Variable inhalation flow rates reduce aerosol drug efficacy, while separate devices for therapy and monitoring hinder patient adherence. This study computationally designs a novel, dual-function valve to solve these issues. Operating on inertial impaction, the valve uses a nozzle to create an aerosol jet and a movable plate to force a sharp airflow turn. Larger, high-inertia particles impact the plate, while smaller therapeutic particles remain entrained. Building on an optimized geometry, a computational fluid dynamics (CFD) model characterized valve performance across clinically relevant conditions. The model simulated aerosol transport during low-flow therapeutic inhalation (10-60 L/min) and high-flow diagnostic peak expiratory flow rate (PEFR) exhalation (100-700 L/min). A custom "valve efficacy" metric quantified selective particle filtration based on aerodynamic diameter. Filtration efficacy is highly dependent on inhalation flow rate and nozzle-to-plate distance. A high-performance therapeutic window was identified at 20-35 L/min, maintaining efficacy above 90%. A direct, linear relationship between aerodynamic drag on the plate and the optimal filtration distance was established. This enables a passive, self-regulating mechanism governed by a linear spring. Augmenting this with a second, stiffer spring in series allows the assembly to function as a PEFR meter. The computational results validate the feasibility of a single, flow-actuated valve that integrates selective therapeutic aerosol filtration with diagnostic PEFR monitoring. This design represents a significant step toward developing more personalized, effective, and user-friendly devices for managing chronic respiratory diseases.

Uncontrolled tipping during incisor retraction with clear aligners continues to limit orthodontic treatment. Different countermeasures have been proposed to improve the predictability of aligners, such as power ridges and attachments, while their efficacy requires further investigation. This work investigated the biomechanics of the overcorrection in reducing the tipping angle and moment during incisor retraction with a clear aligner, through in vitro experiments and finite element analysis (FEA). Specifically, the influence of overcorrection angle (1 deg), retraction distance (0.15 mm), and the combination of both were assessed through in vitro experiments and the finite element simulation. Further computational simulations were conducted to inspect the mechanical performance of clear aligners with different overcorrection angles (from 0 deg to 1 deg and 2 deg) for a fixed retraction distance of 0.15 mm. As the overcorrection angle increased from 0 deg to 1 deg and 2 deg for the clear aligner with a retraction of 0.15 mm, the tipping angle of the incisor decreased from 0.29 deg to 0.25 deg and 0.21 deg, and the tipping moment at the tooth root decreased from 13.60 N·mm to 9.38 N·mm and 6.27 N·mm. However, the retraction force also decreased from 0.84 N to 0.73 N and 0.47 N, indicating a tradeoff between tipping control and retraction efficiency, likely due to increased counteractive moment and the decreased force exerted by the aligner on the tooth crown. In conclusion, this study presents both the benefits and limitations of overcorrection strategies in reducing the tipping angle and moment during incisor retraction using clear aligners, providing biomechanical insight for the optimal design of clear aligners for improving treatment outcomes.

Traumatic brain injury (TBI), resulting from blunt or blast impact to the head, affects approximately 1.6 million Americans per year. Brain biomechanics and injury models play a fundamental role in the TBI community as they can be used to evaluate protective equipment, refine safety standards, and improve individual treatment plans. To maximize clinical relevance, these models require accurate and precise mechanical input parameters relevant to expected injuries. Magnetic resonance elastography (MRE) offers a noninvasive method to quantify the viscoelastic properties of soft tissues by phase-encoding mechanical waves as they propagate through a tissue. While MRE has advanced our understanding of brain tissue mechanics, characterizing nonlinear behavior under large deformations remains a critical challenge for improving TBI models. Here, we coupled ex vivo MRE with incremental compression to characterize brain tissue mechanics beyond the linear elastic and linear viscoelastic regimes, which are more relevant for injury. Using a custom-made MR-compatible compression device, controlled axial pre-strain was applied to fresh brain tissue-agar phantoms. On average, the tissue storage modulus increased by 73.6% when approximately 6.4% of strain was applied. The resulting experimental data, namely storage modulus and applied pre-strain, were successfully fit to a phenomenological equation to output material parameters, including a nonlinearity metric. These findings provide novel, strain-dependent mechanical data for brain tissue that extend beyond small-strain assumptions. The results will improve computational models of TBI by supplying mechanical input parameters for predicting brain response under injury-relevant loading conditions.

