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.

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

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.

Ovarian cancer is one of the gynecological cancer types, which, if metastasized and not detected early, can cause deaths among women. Therefore, there is a need to accurately predict drug responses to ovarian cancer. A gynecological pathologist inspects abnormality in tissues, followed by providing a report about patients; however, such a diagnostic process is (1) hard; (2) requires experience; and (3) time consuming. Moreover, existing tools are far from perfect. Hence, we present a computational pipeline to improve predicting drug response pertaining to ovarian cancer, derived as follows. First, we download digital pathology images pertaining to ovarian bevacizumab response from the cancer imaging archive repository. We employed histogram of oriented gradients to images, constructing feature vectors, provided to Fisher linear discriminant analysis to change the representation through dimensionality reduction. Then, we provide reduced-dimensionality data for regression analysis through support vector regression coupled with various kernels and calculating the area under the ROC curve (AUC). Experimental results against transformer-based models (ViT and Swin) and other deep learning (DL) models (VGG16, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB6) demonstrate that our approach with radial kernel (named SVRD+R) yielded an AUC performance improvements of 17% against the best-performing transformer-based model (ViT) while obtaining an AUC performance improvements of 14.9% when compared against the best DL-based model (MobileNetV2). These results demonstrate the superiority and feasibility of our AI-based pipeline when tackling prediction problems pertaining to gynecologic cancer studies. MSC92B05; 68T09

Research questionCan tissue clearing, combined with volumetric imaging, enable reliable, quantitative three-dimensional analysis of follicles and vasculature in intact human ovarian tissue? DesignA CUBIC-based clearing protocol was adapted for human ovarian medulla and cryopreserved cortex. Tissue from reproductive-aged donors was cleared, fluorescently labeled, and imaged using confocal and light sheet microscopy. Tissue expansion, imaging depth, and vascular morphometrics were quantified and follicle density was compared to conventional histology. ResultsClearing produced optically transparent tissue with a linear expansion factor of 1.2 across cortex and medulla. Imaging depth increased 6.5-11-fold in cortex and 6-8-fold in medulla. Follicle density measurements in immunolabeled cleared cortex were comparable to histology, supporting the validity of volumetric follicle quantification. Light sheet microscopy of lectin-labeled cortex revealed no significant donor-to-donor differences in vascular morphometrics, including mean vessel diameters of 12-14 {micro}m, branch point densities of 632-965 points/mm3, vessel length densities of 117-175 mm/mm3, and volume fractions of 1.9-2.3%. Volumetric imaging further illustrated heterogeneous spatial relationships between follicles and surrounding vessels. ConclusionTissue clearing and volumetric imaging complement routine histology and enable quantitative three-dimensional investigation of follicle-vascular interactions in intact human ovarian tissue, providing a framework for advancing fertility preservation and ovarian tissue transplantation research.

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.

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

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.

Melanocytes become cancerous, forming tumors that may invade and destroy the surrounding tissues. When melanocytes acquire invasive characteristics, the anchored melanoma begins to damage the normal cells. Therefore, early intervention and diagnosis are essential to avoid high morbidity and mortality in malignant melanoma. However, It is challenging to distinguish the difference between malignant melanoma and benign clump of melanocytes. Based on a data set of 10,000 melanocyte tumors, this paper develops a new model system to improve the accuracy of distinguishing between benign and malignant melanocytes. In the first stage, the original CNN architectures are used, such as ResNet18, ResNet50, VGG11, and VGG16. Synthetic medical images, generated via a Diffusion Model to extract informative features from the original dataset, are used to train the CNN architectures. This approach improves classification accuracy from 91.1% to 92.9%. In the second stage, the fully connected layer of each neural network is replaced with a high-level classifier, XGBoost, to perform secondary classification. This hybrid strategy further enhances performance, achieving up to 93.3% accuracy by using the synthetic images.

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.

Background and aimTrauma healing with low-intensity ultrasound is effective for different types of injuries affecting both soft tissues and bones. The work aimed to disclose the healing potential of a new type of ultrasound, ultra-wideband low-intensity mechanical waves (UMUS), and to compare its effects with those of low-intensity pulsed ultrasound (LIPUS) in a model of trauma. Material and methodsThe work was performed on 2-to 3-month-old male Wistar rats. The model of tail amputation was created, and a transducer emitting UMUS (1-7 MHz, 0.22 mW/cm2) was applied daily for 10 days to the surface of the trauma site in animals that were timely immobilized. LIPUS (1.5 mHz, 30.0 mW/cm2) was used in a separate group of animals. Sham-stimulated rats were used as a control. The intensity of collagen expression in the subdermal tissue was assessed in van Gieson-stained sections, whereas in the UMUS group, expression of CD31, CD34, VEGF, and Ki67 was analyzed. ResultsStarting on the 20th day after trauma, UMUS-treated animals demonstrated a statistically significant decrease in the surface area of the traumatic zone compared to the control, whereas LIPUS-treated rats showed this difference on the 30th day of observation. Starting from the 30th day, a significantly greater reduction in the surface of trauma was observed in UMUS, with complete closure achieved in 6 out of 9 rats (P=0.019 vs control), whereas in LIPUS-treated animals, a similar result was observed in 2 out of 8 rats (P>0.05). In UMUS-treated rats, heightened expression of collagen in animals with LIPUS exceeded control data by 7.84% (P=0.034), while the expression in rats with UMUS exceeded data in LIPUS-treated rats by 14.71% (P=0.013). Increased expression of CD31, CD34, VEGF, and Ki67 was observed in UMUS-treated rats. ConclusionsUMUS treatment accelerated healing and reduced wound size, and increased the expression of collagen, CD31, VEGF, CD34, and Ki67, supporting angiogenesis and collagen formation. Effects are more pronounced compared to LIPUS treatment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=176 HEIGHT=200 SRC="FIGDIR/small/717366v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@936775org.highwire.dtl.DTLVardef@16d505eorg.highwire.dtl.DTLVardef@1b75d7dorg.highwire.dtl.DTLVardef@15ae99c_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

