Bioengineering

20 pages, 2948 KiB

Open AccessArticle

A Dual-Branch Residual Network with Attention Mechanisms for Enhanced Classification of Vaginal Lesions in Colposcopic Images

by Haima Yang, Yeye Song, Yuling Li, Zubei Hong, Jin Liu, Jun Li, Dawei Zhang, Le Fu, Jinyu Lu and Lihua Qiu

Bioengineering 2024, 11(12), 1182; https://doi.org/10.3390/bioengineering11121182 - 22 Nov 2024

Abstract

Vaginal intraepithelial neoplasia (VAIN), linked to HPV infection, is a condition that is often overlooked during colposcopy, especially in the vaginal vault area, as clinicians tend to focus more on cervical lesions. This oversight can lead to missed or delayed diagnosis and treatment [...] Read more.

Vaginal intraepithelial neoplasia (VAIN), linked to HPV infection, is a condition that is often overlooked during colposcopy, especially in the vaginal vault area, as clinicians tend to focus more on cervical lesions. This oversight can lead to missed or delayed diagnosis and treatment for patients with VAIN. Timely and accurate classification of VAIN plays a crucial role in the evaluation of vaginal lesions and the formulation of effective diagnostic approaches. The challenge is the high similarity between different classes and the low variability in the same class in colposcopic images, which can affect the accuracy, precision, and recall rates, depending on the image quality and the clinician’s experience. In this study, a dual-branch lesion-aware residual network (DLRNet), designed for small medical sample sizes, is introduced, which classifies vaginal lesions by examining the relationship between cervical and vaginal lesions. The DLRNet model includes four main components: a lesion localization module, a dual-branch classification module, an attention-guidance module, and a pretrained network module. The dual-branch classification module combines the original images with segmentation maps obtained from the lesion localization module using a pretrained ResNet network to fine-tune parameters at different levels, explore lesion-specific features from both global and local perspectives, and facilitate layered interactions. The feature guidance module focuses the local branch network on vaginal-specific features by using spatial and channel attention mechanisms. The final integration involves a shared feature extraction module and independent fully connected layers, which represent and merge the dual-branch inputs. The weighted fusion method effectively integrates multiple inputs, enhancing the discriminative and generalization capabilities of the model. Classification experiments on 1142 collected colposcopic images demonstrate that this method raises the existing classification levels, achieving the classification of VAIN into three lesion grades, thus providing a valuable tool for the early screening of vaginal diseases. Full article

(This article belongs to the Section Biosignal Processing)

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8 pages, 3331 KiB

Open AccessArticle

Computational Simulation of LAVA Treatment of Thyroid Eye Disease Predicts Soft Tissue Outcome Comparable to Two-Wall Resection

by Matthias Krause and Evgeny Gladilin

Bioengineering 2024, 11(12), 1181; https://doi.org/10.3390/bioengineering11121181 - 22 Nov 2024

Abstract

Thyroid eye disease (TED) is a common extrathyroidal manifestation of hyperthyroidism, typically associated with Graves’ disease (GD). This condition can cause severe functional limitations as well as significant aesthetic concerns. Treatment for TED patients aims to restore functionality and address aesthetic concerns. Surgical [...] Read more.

Thyroid eye disease (TED) is a common extrathyroidal manifestation of hyperthyroidism, typically associated with Graves’ disease (GD). This condition can cause severe functional limitations as well as significant aesthetic concerns. Treatment for TED patients aims to restore functionality and address aesthetic concerns. Surgical TED treatment is usually performed by orbital wall resection, which effectively decompresses intraorbital tissues and corrects the orbital/ocular disorders. Several different scenarios of surgical TED treatment including one-, two-, and three-wall resections are known. More recently, a new minimally invasive technique, the so-called lateral valgization (LAVA) of the orbital wall, was reported to show promising results comparable to conventional wall resection techniques. Due to the relatively limited data on TED treatment, only a few quantitative investigations of alternative TED surgery scenarios exist. In this feasibility study, we estimate the soft tissue outcome of LAVA treatment using computational simulation. Our experimental results show that the amount of intraorbital tissue released into the extraorbital space by LAVA treatment is comparable with the outcome of two-wall resection. Our computational simulation confirms previously reported isolated clinical findings suggesting that the minimally invasive LAVA approach represents an attractive alternative to conventional wall resection approaches for surgical TED treatment. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

