Astragaloside IV Attenuates Programmed Death-Ligand 1-Mediated Immunosuppression during Liver Cancer Development via the miR-135b-5p/CNDP1 Axis.

BACKGROUND: Astragaloside IV (AS-IV) is a pivotal contributor to anti-tumour effects and has garnered extensive attention in research. Tumour cell immune suppression is closely related to the increase in Programmed Death-Ligand 1 (PD-L1). Hepatocellular carcinoma (HCC) is a malignant tumour originating from hepatic epithelial tissue, and the role of AS-IV in regulating PD-L1 in anti-HCC activity remains unclear. METHODS: Various concentrations of AS-IV were administered to both human liver immortalised cells (THEL2) and HCC (Huh-7 and SMMC-7721), and cell growth was assessed using the CCK-8 assay. HCC levels and cell apoptosis were examined using flow cytometry. Mice were orally administered AS-IV at different concentrations to study its effects on HCC in vivo. Immunohistochemistry was employed to evaluate PD-L1 levels. Western blotting was employed to determine PD-L1 and CNDP1 protein levels. We carried out a qRT-PCR to quantify the levels of miR-135b-3p and CNDP1. Finally, a dual-luciferase reporter assay was employed to validate the direct interaction between miR-135b-3p and the 3'UTR of CNDP1. RESULTS: AS-IV exhibited a dose-dependent inhibition of proliferation in Huh-7 and SMMC-7721 while inhibiting PD-L1 expression induced by interferon-γ (IFN-γ), thus attenuating PD-L1-mediated immune suppression. MiR-135b-5p showed significant amplification in HCC tissues and cells. AS-IV mitigated PD-L1-mediated immune suppression through miR-135b-5p. MiR-135b-5p targeted CNDP1, and AS-IV mitigated PD-L1-induced immunosuppression by modulating the miR-135b-5p/CNDP1 pathway. CONCLUSION: AS-IV decreases cell surface PD-L1 levels and alleviates PD-L1-associated immune suppression via the miR-135b-5p/CNDP1 pathway. AS-IV may be a novel component for treating HCC.

A combined pig model to determine the net absorption of volatile fatty acids in the large intestine under different levels of crude fiber.

BACKGROUND: This study aimed to develop a combined model to quantify the net absorption of volatile fatty acids (VFA) in the large intestine (LI) of pigs. METHODS: Fifteen female growing pigs (Duroc × Large White × Landrace) were ranked by body weight (30 ± 2.1 kg) on day 0 and assigned to one of three treatments, namely the basal diet containing different crude fiber (CF) levels (LCF: 3.0% CF, MCF: 4.5% CF, and HCF: 6.0% CF). The pigs were implanted with the terminal ileum fistula and the cannulation of the ileal mesenteric vein (IMV), portal vein (PV), and left femoral artery (LFA) from days 6 to 7. [13 C]-Labeled VFA and P-aminohippuric acid were constantly perfused into the terminal ileum fistula and the cannulation of the IMV (day 15), respectively. Blood samples were collected from the PV and the LFA during perfusion (5 h), and LI samples were collected. RESULTS: The net flux of [12 C]-acetic acid in the PV was greater for LCF versus MCF (p = 0.045), but no difference was observed in the net flux of [12 C]-propionic acid (p = 0.505) and [12 C]-butyric acid (p = 0.35) in the PV among treatments. The deposition of [12 C]-acetic acid in the LI was greater for LCF versus MCF (p = 0.014), whereas the deposition of [12 C]-propionic acid (p = 0.007) and [12 C]-butyric acid (p = 0.037) in the LI was greater for LCF versus HCF. CONCLUSIONS: In conclusion, this pig model was found conducive to study the net absorption of VFAs in the LI, and LCF had more net absorption of VFAs in the LI than MCF and HCF.

Induced human pluripotent stem cells (HEBHMUi013-A) derived from a patient of sporadic Alzheimer's disease.