IntroductionIntraoperative glycemic dysregulation, including unrecognized hypoglycemia and stress-induced hyperglycemia, is common during elective surgery. Conventional point-of-care (POC) monitoring provides only intermittent measurements, limiting the anesthesiologists ability to detect rapid glucose fluctuations. Continuous glucose monitoring (CGM) enables real-time, trend-based assessment, potentially shifting intraoperative glycemic management from reactive to proactive. ObjectiveTo meta-analyze the analytical accuracy, intraoperative glycemic efficacy, and feasibility of subcutaneous CGM in adults undergoing elective surgery, informing anesthesiology practice. MethodsThis systematic review and meta-analysis followed the PRISMA 2020 statement. Searches were conducted in PubMed, Embase, and Cochrane Central Register of Controlled Trials from January 2010 to May 2025. Eligible studies included randomized controlled trials and prospective cohorts of adults undergoing elective surgery under general or neuraxial anesthesia using subcutaneous CGM. Primary outcomes were pooled mean absolute relative difference (MARD) and time in range (TIR, 70-180 mg/dL). Random-effects models were applied. ResultsTen studies (3 RCTs, 7 cohorts; N=557) were included. Pooled MARD was 14.1% (95% CI 11.3-16.9%; I{superscript 2}=78%), lower in non-cardiac surgery (12.7%) than cardiac procedures with hypothermia (19.2%; p=0.03). CGM improved TIR by +14.9 percentage points (95% CI 7.2-22.6; p<0.001). Clinically significant hypoglycemia was detected in 43% of patients, all missed by POC. Sensor availability exceeded 96%, with no serious device-related events. ConclusionSubcutaneous CGM provides acceptable intraoperative accuracy and improves glycemic control, supporting its integration into anesthetic management.

Background and ObjectivesWe 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. MethodsA 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. ResultsA 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). ConclusionFor 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.

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.

Finite element (FE) head models are valuable tools for investigating brain injury mechanics, with their reliability critically dependent on accurate material modelling. White matter (WM) is often considered mechanically anisotropic due to its aligned axonal fiber architecture and is commonly represented using fiber-reinforced hyperelastic formulations such as the Gasser-Ogden-Holzapfel (GOH) model. A fundamental assumption of the GOH model is that fibers contribute only in tension and not in compression, requiring the use of tension-compression switches. However, inconsistencies were noted in the formulation of tension-compression switches with the influence on computational biomechanics unknown. To address this knowledge gap, three commonly used switching schemes - differing in both the switching parameter and the treatment of compressed fibers - were theoretically elaborated and numerical implementation within the GOH framework to simulate the mechanical anisotropy of WM in impact simulations. Results from the case-based and group-level analyses demonstrated that both the switching parameter and the treatment of compressed fibers affected WM deformation. Significant cross-scheme strain differences were noted in the first principal strain at the element level and fiber strain at the fiber level. These findings highlighted the mechanical role of tension-compression switch in the GOH-based brain modelling and advocated the adoption of fiber stretch itself as the switching parameter to discriminate the tensile and compressive fibers. The current study provides important guidance for the anisotropic constitutive models in brain tissue and calls for direct verification of the tension-compression switch hypothesis in axonal fibers.

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.

Cryopreservation offers an option for long-term storage and global distribution of complex in vitro models, yet protocols for multicellular microphysiolgocial systems (MPS) such as brain organoids/spheroids remain limited. Here, we systematically compared three commercially available cryopreservation (mFreSR, CryoStorCS10, and 3dGRO) and two freezing time points, and established a robust workflow for freezing and recovering brain organoids. After defrosting, we assessed morphology and metabolic activity. We also evaluated electrophysiology, calcium transients, and neurite outgrowth. In addition, we measured astrocyte migration, apoptosis, mitochondrial integrity, microglia survival, and neural marker expression. We found that organoids require a 4-week recovery period to regain structural and functional stability. Although organoids frozen at week 6 showed higher metabolic activity after recovery, organoids cryopreserved at week 2 had clearly better functional outcomes. They exhibited stronger spontaneous network firing and maintained calcium transients. Finally, incorporated microglia-like cells survived the freezing and displayed comparable morphology to unfrozen controls. Across the endpoints measured here, 3dGRO showed the most favorable overall performance; formal ranking across media awaits harmonized normalization, single-organoid electrophysiology, and prespecified QC thresholds. Together, these results define a practical and reproducible cryopreservation strategy that preserves key physiological features of brain organoids and supports the establishment of ready-to-use organoid banks. The ability to reliably store and distribute complex brain-like tissues represents an essential step toward global standardization, scalable experimentation, and wider adoption of human-relevant microphysiological systems. Together, these results demonstrate recovery of key physiological features in the subset of organoids that remain viable after thaw and support the feasibility of brain organoid banking.