United States Army Medical Research and Development Command

Repeated traumatic brain injury (TBI) is a significant concern among military personnel, athletes, and abuse victims. However, little is known about the mechanisms that drive the brain's apparent increase in injury susceptibility with repeated loading. One critical factor may be the softening of cerebral blood vessels, which are significantly stiffer than brain tissue and influence its mechanical response during trauma. In this study, we employed a finite element model of a Göttingen minipig head to investigate how progressive vascular softening influences strain changes in brain tissue during both repeated blast and rapid rotation. The model incorporated pig-specific anatomical detail and material properties, including detailed cerebral vasculature. Simulations included six repeated exposures of either blast overpressure or coronal or sagittal rotations at varying severity levels. Additional "no-vasculature" (NV) cases were included for each loading condition to benchmark the mechanical contribution of blood vessels. Vessel softening was applied after each exposure based on previous experiments on Göttingen minipig cerebral arteries. While blast exposures did not generate sufficient strain to induce vessel softening, rotational events led to progressively increasing brain strain with repetition, especially in regions adjacent to softened vessels. These increases progressed toward the NV condition with repetition, consistent with diminishing structural support by softened vessels. Results also showed increasing risk of vessel rupture and axonal injury with repetition. These findings elucidate the biomechanical role of vessel softening in repeated TBI and suggest that even sub-failure vessel damage may exacerbate brain strain in repeated exposures and elevate injury risk.

ObjectiveMusculoskeletal injuries pose a health threat to U.S. Service members. In particular, the physical demands of walking and running with load carriage contribute to a high incidence of musculoskeletal injuries of the lower back. Active ankle exoskeleton devices are promising technologies that may help mitigate the impact of load carriage on the incidence of these injuries. However, the safe extended use of these devices requires an understanding of their beneficial or detrimental effects on the lumbar spine. In this pilot study, we investigated the impact of walking with an ankle exoskeleton device on lumbar biomechanical responses.MethodsWe collected motion-capture data and computed tomography images for five young, healthy men walking with a 22.7 kg (50-lb) load for 5 km at a speed of 1.3 m/s, with and without an active ankle exoskeleton (ExoBoot EB60). We developed individualized musculoskeletal and finite-element models to characterize the effects of walking distance and ExoBoot use on the trunk flexion angle, joint reaction force at the L4-L5 joint, and stress on the L4-L5 intervertebral disc annulus.ResultsWhile not statistically significant, we found that the peak trunk flexion angle and the peak annulus stress increased by 16% and 12%, respectively, after walking 5 km with the ExoBoot, and by 34% and 25%, respectively, without it. Similarly, the peak L4-L5 joint reaction force minimally increased by 4% with the ExoBoot, while it increased by 22% without the device.ConclusionExoBoot use likely attenuates the effect of fatigue on the lumbar spine induced by walking with load carriage.

Musculoskeletal injuries (MSKIs) are pervasive problems in novice training environments. Evaluation of modifiable and nonmodifiable risk factors of MSKI risk prior to entry into these environments is largely understudied. To provide military leaders, civilian and military clinicians, and physical training instructors with an MSKI risk model for identifying low-, moderate-, and high-risk profiles among individuals starting US Army Basic Combat Training (BCT) or a physical training program. In this prospective cohort study, data collection was conducted between August 5, 2017, and April 15, 2023, at 2 US Army BCT sites. The sample consisted of volunteer trainees between the ages of 17 and 41 years. They were followed up from the start of BCT. Data analyses were conducted from April to September 2024. Data for the factors potentially associated with MSKI were collected during the first week of BCT and included blood draws, total body dual-energy x-ray absorptiometry, and muscle power test results; surveys of demographics, medical history, physical activity, psychological characteristics, and sleep patterns; and physical fitness results. MSKIs identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes. Logistic regression-based models estimating the risk of MSKI were generated using 5-fold internal cross-validation for the total cohort, males, and females. Traffic light model examples of low (green), moderate (amber), and high (red) MSKI risk tiers were produced. In this cohort study of 2988 Army trainees (median [IQR] age, 19.0 [19.0-22.0] years; 1880 males [62.9%]), 729 females (49.0%) and 758 males (51.0%) had an ICD-10 code-identified MSKI, and 1067 (35.7%) had more than 1 ICD-10 code-identified MSKI. Factors associated with increased MSKI risk in the total cohort and female- and male-specific MSKI risk models (with areas under the receiver operator characteristic curve of 0.701, 0.678, and 0.661, respectively) encompassed 7 variable categories: demographics; anthropometrics and body composition; nutritional status; medical and health history; history of sports and past or current physical activity or fitness; psychological factors (ie, pain, grit, and hardiness); and sleep parameters. This cohort study presents a tiered approach to identifying persons at increased MSKI risk before the start of a physical training program. Applying a tiered quantification risk metric and incorporating multifactorial interventions from these findings may play a role in reduced MSKI risk.

