Mechanical characterization of human brain tissue

Exploring hyperelastic material model discovery for human brain cortex: Multivariate analysis vs. artificial neural network approaches

The human brain, characterized by its intricate architecture, exhibits complex mechanical properties that underpin its critical functional capabilities. Traditional computational methods, such as finite element analysis, have been instrumental in uncovering the fundamental mechanisms governing the brain's physical behaviors. However, accurate predictions of brain mechanics require effective constitutive models to represent the nuanced mechanical properties of brain tissue. In this study, we aimed to identify well-suited material models for human brain tissue by leveraging artificial neural network and multiple regression techniques. These methods were applied to a generalized framework of widely accepted classic models, and their respective outcomes were systematically compared. To evaluate model efficacy, all setups were maintained consistent across both approaches, except for strategies employed to mitigate potential overfitting. Our findings reveal that artificial neural networks are capable of automatically identifying accurate constitutive models from given admissible estimators. However, the five-term and two-term neural network models trained under single-mode and multi-mode loading scenarios, respectively, were found to be suboptimal. These models could be further simplified into two-term and single-term formulations using multiple regression, achieving even higher predictive accuracy. This refinement underscores the importance of rigorous cross-validations of regularization parameters in neural network-based methods to ensure globally optimal model selection. Additionally, our study demonstrates that traditional multivariable regression methods, when combined with appropriate information criterion, are also highly effective in discovering optimal constitutive models. These insights contribute to the ongoing development of advanced material constitutive models, particularly for complex biological tissues.

Cerebrospinal fluid flow dynamics in the aqueduct of Sylvius for rigid and deformable brain models

The cerebrospinal fluid (CSF) in the subarachnoid space and brain ventricles is crucial for maintaining brain health and influencing conditions such as hydrocephalus. This study explores CSF flow dynamics in the aqueduct of Sylvius (AoS) using rigid and deformable brain models based on MRI-derived 3D geometries. The rigid model captures the main characteristics of the CSF velocity waveform in the AoS, particularly in terms of waveform and timing of peaks and troughs. This model also predicts velocity increases in hydrocephalic conditions and velocity ranges in subarachnoid space. However, the rigid model falls short of representing the full range of velocity variations in the AoS. Using fluid-structure interaction simulation, the deformable model addresses the impact of brain tissue deformation on CSF flow. The deformable brain model have an excellent prediction and agrees with MRI measurements, effectively anticipating CSF flow dynamics and flow reversals, underscoring the importance of incorporating brain tissue deformation for accurate modeling. We also found out that the effect of the inlet waveform is critical on the model prediction for the velocity within AoS during a cardiac cycle.

An Approach for Studying the Direct Effects of Shock Waves on Neuronal Cell Structure and Function

Recent U.S. military conflicts have underscored the knowledge gap regarding the neurological changes associated with blast-induced traumatic brain injury (bTBI). In vitro models of TBIs have the advantage of following the neuronal response to biomechanical perturbations in real-time, which can be exceedingly difficult in animal models. Here, we sought to develop an in vitro approach with controlled blast biomechanics to study the direct effects of the primary shock wave at the neuronal level. A blast injury apparatus mimicking the human skull and cerebrospinal fluid was developed. Primary neuronal cells were cultured inside the apparatus and exposed to a 70 kPa peak blast overpressure using helium gas in a blast tube. Neuronal viability was measured 24 h after blast exposure. The transmission of the pressure wave through the skull is believed to be a factor in injury to the cells of the brain. Three thicknesses in the apparatus wall were studied to represent the range of thicknesses in a human skull. To study the transmission of the shock wave to the neurons, the incident pressure at the apparatus location, as well as internal apparatus pressure, were measured. Analysis of the internal pressure wave revealed that wave oscillation frequency, not amplitude, was a significant factor in cell viability after a bTBI. This finding is related to the viscoelastic properties of the brain and suggests that the transmission of the shock wave through the skull is an important variable in blast injury.

The pulsing brain: state of the art and an interdisciplinary perspective

Understanding the pulsing dynamics of tissue and fluids in the intracranial environment is an evolving research theme aimed at gaining new insights into brain physiology and disease progression. This article provides an overview of related research in magnetic resonance imaging, ultrasound medical diagnostics and mathematical modelling of biological tissues and fluids. It highlights recent developments, illustrates current research goals and emphasizes the importance of collaboration between these fields.

Actuated Hydrogel Platforms To Study Brain Cell Behavior

The human brain is a highly complex organ characterized by intricate neural networks, biochemical signaling, and unique mechanical properties. The soft and dynamic viscoelastic extracellular matrix (ECM) plays a crucial role in supporting different types of brain cells and influencing their behavior. Understanding how brain cells respond to mechanical stimuli within this complex environment is essential for unraveling fundamental mechanisms of healthy, unhealthy, and regenerative functions within the central nervous system. This requires the development of advanced materials and techniques to study the interplay between mechanical cues and cell responses. Hydrogels have become essential in this research, mimicking the brain's ECM in both chemical composition and mechanical behavior. Conventional hydrogels, while helpful, are static and lack dynamic stimulation. On the other hand, dynamic hydrogels provide reversible, dynamic stimulation, closely replicating the brain's ECM properties. This review discusses current hydrogel platforms used to investigate brain function in health and disease, focusing on traumatic brain injury (TBI)-like conditions and brain tumors. These dynamic materials offer sophisticated tools for understanding brain cell mechanobiology and developing new therapeutic approaches.

Biointegration of soft tissue-inspired hydrogels on the chorioallantoic membrane: An experimental characterization

Soft scaffold materials for cell cultures grafted onto the chorioallantoic membrane (CAM) provide innovative solutions for creating physiologically relevant environments by mimicking the host tissue. Biocompatible hydrogels represent an ideal medium for such applications, but the relationship between scaffold mechanical properties and reactions at the biological interface remains poorly understood. This study examines the attachment and integration of soft hydrogels on the CAM using an accessible ex ovo system. Composite hydrogels of polyvinyl alcohol and Phytagel were fabricated by sterile freeze-thawing. CAM assays, as an alternative to traditional in vivo models, enabled the evaluation of the compatibility, attachment, and biointegration of hydrogels with three distinct compositions. The mechanomimetic properties of the hydrogels were assessed through cyclic compression-tension tests, with nominal peak stresses ranging from 0 . 26 to 2 . 82  kPa in tension and - 0 . 33 to - 2 . 92  kPa in compression. Mechanical attachment to the CAM was measured by pull-off tests after five days of incubation. On the first day, the interface strength was similar for all hydrogel compositions. On day 5 , softer hydrogels showed the greatest increase ( p = 0 . 008 ), followed by intermediate hydrogels ( p = 0 . 020 ), while the denser hydrogels showed negligible changes ( p = 0 . 073 ). Histological analyses revealed cell infiltration in 100 % of soft, 75 % of intermediate, and 13 % of dense hydrogels, suggesting that softer hydrogels integrate better into the CAM by facilitating cell migration and enhancing interface strength. Chicken embryo survival rates and cytotoxicity assays confirmed the biocompatibility of the hydrogels and supported their potential for use in soft, hydrated three-dimensional scaffolds that mimic tissue environments in dynamic biological systems. Statement of significance Current research on soft scaffold materials for cell cultures often overlooks the critical relationship between mechanical properties and biological integration of these materials with host tissues. Although hydrogels, as soft porous materials, hold promise for creating physiologically relevant environments, the mechanisms driving their attachment and biointegration, especially on the chorioallantoic membrane (CAM), remain largely unexplored. This study addresses this gap by investigating the interaction between soft hydrogels and the CAM, providing valuable insights into how material properties and microstructure influence cellular responses. Our findings emphasize the importance of understanding these dynamics to develop biocompatible scaffolds that better mimic tissue environments, advancing applications in three-dimensional cell cultures on CAM assays and other biological systems.

