Annals of Biomedical Engineering

BackgroundConventional left ventricular assist device ramp metrics are load dependent, obscuring intrinsic myocardial recovery. A mechanistic, patient-specific representation of ventricular mechanics, identifiable from routine clinical data, could provide quantitative indices of intrinsic left ventricular (LV) function for longitudinal recovery surveillance. ObjectiveTo develop and verify a ramp-integrated, patient-specific model of HeartMate 3-assisted LV function that can yield indices of intrinsic myocardial contractility and remodeling. MethodsWe represented LV pressure-volume (PV) behavior with a PV envelope composed of a monotonic passive PV relation (pPVR) and a unimodal active PV relation (aPVR). We developed a parameterization procedure to infer the patient-specific shape of this envelope directly from routine ramp-test data. We then embedded the parameterized envelope within the PSCOPE framework, a hybrid platform that couples a lumped-parameter network to a physical HeartMate 3 flow loop, to reproduce clinical ramp hemodynamics. Percent residuals between simulated outputs and the corresponding clinical measurements verified the implementation of the PV envelope within PSCOPE. ResultsIn three HeartMate 3 recipients, the PSCOPE models reproduced ramp hemodynamics with residuals generally [≤] 20% across pump speeds and measured variables. Cardiac index residuals ranged from 0-18.5%, systemic and pulmonary arterial pressure residuals remained [≤] 18.4%, and pulmonary arterial wedge pressure residuals remained [≤] 20%. The PSCOPE models matched central venous pressure within [≤] 3 mmHg in all cases, although one setting yielded a 33.3% residual due to a low reference pressure. For one patient, the model reproduced ramp hemodynamics at a speed deliberately withheld from PV-envelope parameterization with residuals [≤] 10%, supporting cross-speed generalizability. Patient-specific PV envelopes also revealed clinically meaningful heterogeneity in LV diastolic stiffness, volume threshold for declining systolic function, operating PV points for peak systolic function, and contractile reserve. ConclusionsRamp-integrated parameterization of the monotonic pPVR and unimodal aPVR yields a compact, mechanistic PV envelope that is identifiable from routine clinical data and verifiable within PSCOPE. The resulting indices characterize intrinsic LV function and may enhance longitudinal recovery surveillance and inform LVAD management. Prospective multicenter validation is warranted to confirm the generalizability and clinical utility of this approach.

ObjectivePulsed field ablation (PFA) relies on irreversible electroporation to create nonthermal cardiac lesions, yet real-time indicators of electroporation progression and validated lethal electric field thresholds remain limited. This study aimed to develop a bioimpedance-based metric for real-time monitoring of cardiac electroporation, evaluate the impact of myocardial anisotropy under electroporation conditions, and derive waveform-specific lethal electric field thresholds. IntroductionCurrent PFA procedures lack direct intraoperative feedback on lesion formation, and uncertainty remains regarding the role of myocardial fiber orientation in shaping electric field distributions. Because electroporation dynamically alters tissue electrical properties, monitoring these changes during treatment may improve prediction of ablation outcomes. MethodsPFA was delivered to fresh ex vivo porcine ventricular tissue using clinically relevant and energy-matched waveforms with pulse widths from 1 to 100 {micro}s. Inter-burst broadband electrical impedance spectroscopy was performed using a low-voltage diagnostic waveform to quantify burst-resolved impedance changes. Lesions were visualized using metabolic staining, then finite element models incorporating nonlinear electroporation-dependent conductivity were used to compare anisotropic and homogenized electric field distributions. Lethal electric field thresholds were estimated by fitting simulated contours to measured lesion areas and validated using uniform electric fields generated by a parallel electrode array. ResultsAcross all waveforms, impedance measurements showed a rapid initial decrease followed by stabilization, indicating early electroporation saturation. Burst-to-burst percent change in impedance slope provided a consistent, waveform-agnostic metric of electroporation progression. Lesion morphology was not systematically influenced by fiber orientation, and modeling demonstrated that electroporation-induced conductivity increases homogenized tissue anisotropy. Lethal electric field thresholds increased with decreasing pulse width, ranging from 517 {+/-} 46 V/cm (100 {micro}s) to 1405 {+/-} 55 V/cm (1 {micro}s), and were validated under uniform field conditions. ConclusionBioimpedance-assisted monitoring enables real-time assessment of cardiac electroporation, while electroporation-induced homogenization supports simplified modeling and standardized PFA treatment design.