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25 pages, 6761 KiB

Open AccessArticle

Detection of Rat Pain-Related Grooming Behaviors Using Multistream Recurrent Convolutional Networks on Day-Long Video Recordings

by Chien-Cheng Lee, Ping-Wing Lui, Wei-Wei Gao and Zhongjian Gao

Bioengineering 2024, 11(12), 1180; https://doi.org/10.3390/bioengineering11121180 - 21 Nov 2024

Abstract

In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges. In this [...] Read more.

In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges. In this study, we developed and evaluated a deep learning, multistream algorithm to detect pain-related grooming behaviors in rats. Pain-related grooming behaviors were induced by injecting small amounts of pain-inducing chemicals into the rats’ hind limbs. Day-long video recordings were then analyzed with our algorithm, which initially filtered out non-grooming segments. The remaining segments, referred to as likely grooming clips, were used for model training and testing. Our model, a multistream recurrent convolutional network, learned to differentiate grooming from non-grooming behaviors within these clips through deep learning. The average validation accuracy across three evaluation methods was 88.5%. We further analyzed grooming statistics by comparing the duration of grooming episodes between experimental and control groups. Results demonstrated statistically significant changes in grooming behavior consistent with pain expression. Full article

(This article belongs to the Special Issue Application of Convolutional Neural Networks in Bioimaging and Biosignal Processes)

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15 pages, 5380 KiB

Open AccessArticle

Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency

by Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Zaripova Dilnoza, Kudratjon Zohirov, Rashid Nasimov, Sabina Umirzakova and Young-Im Cho

Bioengineering 2024, 11(12), 1179; https://doi.org/10.3390/bioengineering11121179 - 21 Nov 2024

Abstract

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) [...] Read more.

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications. Full article

(This article belongs to the Special Issue Machine Learning and Medicine: The Interface of Medicine, Engineering and Artificial Intelligence)

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19 pages, 1835 KiB

Open AccessSystematic Review

Evaluation of the Effectiveness of Different Types of Professional Tooth Whitening: A Systematic Review

by Andrea Butera, Carolina Maiorani, Gitana Rederiene, Stefano Checchi and Gianna Maria Nardi

Bioengineering 2024, 11(12), 1178; https://doi.org/10.3390/bioengineering11121178 - 21 Nov 2024

Abstract

Background/Objectives: Tooth whitening is a cosmetic dental treatment that improves the color of natural teeth, making them whiter and brighter; this review aimed to evaluate the greater effectiveness of in-office, at-home, and combined bleaching with hydrogen or carbamide peroxide, also in relation to [...] Read more.

Background/Objectives: Tooth whitening is a cosmetic dental treatment that improves the color of natural teeth, making them whiter and brighter; this review aimed to evaluate the greater effectiveness of in-office, at-home, and combined bleaching with hydrogen or carbamide peroxide, also in relation to possible relapses and side effects (tooth sensitivity). Methods: A literature search has been carried out through electronic databases, PubMed/MEDLINE, and Cochrane Library, focused on the use of the effectiveness of in-office, at-home, and combined dental bleaching. This review has been registered on PROSPERO (ID613248). Results: 30 articles have been included. Most of the studies did not find any more effective treatment than the proposed treatments; all types of bleaching have been shown to be effective in changing color; in the studies that have compared in-office and at-home bleaching, there was a lower recurrence of treatment with the use of the at-home trays with carbamide peroxide gel. There were no clear positions regarding tooth sensitivity, which would appear to be higher in professional in-office and combined dental bleaching. Conclusions: Bleaching is an effective treatment for the change of color of the tooth regardless of the type used (concentrations, type of gel, and duration of sessions), although, at home, it would seem to be more effective over time. It is, however, a clinical feature that may give a risk of tooth sensitivity after treatment. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

