An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems

Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care

The integration of artificial intelligence (AI) and machine learning (ML) into regenerative orthopedics heralds a paradigm shift in clinical methodologies and patient management. This review article scrutinizes AI's role in augmenting diagnostic accuracy, refining predictive models, and customizing patient care in orthopedic medicine. Focusing on innovations such as KeyGene and CellNet, we illustrate AI's adeptness in navigating complex genomic datasets, cellular differentiation, and scaffold biodegradation, which are critical components of tissue engineering. Despite its transformative potential, AI's clinical adoption remains in its infancy, contending with challenges in validation, ethical oversight, and model training for clinical relevance. This review posits AI as a vital complement to human intelligence (HI), advocating for an interdisciplinary approach that merges AI's computational prowess with medical expertise to fulfill precision medicine's promise. By analyzing historical and contemporary developments in AI, from the foundational theories of McCullough and Pitts to sophisticated neural networks, the paper emphasizes the need for a synergistic alliance between AI and HI. This collaboration is imperative for improving surgical outcomes, streamlining therapeutic modalities, and enhancing the quality of patient care. Our article calls for robust interdisciplinary strategies to overcome current obstacles and harness AI's full potential in revolutionizing patient outcomes, thereby significantly contributing to the advancement of regenerative orthopedics and the broader field of scientific research.

Application of residuals from purification of bivalve molluscs in Galician to facilitate marine ecosystem resiliency through artificial reefs with shells - One generation

The seas and oceans of the planet provide a wide range of essential resources. However, marine ecosystems are undergoing severe degradation due to the unsustainable exploitation and consumption patterns of the linear economy. On the other hand, many economic activities linked to the sea generate a large amount of waste, leading to negative impacts, such as the cost of treating or disposing of this waste. A case in point is bivalve mollusc production: a purification process is needed to avoid the risk of diseases through faecal contamination. The present work proposes an innovative procedure to convert this waste, calcium carbonate as calcite and aragonite allotropic types, into by-products. These by-products can be used to manufacture green artificial reefs, partially replacing concrete aggregates with a sustainable alternative to the geological sources of CaCO₃. By installing these reefs, marine ecosystems could be created in a sustainable way and an innovative approach based on the circular economy could be taken towards protecting them. To this end, different concrete mixtures with bivalve shells are proposed. Although this study had been carried out for Galicia (NW Spain), the methodology followed could also be valid for other regions. A physicochemical characterisation of the waste from purifying the bivalves, including oysters, mussels, clams and scallops, was performed. Statistical and multi-criteria analyses were done in order to select the best dosage. Both have provided justification for using a mixture of shells with a predominance of calcite (oyster, scallop) instead of shells with a predominance of aragonite. The multi-criteria analysis served to identify the two best alternatives with dosages in which the medium aggregates were substituted with shells mainly from oysters, with a predominance of calcite. Finally, the statistical analysis played a role in estimating the compressive strength and water absorption of each mixture from the design parameter values.

Identification and ranking biomaterials for bone scaffolds using machine learning and PROMETHEE

Purpose

Bones have a complex hierarchical structure that supports their diverse chemical, biological, and mechanical functions. High rates of bone susceptibility to fractures and injury have attracted extensive research interest to find alternate biomaterials for bone scaffolds. Natural bone healing is only successful if the defect is very small and when a defect exceeds 1 cm3 then bone grafting is required. Large bone defects or injuries are very serious problems in orthopedics as they bring great harm to health and normal function of daily life routine. A scaffold should have good strength to maintain its own structure after implantation in a load bearing environment and without being stiff that shields surrounding bone from the load. Therefore, mechanical properties of bone scaffolds should match those of the host tissue and should be part of the natural environment of the body without any harm or further damage.

Methods

In this paper, we present two main contributions. First, we investigate the use of machine learning models in identifying biomaterials that are suitable for bone scaffolds. Second, we rank the best materials for biomedical scaffold applications using the multi-criteria decision analysis methods, the Preference Ranking Organization METhod for the Enrichment of Evaluations (PROMETHEE). Machine learning models investigated are AdaBoost, artificial neural network (ANN), Naïve Bayes (NB), Decision tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Mechanical properties such as comprehensive strength, tensile strength, and Young’s modulus with the cortical bone are used as the standard reference for classification.

