Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

Hierarchical chitin and chitosan-derived heterostructural nanocomposites: From interdisciplinary applications to a sustainable vision

Natural biopolymeric nanomaterials are highly prioritized and indispensable for industrial production and human use due to their exceptional features. In recent years, the development of bioinspired materials has rapidly advanced, driven by their outstanding qualities and versatile applications. Among these, chitin and chitosan stand out for their biodegradability, biocompatibility, and hierarchical structures, captivating researchers worldwide. In order to ameliorate the characteristics of these materials, integrating them with complementary components such as polymers, organics, and nanomaterials to create multifunctional chitinous bio-composites has become increasingly important. This review highlights recent progress in the development of these composite biomaterials, emphasizing biomimetic design, synthesis methodologies, and applications in drug delivery, cancer therapy, tissue engineering, wound healing, antimicrobial activity, food safety, natural bio-adhesives, and various industrial uses, alongside their ecological balance on Earth within a sustainable vision. Additionally, the discussion also addresses ongoing challenges and explores potential prospects for advancing these innovative biocomposites.

Vat photo-polymerization 3D printing of gradient scaffolds for osteochondral tissue regeneration

In recent decades, osteochondral (OC) tissue regeneration has been one of the major challenges in regenerative medicine. The absence of blood vessels, lymphatic vessels, and nerves in OC tissue prevents self-repair, while the structural complexity and differences between bone and cartilage layers make conventional surgical treatments largely ineffective. To address this issue, tissue engineering has emerged as a promising approach to replacing damaged OC tissue, with a particular focus on innovative strategies such as the design of continuous gradient scaffolds that mimic the complex architecture of native OC tissue. In this review vat photopolymerization (VPP) 3D printing technologies are presented as one of the most effective methods for fabricating gradient scaffolds for OC tissue repair. By leveraging photochemical reactions and light-assisted techniques, such as digital light processing (DLP), stereolithography (SLA) and two-photon polymerization (2-PP), highly precise porous structures made of biocompatible photo-crosslinkable resins have been successfully fabricated, with several relevant examples reported herein. DLP, SLA and 2-PP have proven fundamental in creating compositional, architectural, and mechanical gradients within scaffolds. Moreover, scaffold functionalization with bioactive molecules has demonstrated effectiveness in repairing damaged OC tissue in both in vitro and in vivo conditions. Moreover, the adoption of modeling tools such as the design of experiments (DoE) approach and AI-driven computational methods has proven to be valuable in optimizing the fabrication process and enhancing scaffold designs to more closely replicate the architecture and functionality of osteochondral tissues. STATEMENT OF SIGNIFICANCE: Despite the transformative potential of vat photopolymerization (VPP) techniques, such as stereolithography (SLA) and digital light processing (DLP), for developing high-precision gradient 3D scaffolds for osteochondral (OC) tissue repair, achieving full biomimetic restoration remains a significant challenge. This review offers a comprehensive analysis of advancements in VPP, detailing how these techniques enable precise control over scaffold composition, architecture, and mechanical properties to closely replicate the complex structure of OC tissue. Furthermore, it underscores the critical need for standardized protocols and long-term evaluations in scaffold development. Addressing these gaps is essential to advancing the clinical translation of VPP-based scaffolds, paving the way for more effective treatments for OC tissue damage.

Poly(propylene fumarate) Composite Scaffolds for Bone Tissue Engineering: Innovation in Fabrication Techniques and Artificial Intelligence Integration

Over the past three decades, the biodegradable polymer known as poly(propylene fumarate) (PPF) has been the subject of numerous research due to its unique properties. Its biocompatibility and controllable mechanical properties have encouraged numerous scientists to manufacture and produce a wide range of PPF-based materials for biomedical purposes. Additionally, the ability to tailor the degradation rate of the scaffold material to match the rate of new bone tissue formation is particularly relevant in bone tissue engineering, where synchronized degradation and tissue regeneration are critical for effective healing. This review thoroughly summarizes the advancements in different approaches for PPF and PPF-based composite scaffold preparation for bone tissue engineering. Additionally, the challenges faced by each approach, such as biocompatibility, degradation, mechanical features, and crosslinking, were emphasized, and the noteworthy benefits of the most pertinent synthesis strategies were highlighted. Furthermore, the synergistic outcome between tissue engineering and artificial intelligence (AI) was addressed, along with the advantages brought by the implication of machine learning (ML) as well as the revolutionary impact on regenerative medicines. Future advances in bone tissue engineering could be facilitated by the enormous potential for individualized and successful regenerative treatments that arise from the combination of tissue engineering and artificial intelligence. By assessing a patient's reaction to a certain drug and choosing the best course of action depending on the patient's genetic and clinical characteristics, AI can also assist in the treatment of illnesses. AI is also used in drug research and discovery, target identification, clinical trial design, and predicting the safety and effectiveness of novel medications. Still, there are ethical issues including data protection and the requirement for reliable data management systems. AI adoption in the healthcare sector is expensive, involving staff and facility investments as well as training healthcare professionals on its application.

