Annals of Biomedical Engineering

PurposeTo characterize the frequency-dependent bioimpedance properties of major ocular tissues in intact ex vivo porcine eyes under simulated surgical conditions and evaluate tissue separability at discrete frequencies. MethodsBioimpedance spectra were acquired from sclera, corneal epithelium, iris, lens, vitreous, and retina in intact ex vivo porcine eyes using a two-electrode probe and a precision LCR meter over 5 kHz to 1 MHz. Measurements were obtained under balanced salt solution and ophthalmic viscosurgical device conditions. Probe-tissue contact was verified by microscope visualization and optical coherence tomography. Tissue separability at 5, 50, 100, and 900 kHz was evaluated using global and pairwise statistical comparisons, effect sizes, and ROC-based separability metrics. Robotic-stabilized and handheld measurements were also compared. ResultsOcular tissues demonstrated distinct, frequency-dependent impedance magnitude distributions. Across sampled frequencies, 60% to 80% of tissue pairs showed significant differences after multiplicity correction. Median pairwise effect sizes ranged from Cohens d = 0.48 at 5 kHz to 1.04 to 1.06 at 50 to 100 kHz. Median ROC-based separability was 0.91 at 5 kHz and 0.76 to 0.77 at 50 to 900 kHz. Robotic-stabilized measurements showed lower variance than handheld measurements, although tissue-specific impedance ranges and frequency-dependent trends were preserved across acquisition modes. ConclusionsMajor ocular tissues exhibit reproducible, frequency-dependent bioimpedance signatures in intact ex vivo eyes under simulated surgical preparation. These findings establish a physiologically relevant ocular impedance reference dataset and support bioimpedance as a complementary modality for tissue differentiation in ophthalmic microsurgery.

Background and ObjectiveThe foot-ankle complex is a highly articulated and mechanically constrained system, often simplified as a chain of few rigid segments, neglecting many bone-to-bone motions and raising questions about the accurate representation of interaction with ground. This study proposes a new reduced-order multibody formulation that captures intrinsic kinematic constraints of the foot through motion synergies. MethodsBones kinematic coupling, or motion synergies, were experimentally derived from weight-bearing CT scans using principal component analysis. These couplings were embedded in a synergy-based multibody kinematic optimization framework describing the foot-ankle with five degrees of freedom: ankle flexion; foot adduction, pronation, and arching; and toe flexion. Model accuracy was evaluated against bone-level experimental kinematics. The model was applied to gait data from healthy, flat, and diabetic feet and compared with a standard multi-segment foot model, assessing robustness by progressively reducing the number of skin markers. ResultsAverage errors were about 1{degrees} and 0.5 mm when using subject-specific synergies and below 7{degrees} and 4 mm when scaling the generic model, matching or exceeding the accuracy of existing models. Reliable reconstruction was obtained using only four foot markers. In clinical gait analysis, the model showed superior discrimination between populations and enabled assessment of transverse arch deformation, not accessible with conventional models. ConclusionThe proposed synergy-based model provides an accurate, low-complexity framework for reconstructing bone-level foot and ankle kinematics, substantially simplifying gait analysis while improving biomechanical interpretability. This framework supports future integration with dynamic models aimed at studying load transmission in the foot.

Various ankle-foot conditions (e.g., fractures, diabetic foot ulcers, and post-surgical recovery) require periods of complete non-weightbearing followed by gradually increasing partial loadings. However, existing assistive devices often provide inconsistent or uncomfortable offloading during gait. Additionally, prolonged proximal leg offloading can contribute to muscle atrophy, reduced bone density, and overuse of other body segments. We present a novel offloading ankle-foot orthosis (OLAFO) designed to overcome these limitations. The OLAFO features a patient-specific load-bearing shank brace, designed through a digital workflow and fabricated from a 3D-printed core reinforced with carbon-fiber composite lamination. Interlocking serrated side struts, adjustable in 2 mm increments, modulate load sharing between the shank and plantar surfaces. Furthermore, the OLAFO incorporates contact plates with a rocker profile informed by roll-over-shape measurements to support forward progression and gait symmetry. Proof-of-concept biomechanical verification in one able-bodied participant evaluated complete offloading, five partial-loading levels, and normal gait using a pressure walkway to compute vertical ground reaction forces and impulses. In complete offloading, the affected foot generated no contact pressures. Across partial-loading levels, the foot impulse increased from 14% to 53% of the total load and scaled linearly with strut height adjustments, supporting clinician-prescribed loading increments. Contralateral stance duration increased only modestly compared to commonly used assistive devices, indicating reduced compensatory loading on the intact limb. These findings demonstrate the proof-of-concept feasibility of the OLAFO, highlighting its potential for verifying full offloading and prescribing partial-loading targets during rehabilitation. Future research will evaluate performance across patient populations and clinical rehabilitation tasks.