The effects of specific footwear features on biomechanical parameters are often confounded by simultaneous changes in other shoe conditions, making it difficult to identify the isolated effect of material and design properties on relevant biomechanical outcomes. This study aimed to propose a tool, namely the Modular Footwear Setup (MFS), to assess the effects of midsole modifications on lower limb joint kinematics and in-shoe plantar pressure. The MFS uses a micro-hook-and-loop fastening system and a custom alignment device to enable fast, strong, and reliable midsole attachment/detachment to/from the upper. Accuracy and repeatability of the MFS in replicating the biomechanical outcomes of a control shoe featuring the same upper and midsole were tested in 10 healthy participants (5M,5F; age=33.2{+/-}9.2 yrs; BMI=21.5{+/-}2.8 kg/m2). Participants were asked to walk wearing both the MFS and the standard control shoe in three sessions. Kinematics of lower limb joints were measured via inertial measurement units, while capacitive pressure insoles were used to measure in-shoe plantar pressure. Intraclass correlation coefficient (ICC) was used to assess the repeatability of kinematic and pressure measurements between sessions. Statistical Parametric Mapping analysis did not identify significant differences in joint kinematics between conditions. While the MFS exhibited slightly lower peak pressure at the rearfoot, pressure parameters were not statistically different in the other foot regions. The MFS demonstrated good-to-excellent inter-session repeatability (ICC 0.84-0.97) for peak and mean pressure. Participants reported similar levels of comfort and stability in both shoes. The findings of the present study suggest the MFS has the potential to be a reliable and accurate tool for evaluating the effect of midsole features on relevant biomechanical parameters. This modular approach may improve data-driven footwear design by providing a consistent platform for testing the effects of midsole designs and materials across various applications, including therapeutic, safety, and athletic shoes.

Scaling up microvessel culture systems is essential for producing vascularized clinically relevant tissues, yet current platforms offer little guidance on how to preserve flow conditions during scale-up. Here, we present a computational-experimental framework using computational fluid dynamics (CFD) to guide the design and scaling of microvessel bioreactors. Interstitial flow distributions were pre-dicted in two perfusion-based platforms-a permeable insert and a rhomboidal microfluidic chamber-across multiple scaling factors and hydrostatic pressures. CFD identified IF ranges conducive to vascu-logenesis and quantified how geometry and pressure modulate flow uniformity. Scaled-up bioreactors generated microvessel networks with consistent morphology and connectivity over a 30-fold increase in culture volume, confirming that maintaining equivalent IF ensures reproducible outcomes. The permeable insert platform maintained uniform IF across scales, while the rhomboidal chamber produced spatially varying IF resulting in heterogeneous but physiologically relevant networks. These findings establish CFD as a predictive tool for rationally scaling perfusion bioreactors, enabling microvessel production at clinically relevant scales with controllable morphology. Significance StatementScaling up microvessel bioreactors is critical for engineering large pre-vascularized tissues. However, larger scales may disrupt flow conditions that drive vessel formation. This study demonstrates that computational fluid dynamics (CFD) can predict interstitial flow and guide the rational scale-up while preserving the vasculogenic microenvironment. Experiments across 30+-fold size increase confirmed that matching inter-stitial flow results in morphologically identical microvessel networks. By linking simulation-based design with experimental validation, this work establishes CFD as design tool for scalable perfusion bioreactors for production of microvessel networks at clinically relevant scales.

