Bioengineering最新文献

Genetic disorders affect over 6% of the global population and pose substantial obstacles to healthcare systems. Early identification of these rare facial genetic disorders is essential for managing related medical complexities and health issues. Many people consider the existing screening techniques inadequate, often leading to a diagnosis several years after birth. This study evaluated the efficacy of deep learning-based classifier models for accurately recognizing dysmorphic characteristics using facial photos. This study proposes a multi-class facial syndrome classification framework that encompasses a unique combination of diseases not previously examined together. The study focused on distinguishing between individuals with four specific genetic disorders (Down syndrome, Noonan syndrome, Turner syndrome, and Williams syndrome) and healthy controls. We investigated how well fine-tuning a few well-known convolutional neural network (CNN)-based pre-trained models-including VGG16, ResNet-50, ResNet152, and VGG-Face-worked for the multi-class facial syndrome classification task. We obtained the most encouraging results by adjusting the VGG-Face model. The proposed fine-tuned VGG-Face model not only demonstrated the best performance in this study, but it also performed better than other state-of-the-art pre-trained CNN models for the multi-class facial syndrome classification task. The fine-tuned model achieved both accuracy and an F1-Score of 90%, indicating significant progress in accurately detecting the specified genetic disorders.

Background: The upper respiratory mucosa plays a crucial role in both the physical integrity and immunological function of the respiratory tract. However, in certain situations such as infections, trauma, or surgery, it might sustain damage. Tissue engineering, a field of regenerative medicine, has found applications in various medical fields including but not limited to plastic surgery, ophthalmology, and urology. However, its application to the respiratory system remains somewhat difficult due to the complex morphology and histology of the upper respiratory tract. To date, a culture protocol for producing a handleable, well-differentiated nasal mucosa has yet to be developed. The objective of this review is to describe the current state of research pertaining to cell culture techniques used for producing autologous healthy human upper respiratory cells and mucosal tissues, as well as describe its clinical applications.

Methods: A search of the relevant literature was carried out with no time restriction across Embase, Cochrane, PubMed, and Medline Ovid databases. Keywords related to "respiratory mucosa" and "culture techniques of the human airway" were the focus of the search strategy for this review. The risk of bias in retained studies was assessed using the Joanna Briggs Institute's (JBI) critical appraisal tools for qualitative research. A narrative synthesis of our results was then conducted.

Results: A total of 33 studies were included in this review, and thirteen of these focused solely on developing a cell culture protocol without further use. The rest of the studies used their own developed protocol for various applications such as cystic fibrosis, pharmacological, and viral research. One study was able to develop a promising model for nasal mucosa that could be employed as a replacement in nasotracheal reconstructive surgery.

Conclusions: This systematic review extensively explored the current state of research regarding cell culture techniques for producing tissue-engineered nasal mucosa. Bioengineering the nasal mucosa holds great potential for clinical use. However, further research on mechanical properties is essential, as the comparison of engineered tissues is currently focused on morphology rather than comprehensive mechanical assessments.

Throughout the isolation process, human islets are subjected to destruction of the islet basement membrane (BM) and reduced oxygen supply. Reconstruction of the BM represents an option to improve islet function and survival post-transplant and may particularly be relevant for islet encapsulation devices and scaffolds. In the present study, we assessed whether Perlecan, used alone or combined with the BM proteins (BMPs) Collagen-IV and Laminin-521, has the ability to protect isolated human islets from hypoxia-induced damage. Islets isolated from the pancreas of seven different organ donors were cultured for 4-5 days at 2% oxygen in plain CMRL (sham-treated controls) or in CMRL supplemented with BMPs used either alone or in combination. Postculture, islets were characterized regarding survival, in vitro function and production of chemokines and reactive oxygen species (ROS). Individually added BMPs significantly doubled islet survival and increased in vitro function. Combining BMPs did not provide a synergistic effect. Among the tested BMPs, Perlecan demonstrated the significantly strongest inhibitory effect on chemokine and ROS production when compared with sham-treatment (p < 0.001). Perlecan may be useful to improve islet survival prior to and after transplantation. Its anti-inflammatory potency should be considered to optimise encapsulation and scaffolds to protect isolated human islets post-transplant.

