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Biomedicines
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Applications of Biomedical Engineering and Biomaterials in Human Diseases
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Applications of Biomedical Engineering and Biomaterials in Human Diseases

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4663

Special Issue Editor

Dr. Rui Sang

E-Mail Website
Guest Editor
Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: nanoparticle-based technology for cancer treatment and CRISPR-based gene therapy and diagnostic applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical engineering and biomaterials are at the forefront of transforming healthcare through innovative solutions that address prevention, diagnosis, and treatment of human diseases. Rapid advances in biomaterials science, nanotechnology, and biosensing are creating unprecedented opportunities for precision medicine. Emerging trends such as CRISPR-based biosensors, smart nanomaterials, and lab-on-a-chip systems are enabling rapid, amplification-free diagnostics at the point of care. Similarly, breakthroughs in tissue engineering, regenerative medicine, and 3D bioprinting are offering new possibilities for organ repair, transplantation, and disease modeling. Artificial intelligence and computational modeling are increasingly being integrated into biomaterials design and biomedical device development, accelerating the translation of discoveries into clinically relevant applications.

This Special Issue of Biomedicines aims to provide a platform for cutting-edge interdisciplinary research that integrates engineering, materials science, and medicine to tackle critical healthcare challenges. We welcome original research articles, short communications, and comprehensive reviews that showcase innovations in biomaterials, biosensors, medical devices, regenerative approaches, and drug delivery systems. Applications across oncology, infectious diseases, cardiovascular conditions, neurological disorders, and transplantation medicine are of particular interest. By highlighting these advances, this Special Issue seeks to foster collaboration between scientists, engineers, and clinicians and to inspire the next generation of biomedical technologies that will improve patient outcomes worldwide.

Dr. Rui Sang
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosensors and diagnostics
  • biomedical engineering
  • nanotechnology in medicine
  • artificial intelligence in healthcare
  • precision medicine
  • drug delivery systems
  • regenerative medicine
  • 3D bioprinting
  • CRISPR-based technologies

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Published Papers (7 papers)