Sporadic Alzheimer's disease (sAD) is the most common neurodegenerative disease worldwide, which is characterized by the progressive cognitive dysfunction and behavioral impairment. Here, we generated a human induced pluripotent stem cell (iPSC) line from the peripheral blood mononuclear cells (PBMCs) isolated from a 78-year-old male patient clinically diagnosed with sAD. The iPSC line expressed pluripotency markers, showed normal karyotype, and had the ability to differentiate into three germ layers in vitro. This iPSC line may provide a powerful tool for modeling AD in vitro and studying the pathogenesis of sAD.

Brain architecture-based vulnerability to traumatic injury.

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups' dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Effect of Dietary Supplementation with Mannose Oligosaccharides on the Body Condition, Lactation Performance and Their Offspring of Heat-Stressed Sows.

The aim of this study was to determine the effects of dietary supplementation with mannose oligosaccharide (MOS) on the condition of the body and the reproductive and lactation performances of sows. Eighty pregnant sows were randomly assigned to four groups with a 2 × 2 factorial design: with or without MOS (1 g/kg) and with or without heat stress (HS) challenge. The temperature in the HS groups (HS and HM group) was controlled at 31.56 ± 1.22 °C, while the temperature in the active cooling (AC) groups (AC and AM group) was controlled at 23.49 ± 0.72 °C. The weight loss of sows in the AC group was significantly lower than that of sows in the HS group (p < 0.01). The weight and backfat thickness loss of sows supplemented with MOS displayed a downward trend. The average birth weight of the litter significantly increased in the HM group (basic diet + MOS) compared with the HS group (p < 0.05). The milk protein of sows significantly decreased under the HS condition at 2 and 12 h after delivery (p < 0.05). However, the milk immunoglobin G (IgG) of sows in the HS group increased significantly compared with that of sows in the HM group (p < 0.05) at 12 and 24 h after delivery. The levels of serum urea nitrogen (UREA) and glucose (GLU) decreased significantly under the HS condition (p < 0.05), while the level of interleukin-6 (IL-6) increased significantly under the HS condition (p < 0.05). Dietary supplementation with MOS also significantly reduced TNF-α under the AC conditions (p < 0.05). In conclusion, HS significantly affected the body condition, lactation performances and their offspring of sows. However, dietary supplementation with 1 g/kg MOS did not result in statistically significant changes.

Traditional Chinese Medicine Constitution Identification Based on Objective Facial and Tongue Features: A Delphi Study and a Diagnostic Nomogram for Blood Stasis Constitution.

Objective: The aim of this study was to systematically summarize and form an expert consensus on the theoretical experience of tongue and facial features for the identification of nine types of traditional Chinese medicine (TCM) constitution. Additionally, we sought to explore the feasibility of TCM constitution identification through objective tongue and facial features. Methods: We used Delphi method to investigate the opinions of experts on facial and tongue feature items for identifying TCM constitution. We developed and validated a diagnostic nomogram for blood stasis constitution (BSC) based on objective facial and tongue features to demonstrate the reliability of expert consultation. Results: Eleven experts participated in two rounds of expert consultation. The recovery rates of the two rounds of expert consultation were 100.0% and 90.9%. After the first round, 39 items were screened out from 147 initial items, and 2 items were supplemented by experts. In the second round, 7 items were eliminated, leaving 34 items for 8 types of TCM constitution. The coefficient of variation in the first round was 0.11-0.49 for the 147 items and 0.11-0.29 for the included items. The coefficient of variation in the second round was 0.10-0.27 for the 41 items and 0.10-0.20 for the included items. The W value was 0.548 (P < 0.001) in the first round and 0.240 (P < 0.001) in the second round. Based on expert consultation, we selected BSC as an example and developed and validated a diagnostic nomogram consisting of six indicators: sex, hair volume, lip color-dark purple, susceptibility-facial pigmentation/chloasma/ecchymosis, zygomatic texture-red blood streaks, and sublingual vein-varicose and dark purple. The nomogram showed good discrimination (AUC: 0.917 [95% confidence interval [CI], 0.877-0.956] for the primary dataset, 0.902 [95% CI, 0.828-0.976] for the validation dataset) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: This is the first study to systematically summarize the existing knowledge and clinical experience to form an expert consensus on the tongue and facial features of nine types of TCM constitution. Our results will provide important prior knowledge and expert experience for future constitution identification research. Based on expert consultation, this study presents a nomogram for BSC that incorporates objective facial and tongue features, which can be conveniently used to facilitate the individualized identification of BSC.