Cortical Morphometric Similarity Remodeling in Traumatic Brain Injury Links Cognitive Impairments with Transcriptional Changes and Type-Specific Cells

The heterogeneous injuries and resulting cognitive deficits pose significant challenges in the clinical management of mild traumatic brain injury (mTBI). However, the pathophysiological mechanisms related to heterogeneities of mTBI are still unclear. This study aims to explore the mechanisms underlying brain remodeling by examining the morphometric similarity (MS) alterations and corresponding transcriptomic signatures across adult and pediatric mTBI (adult mTBI: 112 acute patients, 47 follow-up chronic patients, 66 healthy controls [HCs]; pediatric mTBI: 30 acute patients, 31 HCs). A healthy adult cohort (N = 840) is included to derive the modularized brain MS networks representing interregional cortical connectivity. Subsequently, cortical MS remodeling patterns are identified involving mostly MS increases in the frontal modules with typical high MS and decreases in the occipital module with typical low MS, with more pronounced changes observed in the developing brain with mTBI. The abnormal MS changes are correlated with variable cognitive impairments. Moreover, cortical MS remodeling is also associated with the genes enriched in CA1 pyramidal cells and neuron-specific biological processes. The transcription-related cortical remodeling in mTBI might reveal the disruption of brain cellular architecture. Therapeutic modalities to intervene in specific cortex and tackle CA1 over-activation might better encircle the neurobiology of TBI.

Mechanical properties of fresh rhesus monkey brain tissue

Studying brain tissue mechanics is critical for understanding how the brain's physical properties influence its biological functions. Non-human primates, such as rhesus monkeys, are a key translational model for human neuroscience research, yet their brain tissue mechanics remain poorly understood. We report the mechanical properties of rhesus monkey white (corona radiata, CR) and gray (basal ganglia, BG) matter during compression relaxation, tension relaxation, tension-compression cycling (strain = 0.15, nCR = 21, nBG = 14), and shear cycling (strain = 0.3, nCR = 17, nBG = 9). Compression relaxation yields short and long-term time constants of 1.13 ± 0.041 s and 26.3 ± 0.68 s for CR and 1.22 ± 0.046 s and 28.3 ± 0.70 s for BG. Tension relaxation yields short and long-term time constants of 1.10 ± 0.052 s and 28.2 ± 0.82 s for CR and 1.19 ± 0.052 s and 29 ± 1.3 s for BG. Tension-compression cycling yields elastic moduli (E₁, E₂, E₃) of 36 ± 3.8 kPa, 0.61 ± 0.096 kPa, and 9.3 ± 0.90 kPa for CR and 27 ± 4.8 kPa, 0.68 ± 0.092 kPa, and 8 ± 1.0 kPa for BG. Shear cycling yields E₁, E₂, and E₃ of 3.9 ± 0.77 kPa, 0.19 ± 0.034 kPa, and 3.1 ± 0.40 kPa for CR and 2.8 ± 0.52 kPa, 0.18 ± 0.058 kPa, and 3.2 ± 0.53 kPa for BG. Hysteresis areas are also captured during tension-compression and shear cycling. These findings extend the translatability of rhesus monkey models for neuroscience. STATEMENT OF SIGNIFICANCE: While rhesus monkeys are a valuable translational model in human neuroscience research, there is a huge gap in knowledge about rhesus monkey brain tissue mechanics. This study serves to increase our understanding of rhesus monkey brain tissue mechanics and is the first to report the stiffness, time constant, and hysteresis parameters for rhesus monkey brain tissue in compression, tension, and shear for both the corona radiata and basal ganglia. The data is available in an open-source format, allowing others to fit and validate their mechanical models.

3D-printed suction clamps for tensile testing of brain tissue

The conventional mounting of ultra-soft biological tissues often involves gluing it between two plates or manually tightening grips. Both methods demand delicate handling skills and are time-consuming. This study outlines the design and practical application of 3D-printed suction clamps for uniaxial tension tests on brain samples. Successful testing was defined by the absence of relevant slippage or the sample being drawn into the clamp. A total of 112 deer brain samples underwent testing using a universal testing machine after one freeze-thaw cycle. These samples were obtained from eight different brain regions. During sample preparation, 7 out of all samples failed. Among the 105 tests, 89 (85%) were successful. Of the 16 unsuccessful tests, 15 samples (14%) slipped, while only one sample (1%) was drawn into the clamp to an extent that testing became impossible. Medulla oblongata samples exhibited exceptionally high slippage at 38%, whereas samples from the temporal cortex, external capsule, and putamen had the lowest slippage in only one single case. In conclusion, suction clamps facilitate high-throughput testing through user-friendly and rapid sample mounting. Testing success is contingent on the specific brain site, with sample slippage being the primary reason for testing failures, while sample inspiration into the clamp is negligible.

Mechanical characteristics of spinal cord tissue by indentation

The mechanical properties of brain and spinal cord tissue have proven to be extremely complex and difficult to assess. Due to the heterogeneous and ultra-soft nature of the tissue, the available literature shows a large variance in mechanical parameters derived from experiments. In this study, we performed a series of indentation experiments to systematically investigate the mechanical properties of porcine spinal cord tissue in terms of their sensitivity to indentation tip diameter, loading rate, holding time, ambient temperature along with cyclic and oscillatory dynamic loading. Our results show that spinal cord white matter tissue is more compliant than grey matter tissue with apparent moduli of 128.7 and 403.8 Pa, respectively. They show similar viscoelastic behavior with stress relaxation time constants of τ1=1.38s and τ2=36.29s for grey matter and τ1=1.46s and τ2=46.10s for white matter, while the initial peak force decreased by 54 % for grey and 59 % for white matter tissue. An increase of the applied loading rate by two orders of magnitude led to an approximate doubling of the apparent modulus for both tissue types. Thermal variations showed a decrease in apparent modulus of up to 30 % after heating from 20 to 37.0 °C. Our dynamic tests revealed a significant influence of cyclic preload on the stiffness, with a drop of up to 20 % and a relative decrease of up to 60 % after the first cycle compared to the total modulus drop after five cycles for spinal cord grey matter tissue. Oscillatory indentation experiments identified similar loss moduli for spinal cord grey and white matter tissue and a higher storage modulus for white matter tissue. This work provides systematic insights into the mechanical properties of spinal cord tissue under different loading scenarios using nanoindentation.

Tuning the mechanical properties of alginate dialdehyde-gelatin (ADA-GEL) bioinks for bioprinting approaches by varying the degree of oxidation

Extrusion-based 3D bioprinting is one of the most promising and widely used technologies in bioprinting. However, the development of bioprintable, biocompatible bioinks with tailored mechanical and biological properties remains a major challenge in this field. Alginate dialdehyde-gelatin (ADA-GEL) hydrogels face these difficulties and enable to tune the mechanical properties depending on the degree of oxidation (% DO) of ADA. Here, we present a holistic approach for characterizing the influence of the % DO on the mechanical properties of ADA-GEL hydrogels under multiple loading modes, compression, tension, and torsional shear in the large-strain regime. We evaluate complex mechanical characteristics including nonlinearity, hysteresis, conditioning, and stress relaxation. We calibrate hyperelastic material models to determine the corresponding material parameters inversely. Our results confirm that decreasing the % DO of ionically crosslinked ADA-GEL hydrogels leads to an increase in stiffness, more distinct nonlinearity, more pronounced hysteresis, and minor preconditioning effects, while the relaxation behavior is slightly affected. The fabrication technique - molding or printing - does only slightly affect the complex mechanical properties and stress relaxation behavior. Ionically and enzymatically dual-crosslinked ADA-GEL hydrogels showed higher stresses during cyclic loading and less viscous effects during stress relaxation in all three loading modes. We conclude that the % DO and the crosslinking procedure are crucial parameters to tune the mechanical behavior of ADA-GEL hydrogels. Careful choice of these parameters might facilitate the fabrication of biomaterials that closely mimic the properties of native tissues for advanced tissue engineering applications.

