Bioengineering最新文献

Bone tissue engineering (BTE) is an important field of research, essential in order to heal bone defects or replace impaired tissues and organs. As one of the most used additive manufacturing processes, 3D printing can produce biostructures in the field of tissue engineering for bones, orthopaedic tissues, and organs. Scaffold manufacturing techniques and suitable materials with final structural, mechanical properties, and the biological response of the implanted biomaterials are an essential part of BTE. In fact, the scaffold is an essential component for tissue engineering where cells can attach, proliferate, and differentiate to develop functional tissue. Fused deposition modelling (FDM) is commonly employed in the 3D printing of tissue-engineering scaffolds. Scaffolds must have a good architecture, considering the porosity, permeability, degradation, and healing capabilities. In fact, the architecture of a scaffold is crucial, influencing not only the physical and mechanical properties but also the cellular behaviours of mesenchymal stem cells. Cells placed on/or within the scaffolds is a standard approach in tissue engineering. For bio-scaffolds, materials that are biocompatible and biodegradable, and can support cell growth are the ones chosen. These include polymers like polylactic acid (PLA), polycaprolactone (PCL), and certain bioglass or composite materials. This work comprehensively integrates aspects related to the optimisation of biocompatible and biodegradable composites with the low cost, simple, and stable FDM technology to successfully prepare the best designed composite porous bone-healing scaffolds. FDM can be used to produce low-cost bone scaffolds, with a suitable porosity and permeability.

Surgery of the aortic arch remains a complex procedure, with neurological events such as stroke remaining its most dreaded complications. Changes in surgical technique and the continuous innovation in neuroprotective strategies have led to a significant decrease in cerebral and spinal events. Different modes of cerebral perfusion, varying grades of hypothermia, and a number of pharmacological strategies all aim to reduce hypoxic and ischemic cerebral injury, yet there is no evidence indicating the clear superiority of one method over another. While surgical results continue to improve, novel hybrid and interventional techniques are just entering the stage and the question of optimal neuroprotection remains up to date. Within this perspective statement, we want to shed light on the current evidence and controversies of cerebral protection in aortic arch surgery, as well as what is on the horizon in this fast-evolving field. We further present our institutional approach as a large tertiary aortic reference center.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

Since three-dimensional (3D) bioprinting has emerged, it has continuously to evolved as a revolutionary technology in surgery, offering new paradigms for reconstructive and regenerative medical applications. This review highlights the integration of 3D printing, specifically bioprinting, across several surgical disciplines over the last five years. The methods employed encompass a review of recent literature focusing on innovations and applications of 3D-bioprinted tissues and/or organs. The findings reveal significant advances in the creation of complex, customized, multi-tissue constructs that mimic natural tissue characteristics, which are crucial for surgical interventions and patient-specific treatments. Despite the technological advances, the paper introduces and discusses several challenges that remain, such as the vascularization of bioprinted tissues, integration with the host tissue, and the long-term viability of bioprinted organs. The review concludes that while 3D bioprinting holds substantial promise for transforming surgical practices and enhancing patient outcomes, ongoing research, development, and a clear regulatory framework are essential to fully realize potential future clinical applications.

In this study, we aimed to develop a novel method for non-invasively determining intracellular protein levels, which is essential for understanding cellular phenomena. This understanding hinges on insights into gene expression, cell morphology, dynamics, and intercellular interactions. Traditional cell analysis techniques, such as immunostaining, live imaging, next-generation sequencing, and single-cell analysis, despite rapid advancements, face challenges in comprehensively integrating gene and protein expression data with spatiotemporal information. Leveraging advances in machine learning for image analysis, we designed a new model to estimate cellular biomarker protein levels using a blend of phase-contrast and fluorescent immunostaining images of epidermal keratinocytes. By iterating this process across various proteins, our model can estimate multiple protein levels from a single phase-contrast image. Additionally, we developed a system for analyzing multiple protein expression levels alongside spatiotemporal data through live imaging and phase-contrast methods. Our study offers valuable tools for cell-based research and presents a new avenue for addressing molecular biological challenges.

