature to develop a high-fidelity model. We performed blunt trauma simulations and determined the intracranial pressure (ICP), the relative displacement (RD), the von Mises stress, and the maximum principal strain. We validated our detailed-vasculature model by comparing the model-predicted ICP and RD values with experimental measurements. To quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model. RESULTS: For an inclined frontal impact, the predicted ICP matched well with the experimental results in the fossa, frontal, parietal, and occipital lobes, with peak-pressure differences ranging from 2.4% to 9.4%. For a normal frontal impact, the predicted ICP matched the experimental results in the frontal lobe and lateral ventricle, with peak-pressure discrepancies equivalent to 1.9% and 22.3%, respectively. For an offset parietal impact, the model-predicted RD matched well with the experimental measurements, with peak RD differences of 27% and 24% in the right and left cerebral hemispheres, respectively. Incorporating the detailed cerebral vasculature did not influence the ICP but redistributed the brain-tissue stresses and strains by as much as 30%. In addition, our detailed-vasculature model predicted strain reductions by as much as 28% when compared to current reduced-vasculature FE models that only include the major cerebral vessels. CONCLUSIONS: Our study highlights the importance of including a detailed representation of the cerebral vasculature in FE models to more accurately estimate the biomechanical responses of the human brain to blunt impact.

A 3-D virtual human thermoregulatory model to predict whole-body and organ-specific heat-stress responses.

OBJECTIVE: This study aimed at assessing the risks associated with human exposure to heat-stress conditions by predicting organ- and tissue-level heat-stress responses under different exertional activities, environmental conditions, and clothing. METHODS: In this study, we developed an anatomically detailed three-dimensional thermoregulatory finite element model of a 50th percentile U.S. male, to predict the spatiotemporal temperature distribution throughout the body. The model accounts for the major heat transfer and thermoregulatory mechanisms, and circadian-rhythm effects. We validated our model by comparing its temperature predictions of various organs (brain, liver, stomach, bladder, and esophagus), and muscles (vastus medialis and triceps brachii) under normal resting conditions (errors between 0.0 and 0.5 °C), and of rectum under different heat-stress conditions (errors between 0.1 and 0.3 °C), with experimental measurements from multiple studies. RESULTS: Our simulations showed that the rise in the rectal temperature was primarily driven by the activity level (~ 94%) and, to a much lesser extent, environmental conditions or clothing considered in our study. The peak temperature in the heart, liver, and kidney were consistently higher than in the rectum (by ~ 0.6 °C), and the entire heart and liver recorded higher temperatures than in the rectum, indicating that these organs may be more susceptible to heat injury. CONCLUSION: Our model can help assess the impact of exertional and environmental heat stressors at the organ level and, in the future, evaluate the efficacy of different whole-body or localized cooling strategies in preserving organ integrity.

Animal Orientation Affects Brain Biomechanical Responses to Blast-Wave Exposure.

In this study, we investigated how animal orientation within a shock tube influences the biomechanical responses of the brain and cerebral vasculature of a rat when exposed to a blast wave. Using three-dimensional finite element (FE) models, we computed the biomechanical responses when the rat was exposed to the same blast-wave overpressure (100 kPa) in a prone (P), vertical (V), or head-only (HO) orientation. We validated our model by comparing the model-predicted and the experimentally measured brain pressures at the lateral ventricle. For all three orientations, the maximum difference between the predicted and measured pressures was 11%. Animal orientation markedly influenced the predicted peak pressure at the anterior position along the midsagittal plane of the brain (P = 187 kPa; V = 119 kPa; and HO = 142 kPa). However, the relative differences in the predicted peak pressure between the orientations decreased at the medial (21%) and posterior (7%) positions. In contrast to the pressure, the peak strain in the prone orientation relative to the other orientations at the anterior, medial, and posterior positions was 40-88% lower. Similarly, at these positions, the cerebral vasculature strain in the prone orientation was lower than the strain in the other orientations. These results show that animal orientation in a shock tube influences the biomechanical responses of the brain and the cerebral vasculature of the rat, strongly suggesting that a direct comparison of changes in brain tissue observed from animals exposed at different orientations can lead to incorrect conclusions.

Effects of body size and load carriage on lower-extremity biomechanical responses in healthy women.

BACKGROUND: Musculoskeletal injuries, such as stress fractures, are the single most important medical impediment to military readiness in the U.S. Army. While multiple studies have established race- and sex-based risks associated with a stress fracture, the role of certain physical characteristics, such as body size, on stress-fracture risk is less conclusive. METHODS: In this study, we investigated the effects of body size and load carriage on lower-extremity joint mechanics, tibial strain, and tibial stress-fracture risk in women. Using individualized musculoskeletal-finite-element-models of 21 women of short, medium, and tall statures (n = 7 in each group), we computed the joint mechanics and tibial strains while running on a treadmill at 3.0 m/s without and with a load of 11.3 or 22.7 kg. We also estimated the stress-fracture risk using a probabilistic model of bone damage, repair, and adaptation. RESULTS: Under all load conditions, the peak plantarflexion moment for tall women was higher than those in short women (p < 0.05). However, regardless of the load condition, we did not observe differences in the strains and the stress-fracture risk between the stature groups. When compared to the no-load condition, a 22.7-kg load increased the peak hip extension and flexion moments for all stature groups (p < 0.05). However, when compared to the no-load condition, the 22.7-kg load increased the strains and the stress-fracture risk in short and medium women (p < 0.05), but not in tall women. CONCLUSION: These results show that women of different statures adjust their gait mechanisms differently when running with external load. This study can educate the development of new strategies to help reduce the risk of musculoskeletal injuries in women while running with external load.