Regional mechanical properties of spinal cord gray and white matter in transverse section

Understanding spinal cord injury requires a comprehensive knowledge of its mechanical properties, which remains debated due to the variability reported. This study aims to characterize the regional mechanical properties of the spinal cord in transverse sections using micro-indentation. Quasi-static indentations were performed on the entire surface of transverse slices obtained from 10 freshly harvested porcine thoracic spinal cords using a 0.5 mm diameter flat punch. No significant difference in average longitudinal elastic modulus was found between white matter (n = 183, E = 0.51 ± 0.21 kPa) and gray matter (n = 51, E = 0.53 ± 0.25 kPa). In the gray matter, the elastic modulus in the dorsal horn (0.48 ± 0.18 kPa) was significantly smaller than in the ventral horn (0.57 ± 0.24 kPa) (GLMM, p < 0.05). The elastic modulus in the dorsal horn was also significantly smaller than in the lateral (0.52 ± 0.22 kPa) and ventral funiculi (0.53 ± 0.18 kPa) of the white matter (GLMM, p < 0.05). However, there was no significant difference in the elastic modulus among the ventral, lateral and dorsal funiculi of the white matter (GLMM, p > 0.05). The average elastic modulus strongly varies between samples, ranging from 0.23 (±0.06) kPa to 0.79 (±0.18) kPa and the testing time postmortem was significantly associated with a decrease in elastic modulus (t = -5.2, p < 0.001). The spinal cord's white matter demonstrated significantly lower elastic modulus compared to published data on brain tissue tested under similar conditions. These findings enhance our comprehension of the mechanical properties of spinal cord white and gray matter, challenging the homogeneity assumption of current models.

Biomechanical perspectives on traumatic brain injury in the elderly: a comprehensive review

Traumatic brain injuries (TBIs) pose a significant health concern among the elderly population, influenced by age-related physiological changes and the prevalence of neurodegenerative diseases. Understanding the biomechanical dimensions of TBIs in this demographic is vital for developing effective preventive strategies and optimizing clinical management. This comprehensive review explores the intricate biomechanics of TBIs in the elderly, integrating medical and aging studies, experimental biomechanics of head tissues, and numerical simulations. Research reveals that global brain atrophy in normal aging occurs at annual rates of -0.2% to -0.5%. In contrast, neurodegenerative diseases such as Alzheimer's, Parkinson's, and multiple sclerosis are associated with significantly higher rates of brain atrophy. These variations in atrophy rates underscore the importance of considering differing brain atrophy patterns when evaluating TBIs among the elderly. Experimental studies further demonstrate that age-related changes in the mechanical properties of critical head tissues increase vulnerability to head injuries. Numerical simulations provide insights into the biomechanical response of the aging brain to traumatic events, aiding in injury prediction and preventive strategy development tailored to the elderly. Biomechanical analysis is essential for understanding injury mechanisms and forms the basis for developing effective preventive strategies. By incorporating local atrophy and age-specific impact characteristics into biomechanical models, researchers can create targeted interventions to reduce the risk of head injuries in vulnerable populations. Future research should focus on refining these models and integrating clinical data to better predict outcomes and enhance preventive care. Advancements in this field promise to improve health outcomes and reduce injury risks for the aging population.

Development and Assessment of a High-Fidelity Simulator for the Microsurgical Dissection of the Sylvian Fissure

BACKGROUND

The dissection of the Sylvian fissure (SF) is a crucial technique requiring considerable expertise and skills traditionally acquired through years of experience. The continuous decline in surgical case-load necessitates the development of efficient alternative training opportunities. However, building a realistic and effective training simulator for the microsurgical dissection of the SF as an integral part of the neurosurgical curriculum remains a challenging endeavor. This work aims to develop and evaluate a high-fidelity phantom simulator for effective and transferable training of the SF dissection with a focus on middle cerebral artery aneurysm clipping.

METHODS

A phantom simulator replicating the anatomy and tactile properties of the human skull, brain, and meninges was developed that incorporates additive manufacturing, neurosurgical expertise, and tissue engineering. Neurosurgical residents and experienced vascular neurosurgeons (n = 16) tested and rated the simulator's face and content validities and answered questions on the model's educational usefulness.

RESULTS

The resulting SF model was found to be anatomically and haptically faithful, reusable, and modifiable. The simulator overwhelmingly received positive ratings in face and content validity, with mean scores of 4.8 and 4.7 of 5, respectively. Neurosurgeons deemed the simulator as highly accurate with respect to anatomical and tactile accuracy. Neurosurgical residents and neurosurgeons rated the simulator as superior in comparison with traditional teaching and training methods.

CONCLUSIONS

The presented methodology demonstrates that the development and assessment of a high-fidelity hands-on simulator for the focused training of one of the most delicate neurosurgical procedures is achievable in a timely manner and without extensive investments.

Stress landscape of folding brain serves as a map for axonal pathfinding

Understanding the mechanics linking cortical folding and brain connectivity is crucial for both healthy and abnormal brain development. Despite the importance of this relationship, existing models fail to explain how growing axon bundles navigate the stress field within a folding brain or how this bidirectional and dynamic interaction shapes the resulting surface morphologies and connectivity patterns. Here, we propose the concept of "axon reorientation" and formulate a mechanical model to uncover the dynamic multiscale mechanics of the linkages between cortical folding and connectivity development. Simulations incorporating axon bundle reorientation and stress-induced growth reveal potential mechanical mechanisms that lead to higher axon bundle density in gyri (ridges) compared to sulci (valleys). In particular, the connectivity patterning resulting from cortical folding exhibits a strong dependence on the growth rate and mechanical properties of the navigating axon bundles. Model predictions are supported by in vivo diffusion tensor imaging of the human brain.

Clinical challenges and opportunities related to the biological responses experienced by indwelling and implantable bioelectronic medical devices

Implantable electrodes have been utilized for decades to stimulate, sense, or monitor a broad range of biological processes, with examples ranging from glucose monitoring devices to cochlear implants. While the underlying science related to the application of electrodes is a mature field, preclinical and clinical studies have demonstrated that there are still significant challenges in vivo associated with a lack of control over tissue-material interfacial interactions, especially over longer time frames. Herein we discuss the current challenges and opportunities for implantable electrodes and the associated bioelectronic interfaces across the clinical landscape with a focus on emerging technologies and the obstacles of biofouling, microbial colonization, and the foreign body response. Overcoming these challenges is predicted to open the door for a new generation of implantable medical devices and significant associated clinical impact. STATEMENT OF SIGNIFICANCE: Implantable electrodes have been utilised for decades to stimulate, sense, or monitor a broad range of biological processes, with examples ranging from glucose monitoring devices to cochlear implants. Next-generation bioelectronic implantable medical devices promise an explosion of new applications that have until this point in time been impossible to achieve. However, there are several persistent biological challenges hindering the realisation of these new applications. We present a clinical perspective on how these biological challenges have shaped the device market and clinical trial landscape. Specifically, we present statistical breakdowns of current device applications and discuss biofouling, the foreign body response, and microbial colonisation as the main factors that need to be addressed before a new generation of devices can be explored.