Results

The results show that the ANN outperforms the other machine learning models in identifying the biomaterials suitable for bone tissue engineering, while the ranking results using PROMETHEE show that Brushite and Titanium alloy are the best appropriate biomaterials for the cancellous and cortical bones, respectively.

Conclusion

Brushite and Titanium alloy are the best biomaterials for the cancellous and cortical bones, respectively.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Erosive Degradation Study of Concrete Augmented by Mussel Shells for Marine Construction

This work proposes a green material for artificial reefs to be placed in Galicia (northwest Spain) taking into account the principles of circular economy and sustainability of the ecosystem. New concrete formulations for marine applications, based on cement and/or sand replacement by mussel shells, are analyzed in terms of resistance to abrasion. The interest lies in the importance of the canning industry of Galicia, which generates important quantities of shell residues with negative environmental consequences. Currently, the tests to determine the abrasion erosion resistance of concrete on hydraulic structures involve large and complex devices. According to this, an experimental test has been proposed to estimate and compare the wear resistance of these concretes and, consequently, to analyze the environmental performance of these structures. First, a numerical analysis validated with experimental data was conducted to design the test. Subsequently, experimental tests were performed using a slurry tank in which samples with conventional cement and sand were partially replaced by mussel shell. The abrasive erosion effect of concrete components was analyzed by monitoring the mass loss. It shows an asymptotic trend with respect to time that has been modeled by Generalized Additive Model (GAM) and nonlinear regression models. The results were compared to concrete containing only conventional cement and sand. Replacing sand and/or cement by different proportions of mussel shells has not significantly reduced the resistance of concrete against erosive degradation, except for the case where a high amount of sand (20 wt.%) is replaced. Its resistance against the erosive abrasion is increased, losing between 0.1072 and 0.0310 wt.% lower than common concrete. In all the remaining cases (replacements of the 5–10 wt.% of sand and cement), the effect of mussel replacement on erosive degradation is not significant. These results encourage the use of mussel shells in the composition of concrete, taking into account that we obtain the same degradation properties, even more so considering an important residue in the canning industry (and part of the seabed) that can be valorized.

The future of bone regeneration: integrating AI into tissue engineering

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

The preparation and application of calcium phosphate biomedical composites in filling of weight-bearing bone defects

Nowadays, artificial bone materials have been widely applied in the filling of non-weight bearing bone defects, but scarcely ever in weight-bearing bone defects. This study aims to develop an artificial bone with excellent mechanical properties and good osteogenic capability. Firstly, the collagen-thermosensitive hydrogel-calcium phosphate (CTC) composites were prepared as follows: dissolving thermosensitive hydrogel at 4 °C, then mixing with type I collagen as well as tricalcium phosphate (CaP) powder, and moulding the composites at 37 °C. Next, the CTC composites were subjected to evaluate for their chemical composition, micro morphology, pore size, Shore durometer, porosity and water absorption ability. Following this, the CTC composites were implanted into the muscle of mice while the 70% hydroxyapatite/30% β-tricalcium phosphate (HA/TCP) biomaterials were set as the control group; 8 weeks later, the osteoinductive abilities of biomaterials were detected by histological staining. Finally, the CTC and HA/TCP biomaterials were used to fill the large segments of tibia defects in mice. The bone repairing and load-bearing abilities of materials were evaluated by histological staining, X-ray and micro-CT at week 8. Both the CTC and HA/TCP biomaterials could induce ectopic bone formation in mice; however, the CTC composites tended to produce larger areas of bone and bone marrow tissues than HA/TCP. Simultaneously, bone-repairing experiments showed that HA/TCP biomaterials were easily crushed or pushed out by new bone growth as the material has a poor hardness. In comparison, the CTC composites could be replaced gradually by newly formed bone and repair larger segments of bone defects. The CTC composites trialled in this study have better mechanical properties, osteoinductivity and weight-bearing capacity than HA/TCP. The CTC composites provide an experimental foundation for the synthesis of artificial bone and a new option for orthopedic patients.