Journal Description
Bioengineering
Bioengineering
is an international, scientific, peer-reviewed, open access journal on the science and technology of bioengineering, published monthly online by MDPI. The Society for Regenerative Medicine (Russian Federation) (RPO) is affiliated with Bioengineering and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Biomedical)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.8 (2023)
Latest Articles
Alveolar Ridge Preservation Using a Novel Species-Specific Collagen-Enriched Deproteinized Bovine Bone Mineral: Histological Evaluation of a Prospective Case Series
Bioengineering 2024, 11(7), 665; https://doi.org/10.3390/bioengineering11070665 (registering DOI) - 28 Jun 2024
Abstract
In recent years, the significance of maintaining the alveolar ridge following tooth extractions has markedly increased. Alveolar ridge preservation (ARP) is a commonly utilized technique and a variety of bone substitute materials and biologics are applied in different combinations. For this purpose, a
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In recent years, the significance of maintaining the alveolar ridge following tooth extractions has markedly increased. Alveolar ridge preservation (ARP) is a commonly utilized technique and a variety of bone substitute materials and biologics are applied in different combinations. For this purpose, a histological evaluation and the clinical necessity of subsequent guided bone regeneration (GBR) in delayed implantations were investigated in a prospective case series after ARP with a novel deproteinized bovine bone material (95%) in combination with a species-specific collagen (5%) (C-DBBM). Notably, block-form bone substitutes without porcine collagen are limited, and moreover, the availability of histological data on this material remains limited. Ten patients, each scheduled for tooth extraction and desiring future implantation, were included in this study. Following tooth extraction, ARP was performed using a block form of C-DBBM in conjunction with a double-folded bovine cross-linked collagen membrane (xCM). This membrane was openly exposed to the oral cavity and secured using a crisscross suture. After a healing period ranging from 130 to 319 days, guided trephine drilling was performed for implant insertion utilizing static computer-aided implant surgery (s-CAIS). Cores harvested from the area previously treated with ARP were histologically processed and examined. Guided bone regeneration (GBR) was not necessary for any of the implantations. Histological examination revealed the development of a lattice of cancellous bone trabeculae through appositional membranous osteogenesis at various stages surrounding C-DBBM granules as well as larger spongy or compact ossicles with minimal remnants. The clinical follow-up period ranged from 2.5 to 4.5 years, during which no biological or technical complications occurred. Within the limitations of this prospective case series, it can be concluded that ARP using this novel C-DBBM in combination with a bovine xCM could be a treatment option to avoid the need for subsequent GBR in delayed implantations with the opportunity of a bovine species-specific biomaterial chain.
Full article
(This article belongs to the Section Regenerative Engineering)
Open AccessCommentary
Bridging the Gap: Integrating 3D Bioprinting and Microfluidics for Advanced Multi-Organ Models in Biomedical Research
by
Marco De Spirito, Valentina Palmieri, Giordano Perini and Massimiliano Papi
Bioengineering 2024, 11(7), 664; https://doi.org/10.3390/bioengineering11070664 (registering DOI) - 28 Jun 2024
Abstract
Recent advancements in 3D bioprinting and microfluidic lab-on-chip systems offer promising solutions to the limitations of traditional animal models in biomedical research. Three-dimensional bioprinting enables the creation of complex, patient-specific tissue models that mimic human physiology more accurately than animal models. These 3D
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Recent advancements in 3D bioprinting and microfluidic lab-on-chip systems offer promising solutions to the limitations of traditional animal models in biomedical research. Three-dimensional bioprinting enables the creation of complex, patient-specific tissue models that mimic human physiology more accurately than animal models. These 3D bioprinted tissues, when integrated with microfluidic systems, can replicate the dynamic environment of the human body, allowing for the development of multi-organ models. This integration facilitates more precise drug screening and personalized therapy development by simulating interactions between different organ systems. Such innovations not only improve predictive accuracy but also address ethical concerns associated with animal testing, aligning with the three Rs principle. Future directions include enhancing bioprinting resolution, developing advanced bioinks, and incorporating AI for optimized system design. These technologies hold the potential to revolutionize drug development, regenerative medicine, and disease modeling, leading to more effective, personalized, and humane treatments.
Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
Open AccessArticle
Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
by
Jiamin Guo, Wenjun Meng, Qian Li, Yichen Zheng, Hongkun Yin, Ying Liu, Shuang Zhao and Ji Ma
Bioengineering 2024, 11(7), 663; https://doi.org/10.3390/bioengineering11070663 (registering DOI) - 28 Jun 2024
Abstract
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its
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The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.
Full article
(This article belongs to the Special Issue Computational Biology and Biostatistics for Public Health)
Open AccessArticle
Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning
by
Guanjie Yuan, Lingli Cai, Weinuo Qu, Ziling Zhou, Ping Liang, Jun Chen, Chuou Xu, Jiaqiao Zhang, Shaogang Wang, Qian Chu and Zhen Li
Bioengineering 2024, 11(7), 662; https://doi.org/10.3390/bioengineering11070662 (registering DOI) - 28 Jun 2024
Abstract
Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with
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Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.
Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Open AccessArticle
Extracellular Overexpression of a Neutral Pullulanase in Bacillus subtilis through Multiple Copy Genome Integration and Atypical Secretion Pathway Enhancement
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Wenkang Dong, Xiaoping Fu, Dasen Zhou, Jia Teng, Jun Yang, Jie Zhen, Xingya Zhao, Yihan Liu, Hongchen Zheng and Wenqin Bai
Bioengineering 2024, 11(7), 661; https://doi.org/10.3390/bioengineering11070661 (registering DOI) - 28 Jun 2024
Abstract
Neutral pullulanases, having a good application prospect in trehalose production, showed a limited expression level. In order to address this issue, two approaches were utilized to enhance the yield of a new neutral pullulanase variant (PulA3E) in B. subtilis. One involved using
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Neutral pullulanases, having a good application prospect in trehalose production, showed a limited expression level. In order to address this issue, two approaches were utilized to enhance the yield of a new neutral pullulanase variant (PulA3E) in B. subtilis. One involved using multiple copies of genome integration to increase its expression level and fermentation stability. The other focused on enhancing the PulA-type atypical secretion pathway to further improve the secretory expression of PulA3E. Several strains with different numbers of genome integrations, ranging from one to four copies, were constructed. The four-copy genome integration strain PD showed the highest extracellular pullulanase activity. Additionally, the integration sites ytxE, ytrF, and trpP were selected based on their ability to enhance the PulA-type atypical secretion pathway. Furthermore, overexpressing the predicated regulatory genes comEA and yvbW of the PulA-type atypical secretion pathway in PD further improved its extracellular expression. Three-liter fermenter scale-up production of PD and PD-ARY yielded extracellular pullulanase activity of 1767.1 U/mL at 54 h and 2465.1 U/mL at 78 h, respectively. Finally, supplementing PulA3E with 40 U/g maltodextrin in the multi-enzyme catalyzed system resulted in the highest trehalose production of 166 g/L and the substrate conversion rate of 83%, indicating its potential for industrial application.
Full article
(This article belongs to the Section Biochemical Engineering)
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Open AccessArticle
Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs
by
Yilu Shou, Zhenpeng Chen, Pujie Feng, Yanan Wei, Beier Qi, Ruijuan Dong, Hongyu Yu and Haiyun Li
Bioengineering 2024, 11(7), 660; https://doi.org/10.3390/bioengineering11070660 (registering DOI) - 28 Jun 2024
Abstract
Background: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. Methods: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model
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Background: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. Methods: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. Results: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. Conclusion: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.
