Bioengineering

BackgroundPlatelet-derived Growth factors play key roles in tissue repair and regeneration, yet conventional platelet-rich plasma (PRP) formulations release these mediators inconsistently in vivo due to variability in platelet yield and activation dynamics. To overcome this limitation, direct administration of concentrated platelet-derived growth factor preparations has gained interest, though current manufacturing approaches for human platelet lysate (hPL), growth factor concentrates (GFC), and conditioned serum remain constrained by batch variability, incomplete platelet degranulation, and reliance on anticoagulants. Here, we examine alternative platelet activation workflows to establish a standardized, efficient, and reproducible method for high-yield growth factor recovery suitable for translational and clinical applications. MethodsNine GFC production protocols were compared, employing different combinations of freeze-thaw (FT) cycling, glass bead (GB) agitation, calcium (Ca2) activation, and a novel Enriched Growth Factor (Enriched-GF) method. The objective was to identify a protocol capable of maximizing growth factor yield within a three-hour workflow. Optimal Ca2 concentrations and GB conditions were determined from prior optimization studies and integrated into the Enriched-GF processing scheme. Platelet concentrates (n = 10 per protocol) were processed under each condition, and growth factor levels were quantified using ELISA. ResultsGrowth factor yields differed significantly across protocols. The greatest and most consistent increases in growth factor release were observed with the Enriched-GF method combining GB activation, FT cycling, and Ca2 stimulation. This approach resulted in markedly elevated concentrations of key regenerative mediators, including enhanced EGF release, a 4.5-fold increase in PDGF, maximal TGF-{beta} liberation, and a four-fold increase in FGF2 relative to conventional platelet lysate or conditioned serum preparations. These results were reproducible across independent donor pools, demonstrating robustness and batch-to-batch consistency. ConclusionWe describe a rapid and reproducible method for producing highly concentrated platelet-derived growth factors using a combined GB-FT-Ca2 activation strategy. The Enriched-GF protocol consistently outperformed existing platelet lysate, conditioned serum, and conventional GFC preparation methods, yielding a standardized product with enhanced growth factor content. This Enriched-GF approach offers a clinically practicable solution for applications in regenerative medicine requiring reliable and high-yield growth factor delivery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/712883v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1f059d9org.highwire.dtl.DTLVardef@9aeffforg.highwire.dtl.DTLVardef@27cd1org.highwire.dtl.DTLVardef@150b7d1_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic overview of platelet concentrate preparation from whole blood and the generation of different platelet lysates and growth factor-enriched serum using freeze-thaw, calcium gluconate, and glass bead activation methods.

BackgroundThree-dimensional in vitro neuronal cultures have emerged as promising platforms for modelling human brain function and disease under controlled conditions. However, their ability to recapitulate in vivo-like complexity and rich dynamics remains underexplored. In this study, we developed and characterized neurospheroids derived from human induced pluripotent stem cells (hiPSCs) to investigate how key features -- three-dimensionality, cellular heterogeneity, and modular organization -- contribute to replicating brain-like network dynamics. MethodsWe engineered neurospheroids with varying excitatory/inhibitory ratios and assembled them into modular constructs (assembloids), evaluating their electrophysiological activity using high-density micro-electrode arrays. We assessed spontaneous and evoked activity through established metrics of dynamical richness and perturbational complexity. ResultsOur findings show that three-dimensionality and modularity significantly enhance the richness and complexity of network activity, approaching levels observed in vivo, while cellular heterogeneity contribute to functional aspects of the network, such as nuanced activity patterns, dynamical variability, and modular coordination. ConclusionsOur work highlights the critical role of spatial organization in reproducing brain-like activity and provide a foundation for future studies using patient-derived neurospheroids to model disease-specific dynamics.