A passive device that attaches to the feet, called an exotendon, can reduce the energetic cost of running at moderate speeds, but its efficacy and optimal design parameters at higher speeds are unknown. Identifying optimal parameters at new speeds experimentally would require many experimental trials with different exotendon designs, which is challenging for participants at higher running speeds. We developed a muscle-driven simulation framework to predict the effect of various exotendon designs on the energetic cost of running at an experimentally untested speed (4 m/s). We used these predictions to select four designs, which we evaluated experimentally as users ran at this speed. The framework correctly predicted that an exotendon that reduced energetic cost at 2.7 m/s would also reduce energetic cost at 4 m/s (10% predicted vs. 5.7% measured) and that a short, stiff exotendon and a long, compliant exotendon would not significantly reduce energetic cost. However, exotendon parameters predicted by the simulation to maximize energetic savings did not significantly reduce energetic cost when evaluated experimentally. There was variability between participants in both the magnitude of maximum energy savings and the exotendon condition associated with those savings. In a 5-km time trial performed with and without the exotendon condition that elicited the largest energy savings for each participant during the experiment, we observed a lower average heart rate (-3.9 {+/-} 3.8 beats/min; P=0.03; mean {+/-} standard deviation) and increased cadence (15.9 {+/-} 9.6 steps/min; P=0.002) when participants ran with the exotendon but did not observe a statistically significant difference in finishing time (-13.5 {+/-} 24.6 sec; P=0.3). These results demonstrate exotendons can reduce energetic cost across multiple running speeds and that predictive simulations provide a framework for guiding experiments to evaluate assistive device designs. Author summaryDesigning assistive devices that help people move more efficiently usually requires many experimental trials. These studies can be time-consuming and physically demanding, especially when testing multiple device designs. In this study, we explored whether computer simulations could help guide the design of an assistive device for running called an exotendon. The exotendon is a simple elastic band that connects the feet and can help runners use less energy. Previous experiments showed that the device reduces the energy needed to run at moderate speeds, but it was unclear whether it would also work at faster speeds or which design would lead to energetic savings. We first used simulations of human running to test many possible exotendon designs at a faster speed. These simulations allowed us to identify promising designs before conducting experiments. We then tested a small number of these designs with runners. The experiments confirmed that the exotendon can reduce the energy required to run at faster speeds, although the efficacy of different designs varied between individuals. Our results show that computer simulations can help researchers rapidly evaluate a variety of assistive device ideas and focus experimental testing on the most promising designs.

PurposeLarge quantities of human pluripotent stem cells (hPSCs) are required for clinical applications. 3D suspension cultures are suitable for large scale manufacturing of hPSCs but yield, viability and quality are affected by the hydrodynamic environment. This paper characterizes the hydrodynamic environment inside vertical wheel bioreactors (VWBRs) as a function of size and agitation rates, measures its effect on cell aggregation and proliferation, and proposes the use of Lagrangian-based shear stress and energy dissipation rate (EDR) exposures to support scale-up. MethodsIn silico: Transient, 3D, turbulent flow simulations are conducted for two VWBR sizes (100, 500 mL) at five agitation rates between 20 and 80 rpm. Trajectories of cell aggregates of sizes from 200 to 1,000 microns are calculated, and shear stress and EDR exposures are collected along these trajectories. In vitro: ESI-017 hPSCs were cultured in VWBRs for 6 days. Aggregation efficiency and daily fold ratios were calculated based on cell counts and initial inoculation density. ResultsAggregate size, agitation rate and bioreactor size modulate cell aggregate exposures to EDR and shear stress, which significantly depart from maximum or volume average metrics used for scale-up. Combined in vitro/in silico results show EDR affects aggregation efficiency, cell counts and aggregate size, and has a small effect on daily fold ratios but a significant effect on total fold ratio. ConclusionHistory of trajectory-based cell aggregate exposures to EDRs provide a better scale-up basis for VWBRs than volume-averaged EDR. Shear stress does not significantly affect hPSC aggregation, proliferation and expansion in VWBRs under the tested conditions.