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12 pages, 4168 KiB

Open AccessArticle

Efficacy of Novel Digital-Based Surgical Guide in the Limited Interocclusal Distance

by Won-Jong Park, Ki-Seong Kim, Seok-Hwan Cho and Su Young Lee

Bioengineering 2024, 11(12), 1177; https://doi.org/10.3390/bioengineering11121177 - 21 Nov 2024

Abstract

Accurate implant placement is essential for achieving successful outcomes. To aid in this, digitally designed surgical guides have been introduced. Both closed-sleeve and open-sleeve designs are commonly utilized. However, the closed-sleeve design has limitations with restricted interocclusal distance, interference with irrigation, and limited [...] Read more.

Accurate implant placement is essential for achieving successful outcomes. To aid in this, digitally designed surgical guides have been introduced. Both closed-sleeve and open-sleeve designs are commonly utilized. However, the closed-sleeve design has limitations with restricted interocclusal distance, interference with irrigation, and limited visibility, while the open-sleeve design is known to be less accurate. To address these limitations, a new slope-sleeve design was introduced. This design reduces the interocclusal distance requirement compared to the closed-sleeve design and provides improved accuracy. A constraint model with a 31 mm interocclusal distance was created, and three types of surgical guides (closed, open, and slope), printed using either a PolyJet or Digital Light Processing (DLP) 3D printer, were tested on resin bone blocks. Horizontal and angular deviations were measured for the accuracy of each guide after drilling, with data analyzed using one-way ANOVA and independent t-tests. The slope-sleeve design showed significantly lower horizontal and angular deviations in wide-sized guides. Additionally, PolyJet-printed guides showed higher accuracy compared to DLP-printed guides. The slope-sleeve guide offers enhanced stability and precision in restricted interarch spaces. When coupled with high-precision 3D printing technologies like PolyJet, the slope-sleeve design provides a reliable solution for improving implant placement accuracy in challenging clinical scenarios. Full article

(This article belongs to the Section Biosignal Processing)

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17 pages, 4258 KiB

Open AccessArticle

Optimization of Culture Conditions for High Cell Productivity and Astaxanthin Accumulation in Vietnam’s Green Microalgae Haematococcus pluvialis HB and a Neuroprotective Activity of Its Astaxanthin

by Nguyen Cam Ha, Luu Thi Tam, Hoang Thi Minh Hien, Ngo Thi Hoai Thu, Dang Diem Hong and Le Thi Thom

Bioengineering 2024, 11(12), 1176; https://doi.org/10.3390/bioengineering11121176 - 21 Nov 2024

Abstract

Background: Haematococcus pluvialis, a green microalga, is a rich source of natural astaxanthin and a potent antioxidant with high commercial value. This study investigates the biological characteristics and potential of H. pluvialis HB isolated from Hoa Binh, Vietnam, for growth and astaxanthin [...] Read more.

Background: Haematococcus pluvialis, a green microalga, is a rich source of natural astaxanthin and a potent antioxidant with high commercial value. This study investigates the biological characteristics and potential of H. pluvialis HB isolated from Hoa Binh, Vietnam, for growth and astaxanthin accumulation using a two-phase culture method. Methods: H. pluvialis HB was cultured in a C/RM medium at 25 °C, and morphological characteristics were examined. NMR spectroscopy was used to determine the structure of the astaxanthin, which was extracted using the Soxhlet method. Results: After 22 days, the highest cell density (4.96 × 10⁶ cells mL⁻¹) was achieved under optimized light and ultraviolet conditions. Nutrient deprivation followed by bicarbonate supplementation resulted in a maximal astaxanthin accumulation of 48.8 mg g⁻¹ dry cell weight within two days. The extracted astaxanthin demonstrated potent antioxidant activity (IC₅₀: 3.74 mg mL⁻¹) compared to ascorbic acid (IC₅₀: 18.53 µg mL⁻¹) and exhibited strong acetylcholinesterase inhibition (IC₅₀: 297.99 µg mL⁻¹). It also showed neuroprotective effects against H₂O₂ and amyloid beta-induced neurotoxicity in C6 cells. Conclusions: This study highlights H. pluvialis HB as a promising source for large-scale astaxanthin production with potential applications in neuroprotective health products. Full article