Selecting suitable tribo-pairs is crucial for measuring the tribological properties of the blinking process, especially for dry eye disease research. The tribo-pairs, lubricants, loads, and sliding speeds used in the friction models reported so far vary greatly, which limits the development of artificial tear fomulations, that could be effective in treating the effects of dry eye disease. This study compares tribo-pairs under the same experimental conditions and provides a test model closer to the real physiological blinking environment. This study proposes a to use the porcine eyeball-eyelid tribo-pair as an ex vitro tissue friction model to explore the tribological behavior during blinking. Additionally, the presence of mucin on the eyelids and cornea was detected. The tribo-pair was compared with the eyeball-glass and eyeball-mucin coated glass tribo-pairs in terms of friction coefficient, relief time, and wear. Artificial tribo-pairs such as contact lens-glass or contact lens-mucin coated glass were not included because of their irrelevance to dry eye disease. The results showed that the static friction coefficient of the eyelid/eyeball tribo-pairs was significantly lower than that of the bare glass/eyeball group. In addition, its dynamic friction coefficient was higher than that of the glass/eyeball tribo-pairs, but the friction damage caused was lower than that of the glass/eyeball group. The relief period (RP) of the eyelid/eyeball tribo-pair was significantly higher than that of bare glass and mucin-coated glass, showing stronger hydrophilicity within this system. To conduct relevant dry eye disease (DED) research, it is critical to simulate the natural eyelid-eyeball friction system as realistically as possible. Despite its limitations, the use of the porcine eye as an in vitro model provides a structurally and biomechanically realistic platform to capture the key interactions between the eyelid and the ocular surface. This approach allows for a more accurate assessment of friction, tear film dynamics, and therapeutic interventions in dry eye.

This study uses a previously reported 3D-printed variable-height flow cell to investigate the viscoelastic properties of chondrocytes from 2D monolayer and 3D alginate cultures. It was hypothesized that chondrocytes could be distinguished by phenotype associated with their culture environment using viscoelastic recovery time, owing to variation in the pericellular matrix (PCM) produced by chondrocytes from different culture methods. The PCM surrounding the chondrocytes was imaged with confocal microscopy during applied deformation and subsequent recovery. The projected cell area was fitted with a Burgers mechanical model to extract the viscoelastic recovery time. No difference between bovine and primary OA cells from monolayer cultures was observed. However, a statistically significant difference in recovery time was observed between cells from monolayer and alginate cultures in both the bovine (31 s vs. 13 s) and primary OA (34 s vs. 13 s) groups. This work shows that viscoelastic recovery time is influenced by the culture method used for chondrocytes and further demonstrates the role of the PCM as a mechanical protector of chondrocytes.

Background and ObjectivesLung cancer is one of the most frequently diagnosed cancers worldwide. While non-surgical treatment options have increased in number and efficacy, lung resection for primary cancers is still a mainstay of treatment. Lung resection has been shown to impair right ventricular function, although the mechanism for the impairment remains unclear. Wave intensity is increasingly used as a metric for increased post-operative afterload. Here, we develop a computational framework to assess the impact of simulated lung resection on wave intensity to establish that post-operative changes in wave intensity are attributable to the change in pulmonary artery morphometry. MethodsWe analyse a 48 pulmonary arterial surfaces segmented from CT images in patients with no evidence of lung disease to obtain 1D representations of the pulmonary vasculature. For each pulmonary vasculature we sequentially remove vessel branches to mimic post-operative morphometric changes to the arterial network. Using an established 1D computational flow model, we simulate pulsate blood flow in 44 pre-operative cases and 1596 post-operative cases. We compute wave intensity in the main, right, and left pulmonary arteries for all simulations. ResultsWe compare the change in computed wave intensities pre-versus post-operatively to the results of an experimental clinical study comparing pre- and post-operative wave intensity in a 27 patient cohort. We see good agreement between the changes in the parameters of wave intensity between this study and those reported in the clinical study. Further, we capture flow distribution the changes pre-versus post-operatively which indicates that the computational model behaves as expected. ConclusionsIn this preliminary study on a computational framework to capture changes in pulmonary arterial haemodynamics following lung resection, we have shown that our model and analysis pipeline is capable of capturing post-operative changes to wave intensity and flow redistribution between the pulmonary arteries following lung resection. These results motivate further research to develop and validate a patient specific model which is an area of active research for us.

