Frontiers in Bioengineering and Biotechnology

Background Occupational exoskeletons are used to reduce physical workload and prevent work-related musculoskeletal disorders in physically demanding jobs. Although laboratory studies demonstrate reduced muscle load during simulated manual work tasks, evidence from long-term, real-world implementations remains very limited. The RELAX project aims to investigate the long-term effects of a passive back-support exoskeleton (BSE) during manual order-picking work in a Danish warehouse, focusing on health and socio-economic outcomes. Methods This 18-month controlled in-field intervention study compares outcomes at two warehouse departments: one where workers use a passive BSE and a control group where workers perform work tasks as usual. Approximately 90 full-time workers will be followed during the intervention period with questionnaires, interviews and company-registered performance indicators. Primary outcomes include perceived work intensity and musculoskeletal discomfort, while secondary outcomes include sickness absence, employee turnover, productivity and cost effectiveness. Furthermore, a process evaluation will be conducted based on questionnaires, focus-group interviews, and reported exoskeleton use. Quantitative effects will be analysed using difference-in-difference analysis with generalized linear mixed models to account for repeated measures over time. Employee turnover will be analysed using time-to-event analysis, and qualitative focus-group interviews will be analysed using reflexive thematic analysis to explore implementation processes and contextual factors. Cost-effectiveness and return on investment will be assessed by comparing the investment with potential savings in costs and resource use. Discussion By combining longitudinal quantitative outcomes with qualitative process evaluation, the study seeks to provide ecologically valid evidence on the effectiveness, feasibility and sustainability of occupational exoskeleton implementation. This approach will help clarify whether long-term exoskeleton use improves worker health without compromising productivity and may inform future workplace guidelines and large-scale adoption strategies.

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.

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.

Cervical spinal cord injuries (cSCI) induce profound denervation in respiratory muscles leading to hypoventilation that compromises quality of life. Respiratory neuromuscular electrical stimulation of extra-diaphragmatic muscles (rNMES) could be a non-invasive approach to improve respiratory function following cSCI. However, it is critical to first synchronize rNMES with spontaneous breathing. An Ordinary Differential Equation (ODE) was solved and fitted to experimental breathing signals obtained via plethysmography in ten mice. Optimal stimulation ODE-based parameters were identified for intercostal and abdominal muscle stimulation for breathing-synchronized rNMES training. Feasibility was tested on tolerance to repetitive anesthesia and stimulation for ten training sessions in six mice. The ODE-based breathing signals matched the experimental ones with an average coefficient of determination (R{superscript 2}) of 81%. The developed algorithm, Algostim, provided average theoretical optimal times of 0.12 s for intercostal and 0.32 s for abdominal muscles contraction. Feasibility and tolerance to rNMES were favorable after ten sessions. This innovative mathematical approach to rNMES allows optimal stimulation of respiratory muscles while accounting for spontaneous breathing rate. Algostim established a framework for personalized rNMES therapies, enabling the delivery of standardized stimulation parameters and allowing detailed investigation into the underlying mechanisms of rNMES.

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.

AO_SCPLOWBSTRACTC_SCPLOWMuscle fibre type distribution influences both the metabolic and contractile properties of individual muscles. However, as humans tend to self-optimize their gait pattern to minimize cost of transport, these changes in muscle properties may influence gait biomechanics in manners that are difficult to isolate in in vivo experiments. The purpose of this study was to predict the influence of muscle fibre type distribution on the metabolic cost and biomechanics of simulated walking and running. We implemented a muscle model that could predict recruitment of slow and fast twitch muscle fibres in a framework for predictive musculoskeletal simulation. Subsequently, we employed the framework to investigate how metabolic cost of transport, stride length, stride frequency, and mechanical work performed by slow and fast twich muscle fibres were influenced by fibre type distribution across locomotion speeds from 1.0 to 4.5 m {middle dot} s-1. Our results predict that cost of transport increases as slow twitch area fraction decreases, while stride length and frequency was minimally affected by fibre type distribution at speeds resulting in walking. In contrast, fibre type distribution interacts with locomotion speed at speeds resulting in running. Specifically, we predict the existence of a threshold speed below which cost of transport decreases with an increasing proportion of slow twitch fibres, while cost of transport increases with increasing proportions of slow twitch fibres above it. The shift in fibre type distribution was accompanied by an increase in stride frequency and decrease in stride length. These shifts in spatiotemporal characteristics appear to allow the muscles to operate at speeds close to those that achieve peak mechanical efficiency. Taken together, the results of this study predict that muscle fibre type distribution may influence both the energetics and biomechanics of gait, and that this influence is dependent upon the locomotion speed.