Aortic stiffness is both heterogenous and anisotropic. Current non-invasive methods to estimate aortic stiffness are limited to characterizing the aortic tissue as isotropic due to the lack the techniques required to extract multi-axial strain from 3D dynamic images. Vascular deformation mapping (VDM) is a nonrigid image registration technique which has thus far been applied to map aortic growth using longitudinal imaging. In this study, we propose to use VDM to assess 3D aortic deformation by mapping diastolic and systolic images. During image registration process, penalty parameters are employed to fine-tune image alignment and penalize non-physiological deformations. These penalty parameters must be calibrated to ensure that VDM successfully reproduces multi-axial aortic motion patterns in health and disease. In this paper, we developed a calibration pipeline for these parameters using synthetic data. A rotation-free shell model was used to generate physics-based synthetic data on aortic motion incorporating patient-specific geometries, root motion, and blood pressure from a cohort of 14 subjects (healthy, Marfans syndrome and thoracic aortic aneurysm). An error metric was defined to quantify the quality of the VDM results. Furthermore, a k-means clustering technique was used to categorize the subjects into three clusters based on ascending aortic motion. Optimal penalty parameters were identified for each of the three clusters. The results indicated that patient clusters with smaller aortic root motion required larger rigidity penalty values. The calibrated parameters successively reduced errors in 3D displacement and multi-axial stretch compared to un-optimized VDM predictions, enhancing the accuracy of capturing aortic deformation from dynamic images. Among the different aortic regions, the ascending thoracic aorta exhibits the largest error reduction.

Tissue engineered constructs are increasingly used for both modeling organs and disease in vitro as well as for therapeutic intervention. In addition to collagen, these constructs commonly include native extracellular matrix proteins (ECM), such as fibronectin and laminin. Given the critical role of inflammatory pathways in disease and in response to implanted materials, it is important to understand the role these proteins play in regulating the inflammatory environment. Fibronectin and laminin influence neutrophil function and endothelial activation in 2D, but their regulation of the inflammatory environment in 3D engineered constructs is not clear. For this study, we used an inflammation-on-a-chip device that includes a model blood vessel surrounded by a collagen I hydrogel with fibronectin and/or laminin. We investigated the additive effects of both proteins and a range of concentrations for each protein to determine concentration dependence. Both fibronectin and laminin have concertation dependent effects on neutrophils and the endothelium. High concentrations (50 {micro}g/mL) of fibronectin reduced neutrophil migration, while 20 {micro}g/mL laminin reduced neutrophil extravasation and migration, potentially due to lower ICAM-1 expression by the endothelium. Interestingly, 50 {micro}g/mL of laminin significantly disrupted endothelial vessel formation and reduced ICAM-1 and VE-cadherin expression, likely due to significant changes in the collagen architecture. The inclusion of fibronectin and laminin, even at physiological levels, results in significant effects on neutrophil behavior, endothelial vessel formation, and collagen architecture. These proteins impact the inflammatory environment and thus need to be considered when modeling diseases and designing therapeutics, especially when neutrophils or an endothelium are involved. Translational Impact StatementThis work uses an inflammation-on-a-chip device to study how fibronectin and laminin impact neutrophil behavior and vascular inflammation as these proteins are commonly used in engineered constructs. We found that fibronectin impairs neutrophil migration, while laminin decreases neutrophil extravasation and migration and at higher concentrations also prevents endothelial vessel formation. Therefore, researchers should be aware that these proteins will alter the inflammatory environment when including them in engineered constructs.