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.

Animal studies are typically utilized to understand the complex mechanisms associated with toxicant-induced hepatotoxicity. Among the alternative approaches to animal studies, in vitro pooled human hepatocytes have the potential to capture population variability. Here, we examined the effect of the hepatotoxicant thioacetamide on pooled human hepatocytes, divided into five lots, obtained from forty diverse donors. For 24 h, pooled human hepatocytes were exposed to vehicle, 1.33 mM (low dose), and 12 mM (high dose) thioacetamide, followed by RNA-seq analysis. We assessed gene expression variability using heat maps, correlation plots, and statistical variance. We used KEGG pathways and co-expression modules to identify underlying physiological processes/pathways. The co-expression module analysis showed that the majority of the lots exhibited activation for the bile duct proliferation module. Despite lot-to-lot variability, we identified a set of common differentially expressed genes across the lots with similarities in their response to amino acid, lipid, and carbohydrate metabolism. We also examined efflux transporters and found larger lot-to-lot variability in their expression patterns, indicating a potential for alteration in toxicant bioavailability within the cells, which could in turn affect the gene expression patterns between the lots. Overall, our analysis highlights the challenges in using pooled hepatocytes to understand mechanisms of toxicity.

Infection with Sudan virus (SUDV) is characterized by an aggressive disease course with case fatality rates between 40-100% and no approved vaccines or therapeutics. SUDV causes sporadic outbreaks in sub-Saharan Africa, including a recent outbreak in Uganda which has resulted in over 100 confirmed cases in one month. Prior vaccine and therapeutic efforts have historically prioritized Ebola virus (EBOV), leading to a significant gap in available treatments. Two vaccines, Erbevo ® and Zabdeno ® /Mvabea ® , are licensed for use against EBOV but are ineffective against SUDV. Recombinant adenovirus vector vaccines have been shown to be safe and effective against filoviruses, but efficacy depends on having low seroprevalence to the vector in the target human population. For this reason, and because of an excellent safety and immunogenicity profile, ChAd3 was selected as a superior vaccine vector. Here, a ChAd3 vaccine expressing the SUDV glycoprotein (GP) was evaluated for immunogenicity and efficacy in nonhuman primates. We demonstrate that a single dose of ChAd3-SUDV confers acute and durable protection against lethal SUDV challenge with a strong correlation between the SUDV GP-specific antibody titers and survival outcome. Additionally, we show that a bivalent ChAd3 vaccine encoding the GP from both EBOV and SUDV protects against both parenteral and aerosol lethal SUDV challenge. Our data indicate that the ChAd3-SUDV vaccine is a suitable candidate for a prophylactic vaccination strategy in regions at high risk of filovirus outbreaks. One Sentence Summary: A single-dose of ChAd3 vaccine protected macaques from lethal challenge with Sudan virus (SUDV) by parenteral and aerosol routes of exposure.

Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.

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.