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(This article belongs to the Section Biomedical Engineering and Biomaterials)
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Open AccessArticle
Validation of Pelvis and Trunk Range of Motion as Assessed Using Inertial Measurement Units
by
Farwa Ali, Cecilia A. Hogen, Emily J. Miller and Kenton R. Kaufman
Bioengineering 2024, 11(7), 659; https://doi.org/10.3390/bioengineering11070659 - 28 Jun 2024
Abstract
Trunk and pelvis range of motion (ROM) is essential to perform activities of daily living. The ROM may become limited with aging or with neuromusculoskeletal disorders. Inertial measurement units (IMU) with out-of-the box software solutions are increasingly being used to assess motion. We
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Trunk and pelvis range of motion (ROM) is essential to perform activities of daily living. The ROM may become limited with aging or with neuromusculoskeletal disorders. Inertial measurement units (IMU) with out-of-the box software solutions are increasingly being used to assess motion. We hypothesize that the accuracy (validity) and reliability (consistency) of the trunk and pelvis ROM during steady-state gait in normal individuals as measured using the Opal APDM 6 sensor IMU system and calculated using Mobility Lab version 4 software will be comparable to a gold-standard optoelectric motion capture system. Thirteen healthy young adults participated in the study. Trunk ROM, measured using the IMU was within 5–7 degrees of the motion capture system for all three planes and within 10 degrees for pelvis ROM. We also used a triad of markers mounted on the sternum and sacrum IMU for a head-to-head comparison of trunk and pelvis ROM. The IMU measurements were within 5–10 degrees of the triad. A greater variability of ROM measurements was seen for the pelvis in the transverse plane. IMUs and their custom software provide a valid and reliable measurement for trunk and pelvis ROM in normal individuals, and important considerations for future applications are discussed.
Full article
(This article belongs to the Special Issue Biomechanics of Human Movement and Its Clinical Applications)
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Open AccessArticle
The Approach to Sensing the True Fetal Heart Rate for CTG Monitoring: An Evaluation of Effectiveness of Deep Learning with Doppler Ultrasound Signals
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Yuta Hirono, Ikumi Sato, Chiharu Kai, Akifumi Yoshida, Naoki Kodama, Fumikage Uchida and Satoshi Kasai
Bioengineering 2024, 11(7), 658; https://doi.org/10.3390/bioengineering11070658 - 28 Jun 2024
Abstract
Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of
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Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.
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(This article belongs to the Section Biosignal Processing)
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Open AccessArticle
Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning
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Sau Nguyen Van, Jinhui Cui, Yanling Wang, Hui Jiang, Feng Sha and Ye Li
Bioengineering 2024, 11(7), 657; https://doi.org/10.3390/bioengineering11070657 - 27 Jun 2024
Abstract
The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate
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The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate for Gestational Age (AGA) fetuses, indicate intrauterine growth restriction and excessive fetal growth, respectively. Early prediction and assessment of latent risk factors associated with these classifications can facilitate timely medical interventions, thereby optimizing the health outcomes for both the infant and the mother. This study aims to leverage first-trimester data to achieve these objectives. This study analyzed data from 7943 pregnant women, including 424 SGA, 928 LGA, and 6591 AGA cases, collected from 2015 to 2021 at the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, China. We propose a novel algorithm, named the Weighted Inheritance Voting Ensemble Learning Algorithm (WIVELA), to predict the classification of fetuses into SGA, LGA, and AGA categories based on biochemical parameters, maternal factors, and morbidity during pregnancy. Additionally, we proposed algorithms for relevance determination based on the classifier to ascertain the importance of features associated with SGA and LGA. The proposed classification solution demonstrated a notable average accuracy rate of on 10-fold cross-validation over 100 loops, outperforming five state-of-the-art machine learning algorithms. Furthermore, we identified significant latent maternal risk factors directly associated with SGA and LGA conditions, such as weight change during the first trimester, prepregnancy weight, height, age, and obstetric factors like fetal growth restriction and birthing LGA baby. This study also underscored the importance of biomarker features at the end of the first trimester, including HDL, TG, OGTT-1h, OGTT-0h, OGTT-2h, TC, FPG, and LDL, which reflect the status of SGA or LGA fetuses. This study presents innovative solutions for classifying and identifying relevant attributes, offering valuable tools for medical teams in the clinical monitoring of fetuses predisposed to SGA and LGA conditions during the initial stage of pregnancy. These proposed solutions facilitate early intervention in nutritional care and prenatal healthcare, thereby contributing to enhanced strategies for managing the health and well-being of both the fetus and the expectant mother.
