Frontiers in Bioengineering and Biotechnology

BackgroundCommercially-available microprocessor-controlled prosthetic knees are unable to fully replicate the biomechanical function of the missing biological limb. While powered prostheses have the capacity to restore joint level kinetics, current systems rely on intrinsic control schemes that do not allow the user to volitionally modulate movement under neural commands. This limitation may compromise functional performance and hinder prosthetic embodiment, the sense that the device is part of the users body. In a case study on one test participant, we evaluate the functional and perceptual benefits of a bone-anchored, neurally-controlled knee prosthesis by comparing it to the participants microprocessor-controlled prosthesis. MethodsWe conducted a within-subject study on an individual with a transfemoral amputation, with an osseointegrated implant and surgically reconstructed agonist-antagonist muscle pairs. We tested a neurally-controlled powered knee and conventional microprocessor knee across a set of activities, including seated volitional control tasks, sit-to-stand transitions, squatting, level-ground walking, stair ascent, and uninstructed standing. Performance metrics included knee kinematics, prosthesis-generated mechanical power, and functional outcomes such as gait speed, stair ascent time, and weight-bearing symmetry derived from ground reaction forces. Functional mobility and control were complemented by self-reported embodiment, assessed through a questionnaire targeting agency, ownership, and body representation. ResultsThe neurally-controlled prosthesis enabled intuitive and responsive control. Compared to the subjects prescribed prosthesis, the prosthesis yielded improved temporal gait symmetry during walking (symmetry index: 0.93 vs. 0.59, with 1 indicating perfect stance time symmetry), increased prosthetic-side weight-bearing during sit-to-stand and squatting, and successful execution of a step-over-step stair ascent strategy--an outcome not achievable with the subjects prescribed device. Embodiment scores were consistently higher with the neurally-controlled prosthesis compared to the prescribed device across multiple domains, including agency, ownership and body representation. ConclusionsThis study is the first to directly compare a prescribed microprocessor knee with a bone-anchored, neurally-controlled powered prosthesis. By combining osseointegration, surgically reconstructed agonist-antagonist muscle pairs, and powered actuation, the system improved gait symmetry, greater prosthetic-side loading, and step-over-step stair ascent. These results demonstrate the novelty and promise of integrating surgical and mechatronic innovations to restore both functional mobility and embodied control after transfemoral amputation. Trial registrationThis study was approved by the Institutional Review Board at MIT (Protocol No. 2503001589).

Unexploded ordnances (UX.Os) and landmines endanger lives and hinder the economic progress of communities living in post-conflict zones. Currently, the primary method for clearing UX.Os relies on metal detection and manual removal of UX.Os - an expensive, time-consuming, and hazardous process. This study, derived from the 2024 EPFL iGEM project SYNPLODE, presents a new approach that integrates synthetic biology and aerial drone robotics, proposing a novel, end-to-end, safe, and efficient solution to address UX.Os. Starting from bacteria engineered to detect and degrade 2,4,6-trinitrotoluene (TNT), a common explosive in landmines, our solution is designed for three main tasks: detecting TNT and RDX, breaking these compounds down into non-explosive byproducts, and confirming explosive neutralisation. To deploy this solution safely in UXO-contaminated areas, we designed, built, and tested an aerial drone capable of spraying explosive-degrading bacteria. Combining synthetic biology, robotics, mathematical modelling, and affected community engagement, our solution aims to improve UXO and landmine clearance by offering a scalable and cost-effective approach for deactivating UX.Os without risking human lives.

Mammalian cell lines are the preferred hosts for producing commercially relevant therapeutic proteins such as antibodies, multispecifics, and cytokine fusion proteins. Even though significant investment is made to optimize upstream and downstream processes, the optimal gene design parameters for heterologous recombinant protein expression remain poorly understood. We describe here a generic approach to gene optimization in which design features are systematically sampled and modulated iteratively using machine learning (ML). Synthetic genes encoding the Dasher fluorescent protein, differing only in synonymous codons, were used to interrogate the gene-sequence preferences of transient antibody-expressing HEK293 cells. Synonymous codon variations influenced expression by more than two orders of magnitude. This variation in protein yield was used to build ML models relating gene design features, which were then employed to design further-improved genes. The ML models were shown to be expression system-specific. Messenger RNA levels and ribosome occupancy were highly correlated with protein levels, suggesting that mRNA lifetime has a causal relationship with coding bias. Our results illustrate a novel, generally applicable method to improve gene expression via synonymous re-coding for any protein target or host cell.