(This article belongs to the Section Biochemical Engineering)

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3 pages, 159 KiB

Open AccessEditorial

Stem and Progenitor Cells for Musculoskeletal Disease Modeling and Tissue Repair

by Songlin He, Wanting Niu and Zhong Alan Li

Bioengineering 2024, 11(12), 1175; https://doi.org/10.3390/bioengineering11121175 - 21 Nov 2024

Abstract

Musculoskeletal conditions such as osteoarthritis (OA), bone fracture, and sarcopenia are highly prevalent [...] Full article

(This article belongs to the Special Issue Stem/Progenitor Cells for Musculoskeletal Disease Modeling and Tissue Repair)

14 pages, 5065 KiB

Open AccessArticle

Accuracy Evaluation of a Three-Dimensional Face Reconstruction Model Based on the Hifi3D Face Model and Clinical Two-Dimensional Images

by Yujia Xiao, Bochun Mao, Jianglong Nie, Jiayi Liu, Shuo Wang, Dawei Liu and Yanheng Zhou

Bioengineering 2024, 11(12), 1174; https://doi.org/10.3390/bioengineering11121174 - 21 Nov 2024

Abstract

Three-dimensional (3D) facial models have been increasingly applied in orthodontics, orthognathic surgery, and various medical fields. This study proposed an approach to reconstructing 3D facial models from standard orthodontic frontal and lateral images, providing an efficient way to expand 3D databases. A total [...] Read more.

Three-dimensional (3D) facial models have been increasingly applied in orthodontics, orthognathic surgery, and various medical fields. This study proposed an approach to reconstructing 3D facial models from standard orthodontic frontal and lateral images, providing an efficient way to expand 3D databases. A total of 23 participants (average age 20.70 ± 5.36 years) were enrolled. Based on the Hifi3D face model, 3D reconstructions were generated and compared with corresponding face scans to evaluate their accuracy. Root mean square error (RMSE) values were calculated for the entire face, nine specific facial regions, and eight anatomical landmarks. Clinical feasibility was further assessed by comparing six angular and thirteen linear measurements between the reconstructed and scanned models. The RMSE of the reconstruction model was 2.00 ± 0.38 mm (95% CI: 1.84–2.17 mm). High accuracy was achieved for the forehead, nose, upper lip, paranasal region, and right cheek (mean RMSE < 2 mm). The forehead area showed the smallest deviation, at 1.52 ± 0.88 mm (95% CI: 1.14–1.90 mm). In contrast, the lower lip, chin, and left cheek exhibited average RMSEs exceeding 2 mm. The mean deviation across landmarks was below 2 mm, with the Prn displaying the smallest error at 1.18 ± 1.10 mm (95% CI: 0.71–1.65 mm). The largest discrepancies were observed along the Z-axis (Z > Y > X). Significant differences (p < 0.05) emerged between groups in the nasolabial, nasal, and nasofrontal angles, while the other 13 linear and 3 angular measurements showed no statistical differences (p > 0.05). This study explored the feasibility of reconstructing accurate 3D models from 2D photos. Compared to facial scan models, the Hifi3D face model demonstrated a 2 mm deviation, with potential for enriching 3D databases for subjective evaluations, patient education, and communication. However, caution is advised when applying this model to clinical measurements, especially angle assessments. Full article

(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)

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23 pages, 4785 KiB

Open AccessArticle

A Unified Multi-Task Learning Model with Joint Reverse Optimization for Simultaneous Skin Lesion Segmentation and Diagnosis

by Mohammed A. Al-masni, Abobakr Khalil Al-Shamiri, Dildar Hussain and Yeong Hyeon Gu

Bioengineering 2024, 11(11), 1173; https://doi.org/10.3390/bioengineering11111173 - 20 Nov 2024

Abstract

Classifying and segmenting skin cancer represent pivotal objectives for automated diagnostic systems that utilize dermoscopy images. However, these tasks present significant challenges due to the diverse shape variations of skin lesions and the inherently fuzzy nature of dermoscopy images, including low contrast and [...] Read more.