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(This article belongs to the Section Biosignal Processing)
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Open AccessSystematic Review
Mesenchymal Stromal Cells for the Enhancement of Surgical Flexor Tendon Repair in Animal Models: A Systematic Review and Meta-Analysis
by
Ilias Ektor Epanomeritakis, Andreas Eleftheriou, Anna Economou, Victor Lu and Wasim Khan
Bioengineering 2024, 11(7), 656; https://doi.org/10.3390/bioengineering11070656 - 27 Jun 2024
Abstract
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Flexor tendon lacerations are primarily treated by surgical repair. Limited intrinsic healing ability means the repair site can remain weak. Furthermore, adhesion formation may reduce range of motion post-operatively. Mesenchymal stromal cells (MSCs) have been trialled for repair and regeneration of multiple musculoskeletal
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Flexor tendon lacerations are primarily treated by surgical repair. Limited intrinsic healing ability means the repair site can remain weak. Furthermore, adhesion formation may reduce range of motion post-operatively. Mesenchymal stromal cells (MSCs) have been trialled for repair and regeneration of multiple musculoskeletal structures. Our goal was to determine the efficacy of MSCs in enhancing the biomechanical properties of surgically repaired flexor tendons. A PRISMA systematic review was conducted using four databases (PubMed, Ovid, Web of Science, and CINAHL) to identify studies using MSCs to augment surgical repair of flexor tendon injuries in animals compared to surgical repair alone. Nine studies were included, which investigated either bone marrow- or adipose-derived MSCs. Results of biomechanical testing were extracted and meta-analyses were performed regarding the maximum load, friction and properties relating to viscoelastic behaviour. There was no significant difference in maximum load at final follow-up. However, friction, a surrogate measure of adhesions, was significantly reduced following the application of MSCs (p = 0.04). Other properties showed variable results and dissipation of the therapeutic benefits of MSCs over time. In conclusion, MSCs reduce adhesion formation following tendon injury. This may result from their immunomodulatory function, dampening the inflammatory response. However, this may come at the cost of favourable healing which will restore the tendon’s viscoelastic properties. The short duration of some improvements may reflect MSCs’ limited survival or poor retention. Further investigation is needed to clarify the effect of MSC therapy and optimise its duration of action.
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Open AccessArticle
Finite Element Analysis of Cervical Spine Kinematic Response during Ejection Utilising a Hill-Type Dynamic Muscle Model
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Yikang Gong, Zhenghan Cheng, Ee-Chon Teo and Yaodong Gu
Bioengineering 2024, 11(7), 655; https://doi.org/10.3390/bioengineering11070655 - 27 Jun 2024
Abstract
To determine the impact of active muscle on the dynamic response of a pilot’s neck during simulated emergency ejection, a detailed three-dimensional (3D) cervical spine (C0–T1) finite element (FE) model integrated with active muscles was constructed. Based on the Hill-type model characterising the
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To determine the impact of active muscle on the dynamic response of a pilot’s neck during simulated emergency ejection, a detailed three-dimensional (3D) cervical spine (C0–T1) finite element (FE) model integrated with active muscles was constructed. Based on the Hill-type model characterising the muscle force activation mechanics, 13 major neck muscles were modelled. The active force generated by each muscle was simulated as functions of (i) active state ( ), (ii) velocity ( (v)), and (iii) length ( (L)). An acceleration-time profile with an initial acceleration rate of 125 G·s−1 in the 0–80 ms period, reaching peak acceleration of 10 G, then kept constant for a further 70 ms, was applied. The rotational angles of each cervical segment under these ejection conditions were compared with those without muscles and with passive muscles derived from the previous study. Similar trends of segmental rotation were observed with S- and C-curvature of the cervical spine in the 150 ms span analysed. With active muscles, the flexion motion of the C0–C2 segments exhibited higher magnitudes of rotation compared to those without muscle and passive muscle models. The flexion motion increased rapidly and peaked at about 95–105 ms, then decreased rapidly to a lower magnitude. Lower C2–T1 segments exhibited less variation in flexion and extension motions. Overall, during emergency ejections, active muscle activities effectively reduce the variability in rotational angles across cervical segments, except C0–C2 segments in the 60–120 ms period. The role of the active state dynamics of the muscles was crucial to the magnitude of the muscle forces demonstrated. This indicates that it is crucial for pilots to consciously contract their muscles before ejection to prevent cervical spine injuries.