Deciphering mechanical cues in the microenvironment: from non-malignant settings to tumor progression

The tumor microenvironment functions as a dynamic and intricate ecosystem, comprising a diverse array of cellular and non-cellular components that precisely orchestrate pivotal tumor behaviors, including invasion, metastasis, and drug resistance. While unraveling the intricate interplay between the tumor microenvironment and tumor behaviors represents a tremendous challenge, recent research illuminates a crucial biological phenomenon known as cellular mechanotransduction. Within the microenvironment, mechanical cues like tensile stress, shear stress, and stiffness play a pivotal role by activating mechanosensitive effectors such as PIEZO proteins, integrins, and Yes-associated protein. This activation initiates cascades of intrinsic signaling pathways, effectively linking the physical properties of tissues to their physiological and pathophysiological processes like morphogenesis, regeneration, and immunity. This mechanistic insight offers a novel perspective on how the mechanical cues within the tumor microenvironment impact tumor behaviors. While the intricacies of the mechanical tumor microenvironment are yet to be fully elucidated, it exhibits distinct physical attributes from non-malignant tissues, including elevated solid stresses, interstitial hypertension, augmented matrix stiffness, and enhanced viscoelasticity. These traits exert notable influences on tumor progression and treatment responses, enriching our comprehension of the multifaceted nature of the microenvironment. Through this innovative review, we aim to provide a new lens to decipher the mechanical attributes within the tumor microenvironment from non-malignant contexts, broadening our knowledge on how these factors promote or inhibit tumor behaviors, and thus offering valuable insights to identify potential targets for anti-tumor strategies.

Hyperelastic constitutive modeling of healthy and enzymatically mediated degraded articular cartilage

This research demonstrates a systematic curve fitting approach for acquiring parametric values of hyperelastic constitutive models for both healthy and enzymatically mediated degenerated cartilage to facilitate finite element modeling of cartilage. Several widely used phenomenological hyperelastic constitutive models were tested to adequately capture the changes in cartilage mechanics that vary with the differential/unequal abundance of matrix metalloproteinases (MMPs). Trauma and physiological conditions result in an increased production of collagenases (MMP-1) and gelatinases (MMP-9), which impacts the load-bearing ability of cartilage by significantly deteriorating its extracellular matrix (ECM). The material parameters in the constitutive equation of each hyperelastic model are significant for developing a comprehensive computational interpretation of MMP mediated degenerated cartilage. Stress-strain responses obtained from indentation test were fitted with selected Ogden, polynomial, reduced polynomial, and van der Waals hyperelastic constitutive models by optimizing their adjustable parameters (material constants). The goodness of fit of the 2nd order reduced polynomial and van der Waals model exhibited the closest data fitting with the experimental stress-strain distributions of healthy and degraded articular cartilage. The coefficient of the shear modulus for the 2nd order reduced polynomial decreased gradually by 21.9% to 80.1% with more enzymatic degradation of collagen fibril due to the relative abundance of MMP-1 (collagenases), and 28.5% to 69.2% for the van der Waals model. Our findings showed that the major materials coefficients of the models were reduced in the degenerated cartilages, and the reduction varied differentially with the relative abundance of MMPs-1 and 9, correlating the severity of degeneration. This work advances the understanding of cartilage mechanics and offers insights into the impact of biochemical (enzymatic) effects on cartilage degradation.

Biomechanics of soft biological tissues and organs, mechanobiology, homeostasis and modelling

The human body consists of many different soft biological tissues that exhibit diverse microstructures and functions and experience diverse loading conditions. Yet, under many conditions, the mechanical behaviour of these tissues can be described well with similar nonlinearly elastic or inelastic constitutive relations, both in health and some diseases. Such constitutive relations are essential for performing nonlinear stress analyses, which in turn are critical for understanding physiology, pathophysiology and even clinical interventions, including surgery. Indeed, most cells within load-bearing soft tissues are highly sensitive to their local mechanical environment, which can typically be quantified using methods of continuum mechanics only after the constitutive relations are determined from appropriate data, often multi-axial. In this review, we discuss some of the many experimental findings of the structure and the mechanical response, as well as constitutive formulations for 10 representative soft tissues or organs, and present basic concepts of mechanobiology to support continuum biomechanical studies. We conclude by encouraging similar research along these lines, but also the need for models that can describe and predict evolving tissue properties under many conditions, ranging from normal development to disease progression and wound healing. An important foundation for biomechanics and mechanobiology now exists and methods for collecting detailed multi-scale data continue to progress. There is, thus, considerable opportunity for continued advancement of mechanobiology and biomechanics.

Best1 mitigates ER stress induced by the increased cellular microenvironment stiffness in epilepsy

Changes in brain tissue stiffness are closely linked to the development and diseases of the nervous system. Endoplasmic reticulum (ER) stress plays a role in various pathological processes related to epilepsy. However, the relationship between stiffness changes, ER stress, and epilepsy remains unclear. This study aimed to investigate the impact of Best1 upregulation on alleviating ER stress and the underlying mechanism. Additionally, we proposed a protective strategy to prevent cell death resulting from ER stress in epilepsy. This study investigated the expression levels of ER stress-related proteins in epileptic tissues of varying stiffness. Atomic force microscopy revealed differences in stiffness across various lesion regions in patients with epilepsy. The expression levels of ECM and ER stress-related proteins were elevated in tissues with higher stiffness. Polypropionamide hydrogels were used to simulate extracellular matrix (ECM) with varying stiffness levels. Basal ER stress increased in the stiffer hydrogel substrates. Furthermore, the calcium-activated anion channel Bestrophin 1 (Best1) mitigated ER stress induced by both the stiffer substrate and thapsigargin. The loss-of-function mutations in Best1 inhibited this activity. The underlying mechanism involves the upregulation of the endosomal sorting complex required for the transport (ESCRT) components by Best1, which helps mitigate ER stress. These findings suggest that increased stiffness of the cellular microenvironment may contribute to neuronal death during epileptogenesis. Additionally, Best1 upregulation may serve as a protective strategy against excessive ER stress-induced neuronal damage in epilepsy.

In Vitro Assessment of Thermo-Responsive Scaffold as a 3D Synthetic Matrix for CAR-T Potency Testing Against Glioblastoma Spheroids

Chimeric antigen receptor (CAR) T cell immunotherapy has demonstrated exceptional efficacy against hematological malignancies, but notably less against solid tumors. To overcome this limitation, it is critical to investigate antitumor CAR-T cell potency in synthetic 3D microenvironments that can simulate the physical barriers presented by solid tumors. The overall goal of this study was the preliminary assessment of a synthetic thermo-responsive material as a substrate for in vitro co-cultures of anti-disialoganglioside (GD2) CAR-T cells and patient-derived glioblastoma (GBM) spheroids. Independent co-culture experiments demonstrated that the encapsulation process did not adversely affect the cell cycle progression of glioma stem cells (GSCs) or CAR-T cells. GSC spheroids grew over time within the terpolymer scaffold, when seeded in the same ratio as the suspension control. Co-cultures of CAR-T cells in suspension with hydrogel-encapsulated GSC spheroids demonstrated that CAR-T cells could migrate through the hydrogel and target the encapsulated GSC spheroids. CAR-T cells killed approximately 80% of encapsulated GSCs, while maintaining effective CD4:CD8 T cell ratios and secreting inflammatory cytokines after interacting with GD2-expressing GSCs. Importantly, the scaffolds also facilitated cell harvesting for downstream cellular analysis. This study demonstrated that a synthetic 3D terpolymer hydrogel can serve as an artificial scaffold to investigate cellular immunotherapeutic potency against solid tumors.

Thermosensitive injectable fibrillar gels based on cellulose nanocrystals grafted with poly(N-isopropylacrylamide) as biocompatible brain implants

Drug treatment of glioblastoma, the most aggressive and widespread form of brain cancer, is complicated due to the difficulty of penetration of chemotherapeutic drugs through the blood-brain barrier (BBB). Moreover, with surgical removal of tumors, in 90 % of cases they reappear near the original focus. To solve this problem, we propose to use hydrogel based on cellulose nanocrystals grafted with poly(N-isopropylacrylamide) (CNC-g-PNIPAM) as a promising material for filling postoperative cavities in the brain with the release of antitumor drugs. The CNC-g-PNIPAM is formed by "grafting to" method for precise control of molecular weight and grafting density. This colloidal system is liquid under injection conditions (at r. t.) and turns into a gel at human body temperature (when filling the postoperative area). It was shown for the first time that due to the rod-shaped of CNC, the gel has a fibrillar structure and, thus, mechanical properties similar to those of brain tissue, including nonlinear mechanics (strain-stiffening and compression softening). The biocompatibility of the hydrogel with primary brain cells is demonstrated. In addition, the release of the antitumor drug paclitaxel from the hydrogel and its antitumor activity is shown. The resulting nanocolloid system provides an innovative alternative approach to filling postoperative cavities and can be used for postoperative treatment due to the programmable release of drugs, as well as for in vitro modeling of tumor interaction with the BBB affecting drug transport in the brain.