Gene therapy using adeno-associated virus (AAV) vectors shows promise for cancer treatment through molecular intervention, yet achieving sufficient and targeted delivery to brain tumors via systemic administration remains limited by the biological barriers. Here, we investigate whether microbubble-enhanced focused ultrasound (MB-FUS) improves targeted delivery of systemically administered AAV9 to orthotopic gliomas, using quantitative PET imaging of 64Cu-radiolabeled AAV9 vectors and fluorescent reporter expression to assess biodistribution and functional efficacy. At 21 hours after injection, 64Cu-AAV9 accumulation was 3.2-fold higher in FUS-treated tumors compared to non-FUS-treated tumors (n=3, p=0.004). Quantitative PCR analysis of tumor tissue at the same timepoint confirmed a 6.4-fold increase in genome copies in FUS-treated tumors (p=0.0003). The enhanced vector delivery translated to a 5.3-fold increase in optical reporter protein expression in FUS-treated compared to control tumors (p=0.0002) at 17 days post-treatment. These results establish that MB-FUS enables spatially-targeted AAV delivery with quantifiable enhancement in both acute vector biodistribution and downstream transgene expression. The integration of radiolabeled AAV with PET imaging provides a non-invasive methodology for real-time assessment of vector delivery and optimization of treatment protocol for brain cancer gene therapy. HighlightsO_LIMB-FUS enables targeted systemic AAV delivery to brain tumors. C_LIO_LIMB-FUS enhanced vector delivery translates to increased transgene expression in gliomas. C_LIO_LIPET imaging of radiolabeled AAV allows non-invasive tracking of gene therapy vectors. C_LIO_LIReal-time imaging validates spatially-controlled gene delivery for brain cancer. C_LI

Efficient production of human proteins for the development of tool compounds and biologics depends on a detailed understanding of the protein expression machinery in mammalian cells. Codon optimization is widely believed to enhance protein yield, yet its impact in homologous mammalian systems remains poorly defined. Here, we systematically compare five codon usage strategies reflecting common assumptions about rare codons, RNA stability, and synthesis efficiency. We developed pTipi, an efficient open-source mammalian expression vector, and evaluated its performance in antibody production. We generated plasmids for common epitope tag antibodies such as V5, anti-biotin and anti-His for distribution by Addgene. To compare codon usage schemes, we performed a bake-off of 18 human and murine Wnt pathway glycoproteins in mammalian cells. Small-scale expression screens revealed that codon optimization did not provide a general advantage over native coding sequences, while strategies prioritizing RNA stability consistently reduced expression. Interestingly, a skewed codon scheme using the most abundant codons produced yields comparable to native sequences and occasionally enhanced protein output. To enable flexible evaluation of codon strategies, we implemented a Golden Gate-compatible pTipi platform for efficient synthetic gene incorporation. We conclude that native codons are sufficient for robust homologous mammalian expression of glycoproteins, while selective codon skewing can be beneficial for some targets.

BackgroundPersonalized computational neuromusculoskeletal models have great potential for optimizing the design of clinical treatments for movement impairments. While many software tools address specific parts of the model personalization and treatment optimization processes, they typically require significant programming experience to use and do not cover the full breadth of these two processes. Furthermore, published neuromusculoskeletal modeling studies typically do not provide all of the minute methodological details needed for others to reproduce the work. Consequently, researchers seeking to develop skills in the model personalization and treatment optimization processes face a steep learning curve due to the lack of detailed training materials that demonstrate both processes for real-life clinical problems using real-life subject movement data. MethodsThis article presents detailed training tutorials for the model personalization and treatment optimization processes using two real-life clinical problems and the Neuromusculoskeletal Modeling (NMSM) Pipeline. The first clinical problem involves the design of personalized gait modifications and high tibial osteotomy surgery for an individual with bilateral medial knee osteoarthritis, where the goal is to reduce the peak adduction moment in both knees to a specified target level. The second clinical problem involves the design of a synergy-based functional electrical stimulation prescription for an individual post-stroke with impaired walking function, where the goal is to equalize the propulsive and braking impulses between the two legs. Both tutorials were evaluated as course projects given to novice users in a combined undergraduate/graduate mechanical engineering course. ResultsBoth tutorials produced personalized neuromusculoskeletal models and associated dynamically consistent tracking optimizations that closely reproduced subject-specific experimental joint angles, joint moments, ground reaction forces and moments, and (if applicable) muscle activations measured during walking. Subsequent design optimizations predicted personalized treatments that achieved target values of peak knee adduction moments or propulsive and braking impulses. ConclusionsThe detailed step-by-step tutorials presented with this article are the first to walk users step-by-step through the entire process of creating personalized neuromusculoskeletal models and then using them to design personalized treatments for clinical problems. These tutorials can be used to introduce new users to the NMSM Pipeline and as projects in neuromusculoskeletal modeling courses.