Advances in Drug Delivery Integrated with Regenerative Medicine: Innovations, Challenges, and Future Frontiers

Advances in drug delivery systems adapted with regenerative medicine have transformed healthcare by introducing innovative strategies to treat (and repair in many instances) disease-impacted regions of the human body. This review provides a comprehensive analysis of the latest developments and challenges in integrating drug delivery technologies with regenerative medicine. Recent advances in drug delivery technologies, including the design of biomaterials, localized delivery techniques, and controlled release systems guided by mathematical models, are explored to illustrate their role in enhancing therapeutic precision and efficacy. Additionally, regenerative medicine approaches are analyzed, with a focus on extracellular matrix components, stem cell-based therapies, and emerging strategies for organ regeneration in both soft and hard tissue and in vitro model engineering. In particular, the review also discusses the applications of cellular components, including stem cells, immune cells, endothelial cells, and specialized cells such as chondrocytes and osteoblasts, and highlights advancements in cell delivery methods and cell-cell interaction modulation. In addition, future directions and pivotal trends emphasizing the importance of interdisciplinary collaboration and cutting-edge innovations are provided to address successful therapeutic outcomes in regenerative medicine.

A multiphysics hybrid continuum - agent-based model of in vitro vascularized organoids

BACKGROUND

Organoids are 3D in vitro models that fulfill a hierarchical function, representing a small version of living tissues and, therefore, a good approximation of cellular mechanisms. However, one of the main disadvantages of these models is the appearance of a necrotic core due to poor vascularization. The aim of this work is the development of a numerical framework that incorporates the mechanical stimulation as a key factor in organoid vascularization. Parameters, such as fluid velocity and nutrient consumption, are analyzed along the organoid evolution.

METHODS

The mathematical model created for this purpose combines continuum and discrete approaches. In the continuum part, the fluid flow and the diffusion of oxygen and nutrients are modeled using a finite element method approach. Meanwhile, the growth of the organoid, blood vessel evolution, as well as their interaction with the surrounding environment, are modeled using agent-based methods.

RESULTS

Continuum model outcomes include the distribution of shear stress, pressure and fluid velocity around the organoid surface, in addition to the concentration of oxygen and nutrients in its interior. The agent models account for cell proliferation, differentiation, organoid growth and blood vessel morphology, for the different case studies considered.

CONCLUSIONS

Two main conclusions are achieved in this work: (i) the results of the study quantitatively predict in vitro data, with an enhanced blood vessel invasion under high fluid flow and (ii) the diffusion and consumption model parameters of the organoid cells determine the thickness of the proliferative, quiescent, hypoxic and necrotic layers.

Harnessing machine learning algorithms for the prediction and optimization of various properties of polylactic acid in biomedical use: a comprehensive review

Machine learning (ML) has emerged as a transformative tool in various industries, driving advancements in key tasks like classification, regression, and clustering. In the field of chemical engineering, particularly in the creation of biomedical devices, personalization is essential for ensuring successful patient recovery and rehabilitation. Polylactic acid (PLA) is a material with promising potential for applications like tissue engineering, orthopedic implants, drug delivery systems, and cardiovascular stents due to its biocompatibility and biodegradability. Additive manufacturing (AM) allows for adjusting print parameters to optimize the properties of PLA components for different applications. Although past research has explored the integration of ML and AM, there remains a gap in comprehensive analyses focusing on the impact of ML on PLA-based biomedical devices. This review examines the most recent developments in ML applications within AM, highlighting its ability to revolutionize the utilization of PLA in biomedical engineering by enhancing material properties and optimizing manufacturing processes. Moreover, this review is in line with the journal's emphasis on bio-based polymers, polymer functionalization, and their biomedical uses, enriching the understanding of polymer chemistry and materials science.

Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury

Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.