Model-driven exploration of poro-viscoelasticity in human brain tissue: be careful with the parameters!

The brain is arguably the most complex human organ and modelling its mechanical behaviour has challenged researchers for decades. There is still a lack of understanding on how this multiphase tissue responds to mechanical loading and how material parameters can be reliably calibrated. While previous viscoelastic models with two relaxation times have successfully captured the response of brain tissue, the Theory of Porous Media provides a continuum mechanical framework to explore the underlying physical mechanisms, including interactions between solid matrix and free-flowing interstitial fluid. Following our previously published experimental testing protocol, here we perform finite element simulations of cyclic compression-tension loading and compression-relaxation experiments on human brain white and gray matter specimens. The solid volumetric stress proves to be a crucial factor for the overall biphasic tissue behaviour as it strongly interferes with porous effects controlled by the permeability. An inverse parameter identification reveals that poroelasticity alone is insufficient to capture the time-dependent material behaviour, but a poro-viscoelastic formulation captures the response of brain tissue well. We provide valuable insights into the individual contributions of viscous and porous effects. However, due to the strong coupling between porous, viscous, and volumetric effects, additional experiments are required to reliably determine all material parameters.

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during in vivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the in vivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive in vivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.

Buckling by disordered growth

Buckling instabilities driven by tissue growth underpin key developmental events such as the folding of the brain. Tissue growth is disordered due to cell-to-cell variability, but the effects of this variability on buckling are unknown. Here, we analyze what is perhaps the simplest setup of this problem: the buckling of an elastic rod with fixed ends driven by spatially varying, yet highly symmetric growth. Combining analytical calculations for simple growth fields and numerical sampling of random growth fields, we show that variability can increase as well as decrease the growth threshold for buckling, even when growth variability does not cause any residual stresses. For random growth, we find numerically that the shift of the buckling threshold correlates with spatial moments of the growth field. Our results imply that biological systems can either trigger or avoid buckling by exploiting the spatial arrangement of growth variability.

A theoretical framework for predicting the heterogeneous stiffness map of brain white matter tissue

Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.

Machine learning reveals correlations between brain age and mechanics

Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and diagnostic tools. Here we systematically probed the mechanical behavior of n=439 brain tissue samples in tension and compression, in different anatomical regions, for different axon orientations, across five age groups. We used Bayesian statistics to characterize the relation between brain age and mechanical properties and quantify uncertainties. Our results, based on our experimental data and material parameters for the isotropic Ogden and the anisotropic Gasser-Ogden-Holzapfel models, reveal a non-linear relationship between age and mechanics across the life cycle of the porcine brain. Both tensile and compressive shear moduli reached peak values ranging from 0.4-1.0 kPa in tension to 0.16-0.32 kPa in compression at three years of age. Anisotropy was most pronounced at six months, and then decreased. These results represent an important step in understanding age-dependent changes in the mechanical properties of brain tissue and provide the scientific basis for more accurate and realistic computational brain simulations. STATEMENT OF SIGNIFICANCE: In this paper, we investigate the age-dependent mechanical properties of brain tissue based on different deformation modes, anatomical regions, and axon orientations. Hierarchical Bayesian modeling was used to identify isotropic and anisotropic material parameters. The study reveals a nonlinear relationship between shear modulus, degree of anisotropy, and tension-compression asymmetry over the life cycle of the brain. By demonstrating the non-linearity of these relationships, the study fills a significant knowledge gap in current research. This work is a fundamental step in accurately characterizing the complex relationship between brain aging and mechanical properties.

Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex

We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, our approach generates elegant hyperelastic models that achieve accurate data fitting with parsimonious mathematic formulas, while strictly adhering to hyperelasticity constraints such as polyconvexity/ellipticity. Our investigation spans three distinct hyperelastic models-invariant-based, principal stretch-based, and normal strain-based-and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain cortex to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression algorithms robustly discover accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch-based and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to the human brain tissue. Polyconvexity/ellipticity assessment affirm the rigorous adherence to convexity requirements both within and beyond the training regime. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. The evaluation of predictive capabilities demonstrates remarkable interpolation capabilities for all three models and acceptable extrapolation performance for stretch-based and strain-based models. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of isotropic hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems. STATEMENT OF SIGNIFICANCE: Our research introduces a pioneering data-driven framework that revolutionizes the automated identification of hyperelastic constitutive models, particularly in the context of soft matter systems such as the human brain. By harnessing the power of symbolic regression, we have unlocked the ability to distill intricate physical phenomena into elegant and interpretable mathematical expressions. Our approach not only ensures accurate fitting to sparse data but also upholds crucial hyperelasticity constraints, including polyconvexity, essential for maintaining physical relevance.

Toward understanding the brain tissue behavior due to preconditioning: an experimental study and RVE approach

Brain tissue under preconditioning, as a complex issue, refers to repeated loading-unloading cycles applied in mechanical testing protocols. In previous studies, only the mechanical behavior of the tissue under preconditioning was investigated; However, the link between macrostructural mechanical behavior and microstructural changes in brain tissue remains underexplored. This study aims to bridge this gap by investigating bovine brain tissue responses both before and after preconditioning. We employed a dual approach: experimental mechanical testing and computational modeling. Experimental tests were conducted to observe microstructural changes in mechanical behavior due to preconditioning, with a focus on axonal damage. Concurrently, we developed multiscale models using statistically representative volume elements (RVE) to simulate the tissue's microstructural response. These RVEs, featuring randomly distributed axonal fibers within the extracellular matrix, provide a realistic depiction of the white matter microstructure. Our findings show that preconditioning induces significant changes in the mechanical properties of brain tissue and affects axonal integrity. The RVE models successfully captured localized stresses and facilitated the microscopic analysis of axonal injury mechanisms. These results underscore the importance of considering both macro and micro scales in understanding brain tissue behavior under mechanical loading. This comprehensive approach offers valuable insights into mechanotransduction processes and improves the analysis of microstructural phenomena in brain tissue.

Cellular signaling pathways in the nervous system activated by various mechanical and electromagnetic stimuli

Mechanical stimuli, such as stretch, shear stress, or compression, activate a range of biomolecular responses through cellular mechanotransduction. In the nervous system, studies on mechanical stress have highlighted key pathophysiological mechanisms underlying traumatic injury and neurodegenerative diseases. However, the biomolecular pathways triggered by mechanical stimuli in the nervous system has not been fully explored, especially compared to other body systems. This gap in knowledge may be due to the wide variety of methods and definitions used in research. Additionally, as mechanical stimulation techniques such as ultrasound and electromagnetic stimulation are increasingly utilized in psychological and neurorehabilitation treatments, it is vital to understand the underlying biological mechanisms in order to develop accurate pathophysiological models and enhance therapeutic interventions. This review aims to summarize the cellular signaling pathways activated by various mechanical and electromagnetic stimuli with a particular focus on the mammalian nervous system. Furthermore, we briefly discuss potential cellular mechanosensors involved in these processes.