During development, the size of the neuroepithelial cell pool plays a key role in establishing brain size, determining the numbers of derived progenitors and subsequent neuronal cell types. While early histogenesis is well modelled in brain organoids, the organ-scale geometry of the telencephalon is not accurately recapitulated. Herein, we present a new approach for generating ventral and dorsal forebrain organoids which develop a large ventricular neuroepithelium, characteristic of the closed telencephalic vesicle. Using a growth medium that supports aerobic glycolysis and is typically used for endothelial cells, we modulate neuroepithelial expansion to induce a more anatomically accurate neuroepithelial layer which, upon maturation, thickens physiologically to generate the typical neurogenic layered architecture. In addition, we present a new method for embedding organoids in miniature collagen spheres which mimics native extracellular matrix, stabilizes the ventricular geometry for dynamic culture conditions, and provides a means for incorporating vascular cells for neurovascular development. Finally, we demonstrate that human organoids grown under these conditions exhibit dramatically enlarged ventricles and delayed maturation compared to mouse. Together, this approach provides a model of the forebrain neuroepithelium with morphogenetic macroscale geometry and tissue architecture, suitable for investigating neurodevelopment and disease.

Recent research has emphasized the critical role of cell state transitions in tissue homeostasis. In lung biology, transitional cells are recognized as a feature of tissue-scale processes during both normal physiology and disease. The precise way that transitional cell states emerge and are regulated remains to be determined. Engineered tissues, built in a laboratory through bioengineering approaches, allow detailed study of cellular states that are not commonly found in native biology, and allow opportunities to directly induce and manipulate cellular transitions. The following study explores and characterizes epithelial cell states that emerge via cellular reprogramming in a tissue engineering context. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/701406v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1d456f3org.highwire.dtl.DTLVardef@1989ba5org.highwire.dtl.DTLVardef@127dd7org.highwire.dtl.DTLVardef@3dd7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Vertical ground reaction force (vGRF) profiles during walking typically exhibit a double-peaked structure with a mid-stance trough, yet the mechanical conditions governing this morphology remain incompletely defined. In this study, we examined how the balance between push-off and collision impulses during the step-to-step transition influences the temporal and structural characteristics of the vGRF trajectory. Empirical relationships describing push-off and collision work were used to compute transition impulses across walking speeds ranging from 0.8 to 1.4 m{middle dot}s{square}1. A normalized Impulse Balance Index (IBI) was defined to quantify the relative dominance of push-off and collision impulses. The temporal position of the mid-stance trough was quantified using a Trough Deficit Index (TDI) derived from quadratic fits of the vGRF trajectory. Across walking speeds, push-off and collision variations produced step-to-step active work performance imbalance. Push-off and collision became approximately balanced near 1.2 m{middle dot}s{square}1, corresponding to the mechanically preferred walking speed. Deviations from this balanced condition were associated with systematic shifts in trough timing: the trough occurred 1.83% and 1.56% earlier in stance at 0.8 and 1.0 m{middle dot}s{square}1, respectively, and 1.31% later at 1.4 m{middle dot}s{square}1 relative to the reference speed. TDI exhibited a strong inverse relationship with impulse balance (IBI), indicating that vGRF morphology is tightly coupled to the mechanical balance of the step transition. A simplified pendular model further demonstrated that active torque, representing work, during single support shifts the quadratic vertex of the force trajectory by approximately 48.6-51.1% of stance, consistent with the observed trough timing variations. These results show that vertical GRF morphology reflects the imbalance between push-off and collision provides a simple signal of step-to-step transition mechanics, that may be used for rehabilitation and exoskeleton modulation.