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

Patellar osteochondral allograft (OCA) transplantation is widely used to treat large full-thickness cartilage defects, yet long-term failure and reoperation rates remain high. Although surface congruity and osseous integration are emphasized clinically, cartilage thickness and mechanical compatibility between donor and recipient are not considered. Our previous work suggests that cartilage thickness mismatch can amplify local deformation at the graft boundary, potentially compromising graft longevity. This study investigates how combined mismatches in cartilage thickness and mechanical properties influence the local strain environment at the patellar OCA interface. Simplified two-dimensional axisymmetric finite element models of patellar OCA repair were developed in ABAQUS. Donor-to-recipient cartilage thickness ratios ranging from 0.33 to 3.25 were evaluated together with donor-recipient Youngs modulus mismatches (2.5-7.0 MPa). Cartilage was modeled using homogeneous linear elastic and functionally graded material formulations to account for depth-dependent stiffness. A compressive pressure of 1.0 MPa was applied to represent patellofemoral joint loading, and peak compressive and shear strains were quantified at the graft boundary. Cartilage thickness mismatch produced localized high-strain regions (HSR) of compressive and shear strain at the donor-recipient interface that were absent in thickness-matched constructs. Strain amplification increased with both thickness and mechanical property mismatch. Compressive strain exhibited directional asymmetry, with donor-side-thicker configurations producing greater amplification than recipient-side-thicker configurations. Incorporating depth-dependent cartilage stiffness reduced peak strain magnitudes but did not eliminate mismatch-driven strain amplification. These findings demonstrate that cartilage thickness and mechanical disparity can create HSR at the patellar OCA graft boundary that may predispose grafts to impaired integration and long-term failure.

BackgroundThe pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL-deficient knees, yet grading remains subjective, as evidenced by poor inter-observer reliability, particularly for Grade 2. Since low-grade (Grade 1) versus high-grade (Grades 2/3) PS is the threshold for recommending lateral extra-articular augmentation, performing the test in awake clinic patients limits grading reproducibility and introduces variability in surgical decision-making. Existing methods to quantify the pivot shift usually require examiner-performed testing under general anaesthesia. No prior approach has ascertained PS grading from a separate patient-performed functional movement. PurposeTo evaluate the feasibility of a machine learning (ML) classifier, trained on kinematic ultrasound bone-tracking signals acquired during a patients sit-stand-sit (SSS) knee movement, to predict their PS grade, and to clinically validate its ability to differentiate low versus high-grade PS. MethodsUltrasound bone-tracking kinematic data were collected during SSS manoeuvres in 23 ACL-injured patients using the GATOR device, and ground truth PS grades (0-3) were assigned under general anaesthesia by fellowship-trained orthopaedic sports surgeons. From the data collected, Leave-one-out cross-validation (LOOCV) was used to train the ML classifier. Clinical SSS data from 6 ACL-deficient patients was used for independent held-out validation of their low-grade (Grade 1) versus high-grade (Grade 2/3) PS. Multiple deep learning architectures (XceptionTime, InceptionTime, FCN, ResNet, ResCNN) and training strategies (including mixup augmentation and supervised contrastive learning) were tested. Performance was measured by one-versus-rest (OVR) AUC under LOOCV and by AUC (low vs high grade PS) from the held-out patient sessions. ResultsThe ML classifier achieved a maximum OVR AUC of 0.928 {+/-} 0.084 under LOOCV. Classifier performance increased with pivot-shift severity: Grade 3 was identified most reliably (AUC ~0.81; sensitivity 0.70-0.80), whereas Grade 2 remained the most challenging boundary (sensitivity 0.20-0.75 across configurations). For the clinically relevant binary classification of low-versus high-grade pivot shift, the classifier generalised well to a completely unseen patient cohort (AUC 0.889; accuracy 0.860; sensitivity 0.850; minimum-class sensitivity 0.767). ConclusionThe study demonstrates that kinematic ultrasound bone-tracking during sit-stand-sit contains transferable information about rotational instability severity in ACL-deficient patients, and represents the first reported approach to predict pivot shift grade from a patient-performed functional movement. The strong cross-validation performance confirms that the signals contain meaningful PS grade-discriminative information, but larger datasets targeting 50-100 sessions per grade will be required to achieve patient-level generalisation and advance this novel rotational instability assessment tool toward full clinical adoption. Level of EvidenceLevel IV, diagnostic feasibility study.