Mucointegration is as important as osseointegration to ensure the survival of implant-supported prosthesis. Indeed, effective soft tissue integration (STI) prevents the appearance of complication through bacterial dissemination. To optimize STI, electrochemical anodization can be used to nanostructure the trans-gingival part of the prosthetic component. Moreover, Selective Laser Melting (SLM) is a new 3D-manufacturing technique that enables the production of customized implant-supported prosthesis with complex geometry. ObjectiveThe aim of this study is to evaluate the effect of a SLM manufactured and anodized Ti6Al4V surface on the behaviour of both, human gingival fibroblasts and oral bacteria. MethodSLM-Ti6Al4V discs were polished and anodized with defined parameters to obtain nanotubes (NTs) with specific morphology. Surface characterization was assessed through surface topography and wettability. Human gingival Fibroblasts were cultured, and cell morphology was observed by SEM at day 7. Proliferation, viability (day 1,4,7) and adhesion (6 h and 36 h) were analyzed. Then immunofluorescence and RT-qPCR were used to detect the distribution and the gene expression of vinculin at 48 h. An early colonizer (Streptococcus gordonii) was used for a parallel evaluation of bacteriological adhesion. ResultsSLM-ANO-Ti6Al4V showed similar performances in terms of cytotoxicity, compared with a machined and polished titanium surface currently used in clinics. Interestingly, cell adhesion was enhanced on anodized SLM surfaces, with a difference in the distribution of focal adhesion plaques in HGFs, while biofilm formation of S. gordonii was not affected by anodization. SignificanceSLM anodized surface showed promising ability to promote STI while controlling bacterial adhesion.

IntroductionBlood glucose monitoring is essential for the management of diabetes mellitus. Continuous interstitial glucose (IG) monitoring systems are less invasive than capillary blood glucose (BG) measurements, but their agreement decreases at higher glucose levels. Artificial intelligence (AI) approaches, particularly recurrent neural networks such as long short-term memory (LSTM), have shown potential to model temporal glucose dynamics and correct inter-method discrepancies. Objective: To develop and validate an AI-based model capable of predicting capillary BG values from IG data, improving agreement between methods and enhancing glycemic status classification. Methods: This retrospective observational study analyzed 708 paired BG-IG measurements obtained from published anonymized datasets. Data preprocessing included Kalman filtering, robust normalization, temporal windowing, and class balancing via oversampling. An LSTM model with dual output was trained to perform both capillary glucose regression and glycemic status classification. Model performance was assessed using regression metrics (MAE, RMSE, R2), classification metrics (accuracy, F1-score), and agreement analysis (Bland-Altman). Results: The AI model substantially reduced the mean bias from +16.27 mg/dL to -2.08 mg/dL and achieved markedly narrower limits of agreement compared with raw BG-IG differences (-129.5 to +162.0 mg/dL vs. -47.3 to +43.2 mg/dL). Glycemic classification accuracy was high for hyperglycemia (94.6%), prediabetes (93.7%) and normoglycemia (100%), with lower performance observed for hypoglycemia (66.7%). Conclusion: LSTM-based AI modeling demonstrated strong capability to predict capillary BG from IG measurements and to correct inter-method discordance. These findings support the potential integration of AI-enhanced glucose estimation into clinical monitoring systems to improve therapeutic decision-making.

Background and Objectives: We report the first intraoperative deployment of a real-time machine vision system in neurosurgery, derived from our previous anatomical detection work, automatically identifying structures during endoscopic endonasal surgery. Existing systems demonstrate promising performance in offline anatomical recognition, yet so far none have been implemented during live operations. Methods: A real-time anatomy detection model was trained using the YOLOv8 architecture (Ultralytics). Following training completion in the PyTorch environment, the model was exported to ONNX format and further optimized using the NVIDIA TensorRT engine. Deployment was carried out using the NVIDIA Holoscan SDK, the system ran on an NVIDIA Clara AGX developer kit. We used the model for real-time recognition of intraoperative anatomical structures and compared it with the same video labelled manually as reference. Model performance was reported using the average precision at an intersection-over-union threshold of 0.5 (AP50). Furthermore, end-to-end delay from frame acquisition to the display of the annotated output was measured. Results: A mean AP50 of 0.56 was achieved. The model demonstrated reliable detection of the most relevant landmarks in the transsphenoidal corridor. The mean end-to-end latency of the model was 47.81 ms (median 46.57 ms). Conclusion: For the first time, we demonstrate that clinical-grade, real-time machine-vision assistance during neurosurgery is feasible and can provide continuous, automated anatomical guidance from the surgical field. This approach may enhance intraoperative orientation, reduce cognitive load, and offer a powerful tool for surgical training. These findings represent an initial step toward integrating real-time AI support into routine neurosurgical workflows.