Biomechanics of brain tissue damage caused by fiber endoscope penetration in third ventriculostomy surgery

Third ventriculostomy is the preferred treatment for obstructive hydrocephalus, but the biomechanics of brain tissue damage caused by fiber endoscopes remains unclear. In this study, brain tissue material parameters were tested based on the Ogden model to simulate needle puncture mechanics, and replicated the entire fiber endoscope advancement process during third ventriculostomy. It was found that a smaller diameter fiber endoscope, a perpendicular puncture angle, and a faster puncture speed would decrease the damage of brain tissue caused by the fiber endoscope. This study provides valuable insights for optimizing the instrumentation and surgical process of third ventriculostomy.

Characterization and selection of a skull surrogate for the development of a biofidelic head model

This research paper explores the advancement of physical models simulating the human skull-brain complex, focusing on applications in simulating mild Traumatic Brain Injury (mTBI). Existing models, especially head forms, lack biofidelity in accurately representing the native structures of the skull, limiting the understanding of intracranial injury parameters beyond kinematic head accelerations. This study addresses this gap by investigating the use of additive manufacturing (AM) techniques to develop biofidelic skull surrogates. Materials such as Polylactic Acid (PLA), a bone-simulant PLA variant, and Hydroxyapatite-coated Poly(methyl methacrylate) (PMMA) were used to create models tested for their flexural modulus and strength. The trabecular bone regions were simulated by adjusting infill densities (30%, 50%, 80%) and print raster directions, optimizing manufacturing parameters for biofidelic performance. Among the tested materials, PLA and its bone-simulating variant printed at 80% infill density with a side (tangential) print orientation demonstrated the closest approximation to the mechanical properties of cranial bone, yielding a mean flexural modulus of 1337.2 MPa and a mean ultimate strength of 56.9 MPa. Statistical analyses showed that infill density significantly influenced the moduli and strength of the printed simulants. Digital Image Correlation (DIC) corroborated the comparable performance of the simulants, showing similar strain and displacement behaviors to native skull bone. Notably, the performance of the manufactured cortical and trabecular regions underscored their crucial role in achieving biofidelity, with the trabecular structure providing critical dampening effects when the native bone is loaded. This study establishes PLA, particularly its bone-simulant variant, as an optimal candidate for cranial bone simulants, offering significant potential for developing more accurate biofidelic head models in mTBI research.

Region specific anisotropy and rate dependence of Göttingen minipig brain tissue

Traumatic brain injury is a major cause of morbidity in civilian as well as military populations. Computational simulations of injurious events are an important tool to understanding the biomechanics of brain injury and evaluating injury criteria and safety measures. However, these computational models are highly dependent on the material parameters used to represent the brain tissue. Reported material properties of tissue from the cerebrum and cerebellum remain poorly defined at high rates and with respect to anisotropy. In this work, brain tissue from the cerebrum and cerebellum of male Göttingen minipigs was tested in one of three directions relative to axon fibers in oscillatory simple shear over a large range of strain rates from 0.025 to 250 s-1. Brain tissue showed significant direction dependence in both regions, each with a single preferred loading direction. The tissue also showed strong rate dependence over the full range of rates considered. Transversely isotropic hyper-viscoelastic constitutive models were fit to experimental data using dynamic inverse finite element models to account for wave propagation observed at high strain rates. The fit constitutive models predicted the response in all directions well at rates below 100 s-1, after which they adequately predicted the initial two loading cycles, with the exception of the 250 s-1 rate, where models performed poorly. These constitutive models can be readily implemented in finite element packages and are suitable for simulation of both conventional and blast injury in porcine, especially Göttingen minipig, models.

Systematic analysis of constitutive models of brain tissue materials based on compression tests

It's crucial to understand the biomechanical properties of brain tissue to comprehend the potential mechanisms of traumatic brain injury. This study, distinct from homogeneous models, integrates axonal coupling in both axial and transverse compressive experiments within a continuum mechanics framework to capture its intricate mechanical behaviors. Fresh porcine brains underwent unconfined compression at strain rates of 0.001/s and 0.1/s to 0.3 strain, allowing for a comprehensive statistical analysis of the directional, regional, and strain-rate-dependent mechanical properties of brain tissue. The established constitutive model, fitted to experimental data, delineates material parameters providing intuitive insights into the stiffness of gray/white matter isotropic matrices and neural fibers. Additionally, it predicts the mechanical performance of white matter matrix and axonal fibers under compressive loading. Results reveal that gray matter is insensitive to loading direction, exhibiting insignificant stiffness variations within regions. White matter, however, displays no significant differences in mechanical properties under axial and transverse loading, with an overall higher average stress than gray matter and a more pronounced strain-rate effect. Stress-strain curves indicate that, under axial compression, white matter axons primarily resist the load before transitioning to a matrix-dominated response. Under transverse loading, axonal fibers exhibit weaker resistance to lateral pressure. The mechanical behavior of brain tissue is highly dependent on loading rate, region, direction, and peak strain. This study, by combining experimentation with phenomenological modeling, elucidates certain phenomena, contributing valuable insights for the development of precise computational models.

A comprehensive review on modeling aspects of infusion-based drug delivery in the brain

Brain disorders represent an ever-increasing health challenge worldwide. While conventional drug therapies are less effective due to the presence of the blood-brain barrier, infusion-based methods of drug delivery to the brain represent a promising option. Since these methods are mechanically controlled and involve multiple physical phases ranging from the neural and molecular scales to the brain scale, highly efficient and precise delivery procedures can significantly benefit from a comprehensive understanding of drug-brain and device-brain interactions. Behind these interactions are principles of biophysics and biomechanics that can be described and captured using mathematical models. Although biomechanics and biophysics have received considerable attention, a comprehensive mechanistic model for modeling infusion-based drug delivery in the brain has yet to be developed. Therefore, this article reviews the state-of-the-art mechanistic studies that can support the development of next-generation models for infusion-based brain drug delivery from the perspective of fluid mechanics, solid mechanics, and mathematical modeling. The supporting techniques and database are also summarized to provide further insights. Finally, the challenges are highlighted and perspectives on future research directions are provided. STATEMENT OF SIGNIFICANCE: Despite the immense potential of infusion-based drug delivery methods for bypassing the blood-brain barrier and efficiently delivering drugs to the brain, achieving optimal drug distribution remains a significant challenge. This is primarily due to our limited understanding of the complex interactions between drugs and the brain that are governed by principles of biophysics and biomechanics, and can be described using mathematical models. This article provides a comprehensive review of state-of-the-art mechanistic studies that can help to unravel the mechanism of drug transport in the brain across the scales, which underpins the development of next-generation models for infusion-based brain drug delivery. More broadly, this review will serve as a starting point for developing more effective treatments for brain diseases and mechanistic models that can be used to study other soft tissue and biomaterials.

An injectable biomimetic hydrogel adapting brain tissue mechanical strength for postoperative treatment of glioblastoma without anti-tumor drugs participation

Adapting the mechanical strength between the implant materials and the brain tissue is crucial for the postoperative treatment of glioblastoma. However, no related study has been reported. Herein, we report an injectable lipoic acid‑iron (LA-Fe) hydrogel (LFH) that can adapt to the mechanical strength of various brain tissues, including human brain tissue, by coordinating Fe3+ into a hybrid hydrogel of LA and its sodium salt (LANa). When LFH, which matches the mechanical properties of mouse brain tissue (337 ± 8.06 Pa), was injected into the brain resection cavity, the water content of the brain tissue was maintained at a normal level (77%). Similarly, LFH did not induce the activation or hypertrophy of glial astrocytes, effectively preventing brain edema and scar hyperplasia. Notably, LFH spontaneously degrades in the interstitial fluid, releasing LA and Fe3+ into tumor cells. The redox couples LA/DHLA (dihydrolipoic acid, reduction form of LA in cells) and Fe3+/Fe2+ would regenerate each other to continuously provide ROS to induce ferroptosis and activate immunogenic cell death. As loaded the anti-PDL1, anti-PDL1@LFH further enhanced the efficacy of tumor-immunotherapy and promoted tumor ferroptosis. The injectable hydrogel that adapted the mechanical strength of tissues shed a new light for the tumor postoperative treatment.