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.

BackgroundThe clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability. ObjectiveThe purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction. MethodsThis prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed [&ge;]6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8 ACL-deficient, 21 ACL-reconstructed [&ge;]6 months post-surgery) were recruited through physician referral at SGH outpatient clinics to perform standardized chair-stand tests. An ensemble deep learning model that combines convolutional (EfficientNet) and time-series (InceptionTime) classifiers is used to output binary stability classifications (ACL-deficient/ACL-reconstructed). The models performance was evaluated using 10-fold stratified cross-validation with patient-wise splitting, repeated across 100 random seeds to assess variability. ResultsAt the halfway point of the trial, the ensemble model performance with regard to the Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.8365 (SD: 0.042, p-value < 0.001), and the classification accuracy was 75.9% (SD: 3.2%) when the model was tested on the 29 CIRB-approved patients. For the ACL-reconstructed class, the performance indicators were as follows: precision 71.4%, recall 93.8%, F1-score 81.1%. For the ACL-deficient class, the indicators were: precision 87.5%, recall 53.8%, F1-score 66.7%.Against the clinical pivot shift tests low sensitivity (24-32%), the model delivers an almost 2X better sensitivity (53.8%)[2, 3], with a comparable specificity (93.8% vs. 90-98%) ConclusionThe multimodal machine learning model was able to perform at a level that was relevant to clinical classification (AUC-ROC 0.8365, accuracy 75.9%) in differentiating between ACL-deficient and ACL-reconstructed knees. Moreover, the model demonstrated far superior sensitivity than previously published estimates for manual pivot shift testing (53.8% vs. 24-32%). These findings demonstrate that rotational knee instability can be reliably differentiated in clinical settings with a ML model deployed on GATOR PRO data.

Robot-assisted hematoma puncture has seen significant development in primary hospitals across the country. Sino Plan software system is the core of the intelligent surgical robot, independently developed by Sinovation.We conducted a comparative study of imaging indicators, such as residual hematoma volume and hematoma clearance rate, as well as prognostic indicators, in patients who underwent hematoma puncture at our hospital over a 9-year period, before and after the introduction of Sino Plan.The results indicated that following the application of Sino Plan, the hematoma clearance rate was significantly enhanced, and the residual hematoma volume was markedly reduced. Regarding patient prognosis, there was no significant difference in GCS scores between the two groups, but the incidence of adverse prognostic events was lower in patients where Sino Plan was utilized.In conclusion, this 9-year retrospective analysis at our hospital reveals that Sino Plan offers distinct advantages. However, its application in certain special cases suggests that further improvements to the software are warranted to better meet the demands of more specific clinical scenarios.

Recent advancements in wearable sensors and machine learning show promise for estimating lower-body joint torques outside of laboratory settings. Inertial Measurement Units combined with Convolutional Neural Networks have proven effective for this task. However, the impact of different input data types and formats remains underexplored. This study investigates how variations in input data influence the prediction of lower-body joint torques during walking. Results indicate that while dataset choice causes only minor differences in prediction performance, the overall quality of the dataset plays a more critical role than the specific input variables in achieving accurate torque predictions using wearable sensors.