Classifying and segmenting skin cancer represent pivotal objectives for automated diagnostic systems that utilize dermoscopy images. However, these tasks present significant challenges due to the diverse shape variations of skin lesions and the inherently fuzzy nature of dermoscopy images, including low contrast and the presence of artifacts. Given the robust correlation between the classification of skin lesions and their segmentation, we propose that employing a combined learning method holds the promise of considerably enhancing the performance of both tasks. In this paper, we present a unified multi-task learning strategy that concurrently classifies abnormalities of skin lesions and allows for the joint segmentation of lesion boundaries. This approach integrates an optimization technique known as joint reverse learning, which fosters mutual enhancement through extracting shared features and limiting task dominance across the two tasks. The effectiveness of the proposed method was assessed using two publicly available datasets, ISIC 2016 and PH², which included melanoma and benign skin cancers. In contrast to the single-task learning strategy, which solely focuses on either classification or segmentation, the experimental findings demonstrated that the proposed network improves the diagnostic capability of skin tumor screening and analysis. The proposed method achieves a significant segmentation performance on skin lesion boundaries, with Dice Similarity Coefficients (DSC) of 89.48% and 88.81% on the ISIC 2016 and PH² datasets, respectively. Additionally, our multi-task learning approach enhances classification, increasing the F1 score from 78.26% (baseline ResNet50) to 82.07% on ISIC 2016 and from 82.38% to 85.50% on PH². This work showcases its potential applicability across varied clinical scenarios. Full article

(This article belongs to the Section Biosignal Processing)

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11 pages, 1225 KiB

Open AccessArticle

AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management

by Joseph Finkelstein, Aref Smiley, Christina Echeverria and Kathi Mooney

Bioengineering 2024, 11(11), 1172; https://doi.org/10.3390/bioengineering11111172 - 20 Nov 2024

Abstract

This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The accurate prediction of symptom escalation is critical in cancer care to enable timely interventions and improve symptom management [...] Read more.

This study presents an advanced method for predicting symptom escalation in chemotherapy patients using Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The accurate prediction of symptom escalation is critical in cancer care to enable timely interventions and improve symptom management to enhance patients’ quality of life during treatment. The analytical dataset consists of daily self-reported symptom logs from chemotherapy patients, including a wide range of symptoms, such as nausea, fatigue, and pain. The original dataset was highly imbalanced, with approximately 84% of the data containing no symptom escalation. The data were resampled into varying interval lengths to address this imbalance and improve the model’s ability to detect symptom escalation (n = 3 to n = 7 days). This allowed the model to predict significant changes in symptom severity across these intervals. The results indicate that shorter intervals (n = 3 days) yielded the highest overall performance, with the CNN model achieving an accuracy of 81%, precision of 87%, recall of 80%, and an F1 score of 83%. This was an improvement over the LSTM model, which had an accuracy of 79%, precision of 85%, recall of 79%, and an F1 score of 82%. The model’s accuracy and recall declined as the interval length increased, though precision remained relatively stable. The findings demonstrate that both CNN’s temporospatial feature extraction and LSTM’s temporal modeling effectively capture escalation patterns in symptom progression. By integrating these predictive models into digital health systems, healthcare providers can offer more personalized and proactive care, enabling earlier interventions that may reduce symptom burden and improve treatment adherence. Ultimately, this approach has the potential to significantly enhance the overall quality of life for chemotherapy patients by providing real-time insights into symptom trajectories and guiding clinical decision making. Full article