Food costs are more significantly impacted by climate change as countries grow. It is well known that climate change has an impact on the productivity of most agricultural goods, but it is unclear how specifically it will affect food costs. The present research explores how the North Atlantic Oscillation (NAO) index, a widely used climate indicator, affects food prices around the world. This is achieved by applying a robust bivariate Hurst exponent (robust bHe). The research creates a color map of this coefficient using a window-sliding technique over various intervals of time, displaying an illustration that changes overtime. Additionally, the NAO index and global food prices are examined for causal connections using variable-lag transfer entropy using a window-sliding technique. The results show that notable rises in a number of international food prices for long as well as short periods are associated with significant increases in the NAO index. Furthermore, the causative function of the NAO index in influencing global food costs is confirmed by variable-lag transfer entropy. Is highly recommended as it directly connects the research to actionable outcomes for policymakers and the overarching goal of sustainability and food security. This study provides the first direct evidence of a robust, long-range cross-correlation and causal link between the North Atlantic Oscillation (NAO) index and key global food prices. It introduces a novel, robust methodological framework to visualize this time-varying relationship, offering a critical tool for policymakers and forecasting models.

Quality and price of fetal bovine serum (FBS) are traditionally determined by geographical origin and parameters listed in the Certificate of Analysis (CoA). Despite its central role in cell culture, selecting suitable FBS batches remains costly and labor-intensive due to substantial batch-to-batch variation. We propose a molecular assessment strategy based on transcriptomic and cytokine profiling of cells cultured in different FBS batches to evaluate performance more reliably. Analysis of differential gene expression in three cell lines - MRC-5, Jurkat, and THP-1 - enables batch grouping and reveals pathway-specific effects, with immune-related pathways showing the most pronounced variability. Although CoA parameters can stratify batches by origin, they do not consistently correlate with cytokine secretion or gene expression across cell lines. These findings demonstrate that geographical origin is an inadequate predictor of functional FBS performance and that molecular profiling provides a more robust and informative assessment.

Falls among older adults can result in hip fractures that requires x-ray based assessment at emergency department (ED). Only 25.7% of patients presenting to EDs are diagnosed with a hip fracture, as such improved diagnosis prior to transportation to hospital could result in fewer hospital visits and improved triaging. Patient with hip fracture could be immediately directed to centres with orthopaedic surgeons, allowing for reduced time-to-surgery, particularly in rural communities. Ultrasound (US) imaging is portable and can identify fractures but requires expertise, particularly related to image interpretation. Deep learning may reduce operator dependence by automating image interpretation. This study aims to develop HipSAFE, a hip fracture detection tool on US, to support triaging by nurses and paramedics. We hypothesize that diagnostic accuracy will be comparable to pelvic x-ray diagnostic performance in a preclinical study. Bilateral hind limbs of 15 porcine cadavers were imaged by US-naive operators before and after an iatrogenic hip fracture. The limbs were divided into training, validation, and test (8 femurs) sets. The training data were augmented (geometric and photometric transformations). The models included MobileNetV3 (S/L), EfficientNet-Lite (0-2), and ResNet (18/50). Using a moving average aggregation on the operator cine clips, EfficientNet-Lite0 achieved the highest performance (F1=0.944 [95% CI:0.880-0.987]; sensitivity=89.5% [78.6-97.5%]; specificity = 100.0% [100.0-100.0]). The majority voting ensemble model ranked second (F1=0.932 [0.857-0.984]). Naive operators and radiologists had lower performance (F1=0.667 [0.596-0.758] and 0.685 [0.597-0.729]). This pre-clinical study demonstrated that HipSAFE has excellent diagnostic accuracy and there may be a role for US in improving hip trauma triaging, especially for rural and resource-constrained environments.