IntroductionCancer progression is driven not only by tumor cells but also by interactions between the extracellular matrix (ECM), stromal cells, and immune cells within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are major drivers of ECM remodeling, assembling ECM with aberrant organization. Extra domain A fibronectin (EDA-FN), a cellular FN containing an extra type III domain, is upregulated in the TME. EDA-FN regulates cellular behavior and has been associated with poor patient prognosis. Macrophages are among the most abundant immune cells within the TME, where they contribute to TME remodeling and inflammation to promote cancer cell invasion and metastasis. However, how tumor-associated matrix-specific cues regulate macrophage behavior remains largely understudied. PurposeHere, we developed a fibroblast-derived matrix platform that captures the structural imprint of tumor-associated EDA-enriched matrices and investigated how matrix-specific cues regulate macrophage behavior in the absence of ongoing soluble factor cues. MethodHuman mammary fibroblasts (HMFs) preconditioned in incubated low-serum media (lNC, or control) and MDA-MB231 metastatic breast cancer cell-conditioned media (mTCM) were cultured on polyacrylamide gels of 2 kPa and 20 kPa, respectively, followed by decellularization. Matrix organization, including fiber alignment, width, and intrafibrillar spacing, was quantified from confocal images. Decellularized EDA-FN-enriched matrices were subsequently reseeded with macrophages to assess macrophage morphology, phenotype, and matrix interactions. ResultsThe combined effects of tumor-derived soluble factors and pathological stiffness induced a CAF-like phenotype in HMFs, accompanied by cytoskeletal reorganization and microarchitectural alterations of EDA-FN-enriched matrices. Tumor-associated matrices exhibited increased alignment, narrower fiber width, and enlarged intrafibrillar spacing compared to control matrices. These aberrant, tumor-associated matrix-derived features were associated with altered macrophage behavior, including heterogeneous morphology, enhanced localized EDA-FN matrix loss beneath the cell body, and a hybrid phenotype with a shift toward a CD206-dominant profile. ConclusionsThese findings demonstrate the feasibility of obtaining EDA-FN-enriched matrices to isolate matrix-specific cues for investigating macrophage-ECM interactions. Furthermore, this platform can be leveraged to identify matrix-targeting therapeutic approaches for modulating macrophage function within the TME.

Tissue-level mechanical stimuli are primary drivers of tissue adaptation and can be optimised during conservative treatments to improve treatment outcomes for many highly prevalent musculoskeletal conditions. Current laboratory-based technologies limit our ability to connect conservative interventions such as exercise and movement modification with muscle, joint, and tissue-level mechanics, in natural environments. We introduce a physics-informed neural network (PINN) to estimate clinically relevant biomechanics from smart garments. By accounting for physiological dynamics of neural activation and muscle contraction, the PINN accurately predicted hip joint angles (RMSE <6 degrees), moments (RMSE 0.12 N*m/kg to 0.30 N*m/kg), and joint forces (RMSE 6 to 16%) from three inertial measurement units and four electromyographic sensors. We demonstrated that the trained PINN can be combined with a smart garment to estimate hip biomechanics, in real-time, during a gait retraining intervention aimed at modifying joint loading to treat hip osteoarthritis. The developed PINN and smart garment system may be adapted and generalised for personalised management or rehabilitation of a broad range of musculoskeletal diseases and injuries, in clinical, home, workplace, and sporting environments.

BackgroundBone graft biomaterials play a critical role in bone regeneration by influencing osteoblast differentiation and mineralization. However, comparative data regarding the osteogenic potential of commonly used graft materials under standardized conditions remain limited. Method and materialIn this in vitro experimental study, osteoblast-like cells (MG-63) were cultured with four bone graft materials, including Bio-Oss, Cerasorb, Bio-Tiss Cerabone, and Pro Osteon. The relative mRNA expression of osteogenic markers (COL1 and OPN) was evaluated at 1, 7, 14, and 21 days using real-time PCR. Alkaline phosphatase (ALP) activity and mineralization capacity were also assessed using colorimetric assay and Alizarin Red staining. Data were analyzed using one-way ANOVA and Tukey post hoc test (P < 0.05). ResultsSignificant differences were observed among the tested materials across all evaluated parameters. Bio-Oss and Cerasorb demonstrated higher gene expression levels and ALP activity compared to Bio-Tiss Cerabone and Pro Osteon (P < 0.05). Mineralization analysis showed significantly greater calcium deposition in the Bio-Oss and Cerasorb groups, whereas Pro Osteon consistently exhibited the lowest osteogenic performance. ConclusionBone graft biomaterials significantly influence osteogenic activity in osteoblast-like cells. Bio-Oss and Cerasorb showed superior osteogenic potential, while Pro Osteon demonstrated weaker performance. These findings highlight the importance of material properties in optimizing bone regeneration.