This study investigates closed kinematic chain biomechanics in cycling with a focus on knee joint loading. Data from 16 cyclists collected on a standardized ergometer were analyzed in OpenSim using inverse dynamics, static optimization, and joint reaction analysis. To keep the pipeline consistent across all subjects, the report summarizes right-knee outputs over a steady-state interval between 120 and 124 s. Peak knee joint moments ranged from 15.79 to 44.85 Nm (mean 30.49 {+/-} 7.66 Nm), while peak resultant knee reaction forces ranged from 1187.61 to 3309.04 N (mean 2317.19 {+/-} 728.19 N). Static optimization showed strong contributions from the rectus femoris and vastus lateralis during power production, with additional stabilization from the biceps femoris long head and gastrocnemius medialis. Mean peak muscle activation was highest for the rectus femoris (0.72 {+/-} 0.19), followed by the biceps femoris long head (0.66 {+/-} 0.20). Mean peak muscle force was highest for the vastus lateralis (1078.1 {+/-} 305.8 N) and rectus femoris (994.1 {+/-} 379.2 N). The results confirm substantial inter-subject variability in knee loading and support the use of personalized training or rehabilitation strategies when cycling is used for performance development or joint recovery.

Optimizing assistive wearable devices is crucial for their efficacy and user adoption, yet state-of-the-art methods like Human-in-the-Loop Optimization (HILO) and biomechanical modeling face limitations. HILO is time-consuming and often restricted to optimizing control parameters, while inverse dynamics assumes invariant kinematics, which is unreliable for adaptive human-device interaction. Predictive simulation offers a powerful alternative, enabling computational exploration of design spaces. However, existing approaches often lack systematic optimization frameworks and rigorous validation against experimental data. To address this, we developed a Design Optimization Platform that integrates predictive simulations within a two-level optimization structure for personalizing assistive device design. This paper primarily validates the platforms predictive simulations against a publicly available dataset of the passive Biarticular Thigh Exosuit (BATEX), assessing its reliability. Our findings show that the model can sufficiently predict the kinematics and major muscle activations, except for the pelvis tilt and some biarticular muscles. The key finding is that successful identification of personalized optimal BATEX stiffness parameters needs acceptable prediction of metabolic cost trends, not their precise values. Our analysis further reveals that the models accuracy in predicting Vasti muscle activation in the baseline condition is a significant indicator of its success in predicting metabolic cost trends. This demonstrates that accurate prediction of performance trends is more important for effective simulation-based design optimization than perfect biomechanical accuracy, advancing targeted and efficient assistive device development.

Head rotation is the leading cause of diffuse brain injuries from cycling accidents, with severe, long-term or even fatal consequences. Here, we present a novel helmet safety technology, the Release Layer System (RLS), designed to enhance conventional helmets and reduce the likelihood of such injuries. RLS is located on the outer side of the helmet and thus gets impacted first. The force of the impact activates a rolling mechanism triggering the release of an outer polycarbonate panel, thereby dispersing and transforming a substantial portion of the incident rotational energy. To evaluate the effectiveness of the technology, we conducted oblique impact tests on three popular helmet types, in conventional and RLS-equipped configurations, at three impact locations. RLS-equipped helmets reduced Peak Angular Velocity (PAV) by 57-66%, averaged across impact locations, compared to their conventional counterparts. This corresponds to a 68-86% reduction in the probability of an AIS2+ brain injury, as estimated by the Brain Injury Criterion. The most notable improvement was observed at the pYrot location (front impacts, mid-sagittal plane), with up to 85% PAV reduction. Testing across headforms further demonstrated the effectiveness of the technology in mitigating head rotation irrespective of variations in evaluation setups. This work introduces a novel mechanism for rotational impact mitigation and provides evidence of its potential benefits compared with conventional helmets. As an outer-layer approach, RLS may offer an alternative pathway for managing rotational kinematics in future helmet designs.

