Understanding the mechanical interactions between surgical probes and brain tissue is essential for optimizing procedures such as deep brain stimulation (DBS). In this study, agar gel phantoms were used as brain tissue surrogates for their well-characterized mechanical properties and extensive use in neurosurgical modeling. Systematic experiments were conducted to quantify the insertion and withdrawal forces of DBS probes, and the evolution of probe-induced channels was analyzed using synchronized high-speed imaging and force measurements. Key parameters, such as peak insertion force, were extracted from filtered curves showing the relationship between force and depth as well as force and time. Classical physical models, such as the Hertz and Fung equations, characterized the force response in the linear regime and in regimes with weak nonlinearity, while a hybrid physics-guided residual neural network (PGNN) was applied to capture complex and highly nonlinear interactions. Our results show that the force response exhibited time-dependent behavior: insertion force showed clear velocity dependence, whereas withdrawal force was predominantly described by a velocity-independent friction term over the tested range. Probe speed and gel-recovery dynamics nevertheless influenced channel closure. Channel measurements revealed that the residual channel is consistently smaller than the probe diameter, which can be attributed to the combined effects of elastic recovery, viscous flow, and hydration. Both probe speed and depth were found to significantly influence the dynamics of channel closure. Model fitting demonstrated that classical models can adequately describe the force response in specific regimes, but the hybrid PGNN model improves prediction accuracy for complex mechanical interactions. Overall, this work offers new insights into phase-specific probe-material interaction mechanics in a controlled homogeneous surrogate, and the integrated experimental and modeling framework developed here provides a baseline dataset for future studies of DBS-relevant insertion mechanics and model refinement.

Traumatic brain injury (TBI) is a global health concern. Cerebrospinal fluid (CSF) dynamics play a key role in the injury process but remain poorly understood due to experimental challenges imposed by the opaque skull and subarachnoid trabeculae (SAT). This study investigates the influence of SAT on CSF pressurization during impact using instrumented head surrogates. Two surrogates were developed, each including a transparent plastic skull, artificial brain tissue, and a CSF layer between. One model incorporated artificial SAT (permeability = 2.12 ± 1.03 × 10⁻⁹ m², porosity = 0.97), while the control model lacked SAT. Both models were instrumented with pressure sensors in the coup and contrecoup regions and subjected to repeated translational impacts monitored with an accelerometer. Results reveal that the presence of SAT structure decreased peak pressure in both coup and contrecoup regions. Across peak linear impact accelerations (126 g to 1071 g on the surrogate frame, corrected to 68 g to 574 g on the surrogate skull) with impact durations of 0.5 ms to 1.1 ms, linear regressions of peak pressure (Pmax) vs. impact acceleration (α) revealed that SAT reduced the slope by 33 % in the coup region (Pmax = 0.0006α without SAT, Pmax = 0.0004α with SAT), and by 60 % in the contrecoup region (Pmax = 0.0054α without SAT, Pmax = 0.0022α with SAT). In the contrecoup region, pressurization was rapid but delayed by 24 % relative to that of coup region, suggesting possible occurrence of local intracranial cavitation and SAT's potential interactions with cavitation bubbles. In summary, this study uncovers the biomechanical function of SAT in modulating pressure dynamics during impact, indicating a protective effect that may mitigate brain injury. STATEMENT OF SIGNIFICANCE: This study provides new insights into how the fibrous structures around the brain, known as subarachnoid trabeculae (SAT), help protect against traumatic brain injury (TBI). While previous TBI studies attribute injury to tissue deformation during impact, this work considers the overlooked role of cerebrospinal fluid (CSF) pressure dynamics within the skull. Using transparent head surrogates equipped with pressure and acceleration sensors, SAT influence on CSF pressurization is directly measured for a reduction in harmful pressure spikes. Findings reveal a previously unrecognized protective mechanism in brain biomechanics. By replicating head anatomy, this work advances modern understanding of brain injury and could guide the development of future helmets, medical models, and injury prevention strategies.