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19 pages, 6477 KiB

Open AccessArticle

First- vs. Second-Generation Autologous Platelet Concentrates and Their Implications for Wound Healing: Differences in Proteome and Secretome

by Hanna L. Stiller, Natarajan Perumal, Caroline Manicam, Emily R. Trzeciak, Julia Todt, Kerstin Jurk, Andrea Tuettenberg, Sven Schumann, Eik Schiegnitz and Sebastian Blatt

Bioengineering 2024, 11(11), 1171; https://doi.org/10.3390/bioengineering11111171 - 20 Nov 2024

Abstract

Differences in cell count and growth factor expression between first- and second-generation autologous platelet concentrates (APCs) have been well described. The debate over which formula best supports wound healing in various surgical procedures is still ongoing. This study aims to assess the whole [...] Read more.

Differences in cell count and growth factor expression between first- and second-generation autologous platelet concentrates (APCs) have been well described. The debate over which formula best supports wound healing in various surgical procedures is still ongoing. This study aims to assess the whole proteome assembly, cell content, immunological potential and pro-angiogenic potential of second-generation APC, Platelet-Rich Fibrin (PRF) vs. first-generation APC, Platelet-Rich Plasma (PRP). The global proteome of the APCs was analyzed using nano-liquid chromatography mass spectrometry. Blood cell concentrations were determined by an automated cell counter. The effect of APCs on macrophage polarization was analyzed by flow cytometry. A yolk sac membrane (YSM) assay was used to monitor the neo-vessel formation and capillary branching in vivo. Cell count analysis revealed a higher number/concentration of leukocytes in PRF vs. PRP. Incubation of macrophages with PRP or platelet-free plasma (PFP) did not induce a significant pro-inflammatory state but led to a shift to the M0/M2 phenotype as seen in wound healing for all tested formulas. Label-free proteomics analysis identified a total of 387 proteins from three biological replicates of the respective designated groups. PRF induced increased formation of neo-vessels and branching points in vivo in comparison to PRP and PFP (each p < 0.001), indicating the enhanced pro-angiogenic potential of PRF. Overall, PRF seems superior to PRP, an important representative of first-generation formulas. Inclusion of leucocytes in PRF compared to PRP suggested rather an anti-inflammatory effect on macrophages. These results are important to support the versatile clinical applications in regenerative medicine for second-generation autologous platelet concentrates to optimize wound healing. Full article

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17 pages, 4587 KiB

Open AccessArticle

Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach

by Yeong-Jae Jeon, Kyung Min Nam, Shin-Eui Park and Hyeon-Man Baek

Bioengineering 2024, 11(11), 1170; https://doi.org/10.3390/bioengineering11111170 - 20 Nov 2024

Abstract

In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, [...] Read more.

In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, which is inherently time-consuming and can lead to participant discomfort, thus posing limitations in clinical settings. This study aimed to develop a hybrid denoising strategy that integrates low-rank approximation and denoising diffusion probabilistic model (DDPM) to enhance MRS data quality and shorten scan times. Using publicly available ¹H MRS datasets from 15 subjects, we applied the Casorati SVD and DDPM to obtain baseline and functional data during a pain stimulation task. This method significantly improved SNR, resulting in outcomes comparable to or better than averaging over 32 signals. It also provided the most consistent metabolite measurements and adequately tracked temporal changes in glutamate levels, correlating with pain intensity ratings after heating. These findings demonstrate that our approach enhances MRS data quality, offering a more efficient alternative to conventional methods and expanding the potential for the real-time monitoring of neurochemical changes. This contribution has the potential to advance MRS techniques by integrating advanced denoising methods to increase the acquisition speed and enhance the precision of brain metabolite analyses. Full article

(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, 3rd Edition)

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16 pages, 11648 KiB

Open AccessArticle

Analysis of Umbilical Artery Hemodynamics in Development of Intrauterine Growth Restriction Using Computational Fluid Dynamics with Doppler Ultrasound

by Xue Song, Jingying Wang, Ke Sun and Chunhian Lee

Bioengineering 2024, 11(11), 1169; https://doi.org/10.3390/bioengineering11111169 - 20 Nov 2024

Abstract

Intrauterine growth restriction (IUGR), the failure of the fetus to achieve his/her growth potential, is a common and complex problem in pregnancy. Clinically, IUGR is usually monitored using Doppler ultrasound of the umbilical artery (UA). The Doppler waveform is generally divided into three [...] Read more.

