Bioengineering最新文献

The aim of this study was to predict Bruch's membrane opening-minimum rim Width (BMO-MRW), a relatively new parameter using conventional optical coherence tomography (OCT) parameter, using retinal nerve fibre layer (RNFL) thickness and visual field (VF) global indexes (MD, PSD, and VFI). We developed an interpretable machine learning model that integrates structural and functional parameters to predict BMO-MRW. The model achieved the highest predictive accuracy in the inferotemporal sector (R² = 0.68), followed by the global region (R² = 0.67) and the superotemporal sector (R² = 0.64). Through SHAP (SHapley Additive exPlanations) analysis, we demonstrated that RNFL parameters were significant contributing parameters to the prediction of various BMO-MRW parameters, with age and PSD also identified as critical factors. Our machine learning model could provide useful clinical information about the management of glaucoma when BMO-MRW is not available. Our machine learning model has the potential to be highly beneficial in clinical practice for glaucoma diagnosis and the monitoring of disease progression.

The construction of artificial microorganisms often relies on the transfer of genomes from donor to acceptor cells. This synthetic biology approach has been considerably fostered by the J. Craig Venter Institute but apparently depends on the use of microorganisms, which are very closely related. One reason for this limitation of the "creative potential" of "classical" transformation is the requirement for adequate "fitting" of newly synthesized polypeptide components, directed by the donor genome, to interacting counterparts encoded by the pre-existing acceptor genome. Transformation was introduced in 1928 by Frederick Griffith in the course of the demonstration of the instability of pneumococci and their conversion from rough, non-pathogenic into smooth, virulent variants. Subsequently, this method turned out to be critical for the identification of DNA as the sole matter of inheritance. Importantly, the initial experimental design (1.0) also considered the inheritance of both structural (e.g., plasma membranes) and cybernetic information (e.g., metabolite fluxes), which, in cooperation, determine topological and cellular heredity, as well as fusion and blending of bacterial cells. In contrast, subsequent experimental designs (1.X) were focused on the use of whole-cell homogenates and, thereafter, of soluble and water-clear fractions deprived of all information and macromolecules other than those directing protein synthesis, including outer-membrane vesicles, bacterial prions, lipopolysaccharides, lipoproteins, cytoskeletal elements, and complexes thereof. Identification of the reasons for this narrowing may be helpful in understanding the potential of transformation for the creation of novel microorganisms.

Over the last several decades, computational biomechanics has been intensively investigated as part of the study of human body systems (musculoskeletal, cardiovascular, digestive, etc [...].

The increasing global demand for natural substances such as the sesquiterpene α-humulene makes optimizing microbial production essential. A production process using the versatile host Cupriavidus necator has been recently improved by adjusting the minimal media and process parameters. Understanding microbial and process robustness is key to ensuring consistent performance under different conditions. This study is the first to investigate and quantify the robustness of microbial α-humulene production and biomass formation using C. necator pKR-hum. Established process improvements and the impact of common or individual precultures were analyzed and quantified for their effect on the robustness of product and biomass formation. We report a robust α-humulene production process with even more consistent biomass formation using C. necator pKR-hum. Even with a simulated process disturbance, 79% of the maximum α-humulene level was still produced. Overall, our results show that the α-humulene production process using C. necator pKR-hum is highly robust, demonstrating its resilience to process disturbances and suitability for further industrial applications.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI) model based on publicly available genomic datasets to predict hypoxia-related immunotherapy responses. Based on the HCC-Hypoxia Overlap (HHO) and immunotherapy response to hypoxia (IRH) genes selected by differential expression and enrichment analyses, a hypoxia model was built and validated on the TCGA-LIHC and GSE233802 datasets, respectively. The training and test sets were assembled from the EGAD00001008128 dataset of 290 HCC patients, and the response and non-response classes were balanced using the Synthetic Minority Over-sampling Technique. With the genes selected via the minimum Redundancy Maximum Relevance and stepwise forward methods, a Kolmogorov-Arnold Network (KAN) model was trained. Support Vector Machine (SVM) combined the Hypoxia and KAN models to predict immunotherapy response. The hypoxia model was constructed using 10 genes (IRH and HHO). The KAN model with 11 genes achieved a test accuracy of 0.7. The SVM integrating the hypoxia and KAN models achieved a test accuracy of 0.725. The established AI model can predict immunotherapy response based on hypoxia risk and genomic factors potentially intervenable in HCC patients.

Background: Hallux limitus (HL) is a condition marked by the restricted dorsiflexion of the first metatarsophalangeal joint, causing pain and functional limitations, especially during the propulsive phase of walking. This restriction affects the gait, particularly in the final phase, and impairs foot stability and support. HL is more common in adults and leads to biomechanical and functional adaptations. The purpose of this study was to investigate the differences in the center of pressure between subjects with hallux limitus and those with healthy feet. Methods: A total of 80 participants (40 with bilateral HL and 40 healthy controls) aged 18 to 64 were selected from a biomechanics center at the Universidade da Coruña, Spain. The gait analysis focused on three key phases: initial contact, forefoot contact, and the loading response. Data were collected using a portable baropodometric platform and analyzed using IBM SPSS Statistics 29.0.2.0; statistical significance was set at p < 0.05, with a 95% confidence interval. Results: The gait analysis indicated that the case group exhibited statistically significant differences, showing lower values in the left foot load response during the foot contact time (77.83 ± 40.17) compared to the control group (100.87 ± 29.27) (p = 0. 010) and in the foot contact percentage (p = 0. 013) during the stance phase (10.02 ± 5.68) compared to the control group (13.05 ± 3.60). Conclusions: Bilateral HL causes subtle gait changes, with individuals showing greater contact time values in the total stance phase versus the control group. Early detection may improve quality of life and prevent complications.