An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks.

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Focus on blood pressure levels and variability in the early phase of acute ischemic stroke with hypertension and carotid stenosis.

To investigate the optimal blood pressure (BP) levels and relative importance of BP and BP variability in the early phase of acute ischemic stroke (AIS) for hypertensive patients with carotid artery stenosis (CAS). A single-center cohort study included 750 AIS patients with hypertension and tests were performed for CAS. Participants were categorized to Group 1 (SBP < 140 mm Hg and DBP < 90 mm Hg), Group 2: (SBP: 140-159 mm Hg and or DBP: 90-99 mm Hg), and Group 3: (SBP ≥160 mm Hg and/or DBP ≥100 mm Hg) according to the guidelines. The associations of mean BP levels and variability with outcomes (recurrent stroke, all-cause death and the composite cardiovascular events) at 6 months were analyzed by Cox proportional hazard models. The associations of BP variability with BP levels and cerebral blood flow (CBF) were analyzed by linear regression and generalized additive models. Both for primary and secondary outcome, more events occurred in Group 1 compared with Group 2, while no significant difference was found in Group 3 with higher BP levels. Lower systolic BP variability showed better prognosis and higher CBF. The associations were more significant in patients with CAS ≥50%. BP variability exhibited a linear negative relationship with BP levels. In the early phase of AIS with hypertension and CAS, maintaining low blood pressure variability may be important to improve outcomes while low BP levels (SBP/DBP < 140/90 mm Hg) were harmful, especially in those patients with CAS ≥ 50%.

Toward subject-specific evaluation: methods of evaluating finite element brain models using experimental high-rate rotational brain motion.

Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many models have been created and evaluated with limited experimental data and without accounting for subject-specific morphometry of the specimens in the dataset. Recent advancements in the measurement of brain motion using sonomicrometry allow for a comprehensive evaluation of brain model biofidelity using a high-rate, rotational brain motion dataset. In this study, four methods were used to determine the best technique to compare nodal displacement to experimental brain motion, including a new morphing method to match subject-specific inner skull geometry. Three finite element brain models were evaluated in this study: the isotropic GHBMC and SIMon models, as well as an anisotropic model with explicitly embedded axons (UVA-EAM). Using a weighted cross-correlation score (between 0 and 1), the anisotropic model yielded the highest average scores across specimens and loading conditions ranging from 0.53 to 0.63, followed by the isotropic GHBMC with average scores ranging from 0.46 to 0.58, and then the SIMon model with average scores ranging from 0.36 to 0.51. The choice of comparison method did not significantly affect the cross-correlation score, and differences of global strain up to 0.1 were found for the morphed geometry relative to baseline models. The morphed or scaled geometry is recommended when evaluating computational brain models to capture the subject-specific skull geometry of the experimental specimens.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach.

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20-40 rad/s), durations (30-60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

The role of carotid stenosis ultrasound scale in the prediction of ischemic stroke.