Metabolic alterations in the erythrocyte during blood-stage development of the malaria parasite.

BACKGROUND: Human blood cells (erythrocytes) serve as hosts for the malaria parasite Plasmodium falciparum during its 48-h intraerythrocytic developmental cycle (IDC). Established in vitro protocols allow for the study of host-parasite interactions during this phase and, in particular, high-resolution metabolomics can provide a window into host-parasite interactions that support parasite development. METHODS: Uninfected and parasite-infected erythrocyte cultures were maintained at 2% haematocrit for the duration of the IDC, while parasitaemia was maintained at 7% in the infected cultures. The parasite-infected cultures were synchronized to obtain stage-dependent information of parasite development during the IDC. Samples were collected in quadruplicate at six time points from the uninfected and parasite-infected cultures and global metabolomics was used to analyse cell fractions of these cultures. RESULTS: In uninfected and parasite-infected cultures during the IDC, 501 intracellular metabolites, including 223 lipid metabolites, were successfully quantified. Of these, 19 distinct metabolites were present only in the parasite-infected culture, 10 of which increased to twofold in abundance during the IDC. This work quantified approximately five times the metabolites measured in previous studies of similar research scope, which allowed for more detailed analyses. Enrichment in lipid metabolism pathways exhibited a time-dependent association with different classes of lipids during the IDC. Specifically, enrichment occurred in sphingolipids at the earlier stages, and subsequently in lysophospholipid and phospholipid metabolites at the intermediate and end stages of the IDC, respectively. In addition, there was an accumulation of 18-, 20-, and 22-carbon polyunsaturated fatty acids, which produce eicosanoids and promote gametocytogenesis in infected erythrocyte cultures. CONCLUSIONS: The current study revealed a number of heretofore unidentified metabolic components of the host-parasite system, which the parasite may exploit in a time-dependent manner to grow over the course of its development in the blood stage. Notably, the analyses identified components, such as precursors of immunomodulatory molecules, stage-dependent lipid dynamics, and metabolites, unique to parasite-infected cultures. These conclusions are reinforced by the metabolic alterations that were characterized during the IDC, which were in close agreement with those known from previous studies of blood-stage infection.

Controlled Multifactorial Coagulopathy: Effects of Dilution, Hypothermia, and Acidosis on Thrombin Generation In Vitro.

BACKGROUND: Coagulopathy and hemostatic abnormalities remain a challenge in patients following trauma and major surgery. Coagulopathy in this setting has a multifactorial nature due to tissue injury, hemodilution, hypothermia, and acidosis, the severity of which may vary. In this study, we combined computational kinetic modeling and in vitro experimentation to investigate the effects of multifactorial coagulopathy on thrombin, the central enzyme in the coagulation system. METHODS: We measured thrombin generation in platelet-poor plasma from 10 healthy volunteers using the calibrated automated thrombogram assay (CAT). We considered 3 temperature levels (31°C, 34°C, and 37°C), 3 pH levels (6.9, 7.1, and 7.4), and 3 degrees of dilution with normal saline (no dilution, 3-fold dilution, and 5-fold dilution). We measured thrombin-generation time courses for all possible combinations of these conditions. For each combination, we analyzed 2 scenarios: without and with (15 nM) supplementation of thrombomodulin, a key natural regulator of thrombin generation. For each measured thrombin time course, we recorded 5 quantitative parameters and analyzed them using multivariable regression. Moreover, for multiple combinations of coagulopathic conditions, we performed routine coagulation tests: prothrombin time (PT) and activated partial thromboplastin time (aPTT). We compared the experimental results with simulations using a newly developed version of our computational kinetic model of blood coagulation. RESULTS: Regression analysis allowed us to identify trends in our data (P < 10). In both model simulations and experiments, dilution progressively reduced the peak of thrombin generation. However, we did not experimentally detect the model-predicted delay in the onset of thrombin generation. In accord with the model predictions, hypothermia delayed the onset of thrombin generation; it also increased the thrombin peak time (up to 1.30-fold). Moreover, as predicted by the kinetic model, the experiments showed that hypothermia increased the area under the thrombin curve (up to 1.97-fold); it also increased the height of the thrombin peak (up to 1.48-fold). Progressive acidosis reduced the velocity index by up to 24%; acidosis-induced changes in other thrombin generation parameters were much smaller or none. Acidosis increased PT by 14% but did not influence aPTT. In contrast, dilution markedly prolonged both PT and aPTT. In our experiments, thrombomodulin affected thrombin-generation parameters mainly in undiluted plasma. CONCLUSIONS: Dilution with normal saline reduced the amount of generated thrombin, whereas hypothermia increased it and delayed the time of thrombin accumulation. In contrast, acidosis in vitro had little effect on thrombin generation.

Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats.

Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.