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.

This article presents the development of an advanced modeling and simulation platform for carbon capture systems, with a focus on integrated process analysis from upstream CO2 capture through to bioethanol production. The platform supports the evaluation of CO2 mitigation technology by coupling mathematical bioprocess models with an interactive desktop application. The biological system employs Chlorella vulgaris microalgae to fix CO2 through photosynthesis and generate carbohydrate substrates, which are subsequently converted to bioethanol by Saccharomyces cerevisiae yeast via fermentation. The simulation integrates three established kinetic models--the Monod, Logistic, and Luedeking-Piret models--to predict biomass growth, substrate consumption, and ethanol yield under varying operational conditions. A closed-loop CO2 recycling subsystem captures fermentation off-gases and reintroduces them into the bioreactor, enhancing overall carbon utilization efficiency. Three representative simulation scenarios demonstrated process efficiencies ranging from 1.09% to 93.78% of the theoretical maximum CO2-to-ethanol conversion efficiency, confirming the platforms capacity to evaluate a wide operational envelope. The Electron/React-based desktop application provides real-time visualization, interactive 3D bioreactor models, and a simulation history module, making it accessible to researchers, engineers, and students. The platform serves as a digital twin that bridges rigorous bioprocess mathematics with intuitive user interaction, providing a cost-effective tool for designing and optimizing sustainable carbon capture and biofuel production systems.

The arteriovenous fistula (AVF) is the preferred method of vascular access for hemodialysis; however, 30-50% of AVFs undergo primary failure and are unsuitable for clinical use. As disturbed hemodynamics initiate endothelial injury and intimal hyperplasia, we designed an endovascular flow-conditioning anastomotic device (FCAD) to directly improve AVF hemodynamics and protect the anastomotic region. Using computational fluid dynamics, we characterized the flow field and wall shear stress (WSS) profiles in idealized AVF models with and without the FCAD. Incorporation of the FCAD into a brachiocephalic AVF model reduced regions of oscillatory WSS and generated a symmetrical flow profile in the draining vein compared to a reference AVF. Parametric studies also identified an FCAD geometry with a tab angle, height, and aspect ratio of 30{degrees}, 0.1 diameters, and 1.0 restored time-averaged WSS along the inner venous wall, achieving a physiological level without inducing regions of oscillatory flow throughout the cardiac cycle. Similar findings were observed with an in vitro model using particle imaging velocimetry. This study demonstrates the feasibility of the FCAD to normalize venous flow and WSS while imposing minimal resistance to blood flow. Restoring physiological WSS levels on the venous wall is expected to preserve endothelial function and improve AVF maturation.

Pigs and chickens are not only the most important livestock species for global food production but also serve as key model organisms in various research disciplines. The pig is widely used in translational research due to its anatomical and physiological similarity to humans, providing valuable insights into immunology, metabolism, and disease mechanisms. In contrast, the chicken has become an essential model for studies related to poultry health, animal welfare, and developmental biology. Its externally developing embryo offers exceptional accessibility for experimental manipulation. Recent advances in genome editing technologies, particularly CRISPR/Cas9, have further expanded the potential of these species for functional genomic studies, although the efficient delivery of such tools remains a major challenge. By using virus-like particles (VLPs), we have been able to overcome this limitation. Here, we evaluated VLPs as delivery vehicles for genome engineering tools in pigs and chickens, two key livestock species at the human-animal interface. VLP-mediated delivery enabled efficient Cre recombination and high CRISPR/Cas9 editing rates in porcine cells, organoids, and oocytes, particularly when multiplexed. In chickens, VLPs supported robust Cre recombination and Cas9-mediated editing in cell culture, tracheal organ cultures, and in ovo. Reporter VLPs and dCas9 VLPs further demonstrated the versatility of this platform across porcine and avian systems. Together, these findings establish VLPs as an efficient and time-saving strategy for gene editing in livestock, with relevance for animal health, agricultural productivity, and translational One Health research.