Human pluripotent stem cells (hPSCs) hold significant promise for regenerative medicine, yet optimizing their expansion in three-dimensional bioreactor systems remains challenging due to complex interactions between mechanical forces, metabolic constraints, and aggregate formation dynamics. This study developed and validated a mechanistic mathematical model to predict hPSC proliferation dynamics in vertical-wheel bioreactor (VWBR) systems, incorporating the effects of shear stress and energy dissipation rate (EDR) on cell growth and aggregate dynamics. Seven model variants employing different kinetic formulations for shear stress and energy dissipation rate effects were systematically evaluated through model selection, identifiability analyses, and experimental validation. Experimental data from six bioreactor conditions varying in initial cell density (2 x 104-15 x 104 cells/mL), agitation rate (30-60 RPM), and working volume (100-500 mL) were used for model calibration and selection. Bayesian Information Criterion analysis identified a model combining Michaelis-Menten kinetics for shear stress inhibition with a EDR-mediated aggregate detachment formulation as the best-performing variant, achieving a Mean Relative Prediction Error of 13.97%, comparable to the experimental variability of 16.29%. Independent validation experiments using leave-out data gathered under different media exchange schedules confirmed model accuracy with prediction errors below 14%, consistent with observed experimental variability around 12%. The validated model was used to optimize the media exchange protocol, leading to a 37.5% reduction in media consumption with only a 13.5% reduction in final cell yield, demonstrating its utility for prospective, quantitative bioprocess design in VWBR systems.

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.

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

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.

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.

As the United Kingdom (UK) targets net-zero emissions by 2050, anaerobic digestion (AD) has become a cornerstone of renewable energy infrastructure. However, mathematical models, such as the Anaerobic Digestion Model No. 1 (ADM1), often struggle with high-solids agricultural feedstocks because they rely on Chemical Oxygen Demand (COD), a metric that introduces significant experimental error. To overcome this, this study applies an established mass-based ADM1 framework tailored for the co-digestion of maize silage and cow manure sourced from a UK AD site. This study uses a parallel reactor framework, using two identical laboratory-scale reactors to physically replicate the dynamic conditions of the full-scale site. A Global Sensitivity Analysis was first conducted, identifying biomass decay and carbohydrate breakdown rates as the most influential factors affecting system stability and model accuracy. The model was calibrated using data from the first reactor and then tested against an independent second reactor subjected to significant organic loading stress. Results show high predictive capabilities, with the model achieving a R2 of 0.81 for biogas production during calibration. The model maintained high predictive accuracy during the validation test of the second physical twin, achieving an R2 of 0.85, proving that the framework is robust and not overfitted to a single dataset. While predicting rapid fluctuations in pH and alkalinity remains challenging, the mass-based approach effectively forecasts gas yields and process stability. This methodology provides a reliable foundation for robust process modelling, offering a scalable tool for the UK biogas sector to optimise AD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/721061v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@92c7e2org.highwire.dtl.DTLVardef@80d723org.highwire.dtl.DTLVardef@ac3d24org.highwire.dtl.DTLVardef@1e21a51_HPS_FORMAT_FIGEXP M_FIG C_FIG

Accurate cell counting is essential in tissue engineering and cancer research. The ongoing transition towards advanced 3D in vitro tumour models raises a question about the validity of the standard cell counting protocols, particularly in the systems containing extracellular matrix-based scaffolds. Here, we provide a quantitative analysis of the performance of three popular plate reader-based cell counting/viability assays, such as the Alamar Blue, MTT, CellTiter Glo 3D assays, in 2D monolayer and 3D scaffold-based cultures of U251 human glioblastoma cells, including cell-laden Matrigel plugs, and original tissue engineering constructs based on the decellularised sheep brain scaffolds. We quantitatively characterized the assays linearity, precision, biological and technical reproducibility, proportionality, and inter-assay agreement. The study revealed that assays performance is highly platform-dependent, with 2D cultures allowing significantly more precise and reliable measurements than in 3D ECM scaffold-based cultures. The numerical results provided in this study can help researchers make informed decisions when working with 3D scaffold-based in vitro tumour models and for other tissue engineering purposes where precise cell counting is essential. ToC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/720021v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16018d9org.highwire.dtl.DTLVardef@1ff7d6dorg.highwire.dtl.DTLVardef@838021org.highwire.dtl.DTLVardef@1510d5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

This study presents the MMH dataset, a laboratory-collected in vivo dataset comprising whole-body kinematics, three-dimensional ground reaction forces and two-dimensional centres of pressure under both feet, as well as surface electromyography (sEMG) signals of twelve lower-limb muscles (six muscles per leg) during load lifting and lowering tasks. Ten healthy, normal-weight, young male adults each performed 72 trials combining one- and two-handed load (2 kg) lifting and lowering. These trials include multiple initial and final load locations while using three different lifting techniques (stoop, semi-squat, and full-squat). The kinematic and force-plate measurements provide rich input for ergonomic risk assessment tools and optimisation-based musculoskeletal models aimed at quantifying and managing musculoskeletal risk of injury. Also, the sEMG recordings enable the development of EMG-assisted musculoskeletal models and support validation of predictions from optimisation-based models. These makes the multimodal MMH dataset a valuable resource for biomechanics, ergonomics, and human movement research.