IntroductionTo propose an improved U-Net-based segmentation model for colorectal polyp segmentation, aiming to address the challenges of variable lesion morphology, ambiguous boundaries, complex background interference, and insufficient cross-level feature fusion in endoscopic images [5,12]. MethodsAn improved network termed MCA-UNet was developed based on U-Net [5]. The model incorporates a multi-scale context convolution block (MCCB) to enhance multi-scale feature extraction and an attention-guided feature fusion module (AGFF) to optimize skip-feature selection and fusion in the decoder. Experiments were conducted on publicly available colorectal polyp image datasets, including Kvasir-SEG and CVC-ClinicDB [13-15]. Four models, including U-Net, U-Net+MCCB, U-Net+AGFF, and MCA-UNet, were compared, and all models were trained for 100 epochs. Dice, intersection over union (IoU), and mean absolute error (MAE) were used as the main evaluation metrics [20]. ResultsOn the mixed validation set, the Dice scores of U-Net, U-Net+MCCB, U-Net+AGFF, and MCA-UNet were 0.742, 0.771, 0.754, and 0.783, respectively; the corresponding IoU values were 0.603, 0.635, 0.618, and 0.649; and the MAE values were 0.102, 0.090, 0.097, and 0.086. Compared with the baseline U-Net, MCA-UNet improved Dice and IoU by 5.53% and 7.63%, respectively, while reducing MAE by 15.69%. Comparisons on the Kvasir-SEG and CVC-ClinicDB validation subsets further demonstrated the more stable performance of the proposed model. ConclusionBy jointly integrating multi-scale contextual modeling and attention-guided feature fusion, MCA-UNet effectively improves the accuracy and robustness of colorectal polyp segmentation and may provide useful support for intelligent endoscopic image analysis [12,17,18].

Conventional temporal interference stimulation (TI, TIS, or tTIS) leverages two pairs of electrodes to induce an interfering electrical field in the brain. Both computational and experimental studies show that TI can stimulate deep brain regions without significantly affecting shallow areas. While promising, optimization of the locations and dosages on these two pairs of electrodes for maximal focal modulation remains computationally challenging. We are the first to propose two arrays of electrodes instead of two or multiple pairs of electrodes to boost modulation focality. However, the optimization algorithm outputs too many electrodes with overlaps across two frequencies, making it difficult to implement in practice. Based on recent progress in developing multi-channel TI devices and computational work on TI optimization, here we again advocate two-array TI, but with solid software and hardware evidence to show the feasibility. Specifically, we show that the latest optimization algorithm for two-pair TI innately works for two-array TI with the fastest speed (under 30s) among all major algorithms. With a similar amount of electrodes, two-array TI could achieve better focality (3.03 cm) at the hippocampus even than TI using up to 16 pairs of electrodes (3.19 cm) that takes days to optimize. We also show a hardware implementation of two-array TI using 10 electrodes on our 8-channel TI device. We argue that two-pair TI is only preferred when one does not care about modulation focality and promote two-array TI for its advantages in focality and lower cost in terms of both optimization time and electrodes needed. We restate the focality-intensity tradeoff but in the context of TI and provide a first voxel-level map of achievable focality and modulation strength by TI in the MNI-152 head template. We hope this work will pave the way for future adoptions of two-array TI for more focal non-invasive deep brain stimulation.