Tissue phantoms that mimic microvasculature and perfusion are essential for modelling vascular function, guiding interventions, and calibrating imaging systems, which require faithful replication of vascular geometry and flow. Conventional fabrication strategies, including wire-based molding, lithographic micromachining, and additive manufacturing, offer useful capabilities but remain constrained by predefined designs, rectangular channel cross-sections, limited scalability, and high production costs. Reliance on predefined digital vascular models restricts design flexibility and limits the ability to capture the natural variability and complexity of real vascular systems. Here, we present a lithography-free, fractal-generating approach based on a modified Lifted Hele-Shaw Cell (LHSC) technique, in which vascular networks emerge spontaneously via interfacial fluid instabilities. Unlike pre-designed methods, these structures are governed by fluid properties and flow conditions, enabling adaptive, physiologically relevant geometries with smooth Gaussian cross-sections and natural diameter tapering. We demonstrate four phantom designs: a planar vascular tree, an anatomically guided cerebral network, a retinal vascular model, and a conformable curved substrate phantom. Validation using Laser Speckle Contrast Imaging confirms structural fidelity and physiologically relevant flow consistent with Murrays law. This platform uniquely integrates realistic vascular architecture with emergent, fractal driven formation, highlighting its potential as a reproducible and biologically relevant alternative to conventional vascular phantom fabrication. Furthermore, the availability of such realistic in vitro vascular models can reduce reliance on animal experiments and contribute towards more ethical and sustainable preclinical research.

BackgroundPhenome-wide association studies (PheWAS) can reveal novel associations between variants in drug-target genes and disease and, as such, can be used to predict new drug-indication pairs for repurposing drugs with a known mechanism of action. A platelet-derived growth factor receptor beta (PDGFR{beta}) PheWAS demonstrated that patients with a single nucleotide variant that reduces PDGFR{beta} expression exhibit a higher prevalence of chronic skin ulcers, skin grafts, and reconstructive surgeries. Recombinant human platelet derived growth factor BB (rhPDGF) is a therapeutic that binds to and activates PDGFR{beta} and has received FDA approval for multiple indications, including improving healing of lower extremity diabetic neuropathic ulcers, augmenting periodontal bone and soft tissue reconstruction, and stimulating orthopedic bone regeneration. Leveraging a drug-repurposing methodology informed by PheWAS, we hypothesize that rhPDGF will provide therapeutic benefit in the treatment of other complex wounds, like full-thickness surgical wounds of the head or neck that cannot heal by primary intention following skin cancer excision. MethodsThis prospective, double-blinded, single-site study aims to enroll 40 participants, randomized at a ratio of 1:1, comparing the efficacy of an advanced wound matrix saturated with rhPDGF or saline. Comparisons will be stratified by anatomical location (scalp/forehead versus face/neck) and maximum surgical defect dimensions (< 3cm versus > 3cm). The primary outcome of this study will evaluate the time in days to 81-100% granulation of the wound bed by expert clinical assessment of daily photographs. Secondary outcomes will assess the superiority of the rhPDGF-enhanced wound matrix relative to control with respect to wound granulation rate, epithelialization, complete wound healing, and patient reported outcomes (PROMs). DiscussionAlthough reconstructive techniques are available for healing complex head and neck wounds following skin cancer excision, these procedures are invasive, and older, frail patients are often suboptimal candidates. There remains a need for less invasive therapeutic approaches that reduce the healing time and mitigate the morbidity associated with chronic wounds. A PheWAS analysis identified complex wounds requiring reconstructive surgery as a novel drug-indication pair for repurposing rhPDGF. This protocol is designed to evaluate the efficacy of an rhPDGF-enhanced advanced wound matrix for healing complex head and neck wounds post skin cancer excision that cannot heal by primary intention. Clinical trial registrationThis trial is registered at ClinicalTrials.gov (NCT06634030).