Breast Tumor Cell Survival and Morphology in a Brain-like Extracellular Matrix Depends on Matrix Composition and Mechanical Properties

Triple-negative breast cancer (TNBC) is the most invasive type of breast cancer with high risk of brain metastasis. To better understand interactions between breast tumors with the brain extracellular matrix (ECM), a 3D cell culture model is implemented using a thiolated hyaluronic acid (HA-SH) based hydrogel. The latter is used as HA represents a major component of brain ECM. Melt-electrowritten (MEW) scaffolds of box- and triangular-shaped polycaprolactone (PCL) micro-fibers for hydrogel reinforcement are utilized. Two different molecular weight HA-SH materials (230 and 420 kDa) are used with elastic moduli of 148 ± 34 Pa (soft) and 1274 ± 440 Pa (stiff). Both hydrogels demonstrate similar porosities. The different molecular weight of HA-SH, however, significantly changes mechanical properties, e.g., stiffness, nonlinearity, and hysteresis. The breast tumor cell line MDA-MB-231 forms mainly multicellular aggregates in both HA-SH hydrogels but sustains high viability (75%). Supplementation of HA-SH hydrogels with ECM components does not affect gene expression but improves cell viability and impacts cellular distribution and morphology. The presence of other brain cell types further support numerous cell-cell interactions with tumor cells. In summary, the present 3D cell culture model represents a novel tool establishing a disease cell culture model in a systematic way.

Second harmonic generation microscopy, biaxial mechanical tests and fiber dispersion models in human skin biomechanics

Advanced numerical simulations of the mechanical behavior of human skin require thorough calibration of the material's constitutive models based on experimental ex vivo mechanical tests along with images of tissue microstructure for a variety of biomedical applications. In this work, a total of 14 human healthy skin samples and 4 additional scarred skin samples were experimentally analyzed to gain deep insights into the biomechanics of human skin. In particular, second harmonic generation (SHG) microscopy was used to extract detailed images of the distribution of collagen fibers, which were subsequently processed using a three-dimensional Fourier transform-based method recently proposed by the authors to quantify the distribution of fiber orientations. Mechanical tests under both biaxial and uniaxial loading were performed to calibrate the relevant mechanical parameters of two widely used constitutive models of soft fiber-reinforced biological tissues that account for non-symmetrical fiber dispersion. The calibration of the models allowed us to identify correlations between the mechanical parameters of the constitutive models considered. STATEMENT OF SIGNIFICANCE: Constitutive models for soft collagenous tissues can accurately reproduce the complex nonlinear and anisotropic mechanical behavior of skin. However, a comprehensive analysis of both microstructural and mechanical parameters is still missing for human skin. In this study, these parameters are determined by combining biaxial mechanical tests and SHG stacks of collagen fibers on ex vivo healthy human skin samples. The constitutive parameters are provided for two widely used hyperelastic models and enable accurate characterization of skin mechanical behavior for advanced numerical simulations.

MRI-based quantification of cardiac-driven brain biomechanics for early detection of neurological disorders

We present a pipeline to quantify biomechanical environment of the brain using solely MRI-derived data in order to elucidate the role of biomechanical factors in neurodegenerative disorders. Neurological disorders, like Alzheimer's and Parkinson's diseases, are associated with physical changes, including the accumulation of amyloid-β and tau proteins, damage to the cerebral vasculature, hypertension, atrophy of the cortical gray matter, and lesions of the periventricular white matter. Alterations in the external mechanical environment of cells can trigger pathological processes, and it is known that AD causes reduced stiffness in the brain tissue during degeneration. However, there appears to be a significant lag time between microscale changes and macroscale obstruction of neurological function in the brain. Here, we present a pipeline to quantify the whole brain biomechanical environment to bridge the gap in understanding how underlying brain changes affect macroscale brain biomechanics. This pipeline enables image-based quantification of subject-specific displacement field of the whole brain to subject-specific strain, strain rate, and stress across 133 labeled functional brain regions. We have focused our development efforts on utilizing solely MRI-derived data to facilitate clinical applicability of our approach and have emphasized automation in all aspects of our methods to reduce operator dependance. Our pipeline has the potential to improve early detection of neurological disorders and facilitate the identification of disease before widespread, irreversible damage has occurred.

The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain

Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.

The mechanical effects of chemical stimuli on neurospheres

The formulation of more accurate models to describe tissue mechanics necessitates the availability of tools and instruments that can precisely measure the mechanical response of tissues to physical loads and other stimuli. In this regard, neuroscience has trailed other life sciences owing to the unavailability of representative live tissue models and deficiency of experimentation tools. We previously addressed both challenges by employing a novel instrument called the cantilevered-capillary force apparatus (CCFA) to elucidate the mechanical properties of mouse neurospheres under compressive forces. The neurospheres were derived from murine stem cells, and our study was the first of its kind to investigate the viscoelasticity of living neural tissues in vitro. In the current study, we demonstrate the utility of the CCFA as a broadly applicable tool to evaluate tissue mechanics by quantifying the effect that oxidative stress has on the mechanical properties of neurospheres. We treated mouse neurospheres with non-cytotoxic levels of hydrogen peroxide and subsequently evaluated the storage and loss moduli of the tissues under compression and tension. We observed that the neurospheres exhibit viscoelasticity consistent with neural tissue and show that elastic modulus decreases with increasing size of the neurosphere. Our study yields insights for establishing rheological measurements as biomarkers by laying the groundwork for measurement techniques and showing that the influence of a particular treatment may be misinterpreted if the size dependence is ignored.

Frequency and time dependent viscoelastic characterization of pediatric porcine brain tissue in compression

Understanding the viscoelastic behavior of pediatric brain tissue is critical to interpret how external mechanical forces affect head injury in children. However, knowledge of the viscoelastic properties of pediatric brain tissue is limited, and this reduces the biofidelity of developed numeric simulations of the pediatric head in analysis of brain injury. Thus, it is essential to characterize the viscoelastic behavior of pediatric brain tissue in various loading conditions and to identify constitutive models. In this study, the pediatric porcine brain tissue was investigated in compression with determine the viscoelasticity under small and large strain, respectively. A range of frequencies between 0.1 and 40 Hz was applied to determine frequency-dependent viscoelastic behavior via dynamic mechanical analysis, while brain samples were divided into three strain rate groups of 0.01/s, 1/s and 10/s for compression up to 0.3 strain level and stress relaxation to obtain time-dependent viscoelastic properties. At frequencies above 20 Hz, the storage modulus did not increase, while the loss modulus increased continuously. With strain rate increasing from 0.01/s to 10/s, the mean stress at 0.1, 0.2 and 0.3 strain increased to approximate 6.8, 5.6 and 4.4 times, respectively. The brain compressive response was sensitive to strain rate and frequency. The characterization of brain tissue will be valuable for development of head protection systems and prediction of brain injury.

Computational modeling and uncertainty prediction of hyperelastic constitutive responses of damaged brain tissue under different temperature and strain rates

The effect of strain rate and temperature on the hyperelastic material stress-strain characteristics of the damaged porcine brain tissue is evaluated in this present work. The desired constitutive responses are obtained using the commercially available finite element (FE) tool ABAQUS, utilizing 8-noded brick elements. The model's accuracy has been verified by comparing the results from the previously published literature. Further, the stress-strain behavior of the brain tissue is evaluated by varying the damages at various strain rates and temperatures (13, 20, 27, and 37°C) under compression test. Additionally, the sensitivity analysis of the model is computed to check the effect of input parameters, that is, the temperature, strain rate, and damages on the material properties (shear modulus). The modeling and discussion sections enumerate the inclusive features and model capabilities.