Cranial sutures play critical roles in load distribution and neuroprotection, with their biomechanical performance intimately linked to morphological complexity. The purpose of this study was to investigate the effect of different morphologies of cranial sutures on their biomechanical behavior. Based on the different morphologies of the cranial sutures, six groups of finite element models (closed, straight, sine wave, tight sinusoidal wave, layered sinusoidal wave, and layered sinusoidal wave + sutural bone) of the bone-suture-bone composite structures that ranged from simple to complex were constructed. Each model was subjected to 50 kPa impact and 98 N bilateral tensile loads to evaluate von Mises stress and total deformation variations across all groups under combined loading conditions. Key findings reveal that morphological complexity directly governs stress dynamics and mechanical adaptation; layered sinusoidal configurations delayed peak stress by 19-36% and generated elevated von Mises stresses compared to closed sutures, with stress concentrations correlating with interfacial roughness. Under impact, sutures exhibited localized energy dissipation (<0.2 μm deformation), while tensile loading induced uniform displacements (≤11 μm) across all morphologies (p > 0.05), underscoring their dual roles in localized energy absorption and global strain redistribution. Craniosacral therapy relevant forces produced sub-micron deformations far below pathological thresholds (≥1 mm), which implies the biomechanical safety of recommended therapeutic force. Staggered suture-bone in open sutures (31.93% closure rate) enhances shear resistance, whereas closed sutures prioritize rigidity. The findings provide mechanistic explanations for suture pathological vulnerability and clinical intervention limitations, offering a quantitative foundation for future research on cranial biomechanics and therapeutic innovation.

Intra-experimental factor setting shifts in intensified design of experiments (iDoE) enhance understanding of bioproduction processes by capturing their dynamics and are thus essential to fulfill quality by design (QbD) ambitions. Determining the influence of process history on the cellular responses, often referred to as memory effect, is fundamental for accurate predictions. However, the current iDoE designs do not explicitly consider nor quantify the influence of process history. Therefore, we propose the one-factor-multiple-columns (OFMC)-format for iDoE planning. This format explicitly describes stage-dependent factor effects and potential memory effects as across-stage interactions (ASIs) during a bioprocess. To illustrate its utility, an OFMC-iDoE that considers the characteristic growth phases during a fed-batch process was planned. Data were analyzed using ordinary least squares (OLS) regression as previously described via stage-wise analysis of the time series and compared to direct modeling of end-of-process outcomes enabled by the OFMC-format. This article aims to provide the reader with a framework on how to plan and model iDoE data and highlights how the OFMC-format simplifies planning, and data acquisition, eases modeling and gives a straightforward quantification of potential memory effects. With the proposed OFMC-format, optimization of bioprocesses can leverage which factor settings are most beneficial in which state of the mammalian culture and thus elevate performance and quality to the next level.

The analysis of human gait is a cornerstone in diagnosing and monitoring a variety of neuromuscular and orthopedic conditions. Recent technological advancements have paved the way for innovative methodologies that combine multimodal neuroimaging and eXtended Reality (XR) technologies to enhance the precision and applicability of gait analysis. This review explores the state-of-the-art solutions of an advanced gait analysis approach, a multidisciplinary concept that integrates neuroimaging, extended reality technologies, and sensor-based methods to study human locomotion. Several wearable neuroimaging modalities such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), commonly used to monitor and analyze brain activity during walking and to explore the neural mechanisms underlying motor control, balance, and gait adaptation, were considered. XR technologies, including virtual, augmented, and mixed reality, enable the creation of immersive environments for gait analysis, real-time simulation, and movement visualization, facilitating a comprehensive assessment of locomotion and its neural and biomechanical dynamics. This advanced gait analysis approach enhances the understanding of gait by examining both cerebral and biomechanical aspects, offering insights into brain-musculoskeletal coordination. We highlight its potential to provide real-time, high-resolution data and immersive visualization, facilitating improved clinical decision-making and rehabilitation strategies. Additionally, we address the challenges of integrating these technologies, such as data fusion, computational demands, and scalability. The review concludes by proposing future research directions that leverage artificial intelligence to further optimize multimodal imaging and XR applications in gait analysis, ultimately driving their translation from laboratory settings to clinical practice. This synthesis underscores the transformative potential of these approaches for personalized medicine and patient outcomes.

Pulse oximetry is essential for monitoring arterial oxygen saturation (SpO2) and heart rate (HR) in various medical scenarios. However, the traditional pulse oximeters face challenges related to high costs, motion artifacts, and susceptibility to ambient light interference. This work presents a low-cost experimental pulse oximeter prototype designed to address these limitations through design advancements. The device incorporates a 3D-printed finger support to minimize motion artifacts and excessive capillary pressure, along with an elastic element to enhance stability. Unlike conventional transmission-based oximetry, the prototype employs a reflectance-based measurement approach, improving versatility and enabling reliable readings even in cases of poor peripheral perfusion. Additionally, the integration of light-shielding materials mitigates the effects of ambient illumination, ensuring accurate operation in challenging environments such as surgical settings. Metrological characterization demonstrates that the prototype achieves accuracy comparable to that of the commercial GIMA Oxy-50 pulse oximeter while maintaining a production cost at approximately one-tenth of the commercial alternatives. This study highlights the potential of the prototype to deliver affordable and reliable pulse oximetry for different applications.