Intervertebral disc degeneration (IVDD) compromises disc structures and mechanics, yet systematic evaluations of the mechanical responses and their relationship to morphological changes in preclinical models remain limited. This systematic review and meta-analysis synthesized mechanical and morphological alterations following experimental disc injury in in vivo animal models. Searches of MEDLINE, EMBASE and Web of Science databases were conducted in accordance with PRISMA guidelines. Study quality and risk of bias were assessed using modified CAMARADES and SYRCLE tools. Twenty-eight studies were included. Pooled analyses showed significant reductions in stiffness, Youngs modulus, and disc height, and significant increases in range of motion and degeneration grade, indicating both mechanical and structural deterioration. Youngs modulus appeared to be the most sensitive marker of functional degeneration. By contrast, creep and other viscoelastic responses showed non-significant changes. High heterogeneity was evident across studies, reflecting variability in injury models, species, timepoints, and testing methods. Evidence of publication bias was detected in several domains, and moderate methodological quality was noted with overall insufficient blinding and lack of sample size calculations. In vivo animal models of IVDD demonstrate robust and consistent mechanical and morphological degeneration after injury. Youngs modulus is a sensitive mechanical indicator, supporting its use in future preclinical research. Standardization of outcome definitions, methodology, and reporting is essential to improve comparability and enhance translation of preclinical findings to clinical research.

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

Digital volume correlation (DVC) has become the benchmark experimental technique for full-field strain measurement in bone mechanics. In our previous work we developed a novel data-driven image mechanics (D2IM) approach that learns from DVC data and predicts displacement fields directly from undeformed X-ray computed tomography (XCT) images, deriving strain fields from such predictions. However, strain fields derived through numerical differentiation of displacement fields amplify high-frequency noise, and regularization techniques compromise spatial resolution while incurring substantial computational costs. Here we propose the upgrade D2IM-Strain to predict strain fields directly from XCT images of bone. Two prediction strategies were compared: displacement-derived strain and direct strain prediction. The direct strain prediction model significantly improved accuracy particularly for strain magnitudes below 10000{micro}{varepsilon}, taken as a representative threshold value for bone tissue yielding in compression. In addition, the direct approach reduced false-positive high-strain classifications by 75%. By eliminating numerical differentiation, the approach reduces noise amplification while maintaining computational efficiency. These findings represent a critical step toward developing robust data-driven volume correlation methods for hierarchical materials.

Spectral similarity is often judged with a single metric such as RMSE, yet this can be misleading: physically different errors can produce similar scores. This is a critical limitation for computational biomechanics, where spectral agreement underpins both model validation and machine-learning loss design. Here, we develop a multi-metric framework for objective spectral biofidelity and test whether it better captures meaningful disagreement across complex frequency-domain responses. We evaluated 12 complementary similarity metrics, including CORA and ISO/TS 18571, using controlled spectral perturbations that mimic common real-world deviations such as resonance shifts, localized spikes, and broadband tilts. We then applied the framework to an SBI-tuned finite-element middle-ear model to assess convergence with training dataset size and robustness to measurement noise across repeated stochastic runs. No single metric performed reliably across all distortion types. Shape-based metrics tracked resonance morphology but could miss vertical scaling, whereas MaxError remained important for narrowband anomalies that smoother metrics underweighted. CORA and ISO 18571 did not consistently outperform simpler metrics. Rank aggregation using Borda count provided a robust consensus across metrics, enabling objective identification of training-data saturation and noise thresholds beyond which similarity rankings became unstable. These results show that spectral biofidelity cannot be reduced to a single norm. A multi-metric consensus provides a clearer and more physically meaningful basis for comparing experimental and simulated spectra, and offers a more defensible foundation for data-fidelity terms in physics-informed and simulation-based machine learning.

Wearable devices for gravity balancing have high potential for impact across domains, including neuromotor rehabilitation and occupational systems. Devices made from compliant mechanisms, optimized to achieve specific compensation moments at target joints, have proven effective, but thus far have solely been optimized towards gravity compensation and not other wearability criteria. In this work, we propose a multi-objective optimization framework, based on particle swarm optimization, to design a soft, gravity balancing shoulder orthosis, while taking into account wearability constraints such as undesired loading directions and device size. Using this custom framework, we pursued multiple stages of orthosis design and optimization, selecting multiple solutions to be translated to real-world prototypes. These solutions were realized via 3D printing with thermoplastic polyurethane and evaluated for mechanical performance on benchtop and in-vivo. In-vivo testing on 6 healthy individuals demonstrated relative reductions in muscle activity for the anterior deltoid and upper trapezius, by 53 % and 71 % respectively when operating the orthosis for static tasks within functional shoulder ranges of motion. Changes in muscle activation were also were observed across other muscles, including the posterior deltoid, as well as in dynamic tasks at different speeds.