Exploring how neurons in the mammalian body interact with the artificial interface of implants can be used to learn about fundamental cell behavior and to refine medical applications. For fundamental and applied research, it is crucial to determine the conditions that encourage neurons to maintain their natural behavior during interactions with non-natural interfaces. Our previous investigations quantified the deterioration of neuronal connectivity when their dendrites deviate from their natural fractal geometry. Fractal resonance proposes that neurons will exhibit enhanced connectivity if an implant's electrode geometry is matched to the fractal geometry of the neurons. Here, we use in vitro imaging to quantify the fractal geometry of mouse retinal neurons and show that they change during interaction with the electrode. Our results demonstrate that it is crucial to understand these changes in the fractal properties of neurons for fractal resonance to be effective in the in vivo mammalian system.

The intersection of biomedical and bioengineering technologies with sports monitoring and healthcare has recently emerged as a key area of innovation and research [...].

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.

Background: Physiological curvature changes of the lumbar spine and disc herniation can cause abnormal biomechanical responses of the lumbar spine. Finite element (FE) studies on special weightlifter models are limited, yet understanding stress in damaged lumbar spines is crucial for preventing and rehabilitating lumbar diseases. This study analyzes the biomechanical responses of a weightlifter with lumbar straightening and L4-L5 disc herniation during symmetric bending and lifting to optimize training and rehabilitation.

Methods: Based on the weightlifter's computed tomography (CT) data, an FE lumbar spine model (L1-L5) was established. The model included normal intervertebral discs (IVDs), vertebral endplates, ligaments, and a degenerated L4-L5 disc. The bending angle was set to 45°, and weights of 15 kg, 20 kg, and 25 kg were used. The flexion moment for lifting these weights was theoretically calculated. The model was tilted at 45° in Abaqus 2021 (Dassault Systèmes Simulia Corp., Johnston, RI, USA), with L5 constrained in all six degrees of freedom. A vertical load equivalent to the weightlifter's body mass and the calculated flexion moments were applied to L1 to simulate the weightlifter's bending and lifting behavior. Biomechanical responses within the lumbar spine were then analyzed.

Results: The displacement and range of motion (ROM) of the lumbar spine were similar under all three loading conditions. The flexion degree increased with the load, while extension remained unchanged. Right-side movement and bending showed minimal change, with slightly more right rotation. Stress distribution trends were similar across loads, primarily concentrated in the vertebral body, increasing with load. Maximum stress occurred at the anterior inferior margin of L5, with significant stress at the posterior joints, ligaments, and spinous processes. The posterior L5 and margins of L1 and L5 experienced high stress. The degenerated L4-L5 IVD showed stress concentration on its edges, with significant stress also on L3-L4 IVD. Stress distribution in the lumbar spine was uneven.

Conclusions: Our findings highlight the impact on spinal biomechanics and suggest reducing anisotropic loading and being cautious of loaded flexion positions affecting posterior joints, IVDs, and vertebrae. This study offers valuable insights for the rehabilitation and treatment of similar patients.

This study uses machine learning to identify critical factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) based on user ratings and prices, we analyzed data from Amazon. The findings indicate that Flexible Spending Account (FSA)/Health Savings Account (HSA) eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

Background: Glaucoma is a leading cause of irreversible blindness worldwide and is particularly challenging to treat in its refractory forms. The Ahmed valve offers a potential solution for these difficult cases. This research aims to assess the initial clinical experience with Ahmed valve implantation in Romania, evaluating its effectiveness, associated complications, and overall patient outcomes over a five-year period.

Methods: We conducted a prospective study on 50 patients who underwent Ahmed valve implantation due to various types of glaucoma. Patients were monitored at several intervals, up to five years post-surgery. Intraocular pressure and visual acuity were the primary measures of success.

Results: On average, patients maintained the intraocular pressure within the targeted range, with the mean intraocular pressure being 17 mmHg 5 years post-surgery. Success, defined as maintaining target intraocular pressure without additional surgery, was achieved in 82% at 1 year, 68% at 3 years, and 60% after 5 years postoperative.

Conclusion: Ahmed valve implantation is a viable treatment option for refractory glaucoma, demonstrating significant intraocular pressure reduction and manageable complication rates over a five-year follow-up period. Future research should focus on long-term outcomes and optimization of surgical techniques to further reduce complication rates and improve patient quality of life.

Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687-0.781) and 0.747 (95% CI 0.701-0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features.