A helmet will not fit the same way on different individuals with different head sizes and shapes. However, the degree to which subject-specific fit differences may affect the risk of injury remains understudied. In this study, we used experimentally measured head/helmet contact force to generate different levels of initial padding compression when applied to a finite element head model. The deformed padding configuration was used as the initial condition for subsequent blunt impact analyses. As the level of initial padding compression increased, the resulting maximum force, total impulse, and the resulting head injury metrics increased by up to 20%. The results of this study highlight the importance of accurate modeling of initial pad deformation before blunt impact and provide a workflow for establishing such a configuration in other helmet applications. Intuitively, two different people wearing an identical helmet will experience a different fit, since the size and shape of their heads will differ. However, it is unclear how that variation in fit will affect the risk of injury for most helmet types. In this study, we generated different helmet fit conditions in a simulated model of a head and helmet, based on previous experimental measurements of the force between the helmet padding and the head. Four conditions were generated: zero, low, medium, and high initial fitting forces. Using the different fit configurations, we simulated two types of impacts on the front, back, side, and top of the helmet, and calculated the risk of injury to the brain and skull. Our results suggest that differences in padding compression arising from differences in head shape can significantly change the risk of injury. Higher initial padding compression resulted in greater force applied to the head and higher values of brain and skull injury metrics. These effects were more apparent for lower energy impacts, suggesting that the differences might be important to consider for helmets where the user sees repeated, low-intensity impacts such as in American football. Our analysis was limited to one type of helmet, and we were unable to vary the geometry of the head, meaning that the data should be interpreted cautiously. However, our results provide compelling evidence that the level of initial compression in the padding due to the user's head is an important factor to incorporate when designing new helmets.

Root canal treatment is crucial for preventing oral diseases, and its success hinges on the effective removal of biofilm and debris from the canal wall by the irrigant. However, the intricate and narrow anatomy of root canals limits the efficacy of irrigant jet in the critical apical region, hindering fluid mixing and the removal of wall-adherent material. To address this, a passive self-exciting jet technique using a novel slot jet needle is proposed. This design aims to reorganize the flow and induce secondary jet self-excitation within the narrow cavity, thereby enhancing root canal irrigation effect. The influence of the slot angle and aperture diameter is first investigated, and the results show that the efficacy of jet coupling depends not only on the slot jet direction but also on the relative strength of the jets. The irrigation performance is optimal when the slot angle is 90 deg and the aperture diameter is 0.1 mm. Furthermore, the influence of the tapering angle is investigated using the principle of a tapering nozzle. Compared to the clinically common prototype 30 G needle, the optimized design increases the effective cleaning area by up to 20.82% and the extending depth by up to 50.79%, while reducing the mean apical pressure by up to 23.39%. These improvements demonstrate that the cleaning performance, mass exchange capability, and safety are improved simultaneously.

This study systematically compares two personalized control strategies for ankle exoskeletons: human-in-the-loop (HIL) optimized torque profiles and stride-wise adaptive torque control. Through biomechanical analysis of 11 healthy participants walking at 1.50 m/s, we evaluated these approaches across multiple performance metrics. Both strategies demonstrated comparable effectiveness in reducing soleus (SOL) muscle activity (36.8% versus 35.8%) and biological ankle torque (34.1% versus 36.6%), with no significant differences observed at the group level (p > 0.05). However, the optimized torque mode showed 22.4% higher energy efficiency (1.189 versus 1.793 J/kg reduction in muscle activity) and induced less compensatory tibialis anterior (TA) activation (p < 0.05). While gait symmetry remained unimpaired (p > 0.05) under both modes, individual responses varied substantially, particularly in adaptation to dynamic parameter adjustments. These findings indicate that pre-optimized fixed parameters are preferable for stable walking conditions, whereas adaptive control requires further refinement for dynamic environments, providing empirical guidance for selecting ankle exoskeleton control strategies based on application needs.

Given the strong ties to data sharing and the responsible use of resources, reproducibility of modeling and simulation practice is of paramount importance in science. Computational models in orthopedics provide insight into healthy and injured joint mechanics and can inform clinical decision-making. The KneeHub project investigated the influence of modelers' decisions and thus their "art" in simulation and modeling; five teams developed and calibrated knee models using the same experimental data. Model benchmarking evaluated the predictive ability of the models under loading scenarios that were not considered in the development and calibration process. The objective of this study was to evaluate the accuracy of predictions of knee-specific joint biomechanics for benchmark scenarios of simulating a resected anterior cruciate ligament (ACL) using models of one knee and a combined pivot shift loading using models of another knee. The models predicted the major trends in kinematics and kinetics; however, differences were observed in comparison to experimental data and between teams. Model-to-experiment root-mean-square (RMS) errors were up to 6.6±2.4 mm in anterior-posterior (AP) translation, 13.5±12.9 deg in internal-external (IE) rotation, and 5.3±3.4 deg in varus-valgus (VV) rotation; errors were largest in internal-external rotation, and standard deviations reflected differences between teams. While calibrated models were tuned to a similar set of conditions (albeit with different decisions), the optimized stiffness and reference length/strain of ligament structures may not fully reproduce the contributions of these structures to joint kinematics that were measured experimentally in the benchmark scenarios. As researchers often extend models beyond the conditions used to calibrate them, quantifying model accuracy and limitations with benchmarking represents a crucial step toward reproducibility and can help establish best practices for credible modeling in our community.