Cardiovascular disease remains one of the leading causes of mortality worldwide, with endothelial dysfunction playing a pivotal role in its initiation and progression. Early detection and accurate evaluation of endothelial dysfunction are therefore essential for effective risk assessment and intervention. This chapter reviews recent developments in a widely used method, flow-mediated dilation (FMD), for assessing endothelial function in clinical research. We begin with an overview of vascular structure and endothelial physiology, laying the groundwork for a deeper exploration of the mechanotransduction processes at the cellular level that drive vasodilation during FMD. Next, we describe the FMD procedure in detail, which evaluates arterial functions by measuring ultrasound-based arterial vasodilation in response to a period of temporary ischemia. We then discuss the limitations of using FMD%, the traditional marker representing the percentage of vasodilation, as the sole output of the FMD test. Although widely used, FMD% fails to capture the full mechanotransduction process linking shear stress to arterial dilation, leading to potentially incomplete or biased interpretations. To overcome this limitation, we introduce a novel physics-based framework for interpreting FMD results. This approach utilizes a set of biophysical models to extract physiologically meaningful parameters by integrating theoretical insights with experimental FMD measurements. Finally, we outline the significant potential of this advanced FMD analysis tool, which may enable a more comprehensive and mechanistically informed assessment of endothelial function.

BACKGROUND:Accurate electrode placement is essential for effective deep brain stimulation (DBS). However, the mechanics governing probe insertion and withdrawal under controlled conditions remain insufficiently characterized. Experimentally validated computational frameworks capable of quantifying probe insertion mechanics and post-removal cavity formation in controlled surrogate materials are still limited. METHODS:A Coupled Eulerian-Lagrangian (CEL) finite element model was implemented to simulate probe insertion and removal in agar gel, used here as a controlled surrogate material. The computational domain consisted of a 50 × 50 × 70 mm Eulerian gel block and a 50 × 50 × 20 mm void region above the insertion site to allow material transport and minimize boundary effects. The gel was modeled using Eulerian elements to accommodate large deformation, while the Nitinol probe was treated as a rigid body. Mesh convergence analysis identified an optimal resolution of 0.6 million elements. Experiments at four clinically relevant insertion speeds (3.24-9.07 mm/s) were performed to validate simulated reaction forces and post-removal cavity deformation under controlled laboratory conditions. RESULTS:The CEL model accurately reproduced the force-time response throughout insertion and withdrawal, achieving R2 values up to 0.98 and peak force errors between 2% and 12%. The simulation also captured cavity formation and subsequent contraction, with the diameter stabilizing at roughly 60% of the probe diameter, consistent with experiments. Speed-dependent cavity closure observed experimentally was not fully captured by the current rate-independent elastic-plastic material model, highlighting the limitations of the simplified constitutive description. CONCLUSIONS:This work presents the first experimentally validated application of a CEL-based modeling framework for DBS probe insertion and withdrawal in homogeneous agar gel. The model reproduces probe-gel interaction forces and cavity evolution, establishing a mechanically validated baseline that can support future extensions to multilayer tissue models and more physiologically representative simulations. The validated contact mechanics and CEL framework are structurally independent of the specific constitutive description, although the quantitative mechanical response remains dependent on the selected material model. While not intended as a direct clinical predictive tool, the framework provides a controlled foundation for systematic investigation of probe insertion mechanics in surrogate materials relevant to DBS research.

Background: Traditional cardiovascular disease (CVD) risk factors, such as elevated C-reactive protein (CRP) and triglycerides, contribute to vascular endothelial dysfunction before the presence of overt CVD. However, their correlation with the structural integrity of arteries has not been explored. Our group developed a novel analysis method for the flow-mediated dilation (FMD) test to provide biophysical insights into arterial integrity. In this study, we apply this novel method to female participants with and without Type 1 Diabetes (T1D) to examine how CVD risk factors influence the structural function of the arterial system. Method: A total of 37 Women with T1D (HbA1c = 8.4 ± 1.6%) and 34 apparently healthy women (HbA1c = 5.2 ± 0.2%) were enrolled. All participants attended the study visit during the menses phase, provided written informed consent, and underwent body composition assessment, blood sample collection and analysis (including circulating concentrations of estradiol, insulin, glucose, CRP, HbA1c, and a lipid panel), and a brachial artery FMD test conducted per published guidelines. The ultrasound screen was continuously recorded during baseline and reactive hyperemia to generate time-based diameter profiles, which were then processed to derive conventional FMD% and 5 biophysical parameters: Mechanotransduction Strength (γ), Artery’s Stiffness Ratio (E min *), Softening Sensitivity (B), Characteristic response time (T (s), characterizing the artery’s response time to vasodilators), and Recovery Propensity (ζ*). Results: Group comparisons revealed differences in not only FMD% (p = 0.044) but also biophysical parameters, most notably mechanotransduction strength γ (p = 0.088), indicating impaired mechanical signaling through the vessel wall in T1D. Multiple logistic regression analysis showed that variability in traditional CVD risk factors was associated with novel biophysical parameters from the FMD test, but not strongly with FMD%. Across all subjects, glucose was positively associated with recovery propensity (ζ*, Coeff = 0.58, p< 0.001) and characteristic response time (T, Coeff = 1.61, p< 0.001). Percent Fat was positively associated with the arterial stiffness ratio (E min *, Coeff = 0.047, p =0.038). HDL cholesterol was inversely related to T (Coeff = -0.87, p=0.01). In the T1D group, CRP was negatively associated with the softening sensitivity (B, Coeff = -2.3, p=0.023), and glucose was negatively related to FMD% (Coeff = -0.019, p=0.005). At the model level, traditional CVD risk factors significantly predicted E min * (R 2 = 0.35, p=0.034), T (R 2 = 0.47, p< 0.001), and ζ*(R 2 = 0.35, p=0.029), with even higher explanatory power in the T1D subgroup for T (R 2 = 0.64, p=0.029), and ζ*(R 2 = 0.61, p=0.043). No significant models (p< 0.1) were observed for FMD%. Conclusion: Findings from the present investigation have identified that CVD risk factors influence distinct aspects of arterial integrity as measured by biophysical parameters derived from the FMD test. Further studies are warranted to deepen the understanding of these parameters and to explore their value in assessing arterial health. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