Neural feedback is important for healthy control of movement, and multiple neurological disorders (e.g., stroke, cerebral palsy, Parkinsons disease, incomplete spinal cord injury) can be described by how they impair healthy feedback or induce unhealthy feedback. Researchers have created numerous computational neuromusculoskeletal models controlled by simulated neural feedback mechanisms, but these models rarely represent actual human subjects and thus have not found practical application in treating patients with movement impairments. As a step toward designing patient-specific treatments for individuals with neurological disorders, this study used the Neuromusculoskeletal Modeling Pipeline to develop and evaluate a novel synergy-based feedforward (FF)+feedback (FB) model using a personalized, three-dimensional neuromusculoskeletal walking model of an actual human subject post-stroke. Experimental walking data collected from the subject were used to create the subjects personalized walking model. This model was used to calculate lower body muscle activations consistent with the subjects electromyographic, joint motion, and ground reaction data for 5 calibration walking cycles. Nominal FF synergy controls were calculated by averaging the muscle synergies that closely reconstructed the 5 cycles of muscle activations and associated joint moments simultaneously. These nominal FF controls were then scaled by 0, 25, 50, 75, 100, and 125%, and the gap in reproducing individual cycle muscle activations was filled by fitting FB synergy controls as a function of joint positions, velocities, and moments as surrogates for muscle lengths, muscle velocities, and tendon forces. Finally, the six synergy-based FF+FB models controlled the subjects personalized walking model in predictive simulations performed for 3 testing walking cycles withheld from calibration. The 100% FF model (which still had minimal FB) reproduced the testing walking cycles the most closely, and only the 75%, 100%, and 125% FF models generated near-periodic walking motions using initial conditions consistent with experimental values. The 0, 25, and 50% FF models could generate near-periodic walking motions only when the initial conditions were allowed to diverge substantially from experimental values. Our findings suggest that predictive simulations of walking using real experimental data may require a minimum level of feedforward control and sufficient fitting data to predict a subjects actual dynamically consistent motion.

Pathologic vertebral fractures are a major complication in metastatic spine disease. However, current clinical scores, such as Spinal Instability Neoplastic Score (SINS), show limited predictive capability, particularly within the indeterminate range where most clinical uncertainty lies. This study aimed to develop and evaluate quantitative computed tomography (qCT)-based subject-specific finite element (SSFE) models to predict vertebral strength in presence of different metastatic lesion types. Twelve ex vivo human spine segments, each containing one metastatic (n=12) and one adjacent control vertebra (n=12), were scanned using qCT and calibrated using a calibration phantom. Homogenised nonlinear finite element models were developed with spatially heterogeneous, isotropic, density-dependent material properties and loaded under uniaxial compression corresponding to 1.9% apparent strain. Ultimate failure load, stiffness, and strain distributions were compared between metastatic and control vertebrae. Predicted failure load ranged from 0.2 kN to 6.2 kN (mean. {+/-} standard deviation: 1.8 {+/-} 1.6 kN metastatic; 1.7 {+/-} 1.5 kN control), with no statistically significant difference between groups (p > 0.05). Normalised failure load varied widely, reflecting lesion-specific mechanical heterogeneity. Lytic lesions generally weakened vertebrae, whereas mixed and blastic lesions occasionally enhanced strength, likely due to localised sclerosis or reactive bone formation. High compressive axial strains (greater than 0.019) were frequently concentrated near the endplates, particularly in lytic vertebrae. qCT-derived bone mineral density strongly correlated with failure load (R{superscript 2} = 0.74-0.77). These findings highlight the complexity of metastatic vertebral mechanics and demonstrate that qCT-based SSFE modelling provides a quantitative framework for assessing fracture risk, complementing conventional imaging-based tools.

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.