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(This article belongs to the Special Issue Computational Biomechanics, Volume II)
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Open AccessArticle
The Emerging Role of Large Language Models in Improving Prostate Cancer Literacy
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Marius Geantă, Daniel Bădescu, Narcis Chirca, Ovidiu Cătălin Nechita, Cosmin George Radu, Ștefan Rascu, Daniel Rădăvoi, Cristian Sima, Cristian Toma and Viorel Jinga
Bioengineering 2024, 11(7), 654; https://doi.org/10.3390/bioengineering11070654 - 27 Jun 2024
Abstract
This study assesses the effectiveness of chatbots powered by Large Language Models (LLMs)—ChatGPT 3.5, CoPilot, and Gemini—in delivering prostate cancer information, compared to the official Patient’s Guide. Using 25 expert-validated questions, we conducted a comparative analysis to evaluate accuracy, timeliness, completeness, and understandability
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This study assesses the effectiveness of chatbots powered by Large Language Models (LLMs)—ChatGPT 3.5, CoPilot, and Gemini—in delivering prostate cancer information, compared to the official Patient’s Guide. Using 25 expert-validated questions, we conducted a comparative analysis to evaluate accuracy, timeliness, completeness, and understandability through a Likert scale. Statistical analyses were used to quantify the performance of each model. Results indicate that ChatGPT 3.5 consistently outperformed the other models, establishing itself as a robust and reliable source of information. CoPilot also performed effectively, albeit slightly less so than ChatGPT 3.5. Despite the strengths of the Patient’s Guide, the advanced capabilities of LLMs like ChatGPT significantly enhance educational tools in healthcare. The findings underscore the need for ongoing innovation and improvement in AI applications within health sectors, especially considering the ethical implications underscored by the forthcoming EU AI Act. Future research should focus on investigating potential biases in AI-generated responses and their impact on patient outcomes.
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(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
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Open AccessArticle
Pectin as a Biomaterial in Regenerative Endodontics—Assessing Biocompatibility and Antibacterial Efficacy against Common Endodontic Pathogens: An In Vitro Study
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Raghda Magdy Abdelgawad, Nailê Damé-Teixeira, Katarzyna Gurzawska-Comis, Arwa Alghamdi, Abeer H. Mahran, Rania Elbackly, Thuy Do and Reem El-Gendy
Bioengineering 2024, 11(7), 653; https://doi.org/10.3390/bioengineering11070653 - 26 Jun 2024
Abstract
Regenerative endodontics (REP) is a new clinical modality aiming to regenerate damaged soft and hard dental tissues, allowing for root completion in young adults’ teeth. Effective disinfection is crucial for REP success, but commonly used antimicrobials often harm the niche dental pulp stem
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Regenerative endodontics (REP) is a new clinical modality aiming to regenerate damaged soft and hard dental tissues, allowing for root completion in young adults’ teeth. Effective disinfection is crucial for REP success, but commonly used antimicrobials often harm the niche dental pulp stem cells (DPSCs). To our knowledge, this is the first study to explore the biocompatibility and antimicrobial potential of pectin as a potential natural intracanal medicament for REPs. Low methoxyl commercial citrus pectin (LM) (pectin CU701, Herbstreith&Fox.de) was used in all experiments. The pectin’s antibacterial activity against single species biofilms (E. faecalis and F. nucleatum) was assessed using growth curves. The pectin’s antimicrobial effect against mature dual-species biofilm was also evaluated using confocal laser scanning microscopy (CLSM) after 30 min and 7 days of treatment. The DPSC biocompatibility with 2% and 4% w/v of the pectin coatings was evaluated using live/dead staining, LDH, and WST-1 assays. Pectin showed a concentration-dependent inhibitory effect against single-species biofilms (E. faecalis and F. nucleatum) but failed to disrupt dual-species biofilm. Pectin at 2% w/v concentration proved to be biocompatible with the HDPSCs. However, 4% w/v pectin reduced both the viability and proliferation of the DPSCs. Low concentration (2% w/v) pectin was biocompatible with the DPSCs and showed an antimicrobial effect against single-species biofilms. This suggests the potential for using pectin as an injectable hydrogel for clinical applications in regenerative endodontics.