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Research

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23 pages, 2724 KB  
Article
CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net–ELM Model
by Zeki Ogut, Eser Sert, Ertugrul Kaya and Muhammed Yildirim
Biomedicines 2026, 14(6), 1298; https://doi.org/10.3390/biomedicines14061298 - 7 Jun 2026
Abstract
Background: Accurate liver segmentation from abdominal computed tomography (CT) images is an important task for surgical planning, volumetric analysis, and tumor assessment. Although recent deep learning-based three-dimensional segmentation approaches provide high segmentation performance, these models generally require high computational resources and long training [...] Read more.
Background: Accurate liver segmentation from abdominal computed tomography (CT) images is an important task for surgical planning, volumetric analysis, and tumor assessment. Although recent deep learning-based three-dimensional segmentation approaches provide high segmentation performance, these models generally require high computational resources and long training times. Methods: In this study, a hybrid liver segmentation framework combining the 3D U-Net architecture with the extreme learning machine (ELM) method was proposed. In the proposed approach, deep volumetric feature maps extracted from the bottleneck layer of the trained 3D U-Net were used as input to an ELM-based classifier for final segmentation refinement. All experiments were performed on the Task03_Liver_rs dataset, which is a rescaled version of the Medical Segmentation Decathlon liver dataset. To provide a more reliable evaluation, fivefold cross-validation experiments were conducted using the same preprocessing pipeline, training protocol, and hyperparameter settings for all comparison models. In addition to overlap-based metrics, boundary-based and clinically relevant metrics including HD95, ASD, surface Dice, and volumetric error were also evaluated. Results: Experimental results demonstrated that the proposed 3D U-Net–ELM framework achieved competitive and stable segmentation performance compared with nnU-Net, standard 3D U-Net, SwinUNet, and SwinUNet–ELM models. The proposed model achieved a mean Dice score of 0.9399 ± 0.0210 and an IoU score of 0.8874 ± 0.0358 under fivefold cross-validation. Furthermore, the proposed approach produced lower HD95 and ASD values together with higher surface Dice scores, indicating improved boundary consistency and volumetric segmentation quality. In addition, the hybrid ELM-based structure provided advantages in computational efficiency and training cost. Conclusions: The obtained findings indicate that the proposed 3D U-Net–ELM framework provides a balanced and computationally efficient alternative for volumetric liver segmentation. Nevertheless, the absence of independent multicenter external validation remains an important limitation of the study. Future studies will focus on evaluating the proposed framework using larger and more diverse multicenter datasets to further investigate its clinical applicability and generalizability. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
21 pages, 1764 KB  
Article
APOE ε4 Allele Dose and Time to Clinical Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Dementia: An ADNI Survival Analysis
by Faizaan Fazal Khan and Goo-Rak Kwon
Biomedicines 2026, 14(6), 1280; https://doi.org/10.3390/biomedicines14061280 - 4 Jun 2026
Viewed by 255
Abstract
Background/Objectives: Existing Alzheimer’s disease (AD) prediction studies often treat APOE ε4 as a binary carrier variable and emphasize classification rather than time-to-event progression. This study evaluated whether APOE ε4 allele dose predicts clinical conversion from mild cognitive impairment (MCI) to AD dementia/probable AD [...] Read more.
Background/Objectives: Existing Alzheimer’s disease (AD) prediction studies often treat APOE ε4 as a binary carrier variable and emphasize classification rather than time-to-event progression. This study evaluated whether APOE ε4 allele dose predicts clinical conversion from mild cognitive impairment (MCI) to AD dementia/probable AD in a longitudinal survival framework adjusted for hippocampal volume and baseline cognition. Methods: We analyzed 1115 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline MCI, APOE genotype data, and at least one follow-up visit, grouped by APOE ε4 allele count (0, 1, or 2). Kaplan–Meier curves, Bonferroni-corrected log-rank tests, nested Cox models, interaction testing, and twelve sensitivity and robustness analyses were performed. Results: During 3.73 ± 3.38 years of mean follow-up, 399 participants (35.8%) clinically converted. Median conversion-free survival was 18.47 years for non-carriers, 4.32 years for heterozygotes, and 3.41 years for homozygotes, although the non-carrier median occurred late in follow-up. In the fully adjusted Cox model, APOE ε4 dose remained associated with conversion hazard (HR = 1.580, 95% CI 1.362–1.834, p < 0.0001). Intracranial Volume (ICV)-adjusted hippocampal volume was protective (HR = 0.620, 95% CI 0.566–0.680, p < 0.0001), and the model achieved a Concordance Index (C-index) of 0.805. The APOE ε4 × hippocampal volume interaction was not significant (likelihood ratio test p = 0.098). Sensitivity analyses supported robustness, although the APOE ε4 association was attenuated in the exploratory amyloid-positive CSF subgroup. Conclusions: These findings support APOE ε4 allele dose as a statistical marker of clinical progression risk in ADNI, not as evidence of biomarker-confirmed AD progression or distinct mechanisms. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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20 pages, 1948 KB  
Article
Efficacy and Safety of a Bioinspired Chitosan–Catechol/Gelatin Hemostatic Patch vs. TachoSil in Hepatectomy: A Randomized Noninferiority Trial
by Seoung Hoon Kim, Keumyeon Kim, Kyoungok Yun and Gyu-Seong Choi
Biomedicines 2026, 14(5), 1087; https://doi.org/10.3390/biomedicines14051087 - 12 May 2026
Viewed by 418
Abstract
Background/Objectives: Topical hemostatic biomaterials are used to control diffuse parenchymal bleeding during hepatectomy. TachoSil is a widely used standard fibrin sealant patch. We evaluated the efficacy and safety of InnoSEAL Plus DL, a novel bioinspired absorbable chitosan–catechol/gelatin hemostatic patch, compared with TachoSil. [...] Read more.