Intrauterine growth restriction (IUGR), the failure of the fetus to achieve his/her growth potential, is a common and complex problem in pregnancy. Clinically, IUGR is usually monitored using Doppler ultrasound of the umbilical artery (UA). The Doppler waveform is generally divided into three typical patterns in IUGR development, from normal blood flow (Normal), to the loss of end diastolic blood flow (LDBF), and even to the reversal of end diastolic blood flow (RDBF). Unfortunately, Doppler ultrasound hardly provides complete UA hemodynamics in detail, while the present in silico computational fluid dynamics (CFD) can provide this with the necessary ultrasound information. In this paper, CFD is employed to simulate the periodic UA blood flow for three typical states of IUGR, which shows comprehensive information on blood flow velocity, pressure, and wall shear stress (WSS). A new finding is the “hysteresis effect” between the UA blood flow velocity and pressure drop in which the former always changes after the latter by 0.1–0.2 times a cardiac cycle due to the unsteady flow. The degree of hysteresis is a promising indicator characterizing the evolution of IUGR. CFD successfully shows the hemodynamic details in different development situations of IUGR, and undoubtedly, its results would also help clinicians to further understand the relationship between the UA blood flow status and fetal growth restriction. Full article

(This article belongs to the Section Biosignal Processing)

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3 pages, 153 KiB

Open AccessEditorial

Computational Fluid Dynamics in Medicine and Biology

by Amirtahà Taebi

Bioengineering 2024, 11(11), 1168; https://doi.org/10.3390/bioengineering11111168 - 20 Nov 2024

Abstract

This Special Issue of Bioengineering presents cutting-edge research on the applications of computational fluid dynamics (CFD) in medical and biological contexts [...] Full article

(This article belongs to the Special Issue Computational Fluid Dynamics in Medicine and Biology)

17 pages, 3033 KiB

Open AccessArticle

Proteoglycans Enhance the Therapeutic Effect of BMSC Transplantation on Osteoarthritis

by Chunxiao Ran, Tianhao Liu, Yongming Bao, Weidan Wang, Dongling Xue, Guangxiao Yin, Xiuzhi Zhang and Dewei Zhao

Bioengineering 2024, 11(11), 1167; https://doi.org/10.3390/bioengineering11111167 - 20 Nov 2024

Abstract

Background: The injection of bone mesenchymal stem cells (BMSCs) for osteoarthritis (OA) treatment fails to address the disrupted extracellular microenvironment, limiting the differentiation and paracrine functions of BMSCs and resulting in suboptimal therapeutic outcomes. Proteoglycans (PGs) promote cell differentiation, tissue repair, and microenvironment [...] Read more.

Background: The injection of bone mesenchymal stem cells (BMSCs) for osteoarthritis (OA) treatment fails to address the disrupted extracellular microenvironment, limiting the differentiation and paracrine functions of BMSCs and resulting in suboptimal therapeutic outcomes. Proteoglycans (PGs) promote cell differentiation, tissue repair, and microenvironment remodeling. This study investigated the potential of combining PGs with BMSCs to increase the efficacy of OA treatment. Methods: We evaluated the effects of PG on BMSC and chondrocyte functions by adding various PG concentrations to the culture media. Additionally, a Transwell system was used to assess the impact of PG on the communication between BMSCs and chondrocytes. The results of the in vitro experiment were verified by tissue staining and immunohistochemistry following the treatment of OA model rats. Results: Our findings indicate that PG effectively induces Col II expression in BMSCs and enhances the paracrine secretion of TGF-β1, thereby activating the TGF-β signaling pathway in chondrocytes and increasing PRG4 gene expression. Compared with the other groups, the BMSC/PG treatment group presented a smoother articular surface and more robust extracellular matrix than the other groups in vivo, with significantly increased expression and distribution of Smad2/3 and PRG4. Conclusions: PG enhances BMSC differentiation into chondrocytes and stimulates paracrine TGF-β1 secretion. Proteoglycans not only promote chondrocyte differentiation and paracrine TGF-β1 signaling in BMSCs but also increase the sensitivity of chondrocytes to TGF-β1 secreted from BMSCs, leading to PRG4 expression through the TGFR/Smad2/3 pathway. Proteoglycans can enhance the therapeutic effect of BMSC treatment on OA and have the potential to delay the degeneration of OA cartilage. Full article