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(This article belongs to the Special Issue Microbial Biopolymers: From Synthesis to Properties and Applications)
Open AccessEditorial
The Collaborative Spark That Ignited the Field of Stromal Stem Cell Biology
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James T. Triffitt
Bioengineering 2024, 11(7), 652; https://doi.org/10.3390/bioengineering11070652 - 26 Jun 2024
Abstract
Russia has produced many scientists of great renown in a multitude of fields from chemistry, physics, astronautics, and mathematics to biology, pathology, and medicine [...]
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(This article belongs to the Special Issue A Tribute to Professor Alexander Friedenstein and His Outstanding Achievements in the Area of Stromal Stem Cells)
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Open AccessReview
Chromophore-Assisted Light Inactivation for Protein Degradation and Its Application in Biomedicine
by
Lvjia Zhou, Jintong Na, Xiyu Liu and Pan Wu
Bioengineering 2024, 11(7), 651; https://doi.org/10.3390/bioengineering11070651 - 26 Jun 2024
Abstract
The functional investigation of proteins holds immense significance in unraveling physiological and pathological mechanisms of organisms as well as advancing the development of novel pharmaceuticals in biomedicine. However, the study of cellular protein function using conventional genetic manipulation methods may yield unpredictable outcomes
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The functional investigation of proteins holds immense significance in unraveling physiological and pathological mechanisms of organisms as well as advancing the development of novel pharmaceuticals in biomedicine. However, the study of cellular protein function using conventional genetic manipulation methods may yield unpredictable outcomes and erroneous conclusions. Therefore, precise modulation of protein activity within cells holds immense significance in the realm of biomedical research. Chromophore-assisted light inactivation (CALI) is a technique that labels photosensitizers onto target proteins and induces the production of reactive oxygen species through light control to achieve precise inactivation of target proteins. Based on the type and characteristics of photosensitizers, different excitation light sources and labeling methods are selected. For instance, KillerRed forms a fusion protein with the target protein through genetic engineering for labeling and inactivates the target protein via light activation. CALI is presently predominantly employed in diverse biomedical domains encompassing investigations into protein functionality and interaction, intercellular signal transduction research, as well as cancer exploration and therapy. With the continuous advancement of CALI technology, it is anticipated to emerge as a formidable instrument in the realm of life sciences, yielding more captivating outcomes for fundamental life sciences and precise disease diagnosis and treatment.