Background/Objectives: Topical hemostatic biomaterials are used to control diffuse parenchymal bleeding during hepatectomy. TachoSil is a widely used standard fibrin sealant patch. We evaluated the efficacy and safety of InnoSEAL Plus DL, a novel bioinspired absorbable chitosan–catechol/gelatin hemostatic patch, compared with TachoSil. Methods: This multicenter, randomized, single-blind, active-controlled, parallel-group noninferiority trial enrolled adults undergoing hepatectomy who had persistent oozing from the hepatic transection surface despite primary hemostasis. Participants were randomized in a 1:1 ratio to receive InnoSEAL Plus DL or TachoSil. The primary endpoint was hemostatic success within 3 min of application, with a prespecified noninferiority margin of −19.4 percentage points (pp). Safety was assessed up to 30 days postoperatively. Results: Ninety patients were randomized (45 per group). In the per-protocol population, 3 min hemostatic success was achieved in 100.0% of both the InnoSEAL Plus DL (43/43) and TachoSil (41/41) groups. The risk difference was 0.0 pp, and the lower bound of the one-sided 97.5% confidence interval was −8.2 pp, confirming noninferiority. The mean time to hemostasis was similar between groups (1.2 vs. 1.3 min), and no intraoperative rebleeding occurred. Adverse events were reported in 78/90 patients (86.7%) and serious adverse events in 6/90 (6.7%); the latter were typical post-hepatectomy events unrelated to the study devices. No deaths were reported. Conclusions: InnoSEAL Plus DL was noninferior to TachoSil for achieving rapid intraoperative hemostasis during hepatectomy, with no unexpected safety concerns. This bioinspired hemostatic patch is an effective alternative to fibrin sealant, without the use of human-derived proteins. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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23 pages, 522 KB  
Article
Privacy-Preserving Hybrid GA–LSTM Ensemble for Typhoid Detection Using Optimised Clinical Feature Selection
by Karim Gasmi, Afrah Alanazi, Sahar Almenwer, Sarah Almaghrabi, Hamoud Alshammari, Kais Khaldi and Hassen Chouaib
Biomedicines 2026, 14(5), 1010; https://doi.org/10.3390/biomedicines14051010 - 29 Apr 2026
Viewed by 541
Abstract
Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to develop a reliable and efficient diagnostic framework that automates typhoid fever [...] Read more.
Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to develop a reliable and efficient diagnostic framework that automates typhoid fever detection from clinical data while preserving patient privacy. Methods: To achieve this objective, we propose a hybrid framework combining genetic algorithm (GA)–based feature selection, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) deep learning classifier, and federated learning. The GA identifies the most informative clinical features, reducing redundancy and computational complexity. The selected features are then used to train a CNN–LSTM model in a federated learning setup using the Federated Averaging (FedAvg) algorithm, enabling collaborative model training across multiple clients without sharing raw patient data. Results: Experimental results show that the proposed framework achieves 92% accuracy, with a strong F1-score and satisfactory sensitivity. Compared to models trained on the full feature set, the proposed approach requires less memory and shorter training time, while maintaining balanced performance under class imbalance. Conclusions: These results demonstrate that integrating evolutionary feature selection, deep sequential learning, and federated training provides an effective and privacy-aware solution for multi-class typhoid fever diagnosis. The proposed framework is particularly suitable for clinical environments with limited data access and constrained resources. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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22 pages, 2585 KB  
Article
Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs
by Behnam Kiani Kalejahi, Sajid Khan and Rakhim Zakirov
Biomedicines 2026, 14(2), 299; https://doi.org/10.3390/biomedicines14020299 - 29 Jan 2026
Cited by 2 | Viewed by 912
Abstract
Background/Objectives: Accurate classification of primary bone tumors from radiographic images is essential for early diagnosis, appropriate treatment planning, and informed clinical decision-making. While deep convolutional neural networks (CNNs) have shown strong performance in medical image analysis, their high computational complexity often limits real-world [...] Read more.
Background/Objectives: Accurate classification of primary bone tumors from radiographic images is essential for early diagnosis, appropriate treatment planning, and informed clinical decision-making. While deep convolutional neural networks (CNNs) have shown strong performance in medical image analysis, their high computational complexity often limits real-world clinical deployment. This study aims to develop a lightweight yet highly accurate model for multi-class bone tumor classification. Methods: We propose Bone-CNN, a computationally efficient CNN architecture specifically designed for radiograph-based classification of primary bone tumors. The model was evaluated using the publicly available Figshare Radiograph Dataset of Primary Bone Tumors, which includes nine distinct tumor classes ranging from benign to malignant lesions and originates from multiple imaging centres. Performance was assessed through extensive experiments and compared against established baseline models, including DenseNet121, EfficientNet-B0, and MobileNetV2. Results: Bone-CNN achieved a test accuracy of 96.52% and a macro-AUC of 0.9989, outperforming all baseline architectures. Both quantitative and qualitative evaluations, including confusion matrices and ROC curve analyses, demonstrated robust and reliable discrimination between challenging tumor subtypes. Conclusions: The results indicate that Bone-CNN offers an excellent balance between accuracy and computational efficiency. Its strong performance and lightweight design highlight its suitability for clinical deployment, supporting effective and scalable radiograph-based assessment of primary bone tumors. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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Review