INTRODUCTION: To improve the accuracy of ultrasound techniques for the assessment of carotid stenosis, we designed a novel carotid artery stenosis ultrasound scale (CASUS), and evaluated its accuracy, reliability, and its value in predicting the occurrence of cardiovascular and cerebrovascular diseases in a prospective study. METHODS: A total of 750 patients with first-time ischemic stroke and hospitalized within 24 h were enrolled in the study. Using color Doppler ultrasound (CDUS), the degree of stenosis and blood flow (BF) in bilateral internal carotid arteries (ICA) and the V1-V3 segment of vertebral arteries (VA) was assessed. Cubic simulation curves for BF and global blood flow (GBF) over the stenosis score (SS), total stenosis score (TSS), and radiological imaging- total stenosis score (RI-TSS) were fitted and compared. The receiver operating characteristic (ROC) curves using TSS, RI-TSS, or GBF to predict various ischemic stroke endpoints were also analyzed and compared. RESULTS: There was a linear relationship between SS and BF both ICA and VA (R2 were 0.734 and 0.783, respectively, both P < 0.05). Both TSS and RI-TSS with GBF showed an inverse "S" curve relationship (R2 was 0.839 and 0.843, all P < 0.05). The AUC values of TSS-based and RI-TSS-based predictions of each endpoint were all greater than 0.7 (all P < 0.05), but the differences of the AUC values between TSS, RI-TSS, and GBF were not statistically significant (all P > 0.05). CONCLUSIONS: The novel CASUS can better reflect the level of cerebral reperfusion in patients with ischemic stroke and can better predict the occurrence of cardiovascular and cerebrovascular diseases.

Biomechanics of the Human Brain during Dynamic Rotation of the Head.

Traumatic brain injuries (TBI) are a substantial societal burden. The development of better technologies and systems to prevent and/or mitigate the severity of brain injury requires an improved understanding of the mechanisms of brain injury, and more specifically, how head impact exposure relates to brain deformation. Biomechanical investigations have used computational models to identify these relations, but more experimental brain deformation data are needed to validate these models and support their conclusions. The objective of this study was to generate a dataset describing in situ human brain motion under rotational loading at impact conditions considered injurious. Six head-neck human post-mortem specimens, unembalmed and never frozen, were instrumented with 24 sonomicrometry crystals embedded throughout the parenchyma that can directly measure dynamic brain motion. Dynamic brain displacement, relative to the skull, was measured for each specimen with four loading severities in the three directions of controlled rotation, for a total of 12 tests per specimen. All testing was completed 42-72 h post-mortem for each specimen. The final dataset contains approximately 5,000 individual point displacement time-histories that can be used to validate computational brain models. Brain motion was direction-dependent, with axial rotation resulting in the largest magnitude of displacement. Displacements were largest in the mid-cerebrum, and the inferior regions of the brain-the cerebellum and brainstem-experienced relatively lower peak displacements. Brain motion was also found to be positively correlated to peak angular velocity, and negatively correlated with angular velocity duration, a finding that has implications related to brain injury risk-assessment methods. This dataset of dynamic human brain motion will form the foundation for the continued development and refinement of computational models of the human brain for predicting TBI.

Investigation of Cross-Species Scaling Methods for Traumatic Brain Injury Using Finite Element Analysis.

Scaling methods are used to relate animal exposure data to humans by determining equivalent biomechanical impact conditions that result in similar tissue-level mechanics for different species. However, existing scaling methods for traumatic brain injury (TBI) do not account for the anatomical and morphological complexity of the brains for different species and have not been validated based on accurate anatomy and realistic material properties. In this study, the relationship between the TBI condition and brain tissue deformation was investigated using human, baboon, and macaque brain finite element (FE) models, which featured macro- and mesoscale anatomical details. The aim was to evaluate existing scaling methods in predicting similar biomechanical responses in the different species using both idealized and real-world TBI pulses. A second aim was to develop a new method to improve how animal data are scaled to humans. As previously found in humans, the animal's brain response to the rotational head motion was well characterized by single-degree-of-freedom (sDOF) mechanical systems with resonance at certain natural frequency, and this concept was leveraged to develop a new TBI scaling method based the natural frequency of the sDOF models representing each species. Previously described biomechanical scaling methods based on mass or inertia ratios were poor predictors of equivalent strain. The novel frequency-based scaling method was an improved approach to scaling the equivalent loading conditions. The findings of this study enable better interpretation of mechanical-trauma responses obtained from animal data to the human, thus effectively advancing the development of human injury criteria and contributing toward the mitigation of TBI.

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury.

With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s-1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.