BackgroundThe use of autologous or allogeneic cell therapies has now entered to the clinical practice in several fields of medicine, especially in oncology and hematology. From this regard, 2D-cell manufacturing is complex and costly and bioreactors have attracted major interest for efficient and cost-effective mass production of cells. Bioreactors have several advantages such as homogeneous repartition of nutrients and gas, control of all culture parameters and increased yield. However, the important shear stress generated by those bioreactors is an important disadvantage as it can affect cell survival or cell quality. This important shear stress is the result of the mixing method using either blades (used in stirred-tanked bioreactors) or gas bubbles (used in airlift bioreactors). Another downside of the use of bioreactors is the difficulty to scale-up. As the volume increases, the shear stress generated by blades radically increases leading to cell death and a decrease of cell quality. DescriptionIn this study, we describe a bioreactor developed using a different mixing method effectively reducing the shear stress and facilitating scale-up. This bladeless method uses an inclination of the bioreactor as well as rotation to mix fluids in a container. Here we described different steps that led to the adaptation of this bioreactor, initially developed for fragile microalgae culture, for mammalian cell culture amplification. The bioreactor was tested to amplify a natural killer (NK) cell line NK92 which is an IL-2 dependent cell line used in clinical trials for cancer therapy. We have tested the influence of 1-The number of cells seeded; 2-The influence of the rotation speed on cell growth and viability; 3-The influence of the bioreactor angle on the above parameters; 4-The duration of the culture. ResultsCells were initially seeded at 2.5.105 / ml in a volume of 380 ml. According to the rotation speed of 15, 30, 45 and 60 rpm, we have observed an increase of cell numbers at day 3 (3-fold), day 5 (7-fold) and day 7 (10-fold) compared to seeding, the best expansion being obtained at day 7 with a rotation speed of 45 rpm. The optimal angle of rotation was found to be 3 degree, with an optimal amplification at day 7 versus day 3 (p < 0.01). The viability was also found to be optimal in the latter condition. ConclusionsThese preliminary results demonstrate that NK92 cells could be amplified using this bioreactor. In the best tested condition, neither cell viability nor cell growth was impacted. These results strongly suggest the potential use of this device in future clinically applicable conditions.

Cardiac rhythm is a critical clinical indicator for cardiac arrhythmias and adverse events during drug toxicity studies. In vivo, cardiomyocyte responses to pharmacological agents occur within minutes and are strongly influenced by dynamic drug delivery through blood flow. However, conventional 2D and 3D static culture systems fail to replicate these fluid flow kinetics, limiting their physiological relevance for assessing beat rate responses. Here, we present Mera, an advanced microphysiological system (MPS) developed by Hooke Bio, designed for high-throughput, long-term culture and functional analysis of 3D cardiac spheroids composed of human induced pluripotent stem cell-derived cardiomyocytes and cardiac fibroblasts. Mera enables dynamic perfusion, allowing investigation of cardiomyocyte beat rates under physiologically relevant flow conditions. The platform supports up to 640 spheroids per run and integrates automated imaging, fluid handling, and user-friendly software, operating under controlled physiological conditions (37{degrees}C, 5% CO2). Flow rates are tunable between 0 and 12.5 mL/min to mimic in vivo environments. Pharmacological testing with verapamil, isoproterenol, calcium chloride, and propranolol demonstrated real-time, reversible modulation of beat rate under flow, including recovery following drug-induced suppression. System variability was comparable to a temperature-controlled reference platform, supporting robust statistical analysis. Dose-response studies yielded IC values consistent with literature, confirming physiological relevance. Collectively, these results demonstrate that Mera provides a reproducible, scalable, and human-relevant platform for cardiac drug testing. By enabling dynamic drug exposure and automated analysis, Mera represents a powerful new approach methodology (NAM) for improving the predictive assessment of cardiac safety and beat-rate modulation drug responses.