Prosthetic devices balance functionality and usability to support activities of daily living (ADLs). However, many designs rely on rigid end effectors that, while anthropomorphic in form, lack biomimetic design principles. This mismatch increases cognitive and physical burden, reducing adoption rates. We developed the Human-inspired Actuator Modeling and Reconstruction (HAMR) process, a user-centered framework informed by individual morphology and functional needs, to generate customized agonist/antagonist tendon-actuated end effectors. Using HAMR, we created the Tendon Actuated Prosthetic Hand (TAPH), which integrates human-derived geometry with adaptive force distribution to promote natural object interaction. In a study with 12 participants without limb difference, TAPH was compared to a structurally similar tendon-actuated hand with generalized anthropomorphic geometry across three ADL tasks of varying complexity. TAPH significantly improved task performance and reduced physical effort, mental workload, and frustration, particularly during gross motor tasks. For fine motor tasks, performance improved under stable conditions but not during tasks requiring dynamic precision and continuous coordination. These findings highlight the functional benefits of biologically informed prosthesis design and support biomimetic principles in enhancing performance and user experience.

ObjectiveTo investigate the effects of different gait patterns on knee joint biomechanics and dynamic stability during stair ascent. MethodsFourteen healthy males were recruited to ascend stairs using two distinct gait patterns: the "single-step" (leading with the same leg) and "cross-step" (alternating legs) strategies. Kinematic and kinetic data were collected synchronously using a Qualisys infrared motion capture system and a Kistler 3D force plate. Dynamic stability was quantified using the Margin of Stability (MOS), and knee joint biomechanics were evaluated using Patellofemoral Joint Stress (PFJS) and other relevant metrics. ResultsThroughout the gait cycle, there was no significant difference in the Medio-Lateral (ML) MOS between the single-step and cross-step patterns (P=0.318). However, in the Anterior-Posterior (AP) direction, the MOS for both patterns remained negative and decreased over time, with the cross-step pattern exhibiting significantly lower AP MOS values than the single-step pattern (P=0.002). At the moment of left foot-off, significant differences were observed in the right knee joint angle, right knee joint moment, net joint moment, effective quadriceps muscle lever arm, Quadriceps Force (QF), the angle between the quadriceps tendon and patellar ligament, Patellofemoral Joint Force (PFJF), patellofemoral joint stress, and patellofemoral contact area (all P<0.001). ConclusionsDuring stair ascent, the cross-step pattern reduces body stability, thereby increasing the risk of backward falls. Furthermore, this pattern increases patellofemoral joint stress, subjecting the knee to greater loading. Therefore, it is recommended to enhance lower limb muscle strength through targeted training to reduce fall risk. Additionally, adopting a more cautious gait strategy (such as the single-step pattern) can help minimize patellofemoral joint loading and mitigate the risk of patellofemoral pain.