Transcranial Magnetic Stimulation (TMS) involves pulsed magnetic fields that pass through the scalp to stimulate the brain, with incidental stimulation to superficial nerves and muscles. From a research perspective, the tactile sensations can be a problematic confound, particularly when stimulation approaches an unpleasant or painful level. Additionally, tactile sensations contribute to difficult challenges in establishing an appropriate sham control condition. Clinically, some patients find stimulation uncomfortable or intolerable. Clinicians need data on adjustments to stimulation parameters to improve tolerability and efficacy. The primary objective of this study was to characterize the tolerability of TMS by location (over modified Beam F3 prefrontal, THREE-D prefrontal, right orbitofrontal, medial prefrontal, motor, and parietal cortical targets, as well as the knee) and by frequency (1 Hz, 10 Hz, or iTBS), with increasing levels of stimulation intensity. We also characterized sensory thresholds and qualitative aspects of stimulation across locations and frequencies. For location, sites distal to the facial nerves and muscle (Knee, P3, M1, mPFC) were more tolerable, followed by Beam F3, with the THREE-D and AF8 locations as least tolerable. For frequency, we found that 1 Hz was significantly more tolerable than 10 Hz and iTBS. iTBS was more annoying than 10 Hz but only marginally different in tolerability. TMS researchers and clinicians should understand the impact of sensation based on location and frequency, with increasing stimulation intensity. This is a single-session study in generally healthy individuals, and there is a need for additional data to further inform research and clinical practice.

As cell imaging grows in scale, precision, and complexity, data integration and harmonization become increasingly important for studying cell-material interactions. Quantitative understanding of how cells respond to mechanical cues, such as substrate stiffness and topography, is often limited by differences in experimental conditions and imaging formats. This study presents a framework that combines compact, interpretable cell shape models with generative artificial intelligence to harmonize 2D and 3D immunofluorescent datasets within defined experimental contexts. By efficiently capturing morphology and associated biological features, the approach enables generation of realistic synthetic cells, including rare or intermediate phenotypes, to augment machine-learning analyses and support scalable in silico studies. This work advances data-driven investigation of cellular responses to biomaterial-derived mechanical cues.

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and time-effective detection methods. In this paper, this paper introduces a novel approach to the diagnosis of CAD based on a Convolutional Neural Network (CNN) with a temporal attention mechanism. The model will be developed on an architecture that will automatically extract and emphasize critical features from sequential medical imaging data from coronary angiograms, allowing subtle signs of CAD to be easily spotted, which could not have been detected by convention. The temporal attention mechanism strengthens the ability of a model to focus on relevant temporal patterns, thus improving sensitivity and robustness in detecting CAD for various stages of the disease. Experimental validation on a large and diverse dataset demonstrates the efficacy of the proposed method, with significant improvements in both detection accuracy and processing time compared to traditional CNN architectures. The results of this study propose a scalable solution system for the diagnosis of CAD. This proposed system can be integrated into clinical workflows to assist healthcare professionals. Ultimately, this research contributes to the field of AI-driven healthcare solutions and has the potential to reduce the global burden of CAD through early automated detection.

Abstract The ISBAR framework is used to standardize clinical handovers and enhance patient safety. Observational tools based on ISBAR have been developed to assess the completeness of information transfer. However, these instruments have primarily been developed in non-Chinese contexts, and validated Chinese-language observational tools suitable for clinical practice remain limited. In this study, a cross-cultural adaptation and psychometric validation of the ISBAR Structured Handover Observation Tool was conducted, examining its reliability and discriminant validity in Chinese clinical settings. The study was conducted in two phases: cross-cultural adaptation and psychometric evaluation in real-world clinical settings. Content validity was assessed using the Content Validity Index (CVI), and inter-rater reliability was evaluated using the Intraclass Correlation Coefficient (ICC) based on a two-way mixed-effects model with absolute agreement. Discriminant validity was examined using the Mann-Whitney U test to compare scores across nurses with varying levels of clinical experience. A total of 233 handover cases involving patient transfers from the intensive care unit (ICU) to general wards were collected, involving 84 nurses. The scale demonstrated good content validity, with item-level content validity indices (CVI) ranging from 0.88 to 1.00 and a scale-level CVI/Ave of 0.98. The inter-rater reliability, assessed using fifty randomly selected cases, was high, with an intraclass correlation coefficient (ICC) of 0.885 for single-rater assessments and 0.939 for average-rater assessments. Discriminant validity analysis showed that nurses with more clinical experience had significantly higher total scores than those with less experience (Z = -4.772, p < 0.001). The Chinese version of the ISBAR Structured Handover Observation Tool demonstrates good content validity, high inter-rater reliability, and acceptable discriminant validity. This tool provides a standardized and practical method for assessing the completeness of information transfer and is expected to support quality improvement in patient handover from the ICU to general wards in Chinese clinical settings.

