Bioengineering最新文献

Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms' classification accuracy and patients' rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients' rehabilitation prognosis.

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia, with an estimated five million cases globally. This condition increases the likelihood of developing cardiovascular complications such as thromboembolic events, with a fivefold increase in risk of both heart failure and stroke. Contemporary challenges include a better understanding AF pathophysiology and optimizing therapeutical options due to the current lack of efficacy and adverse effects of antiarrhythmic drug therapy. Hence, the identification of novel biomarkers in biological samples would greatly impact the diagnostic and therapeutic opportunities offered to AF patients. Long noncoding RNAs, micro RNAs, circular RNAs, and genes involved in heart cell differentiation are particularly relevant to understanding gene regulatory effects on AF pathophysiology. Proteomic remodeling may also play an important role in the structural, electrical, ion channel, and interactome dysfunctions associated with AF pathogenesis. Different devices for processing RNA and proteomic samples vary from RNA sequencing and microarray to a wide range of mass spectrometry techniques such as Orbitrap, Quadrupole, LC-MS, and hybrid systems. Since AF atrial tissue samples require a more invasive approach to be retrieved and analyzed, blood plasma biomarkers were also considered. A range of different sample preprocessing techniques and bioinformatic methods across studies were examined. The objective of this descriptive review is to examine the most recent developments of transcriptomics, proteomics, and bioinformatics in atrial fibrillation.

The majority of the current cancer research is based on two-dimensional cell cultures and animal models. These methods have limitations, including different expressions of key factors involved in carcinogenesis and metastasis, depending on culture conditions. Addressing these differences is crucial in obtaining physiologically relevant models. In this manuscript we analyzed the plasticity of the expression of stem cell and epithelial/mesenchymal markers in breast cancer cells, depending on culture conditions. Significant differences in marker expression were observed in different growth models not only between 2D and 3D conditions but also between two different 3D models. Differences observed in the levels of adherent junction protein E-cadherin in two different 3D models suggest that spatial parameters of cell growth and physical stress in the culture may affect the expression of junction proteins. To provide an explanation of this phenomenon on the grounds of mechanobiology, these parameters were analyzed using a mathematical model of the 3D bioprinted cell culture. The finite element mechanical model generated in this study includes an extracellular matrix and a group of regularly placed cells. The single-cell model comprises an idealized cytoskeleton, cortex, cytoplasm, and nucleus. The analysis of the model revealed that the stress generated by external pressure is transferred between the cells, generating specific stress fields, depending on growth conditions. We have analyzed and compared stress fields in two different growth conditions, each corresponding to a different elasticity of extracellular matrix. We have demonstrated that soft matrix conditions produce more stress than a stiff matrix in the single cell as well as in cellular spheroids. The observed differences can explain the plasticity of E-cadherin expression in response to mechanical stress. These results should contribute to a better understanding of the differences between various growth models.

In this study, we present an oncolytic virus (OV) evaluation system established using microfluidic organ-on-a-chip (OOC) systems and patient-derived organoids (PDOs), which was used in the development of a novel oncolytic virus, AD4-GHPE. An OV offers advantages such as good targeting ability and minimal side effects, and it has achieved significant breakthroughs when combined with immunotherapy in recent clinical trials. The development of OVs has become an emerging research focus. PDOs can preserve the heterogeneity of in situ tumor tissues, whereas microfluidic OOC systems can automate and standardize various experimental procedures. These systems have been applied in cutting-edge drug screening and cell therapy experiments; however, their use in functionally complex oncolytic viruses remains to be explored. In this study, we constructed a novel recombinant oncolytic adenovirus, AD4-GHPE, and evaluated OOC systems and PDOs through various functional validations in hypopharyngeal and breast cancer organoids. The results confirmed that AD4-GHPE exhibits three antitumor mechanisms, namely, tumor-specific cytotoxicity, a reduction in programmed death ligand 1 (PD-L1) expression in tumor cells to increase CD8⁺ T-cell activity, and granulocyte-macrophage colony-stimulating factor (GM-CSF) secretion. The evaluation system combining OOC systems and PDOs was efficient and reliable, providing personalized OV treatment recommendations for patients and offering industrialized and standardized research ideas for the development of OVs.

Actions for improving water quality are critical and include the remediation of polluted groundwater. The effectiveness of the remediation strategy to remove contamination by chlorinated solvents may be increased by combining physicochemical treatments (i.e., adsorption) and biological degradation (i.e., biological reductive dechlorination (BRD)). Recent studies have shown the potentialities of bio-based materials for bioremediation purposes, including polyhydroxybutyrate (PHB), a biodegradable microbial polyester tested as a fermentable source of slow-release electron donors. Further, a low-cost biochar derived from the pyrolysis of pinewood waste (PWB), used as sorbent material, has recently been proposed to accelerate reductive microbial dehalogenation. Here, we propose a coupled adsorption and biodegradation (CAB) process for trichloroethylene (TCE) removal in a mini pilot-scale reactor composed of two reactive zones, the first one filled with PHB and the second one with PWB. This work aimed to evaluate the performance of the CAB process with particular regard to the effectiveness of the PWB in sustaining the biofilm, mostly enriched by Dehalococcoides mccartyi. The main results showed the CAB system treated around 1300 L of contaminated water, removing 102 mg TCE per day. Combining PHB and PWB had a positive effect on the growth of the dechlorinating community with a high abundance of Dhc cells.

Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual's ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor function and improving patient outcomes. This systematic review examines the application of machine learning and deep learning techniques in myoelectric-controlled systems for upper limb rehabilitation, focusing on the use of electroencephalography and electromyography signals. By integrating non-invasive signal acquisition methods with advanced computational models, the review highlights how these technologies can enhance the accuracy and efficiency of rehabilitation devices. A comprehensive search of literature published between January 2015 and July 2024 led to the selection of fourteen studies that met the inclusion criteria. These studies showcase various approaches in decoding motor intentions and controlling assistive devices, with models such as Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks showing notable improvements in control precision. However, challenges remain in terms of model robustness, computational complexity, and real-time applicability. This systematic review aims to provide researchers with a deeper understanding of the current advancements and challenges in this field, guiding future research efforts to overcome these barriers and facilitate the transition of these technologies from experimental settings to practical, real-world applications.

Our group has previously demonstrated that tissue-engineered dermis containing cultured fibroblasts or adipose-derived stromal vascular fraction cells is superior to artificial dermis in terms of scar quality for covering facial defects. However, using these cells for clinical applications requires Food and Drug Administration approval and involves complex procedures for cell culture or isolation. This retrospective study aimed to compare effects of tissue-engineered dermis containing micronized adipose tissue (MAT) and artificial dermis for facial reconstruction. Tissue-engineered dermis consisting of MAT seeded on artificial dermis was applied in 30 cases, while artificial dermis without MAT was grafted in 35 cases. Healing time and severities of scar contraction, color mismatch, and landmark distortion at one year after healing were evaluated. Wounds in the tissue-engineered dermis group re-epithelialized in 30.0 ± 4.3 days compared to 34.3 ± 5.4 days in the artificial dermis group (p < 0.05). The average dE2000 score in color mismatch analysis was 4.9 ± 1.7 in the tissue-engineered dermis group and 5.1 ± 1.7 in the artificial dermis group (p = 0.57). The extent of scar contraction was 16.2 ± 12.3% in the tissue-engineered dermis group and 23.2 ± 12.8% in the artificial dermis group (p < 0.05). The average severity grade of landmark distortion was 0.20 ± 0.50 in the tissue-engineered dermis group and 0.50 ± 0.71 in the artificial dermis group (p < 0.05). These findings indicate that tissue-engineered dermis grafts containing MAT are superior to artificial dermis grafts for facial reconstruction in terms of healing time, scar contraction, and landmark distortion severity. However, there was no significant difference in color mismatch between the two groups.

Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the "butterfly pattern" (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the "medial arch + metatarsal area" pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.

The present study explores the anatomical differences between sexes of the carotid artery using non-invasive magnetic resonance imaging (MRI) and a DICOM processing protocol. Bilateral three-dimensional models of the carotid artery were constructed for 20 healthy young adults, 10 males and 10 females, in order to evaluate key anatomical landmarks; these include the bifurcation diameter and angle, as well as the internal and external carotid arteries (ICA and ECA) for both sides (left and right). The results show that males exhibit larger bifurcation and ECA diameters, which could indicate reduced endothelial shear stress (ESS). However, as there is no previously observed sex difference in ESS between sexes, compensatory factors might be in play, such as blood pressure. This underscores the interaction between vascular geometry and stroke risk disparities; future research is encouraged to analyze diverse demographics and employ flow modeling techniques to further asses the connection between anatomical differences within a given population and vascular outcomes.

The ability to measure vital signs using electromagnetic waves has been extensively investigated as a less intrusive method capable of assessing different biosignal sources while using a single device. On-body antennas, when directly coupled to the human body, offer a comfortable and effective alternative for daily monitoring. Nonetheless, on-body antennas are challenging to design primarily due to the high dielectric constant of body tissues. While the simulation process may often include a body model, a unique model cannot account for inter-individual variability, leading to discrepancies in measured antenna parameters. A potential solution is to increase the antenna's bandwidth, guaranteeing the antenna's impedance matching and robustness for all users. This work describes a new on-body microstrip antenna having a stacked structure with parasitic elements, designed and optimized using artificial intelligence (AI). By using an AI-driven design approach, a self-adaptive Bayesian neural network surrogate-model-assisted differential evolution for antenna optimization (SB-SADEA) method to be specific, and a stacked structure having parasitic elements and a defected ground structure with 27 tuneable design parameters, the simulated impedance bandwidth of the on-body antenna was successfully enhanced from 150 MHz to 1.3 GHz, while employing a single and simplified body model in the simulation process. Furthermore, the impact of inter-individual variability on the measured S-parameters was analyzed. The measured results relative to ten subjects revealed that for certain subjects, the SB-SADEA-optimized antenna's bandwidth reached 1.6 GHz.