This paper presents a theoretical and experimental study of oscillatory flow through a finite compliant tube, aiming to investigate fluid–structure interactions in structures like blood vessels. The setup consists of a horizontally submerged polydimethylsiloxane tube mounted between two fixed ends, each instrumented with a pressure sensor. The device that provides the flow was custom-designed and manufactured by our team. It consists of two rigidly connected piston–cylinder assemblies that operate 180° out of phase, ensuring that the amount of fluid pushed into the tube from one end is identical to the amount pulled out from the other end, thereby making the flow purely oscillatory. Visualizing the flow inside a narrow tube with a constantly changing shape can be quite cumbersome, making corrections for light refraction distinctly challenging. Therefore, the tube's undulating shape is monitored using a high-speed camera. The instantaneous tube profile is obtained by processing the video offline in MATLAB. A theoretical model was developed to describe the tube's wall radial motion. The resulting equation was solved analytically for small deformations to obtain the predicted local deformation history. For radial deformations within ∼7%, the analytical solution agreed well with observation. Driven by this validation, a complete solution for the flow field is proposed within the small deformation limit. This study showcases a setup where a compliant tube's flow conditions are measured in tandem with its deformation, offering a unique avenue through which the fluid–structure interaction models in compliant tubes can be tested and refined.

BACKGROUND AND OBJECTIVES:The efficacy of deep brain stimulation (DBS) relies on accurate electrode placement. Unfortunately, electrode deviation poses a persistent problem, with most electrodes demonstrating some degree of bending. Although such bending does not always result in target deviation, an estimated 3% to 8% of patients still require revision surgery to address suboptimal electrode placement. DBS electrode deviation may occur at mechanical tissue interfaces, with denser internal capsule (IC) fibers being the most likely factor. Based on basic principles of physics, we hypothesized that the angle of a planned trajectory relative to tissue interfaces created by the IC induces deviation. METHODS:Ten patients with Parkinson disease scheduled for DBS surgery underwent preoperative 3T magnetic resonance elastography (MRE) using synchronized external vibrations to measure brain tissue stiffness. The IC stiffness interface (ICSI) was defined as the transition between the corona radiata and IC on MRE. The rate of transition was calculated as the change in stiffness across the ICSI. Postoperative computed tomography was used to measure target deviation . The angle of approach was calculated as the angle between the planned trajectory and the normal vector to the ICSI. Pearson correlations and t -tests were performed to evaluate associations between the angle of approach and target deviation. RESULTS:Twenty-one electrode trajectories were analyzed. The mean electrode deviation was 1.27 ± 0.63 mm. A significant correlation (r = 0.57, 95% CI [0.18, 0.80], P = .007) was found between angle of approach and target deviation, with larger angles associated with greater deviations. The rate of transition did not correlate with deviation ( P = .874). CONCLUSION:MRE effectively quantifies in vivo brain tissue stiffness in Parkinson disease. The angle between the planned trajectory and the ICSI correlates with target deviation, supporting the hypothesis that tissue mechanics influence electrode bending. MRE has potential to quantify the likelihood of DBS electrode deviation, which could reduce revision surgeries and enhance clinical outcomes.