Distributed manufacturing of open-source hardware shows potential to offer accessible, affordable, and customizable solutions for users in low-resource contexts. Their real-world adoption, however, depends not only on the availability of openly shared designs but also on their replicability when fabricated in different local contexts. This work investigates the replicability of open-source hardware through a practical design-driven approach, using the development and experimental evaluation of a two-piece open-source forearm crutch as a case study. Replicability was considered from early-stage design and evaluated by introducing controlled variations from distributed manufacturing contexts, e.g., material feedstock, manufacturing equipment, and fabrication strategies. Four batches of crutches were fabricated and assembled, using virgin and recycled filaments on small- and large-format 3D printers. After the qualitative evaluation, mechanical static load testing was performed following ISO 11334:2007, together with economic analysis. Comparable mean load-bearing and consistent failure behavior were achieved across batches, making them suitable for use in pairs. Limited cost variability was achieved, supporting repairability and product lifecycle extension. Beyond the specific case study, replicability of open-source hardware needs to be considered as an early-stage design constraint by developing products that allow for variability from local contexts and by including product-specific approaches to assess replicability during development.

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive procedure for the treatment of thoracic aortic pathologies, such as Thoracic Aortic Aneurysm (TAA). Computational simulations can provide valuable insights into TEVAR outcomes and complications prior to surgery, making them a useful tool in the procedural planning. In this work, Fluid-Structure Interaction (FSI) computational simulations are carried out in ten pre-TEVAR patient-specific TAA cases, for which post-TEVAR outcomes are known, to quantify the hemodynamic drag forces acting on the aortic wall. Based on these results, this study proposes a new risk factor R to predict the occurrence of type I and III endoleaks. The patient cohort is divided in a calibration set, used to associate specific R values with three different risk levels, and a validation set, to test the risk factor efficacy. Based on the risk factor values obtained for the calibration set, R[&le;] 0.33 is associated with low risk of endoleak formation, 0.33 < R[&le;] 0.67 with moderate risk, and R > 0.67 with high risk. Once it is applied to the validation set,the risk factor is able to predict the formation of a type Ia endoleak. The risk factor proposed in this work is capable of identifying all the endoleak cases analysed, as well as conditions known to increase the risk of TEVAR complications. This study represents a preliminary attempt to determine whether pre-TEVAR hemodynamics can effectively predict post-TEVAR complications and thereby aid clinicians in the pre-operative planning.

BackgroundVascular mural cells (MC) are essential components of vasculature, playing critical roles in tissue regeneration and cell therapy. The use of animal derived ancillary materials, like fetal bovine serum (FBS), in the induction of MC from human pluripotent stem cells (hPSCs), represents one of the biggest limitations to guarantee preclinical safety standards required to use this products in clinical settings. This study aimed to validate human platelet lysate (hPL) as a serum-free alternative for MC differentiation from hPSCs. MethodsComparison of MC differentiation efficiency from hiPSC using FBS vs hPL supplemented cultures was performed, along with functionality and gene expression assessment through bulk RNA sequencing. ResultsOptimization of hPL concentration identified hPL1% as the most effective condition, yielding PDGFR-{beta}+/CNN1+ MC, with a comparable efficiency to FBS10% and similar interaction with endothelial cells in vascular formation assays. However, distinct transcriptional profiles revealed that FBS10% and hPL1% drive differentiation toward different MC subphenotypes; hPL1% promoted contractile gene expression, while FBS10% enriched extracellular matrix pathways. Higher hPL concentrations further shifted differentiation toward cardiomyocytes. ConclusionIn monolayer in vitro differentiation of MC from hiPSC, the differentiation efficiency using hPL 1% supplementation is equivalent to FBS 10%, while supporting a more contractile phenotype. These findings establish hPL as a xeno-minimized, clinically compliant substitute for FBS for hPSC-derived MC differentiation, an important breakthrough for regenerative medicine.