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(This article belongs to the Section Biochemical Engineering)
Open AccessArticle
Anatomical Features and Material Properties of Human Surrogate Head Models Affect Spatial and Temporal Brain Motion under Blunt Impact
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Michael Hanna, Abdus Ali, Prasad Bhatambarekar, Karan Modi, Changhee Lee, Barclay Morrison III, Michael Klienberger and Bryan J. Pfister
Bioengineering 2024, 11(7), 650; https://doi.org/10.3390/bioengineering11070650 - 25 Jun 2024
Abstract
Traumatic brain injury (TBI) is a biomechanical problem where the initiating event is dynamic loading (blunt, inertial, blast) to the head. To understand the relationship between the mechanical parameters of the injury and the deformation patterns in the brain, we have previously developed
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Traumatic brain injury (TBI) is a biomechanical problem where the initiating event is dynamic loading (blunt, inertial, blast) to the head. To understand the relationship between the mechanical parameters of the injury and the deformation patterns in the brain, we have previously developed a surrogate head (SH) model capable of measuring spatial and temporal deformation in a surrogate brain under blunt impact. The objective of this work was to examine how material properties and anatomical features affect the motion of the brain and the development of injurious deformations. The SH head model was modified to study six variables independently under blunt impact: surrogate brain stiffness, surrogate skull stiffness, inclusion of cerebrospinal fluid (CSF), head/skull size, inclusion of vasculature, and neck stiffness. Each experimental SH was either crown or frontally impacted at 1.3 m/s (3 mph) using a drop tower system. Surrogate brain material, the Hybrid III neck stiffness, and skull stiffness were measured and compared to published properties. Results show that the most significant variables affecting changes in brain deformation are skull stiffness, inclusion of CSF and surrogate brain stiffness. Interestingly, neck stiffness and SH size significantly affected the strain rate only suggesting these parameters are less important in blunt trauma. While the inclusion of vasculature locally created strain concentrations at the interface of the artery and brain, overall deformation was reduced.
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(This article belongs to the Special Issue Advances in Trauma and Injury Biomechanics)
Open AccessArticle
Poly(2-Hydroxyethyl Methacrylate) Hydrogel-Based Microneedles for Bioactive Release
by
Manoj B. Sharma, Hend A. M. Abdelmohsen, Özlem Kap, Volkan Kilic, Nesrin Horzum, David Cheneler and John G. Hardy
Bioengineering 2024, 11(7), 649; https://doi.org/10.3390/bioengineering11070649 - 25 Jun 2024
Abstract
Microneedle arrays are minimally invasive devices that have been extensively investigated for the transdermal/intradermal delivery of drugs/bioactives. Here, we demonstrate the release of bioactive molecules (estradiol, melatonin and meropenem) from poly(2-hydroxyethyl methacrylate), pHEMA, hydrogel-based microneedle patches in vitro. The pHEMA hydrogel microneedles had
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Microneedle arrays are minimally invasive devices that have been extensively investigated for the transdermal/intradermal delivery of drugs/bioactives. Here, we demonstrate the release of bioactive molecules (estradiol, melatonin and meropenem) from poly(2-hydroxyethyl methacrylate), pHEMA, hydrogel-based microneedle patches in vitro. The pHEMA hydrogel microneedles had mechanical properties that were sufficiently robust to penetrate soft tissues (exemplified here by phantom tissues). The bioactive release from the pHEMA hydrogel-based microneedles was fitted to various models (e.g., zero order, first order, second order). Such pHEMA microneedles have potential application in the transdermal delivery of bioactives (exemplified here by estradiol, melatonin and meropenem) for the treatment of various conditions.
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(This article belongs to the Special Issue Advances in Biomedical Instrumentation: Diagnosis, Therapy, and Rehabilitation (Featuring Selected Contributions Presented at the BEI-2023 Conference))
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A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging
by
Sanaz Vahdati, Bardia Khosravi, Kathryn A. Robinson, Pouria Rouzrokh, Mana Moassefi, Zeynettin Akkus and Bradley J. Erickson
Bioengineering 2024, 11(7), 648; https://doi.org/10.3390/bioengineering11070648 - 25 Jun 2024
Abstract
Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two
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Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75–0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (p-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.
Full article
(This article belongs to the Special Issue Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment)
Open AccessArticle
Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data
by
Lan Lin, Yutong Wu, Lingyu Liu, Shen Sun and Shuicai Wu
Bioengineering 2024, 11(7), 647; https://doi.org/10.3390/bioengineering11070647 - 25 Jun 2024
Abstract
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is
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The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain’s alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
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(This article belongs to the Section Biosignal Processing)
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HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction
by
Guang Li, Zhenhao Deng, Yongshuai Ge and Shouhua Luo
Bioengineering 2024, 11(7), 646; https://doi.org/10.3390/bioengineering11070646 - 25 Jun 2024
Abstract
X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of
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X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network’s feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.
Full article
(This article belongs to the Special Issue Recent Advancements in Spectral CT Imaging Techniques)
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