Pulmonary hypertension (PH) is a serious condition affecting patients with end-stage renal disease (ESRD), yet the hemodynamic mechanisms underlying development remain poorly understood. Novel alternative methods (NAMs), such as computational fluid dynamics (CFD), provide a powerful and ethical approach to investigate vascular physiology using patient-specific data. We developed a CFD model of the pulmonary artery (PA) informed by noninvasive magnetic resonance imaging (MRI) from an ESRD patient to characterize flow dynamics and wall shear metrics relevant to PH. Simulations were performed using image-based geometry, and velocity fields, wall shear stress (WSS), time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were quantified. Results demonstrated physiologically consistent flow distributions, with higher velocities localized near outlet regions and lower velocities in branches. Spatially averaged TAWSS was approximately 9 dyn/cm2, in agreement with previously reported ranges. OSI values were low across the pulmonary vasculature, suggesting limited flow reversal. Together, these results highlight the feasibility of using patient-specific CFD to capture PA hemodynamics in ESRD and demonstrate consistency with published physiological values. This framework demonstrates the utility of NAMs to provide insight into complex biomechanical systems and a foundation for future studies seeking to clarify mechanistic links between ESRD development, arteriovenous fistula (AVF) creation, and eventual PH development, ultimately informing development of patient-specific diagnostic and therapeutic strategies. As NAMs gain regulatory and scientific traction, approaches like this will play an important role in reducing reliance on animal models while enabling ethically responsible, patient-specific discovery in cardiovascular research.

Operative repair of the rotator cuff (RC) of the shoulder can return a patient to normal function but is not without complications. An understanding of the tissue strains in the RC of the shoulder under normal and pathological conditions can inform surgeons about the conditions needed for restoration of function and the state of the tissue under repair. The current work applied digital image correlation (DIC) to quantify rotator cuff strains with the goals of (1) determining whether the tension created by the supraspinatus (SS) muscle is transmitted without diversion by the structure of the cuff from the supraspinatus to the cuff insertion; and (2) whether releases of the SS tendons at their insertions alter the strain field in the region away from the cuff insertion. DIC methods recorded the bursal-side cuff strains created using a shoulder simulator, which could apply physiologic loads to the cuff muscles. The SS and infraspinatus (IS) insertions of the humerus were sequentially released while muscle loads were applied. The first principal strains and their directions showed changes after releases of both the anterior and posterior SS insertions and after the IS release. The results demonstrated (1) that the RC transmits SS muscle forces without diversion and (2) that RC releases do affect the strain field. Release of both heads of the SS led to statistically significant changes in strain magnitude and direction.

This study aids in the development of a heterogeneous lung model for automobile crashes and blunt trauma. Existing finite element models of the lung tissue are modeled as homogeneous and consider only the parenchyma while ignoring the bronchi. Though the homogeneous model representation is computationally efficient, it cannot capture the true dynamics of the lung tissue. Therefore, in this study, dynamic compression tests were performed on porcine parenchyma at 1.6, 3, and 5 m/s. The maximum load increased from 23.4 to 55.6 N, with the average strain rate varying from 84 to 309 s-1. Strain rate-dependent bilinear material properties were estimated using a genetic algorithm-based inverse finite element (FE)-based optimization. These optimized bilinear parameters of parenchyma and porcine bronchi properties were used to develop an FE model of the heterogeneous lung sample. This heterogeneous model was then validated with test data obtained from dynamic compression tests on heterogeneous lung samples. Finally, a full-organ heterogeneous lung model was developed and studied for both frontal and side-impact scenarios. The side impact simulation revealed a rapid increase in maximum principal strain, ranging from 0.21 to 0.35 within 5.3 ms. In contrast, the homogeneous full-organ model exhibited a more gradual increase in strain, varying from around 0.23 to 0.35 over a span of 15.6 ms. Similarly, a significant difference was observed in the maximum strain rate between the homogeneous (207 s-1) and the heterogeneous models (352 s-1).