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22 pages, 1104 KB  
Review
Functionalized Lipid Nanoparticles for Targeted RNA Delivery in Immune and Inflammatory Diseases
by Yeongji Jang, Hyun Kyu Song, Man Kyu Shim and Yoosoo Yang
Biomedicines 2026, 14(5), 957; https://doi.org/10.3390/biomedicines14050957 - 22 Apr 2026
Viewed by 691
Abstract
Lipid nanoparticles (LNPs) have become an important platform for the delivery of RNA therapeutics, including messenger RNA (mRNA) and small interfering RNA (siRNA). However, most clinically approved LNP formulations exhibit strong liver tropism following systemic administration, which limits efficient delivery to extrahepatic tissues. [...] Read more.
Lipid nanoparticles (LNPs) have become an important platform for the delivery of RNA therapeutics, including messenger RNA (mRNA) and small interfering RNA (siRNA). However, most clinically approved LNP formulations exhibit strong liver tropism following systemic administration, which limits efficient delivery to extrahepatic tissues. This inherent biodistribution profile has therefore been recognized as a key challenge for expanding the therapeutic applications of RNA nanomedicine. Recent efforts have focused on engineering functionalized LNP systems to improve delivery specificity beyond the liver. Surface modification with targeting ligands—such as antibodies, peptides, and nucleic acid aptamers—can promote receptor-mediated uptake by specific immune cell populations, including macrophages, dendritic cells and T lymphocytes. In parallel, advances in lipid design have improved intracellular RNA delivery by facilitating endosomal escape. These developments have broadened the potential use of RNA nanomedicine for inflammatory disorders, including autoimmune diseases, neuroinflammation, and cardiovascular inflammation. Functionalized LNPs are also being investigated for in vivo engineering of immune cells. This review summarizes current strategies for designing functionalized LNP systems, highlights their emerging applications in immune and inflammatory diseases, and discusses key challenges for clinical translation. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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22 pages, 3397 KB  
Review
Advances in Bone-on-a-Chips for In Vitro Modeling of Bone Physiology and Pathology
by Xiuyun Cheng, Mingxia Lu, Ming Ma, Shumin Zhou, Jun Xu, Yuhao Li and Hongxu Lu
Biomedicines 2026, 14(3), 710; https://doi.org/10.3390/biomedicines14030710 - 19 Mar 2026
Viewed by 1243
Abstract
Bone is a dynamic and multifunctional tissue that provides mechanical support, regulates mineral homeostasis, supports hematopoiesis, and relies on complex interactions among multiple cell types. The increasing incidence of bone-related diseases, such as osteoporosis, osteoarthritis, fracture non-union, and bone cancer, highlights the need [...] Read more.
Bone is a dynamic and multifunctional tissue that provides mechanical support, regulates mineral homeostasis, supports hematopoiesis, and relies on complex interactions among multiple cell types. The increasing incidence of bone-related diseases, such as osteoporosis, osteoarthritis, fracture non-union, and bone cancer, highlights the need for in vitro models that better reflect human bone physiology. Bone-on-a-chip technology, developed through advances in microfluidics, biomaterials, and tissue engineering, offers a promising approach to recreate key features of the bone microenvironment in vitro. By incorporating bone-mimicking materials, relevant bone cells, vascular components, fluid perfusion, and mechanical stimulation, these platforms allow more realistic investigation of bone remodeling, regeneration, disease mechanisms, and drug responses. In parallel, bone organoids and their integration with microfluidic chips have further expanded the capabilities of in vitro bone models by enabling the formation of self-organized, human-relevant bone tissues with increased cellular complexity. This review summarizes recent progress in bone-on-a-chip systems, including models for osteogenesis and bone regeneration, vascularized bone, bone marrow and hematopoietic niches, cancer bone metastasis, and mechanobiological studies. Key design principles, materials, cellular components, and applications in disease modeling, drug screening, toxicity assessment, and personalized medicine are discussed. Current challenges and future directions are also discussed to support the continued development of more physiologically relevant in vitro bone models. Full article
(This article belongs to the Special Issue Applications of Biomedical Engineering and Biomaterials in Human Diseases)
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