Beyond stiffness: deciphering the role of viscoelasticity in cancer evolution and treatment response

There is increasing evidence that cancer progression is linked to tissue viscoelasticity, which challenges the commonly accepted notion that stiffness is the main mechanical hallmark of cancer. However, this new insight has not reached widespread clinical use, as most clinical trials focus on the application of tissue elasticity and stiffness in diagnostic, therapeutic, and surgical planning. Therefore, there is a need to advance the fundamental understanding of the effect of viscoelasticity on cancer progression, to develop novel mechanical biomarkers of clinical significance. Tissue viscoelasticity is largely determined by the extracellular matrix (ECM), which can be simulatedin vitrousing hydrogel-based platforms. Since the mechanical properties of hydrogels can be easily adjusted by changing parameters such as molecular weight and crosslinking type, they provide a platform to systematically study the relationship between ECM viscoelasticity and cancer progression. This review begins with an overview of cancer viscoelasticity, describing how tumor cells interact with biophysical signals in their environment, how they contribute to tumor viscoelasticity, and how this translates into cancer progression. Next, an overview of clinical trials focused on measuring biomechanical properties of tumors is presented, highlighting the biomechanical properties utilized for cancer diagnosis and monitoring. Finally, this review examines the use of biofabricated tumor models for studying the impact of ECM viscoelasticity on cancer behavior and progression and it explores potential avenues for future research on the production of more sophisticated and biomimetic tumor models, as well as their mechanical evaluation.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

In vivo biocompatibility assessment of 3D printed bioresorbable polymers for brain tissue regeneration. A feasibility study

Introduction

The limited capacity of brain tissue to regenerate after acute injury, hampered by cell death, edema and inflammation, has led to an interest in promising and innovative approaches such as implantable regenerative scaffolds designed to improve brain plasticity. Leveraging the capabilities of bioprinting, these scaffolds can be tailored to match the intricate architecture of the brain.

Methods

In this methodological study, we performed in vivo biocompatibility assessments after a brain lesion on three distinct bioeliminable or bioresorbable materials: Poly(ethylene glycol) diacrylate (PEGDA), Polycaprolactone (PCL) and a PEGDA mixed with gelatin methacrylate (PEGDA-GelMA).

Results

A scaffold with a complex shape was printed with patterns, spatial resolution and porosity adapted to cerebral cortex reconstruction. In vivo evaluations were complemented by behavioral monitoring, affirming the safety of these materials. High-resolution T2 MRI imaging effectively captured scaffold structures and demonstrated their non-invasive utility in monitoring degradability. ASL MRI imaging quantified cerebral blood flow and was positively and significantly correlated with lectin immunofluorescent labeling. It may be used to non-invasively monitor progressive revascularization of implants.PEGDA produced an intense foreign-body response, encapsulated by a fibro-inflammatory barrier. On the other hand, PCL provoked a controlled inflammatory reaction and facilitated cell migration into the scaffold, although it induced a fibrotic response around PCL fibers. Conversely, the PEGDA-GelMA composite emerged as a promising candidate for intracerebral implantation. It facilitated the creation of a permissive glial layer, while also inducing neovascularization and attracting neuronal progenitors.

Conclusion

Behavior, MRI monitoring and histology allowed a thorough following of biomaterial biocompatibility. The collective findings position PEGDA-GelMA as a convincing biomaterial option as a basis for treating severe brain lesions, offering new avenues in the search for effective treatments.

Kirchhoff's law-based velocity-controlled motion models to predict real-time cutting forces in minimally invasive surgeries

A theoretical framework, united by a "system effect" is formulated to model the cutting/haptic force evolution at the cutting edge of a surgical cutting instrument during its penetration into soft biological tissue in minimally invasive surgery. Other cutting process responses, including tissue fracture force, friction force, and damping, are predicted by the model as well. The model is based on a velocity-controlled formulation of the corresponding equations of motion, derived for a surgical cutting instrument and tissue based on Kirchhoff's fundamental energy conservation law. It provides nearly zero residues (absolute errors) in the equations of motion balances. In addition, concurrent closing relationships for the fracture force, friction coefficient, friction force, process damping, strain rate function (a constitutive tissue model), and their implementation within the proposed theoretical framework are established. The advantage of the method is its ability to make precise real-time predictions of the aperiodic fluctuating evolutions of the cutting forces and the other process responses. It allows for the robust modeling of the interactions between a medical instrument and a nonlinear viscoelastic tissue under any physically feasible working conditions. The cutting process model was partially qualitatively verified through numerical simulations and by comparing the computed cutting forces with experimentally measured values during robotic uniaxial biopsy needle constant velocity insertion into artificial gel tissue, obtained from previous experimental research. The comparison has shown a qualitatively similar adequate trend in the evolution of the experimentally measured and numerically predicted cutting forces during insertion of the needle.

Harnessing the Potential of Self-Assembled Peptide Hydrogels for Neural Regeneration and Tissue Engineering

Spinal cord injury, traumatic brain injury, and neurosurgery procedures usually lead to neural tissue damage. Self-assembled peptide (SAP) hydrogels, a type of innovative hierarchical nanofiber-forming peptide sequences serving as hydrogelators, have emerged as a promising solution for repairing tissue defects and promoting neural tissue regeneration. SAPs possess numerous features, such as adaptable morphologies, biocompatibility, injectability, tunable mechanical stability, and mimicking of the native extracellular matrix. This review explores the capacity of neural cell regeneration and examines the critical aspects of SAPs in neuroregeneration, including their biochemical composition, topology, mechanical behavior, conductivity, and degradability. Additionally, it delves into the latest strategies involving SAPs for central or peripheral neural tissue engineering. Finally, the prospects of SAP hydrogel design and development in the realm of neuroregeneration are discussed.

Specimen width affects vascular tissue integrity for in-vitro characterisation

The preparation of slender specimens for in-vitro tissue characterisation could potentially alter mechanical tissue properties. To investigate this factor, rectangular specimens were prepared from the wall of the porcine aorta for uniaxial tensile loading. Varying strip widths of 16 mm, 8 mm, and 4 mm were achieved by excising zero, one, and three cuts within the specimen along the loading direction, respectively. While specimens loaded along the vessel's circumferential direction acquired consistent tissue properties, the width of test specimens influenced the results of axially loaded tissue; vascular wall stiffness was reduced by approximately 40% in specimens with strips 4 mm wide. In addition, the cross-loading stretch was strongly influenced by specimen strip width, and fiber sliding contributed to the softening of slender tensile specimens, an outcome from finite element analysis of test specimens. We may, therefore, conclude that cutting orthogonal to the main direction of collagen fibers introduces mechanical trauma that weakens slender tensile specimens, compromising the determination of representative mechanical vessel wall properties.

A comparison of brain retraction mechanisms using finite element analysis and the effects of regionally heterogeneous material properties

Finite element (FE) simulations of the brain undergoing neurosurgical procedures present us with the great opportunity to better investigate, understand, and optimize surgical techniques and equipment. FE models provide access to data such as the stress levels within the brain that would otherwise be inaccessible with the current medical technology. Brain retraction is often a dangerous but necessary part of neurosurgery, and current research focuses on minimizing trauma during the procedure. In this work, we present a simulation-based comparison of different types of retraction mechanisms. We focus on traditional spatulas and tubular retractors. Our results show that tubular retractors result in lower average predicted stresses, especially in the subcortical structures and corpus callosum. Additionally, we show that changing the location of retraction can greatly affect the predicted stress results. As the model predictions highly depend on the material model and parameters used for simulations, we also investigate the importance of using region-specific hyperelastic and viscoelastic material parameters when modelling a three-dimensional human brain during retraction. Our investigations demonstrate how FE simulations in neurosurgical techniques can provide insight to surgeons and medical device manufacturers. They emphasize how further work into this direction could greatly improve the management and prevention of injury during surgery. Additionally, we show the importance of modelling the human brain with region-dependent parameters in order to provide useful predictions for neurosurgical procedures.