Polymeric nanoparticles (NPs) are promising tools for transporting and localizing therapeutics with intravenous delivery. Targeting these vehicles to specific tissue sites is challenging. Here, we investigate the use of low-frequency acoustic fields to drive polymeric NPs from circulating blood onto blood vessel walls by using concepts of elastic material deformation. By varying the shear flow rate and duration of acoustic field exposure, we achieved a 1000-fold increase in NP fluorescence intensity on vascular tissue compared with no acoustic field at a flow rate of 2 m/min. Interestingly, we found that acoustic-field-enhanced NP deposition is independent of NP surface chemistry. We also showcase a 100-fold increase in the area of fluorescence detected following NP delivery to an intact, ex vivo human vessel wall when a localized acoustic field is applied. This work suggests that local administration of acoustic fields can control polymeric NP biodistribution after intravenous delivery and enhance the treatment of tissue-specific pathologies.

Traumatic brain injury (TBI) is a serious health issue. Studies have highlighted the severity of rotation-induced TBI. However, the role of cerebrospinal fluid (CSF) in transmitting the impact from the skull to the soft brain matter remains unclear. Herein, we use experiments and computations to define and probe this role in a simplified setup. A spherical hydrogel ball, serving as a soft brain model, was subjected to controlled rotation within a water bath, emulating the CSF, and filling a transparent cylinder. The cylinder and ball velocities, as well as the ball's deformation over time, were measured. We found that the soft hydrogel ball is very sensitive to decelerating rotational impacts, experiencing significant deformation during the process. A finite-element code is written to simulate the process. The hydrogel ball is modeled as a poroelastic material infused with fluid and its coupling with the suspending fluid is handled by an arbitrary Lagrangian-Eulerian method. The results indicate that the density contrast, as well as the rotational velocity difference, between the hydrogel ball and the suspending fluid, play a central role in the ball's deformation due to centrifugal forces. This approach contributes to a deeper understanding of brain injuries and may portend the development of preventive measures and improved treatment strategies.

This paper presents a theoretical model examining the interaction between a fibrous network and viscous fluid flow driven by an oscillating boundary. The aim is to understand how oscillating impacts are transmitted from the skull, through the arachnoid trabeculae network filled with cerebrospinal fluid, as observed in shaken baby syndrome. The model uses an effective medium approach to determine the fluid velocity field while each fiber is treated as a soft string undergoing deformation. Results indicate that the frequency of oscillation, fiber stiffness, and porous structure resistance significantly influence the oscillating shearing flow, as indicated by the Womersley (Wo), Brinkman (α), and Bingham (Bm) numbers. Application of the model to shaken baby syndrome suggests that oscillations in the cerebrospinal fluid and arachnoid trabeculae can significantly surpass those on the skull, leading to intense shear stress penetration to the brain. This model is the first study to integrate the dynamic response of string-like fibrous networks in fluid flows with oscillating boundaries and offers a quantitative framework for predicting the transmission of shearing forces from the skull to the brain matter.

Traumatic brain injury remains a significant global health concern, requiring advanced understanding and mitigation strategies. In current brain concussion research, there is a significant knowledge gap: the critical role of transient cerebrospinal fluid (CSF) flow in the porous subarachnoid space (SAS) has long been overlooked. To address this limitation, we are developing a simplified mathematical model to investigate the CSF pressurization in the porous arachnoid trabeculae and the resulting motion of brain matter when the head is exposed to a translational impact. The model simplifies the head into an inner solid object (brain) and an outer rigid shell (skull) with a thin, porous fluid gap (SAS). The CSF flow in the impact side (coup region) and the opposite side (contrecoup region) is modeled as porous squeezing and expanding flows, respectively. The flow through the side regions, which connect these regions, is governed by Darcy's law. We found that the porous arachnoid trabeculae network significantly dampens brain motion and reduces pressure variations within the SAS compared to a SAS without the porous arachnoid trabeculae (AT). This effect is particularly pronounced under high-frequency, periodic acceleration impacts, thereby lowering the risk of injury. The dampening effect can be attributed to the low permeability of the AT, which increases resistance to fluid movement and stabilizes the fluid and pressure responses within the SAS, thereby reducing extreme pressure fluctuations and brain displacement under impact. This work provides a foundational understanding of CSF flow dynamics, paving the way for innovative approaches to brain injury prevention and management.