ObjectivesEpidural spinal cord stimulation (SCS) is an emerging therapy for motor rehabilitation following spinal cord injury (SCI) and other motor disorders. Conventionally, SCS leads are placed along the dorsal spinal cord (SCSD), where stimulation activates large diameter afferent fibers, which indirectly activate motoneurons through reflex pathways. This leads to broad activation of flexor and extensor muscles and limited fine-tuned control of motor output. Targeting the ventral spinal cord (SCSV) may enable more direct activation of motoneuron pools, potentially improving the specificity of muscle activation; however, there is currently no established method to place leads ventrally. To address this, we evaluated the feasibility of four modified percutaneous implantation techniques to target the ventrolateral thoracolumbar spinal cord. Materials and methodsPercutaneous SCSV implantation was performed in three human cadaver torso specimens under fluoroscopic guidance. The following approaches were evaluated: sacral hiatus, transforaminal, interlaminar contralateral, and interlaminar ipsilateral. The leads in the latter 3 approaches were inserted between L1 and L5. Eighteen implants were attempted, with nine leads retained for analysis. Lead and electrode position were assessed using computed tomography (CT) with three-dimensional reconstruction, along with anatomical dissection to verify lead and electrode placement within the epidural space. ResultsSuccessful ventral epidural lead placement was achieved using all four implantation approaches. The sacral hiatus (16/16 electrodes) and transforaminal (8/8 electrodes) approaches resulted in exclusively ventrolateral placement. The interlaminar contralateral approach led to 27/32 electrodes positioned ventrolaterally and 5/32 dorsally. The interlaminar ipsilateral implantation approach led to 14/32 electrodes positioned ventrolaterally and 18/32 positioned ventromedially. ConclusionsThese findings demonstrate that ventral epidural SCS lead placement can be achieved using modified percutaneous implant techniques. The four approaches outlined here provide a clinically feasible pathway to SCSV and establishes a foundation for future clinical studies investigating SCSV for motor rehabilitation following SCI.

Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fishs size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish-turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

Precision-cut lung slices (PCLS) retain the native cells and extracellular matrix that contribute to the structural and functional integrity of lung tissue. This technique enables the study of cell-matrix interactions and is particularly useful for pre-clinical pharmacological studies. More specifically, PCLS are widely used to model the complex pathophysiology of pulmonary fibrosis, an uncurable and progressive interstitial lung disease. Current ex vivo pulmonary fibrosis models expose PCLS to pro-fibrotic biochemical cues over a short timeframe (hours to days) and quickly collect samples for analysis due to viability concerns. This condensed timeline is a limitation to understanding chronic disease mechanisms. To extend the utility of ex vivo pulmonary fibrosis models, PCLS were embedded in engineered hydrogels and exposed to pro-fibrotic biochemical and biophysical cues. Hydrogel-embedded PCLS maintained greater than 80% total cell viability over 3 weeks in culture. Gene expression patterns in samples exposed to pro-fibrotic cues matched trends measured in human fibrotic lung tissue. Finally, treatment with Nintedanib, a Food and Drug Administration approved pulmonary fibrosis drug, moderately reduced fibroblast activation and influenced epithelial cell differentiation. Collectively, these results show that hydrogel-embedded PCLS models of pulmonary fibrosis extend our ability to study fibrotic processes ex vivo and, when applied to human tissues, present a new approach methodology for studying lung disease and treatment.

BackgroundLarge Language Models (LLMs) have demonstrated expert-level performance across many medical domains, suggesting potential utility in clinical practice. However, their reliability in the highly specialized domain of moderate hyperthermia (HT) remains unknown. We therefore evaluated the performance of three modern LLMs in answering HT-related questions. MethodsWe conducted an evaluation study by posing 40 open-ended questions--22 clinical and 18 physics-related--to three modern LLMs (DeepSeek-V3, Llama-3.3-70B-Instruct, and GPT-4o). Responses were blinded, randomized, and evaluated by 19 international experts with either a clinical or physics background for quality (5-point Likert scale: 1=very bad, 2=bad, 3=acceptable, 4=good to 5=very good) and for potential harmfulness in clinical decision-making. ResultsA total of 1144 quality evaluation responses were collected. Overall reported mean quality scores were similar across models, with DeepSeek scoring 3.26, Llama 3.18, and GPT-4o 3.07, corresponding to an "acceptable" rating. Across expert evaluations, responses were considered potentially harmful in 17.8% of cases for DeepSeek, 19.3% for Llama, and 15.3% for GPT-4o. Notably, despite "acceptable" mean scores, approximately 25% of responses were rated "bad" to "very bad," and potentially harmful answers occurred in [~]15-19% of evaluations, indicating a non-trivial risk if used without domain expertise. ConclusionOur findings indicate that the performance of LLMs in HT in versions available at the time of investigation is only partially satisfactory. The proportion of poor-quality responses is too high and may lead non-domain experts to misinterpret the available clinical evidence and draw inappropriate clinical conclusions.