Motor rehabilitation requiring sustained physical exercise faces poor adherence in neurological populations due to insufficient supervision and monotony. While virtual reality and musical biofeedback independently improve engagement and motivation, their comparative and combined impact on intensity control strategies during high-intensity interval training (HIIT) remains unexplored. Thirty healthy adults (16 males, 14 females; mean age 27.5 {+/-} 7.2 years) were sequentially assigned to three feedback modalities (n=10 each) during intensity-guided stationary cycling: visual-only (position-based), musical-only (speed-based), and combined audiovisual (position-based). Participants completed two 9-minute moderate-to-high intensity sessions (Set 1 and Set 2) maintaining pedaling speed within a target speed zone. Performance distinguished control strategy from effectiveness: stability via target zone exits, correction capacity via recovery time and sustained deviations, and overall effectiveness via time in zone. Heart rate (HR) assessed physiological intensity; usability and cognitive workload were evaluated via e-Rubric and NASA-TLX. Distinct regulation strategies emerged. Musical-only showed significantly lower stability (Set 1: 14.52 exits/min vs. 1.48 visual and 1.79 combined; corrected p < 0.0167) but superior correction (0.21s recovery vs. 2.48s and 1.06s; p < 0.0001) with minimal sustained deviations. Combined feedback achieved highest Set 2 effectiveness (98.13% vs. 95.17% time in zone; corrected p < 0.0167) but elevated physical demand (corrected p < 0.0167). HR variability was comparable (p = 0.85), confirming consistent cardiovascular workload despite differing strategies. Satisfaction was high, with slight preference for musical feedback; cognitive workload did not differ. Musical biofeedback promotes reactive control with frequent but rapidly corrected oscillations, maintaining physiological safety and engagement. Visual feedback ensures stable target adherence at the cost of compensatory physical effort. Combined modality offered no synergy, increasing demand without improving effectiveness. Findings reveal a trade-off between stability and correction agility, supporting tailored modality selection: musical feedback suits unsupervised rehabilitation prioritizing engagement, rapid error correction, and sustainable effort, while visual feedback suits supervised protocols requiring stable preventive control and precise adherence quantification. Author summaryMany people undergoing neurological rehabilitation struggle to maintain adherence to high-intensity exercise programs, particularly without direct supervision. While virtual reality and musical feedback have shown promise for improving engagement and motivation, we didnt know which type works best for controlling exercise intensity, or whether combining them would be better. We tested three feedback systems with 30 healthy adults performing stationary cycling: visual-only, musical-only, and both combined. We measured how well participants stayed within target speed and assessed their experience. Musical feedback prompted frequent but instant adjustments--a reactive strategy that was less physically demanding and most enjoyable. Visual feedback kept participants more precisely in the target zone but required significantly more effort. Surprisingly, combining both didnt improve performance and instead increased physical demand. Our results show that different feedback types suit different rehabilitation contexts. Musical feedback may be ideal for unsupervised home-based exercise because it keeps people engaged without requiring exhausting effort. Visual feedback works better when precise control is essential in supervised clinical settings, despite being more demanding. Combining both offers no advantage. These findings help clinicians choose the right feedback approach based on their specific rehabilitation goals.

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.

In "Experiment-free exoskeleton assistance via learning in simulation", Luo et al. [1] present an ambitious framework for developing exoskeleton controllers through reinforcement learning exclusively in computer simulation. The authors report that a control policy trained on a small dataset from one subject was directly transferred to physical hardware, reducing human metabolic cost during walking, running, and stair climbing by more than any prior device. If confirmed, this would represent a major breakthrough for the field of wearable robotics and their clinical applications. However, a close examination of the published materials casts doubt on these claims. The reported experimental results violate physiological limits on the relationship between mechanical power and muscle energy use during gait2,3,4. The algorithmic claims are surprising and cannot be verified; in contrast with established replicability standards in machine learning5,6, executable code has not been made available. We conclude that the goals of this study have not yet been verifiably achieved and make recommendations for avoiding publication errors of this type in the future.

Conventional therapy after stroke focuses on reducing physical impairments. However, the decisions that guide peoples movements may have far-reaching consequences towards recovery. We lack the tools to characterize these decisions. Recently, researchers have created a quantitative behavioral assessment of effort-based decision-making and applied it to some clinical populations. The purpose of this paper is to examine the feasibility of evaluating effort-based decision-making during walking after stroke. We recruited five neurotypical participants in an initial study. We conducted a subjective effort valuation on the neurotypical individuals with and without a knee immobilizer to simulate the biomechanics of reduced knee flexion during post-stroke gait. Participants cleared obstacles of varying heights during overground walking, followed by rating their perceived effort and then completing an effort choice paradigm to calculate subjective effort value. In a second experiment, we recruited five individuals with stroke to perform a similar protocol without an immobilizer during harnessed treadmill walking. We found that rated perceived effort increased monotonically with obstacle height across groups, that individuals could recall obstacle heights without cues, and that subjective effort value increased with knee immobilization in the control group as expected. We conclude that adapting an effort-based decision-making assessment to a walking context in people with stroke is feasible.