Imaging nanomaterials in hybrid systems is critical to understanding the structure and functionality of these systems. However, current technologies such as scanning electron microscopy (SEM) may obtain high resolution/contrast images at the cost of damaging or contaminating the sample. For example, to prevent the charging of organic substrate/matrix, a very thin layer of metal is coated on the surface, which will permanently contaminate the sample and eliminate the possibility of reusing it for following processes. Conversely, examining the sample without any modifications, in pursuit of high-fidelity digital images of its unperturbed state, can come at the cost of low-quality images that are challenging to process. Here, a solution is proposed for the case where no brightness threshold is available to reliably judge whether a region is covered with nanomaterials. The method examines local brightness variability to detect nanomaterial deposits. Very good agreement with manually obtained values of the coverage is observed, and a strong case is made for the method's automatability. Although the developed methodology is showcased in the context of SEM images of Polydimethylsiloxane (PDMS) substrates on which silicone dioxide (SiO2) nanoparticles are assembled, the underlying concepts may be extended to situations where straightforward brightness thresholding is not viable.

In this study, a physics-based model is developed to describe the entire flow mediated dilation (FMD) response. A parameter quantifying the arterial wall's tendency to recover arises from the model, thereby providing a more elaborate description of the artery's physical state, in concert with other parameters characterizing mechanotransduction and structural aspects of the arterial wall. The arterial diameter's behavior throughout the full response is successfully reproduced by the model. Experimental FMD response data were obtained from healthy volunteers. The model's parameters are then adjusted to yield the closest match to the observed experimental response, hence delivering the parameter values pertaining to each subject. This study establishes a foundation based on which future potential clinical applications can be introduced, where endothelial function and general cardiovascular health are inexpensively and noninvasively quantified.

The endothelial glycocalyx layer (EGL), with its inherent fibrous architecture enveloping the interior surfaces of blood vessels, paradoxically increases resistance to blood flow. This phenomenon poses a significant question: how do physiological systems overcome the enhanced resistance imparted by the EGL? Addressing this knowledge gap, this study proposes a new theoretical framework to analyze the dynamic behavior of the EGL in the setting of pulsatile blood flow. Central to our investigation is the novel concept of pulsatile soft lubrication, a potential mechanism for mitigating flow resistance. Utilizing a theoretical model that mimics fluid dynamics across parallel fibrous boundaries, we explore the intricate interplay between fluid motion and EGL fibers under pulsatile pressure gradients. The results indicate that the EGL's natural elasticity engenders a dynamic interface that notably lessens flow resistance, thereby enhancing flow rates. Beyond advancing our understanding of the EGL's critical function in hemodynamics, this research also highlights its broader implications, suggesting relevance in engineering and design principles. Insights into fluid dynamics and surface interactions garnered from this study could inform innovative strategies for reducing friction and optimizing flow across a variety of systems.

Background and purposeTraumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.Materials and methodsA machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models.ResultsCompared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%.ConclusionThe machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.

Cloud storage services allow data owners to outsource their potentially sensitive data (e.g., private genome data) to remote cloud servers in a ciphertext form. To enable data owners to further share the data encrypted in ciphertexts, many proxy re-encryption (PRE) schemes are proposed. However, most schemes only support single-recipient or coarse-grained re-encryption, which may limit the flexibility for data sharing. To address this issue, we propose a Policy-based Broadcast Access Authorization (PBAA) scheme by introducing the well-established identity-based broadcast encryption (IBBE) and key-policy attribute-based encryption into PRE. In our PBAA scheme, a data owner can apply IBBE to encrypt his data to a group of recipients. More importantly, the data owner can generate a delegation key with an access policy, and send this key to the cloud such that it can convert any initial ciphertext satisfying the access policy into a new ciphertext for a new group of recipients. With these features, cloud users can share their remote data in a secure and flexible way. Security analysis and performance evaluation show that the PBAA scheme is secure and efficient, respectively.

In this study, mechanotransduction is investigated through a physics-based viscoelastic model describing the arterial diameter response during a brachial artery flow mediated dilation (BAFMD) test. The study is a significant extension of two earlier studies by the same group, where only the elastic response was considered. Experimental BAFMD responses were collected from 12 healthy volunteers. The arterial wall's elastic and viscous properties were treated as local variable quantities depending on the wall shear stress (WSS) sensed by mechanotransduction. The dimensionless parameters, arising from the model which serve as a quantitative assessment of the artery's physical state, were adjusted to replicate the experimental response. Among those dimensionless parameters, the viscoelastic ratio, which reflects the relative strength of the viscous response compared to its elastic counterpart, is of special relevance to this paper's main conclusion. Based on the results, it is concluded that the arterial wall's mechanical behavior is predominantly elastic, at least in the strict context of the BAFMD test. Recommendations for potential future research and applications are provided.