(This article belongs to the Special Issue Integrating Novel Therapeutics and Personalized Medicine into Osteoarthritis Treatment: Advancements in Cartilage Regeneration and Joint Preservation)

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1 pages, 170 KiB

Open AccessCorrection

Correction: Lana et al. Sacral Bioneuromodulation: The Role of Bone Marrow Aspirate in Spinal Cord Injuries. Bioengineering 2024, 11, 461

by José Fábio Lana, Annu Navani, Madhan Jeyaraman, Napoliane Santos, Luyddy Pires, Gabriel Silva Santos, Izair Jefthé Rodrigues, Douglas Santos, Tomas Mosaner, Gabriel Azzini, Lucas Furtado da Fonseca, Alex Pontes de Macedo, Stephany Cares Huber, Daniel de Moraes Ferreira Jorge and Joseph Purita

Bioengineering 2024, 11(11), 1166; https://doi.org/10.3390/bioengineering11111166 - 19 Nov 2024

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Innovations in Nerve Regeneration)

13 pages, 3184 KiB

Open AccessReview

A Comprehensive Review of Performance Metrics for Computer-Aided Detection Systems

by Doohyun Park

Bioengineering 2024, 11(11), 1165; https://doi.org/10.3390/bioengineering11111165 - 19 Nov 2024

Abstract

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers [...] Read more.

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility. Full article

(This article belongs to the Special Issue Artificial Intelligence for Computer-Aided Detection in Biomedical Applications)

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29 pages, 1081 KiB

Open AccessReview

Hydrogel-Enhanced Autologous Chondrocyte Implantation for Cartilage Regeneration—An Update on Preclinical Studies

by Xenab Ahmadpoor, Jessie Sun, Nerone Douglas, Weimin Zhu and Hang Lin

Bioengineering 2024, 11(11), 1164; https://doi.org/10.3390/bioengineering11111164 - 19 Nov 2024

Abstract

Autologous chondrocyte implantation (ACI) and matrix-induced ACI (MACI) have demonstrated improved clinical outcomes and reduced revision rates for treating osteochondral and chondral defects. However, their ability to achieve lasting, fully functional repair remains limited. To overcome these challenges, scaffold-enhanced ACI, particularly utilizing hydrogel-based [...] Read more.

Autologous chondrocyte implantation (ACI) and matrix-induced ACI (MACI) have demonstrated improved clinical outcomes and reduced revision rates for treating osteochondral and chondral defects. However, their ability to achieve lasting, fully functional repair remains limited. To overcome these challenges, scaffold-enhanced ACI, particularly utilizing hydrogel-based biomaterials, has emerged as an innovative strategy. These biomaterials are intended to mimic the biological composition, structural organization, and biomechanical properties of native articular cartilage. This review aims to provide comprehensive and up-to-date information on advancements in hydrogel-enhanced ACI from the past decade. We begin with a brief introduction to cartilage biology, mechanisms of cartilage injury, and the evolution of surgical techniques, particularly looking at ACI. Subsequently, we review the diversity of hydrogel scaffolds currently undergoing development and evaluation in preclinical studies for articular cartilage regeneration, emphasizing chondrocyte-laden hydrogels applicable to ACI. Finally, we address the key challenges impeding effective clinical translation, with particular attention to issues surrounding fixation and integration, aiming to inform and guide the future progression of tissue engineering strategies. Full article

(This article belongs to the Section Regenerative Engineering)

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Bioengineering

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