The impact of traumatic spinal cord injury (SCI) can be extremely devastating, as it often results in the disruption of neural tissues, impeding the regenerative capacity of the central nervous system. However, recent research has demonstrated that mesenchymal stem cells (MSCs) possess the capacity for multi-differentiation and have a proven track record of safety in clinical applications, thus rendering them effective in facilitating the repair of spinal cord injuries. It is urgent to develop an aligned scaffold that can effectively load MSCs for promoting cell aligned proliferation and differentiation. In this study, we prepared an aligned nanofiber scaffold using the porcine decellularized spinal cord matrix (DSC) to induce MSCs differentiation for spinal cord injury. The decellularization method removed 87% of the immune components while retaining crucial proteins in DSC. The electrospinning technique was employed to fabricate an aligned nanofiber scaffold possessing biocompatibility and a diameter of 720 nm. In in vitro and in vivo experiments, the aligned nanofiber scaffold induces the aligned growth of MSCs and promotes their differentiation into neurons, leading to tissue regeneration and nerve repair after spinal cord injury. The approach exhibits promising potential for the future development of nerve regeneration scaffolds for spinal cord injury treatment.

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

The regenerative capacity of the peripheral nervous system is limited, and peripheral nerve injuries often result in incomplete healing and poor outcomes even after repair. Transection injuries that induce a nerve gap necessitate microsurgical intervention; however, even the current gold standard of repair, autologous nerve graft, frequently results in poor functional recovery. Several interventions have been developed to augment the surgical repair of peripheral nerves, and the application of functional biomaterials, local delivery of bioactive substances, electrical stimulation, and allografts are among the most promising approaches to enhance innate healing across a nerve gap. Biocompatible polymers with optimized degradation rates, topographic features, and other functions provided by their composition have been incorporated into novel nerve conduits (NCs). Many of these allow for the delivery of drugs, neurotrophic factors, and whole cells locally to nerve repair sites, mitigating adverse effects that limit their systemic use. The electrical stimulation of repaired nerves in the perioperative period has shown benefits to healing and recovery in human trials, and novel biomaterials to enhance these effects show promise in preclinical models. The use of acellular nerve allografts (ANAs) circumvents the morbidity of donor nerve harvest necessitated by the use of autografts, and improvements in tissue-processing techniques may allow for more readily available and cost-effective options. Each of these interventions aid in neural regeneration after repair when applied independently, and their differing forms, benefits, and methods of application present ample opportunity for synergistic effects when applied in combination.

Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.

This case report presents a virtual treatment simulation of the orthodontic treatment and surgery-first orthognathic surgery employed to treat a patient with a repaired unilateral cleft lip and alveolus with Class III malocclusion and lower third facial asymmetry. The patient exhibited a negative overjet of 9 mm, a missing lower right second premolar, and a 5 mm gap between the upper right central and lateral incisors with midline discrepancy. The three-dimensional virtual planning began with virtual pre-surgical orthodontics, followed by the positioning of the facial bones and teeth in their ideal aesthetic and functional positions. The sequence of steps needed to achieve this outcome was then reverse-engineered and recorded using multiplatform Nemostudio software (Nemotec, Madrid, Spain), which facilitated both surgical and orthodontic planning. The treatment included a two-piece segmental maxillary osteotomy for dental space closure, a LeFort I maxillary advancement, and a mandibular setback with bilateral sagittal split osteotomy to correct the skeletal underbite and asymmetry. A novel approach was employed by pre-treating the patient for orthognathic surgeries at age 11, seven years prior to the surgery. This early phase of orthodontic treatment aligned the patient's teeth and established the dental arch form. The positions of the teeth were maintained with retainers, eliminating the need for pre-surgical orthodontics later. This early phase of treatment significantly reduced the treatment time. The use of software to predict all the necessary steps for surgery and post-surgical orthodontic tooth movements made this approach possible. Multi-step virtual planning can be a powerful tool for analyzing complex craniofacial problems that require multidisciplinary care, such as cleft lip and/or palate.