Elisa Roldan | Manchester Metropolitan University (original) (raw)

Papers by Elisa Roldan

Materialia , 2024

The gold standard to characterise anterior cruciate ligament (ACL) implants is through the evalua... more The gold standard to characterise anterior cruciate ligament (ACL) implants is through the evaluation of mechanical properties such as Young's modulus or ultimate tensile stress. Currently, no studies have been performed to relate the in-vivo hyper-elastic behaviour of the ACL with the design of tissue engineered ligaments. The aim of this work is to determine the most comparable 2D/3D polyvinyl alcohol (PVA) electrospun structure to the in-vivo mechanical behaviour of the natural ligament. Biomechanics of 12 young participants were captured while daily and high impact activities were performed. A musculoskeletal knee model and kinematic data were used to estimate the in-vivo ACL length and strain. The in-vivo ACL tensile forces were determined with a non-linear force/strain relationship. 2D scaffolds, 1 twisted filament scaffolds, 3 twisted filaments scaffolds and 3 twisted/braided filaments scaffolds were fabricated using electrospinning and characterised morphologically and mechanically using scanning electron microscopy and tensile testing respectively. Cyclic tensile and shear tests were performed in dry and wet conditions to crosslinked and non-crosslinked samples. The hyper-elastic behaviour of our PVA scaffolds was characterised with the Mooney Rivlin model and a non-linear string-based model, and both models compared with the in-vivo mechanical behaviour of the native ACL. Crosslinked 3 twisted/braided filaments scaffolds faithfully mimicked the morphology and the hyper-elastic behaviour of the natural ACL, showed a good resistance to shear loading and remained undegraded in phosphate-buffer saline solution. This study demonstrated, for the first time, that 3 twisted/braided filaments PVA electrospun scaffolds have an excellent potential for ACL replacements.

Journal of the Mechanical Behavior of Biomedical Materials, 2024

Currently, the use of autografts is the gold standard for the replacement of many damaged biologi... more Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young's modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young's modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R 2 of 0.93 and 0.8, to predict the ultimate tensile strength and Young's modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications.

Frontiers in Bioengineering and Biotechnology, Jul 12, 2023

Introduction: Gelatin is a natural polymer commonly used in biomedical applications in combinatio... more Introduction: Gelatin is a natural polymer commonly used in biomedical applications in combination with other materials due to its high biocompatibility, biodegradability, and similarity to collagen, principal protein of the extracellular matrix (ECM). The aim of this study was to evaluate the suitability of gelatin as the sole material to manufacture tissue engineering scaffolds by electrospinning. Methods: Gelatin was electrospun in nine different concentrations onto a rotating collector and the resulting scaffold's mechanical properties, morphology and topography were assessed using mechanical testing, scanning electron microscopy and white light interferometry, respectively. After characterizing the scaffolds, the effects of the concentration of the solvents and crosslinking agent were statistically evaluated with multivariate analysis of variance and linear regressions. Results: Fiber diameter and inter-fiber separation increased significantly when the concentration of the solvents, acetic acid (HAc) and dimethyl sulfoxide (DMSO), increased. The roughness of the scaffolds decreased as the concentration of dimethyl sulfoxide increased. The mechanical properties were significantly affected by the DMSO concentration. Immersed crosslinked scaffolds did not degrade until day 28. The manufactured gelatin-based electrospun scaffolds presented comparable mechanical properties to many human tissues such as trabecular bone, gingiva, nasal periosteum, oesophagus and liver tissue. Discussion: This study revealed for the first time that biomimetic electrospun scaffolds with gelatin alone can be produced for a significant number of human tissues by appropriately setting up the levels of factors and their interactions. These findings also extend statistical relationships to a form that would be an excellent starting point for future research that could optimize factors and interactions using both traditional statistics and machine learning techniques to further develop specific human tissue.

The anterior cruciate ligament (ACL) is one of the most frequently injured ligaments of the knee.... more The anterior cruciate ligament (ACL) is one of the most frequently injured ligaments of the knee. It provides joint stability by constraining anterior tibial translations or internal rotations. This ligament suffers from one of the highest injury rates and, due to the fact that it has very poor healing properties, in many cases requires surgery to be reconstructed. Nowadays the “gold standard” for surgical intervention is to reconstruct the ACL from autografts, but this procedure generates problems such as muscle weakness related to the donor site, or graft site morbidity, therefore other grafts solutions should be investigated. The aim of this thesis was to design, manufacture and test tissue engineered prototypes that could be used for surgical reconstruction of the ACL with comparable mechanical behaviour and nano and micro morphology to the native ACL. The in vivo mechanical behaviour of the ACL was investigated during a range of daily activities to understand its failure mechan...

Frontiers in Physics

Procedia CIRP, 2016

This study aims to understand and estimate the forces while the anterior cruciate ligament (ACL) ... more This study aims to understand and estimate the forces while the anterior cruciate ligament (ACL) is subjected to loading during stair negotiation. 9 healthy subjects were recruited and asked to negotiate stairs while their motion was captured. An OpenSim model was developed to estimate the ACL length from kinematic data. The forces were estimated using a force/displacement relationship. The peak ACL force was 0.416±0.089 N/BW and was recorded at full extension of the knee while the participants were descending stairs. The forces experienced by the right and left ACL of women were highly significant compared to ACL forces in men and the ACL forces in old people were significantly higher than in younger people. These forces will be used as boundary conditions in a novel finite element model (FEM) to estimate tensile and shear stress levels in the ACL to gain design information to create a tissue engineered ACL implant. The CIRP-Biomanufacturing conference promotes the exchange of knowledge on biodesign and biofabrication in order to develop novel medical devices for improving quality of life. This work is relevant to the conference since it contributes to a better understanding of the mechanical behavior of ACL throughout its range of motion and it is the starting point for the design and manufacture of new ACL implants.

Gait & posture, 2017

Procidia CIRP, 2016

Gait and Posture, 2017

The anterior cruciate ligament (ACL) plays a key role in the stability of the knee joint restrict... more The anterior cruciate ligament (ACL) plays a key role in the stability of the knee joint restricting the rotation and anterior tibial translation. However, there is a lack of knowledge of the in vivo ACL mechanical behaviour during high impact manoeuvres. The motion of 12 young participants with healthy knees was captured while they performed the following activities: walking, running, cross-over cutting, sidestep cutting, jumping and jumping with one leg. The in vivo ACL length and strain were estimated using experimental kinematic data and three degree of freedom (DOF) knee model. The in vivo ACL tensile forces were determined with a well-established force/strain relationship obtained through ACL tensile tests. Statistical regression models between ACL length with respect to angles for each activity have been performed in order to better understand the ACL failure mechanisms. The maximum ACL tensile force was observed during jumping vertically at maximum effort with two legs (1.076 ± 0.113 N/BW). Surprisingly, the peak tensile ACL force for all subjects during crossover cutting (0.715 ± 0.2647 N/BW) was lower than during walking (0.774 ± 0.064 N/BW). Regression coefficients for crossover cutting indicated that excessive knee rotation and abduction angles contribute more significantly to the ACL elongation than in activities such as walking or running. These findings suggested that the ACL is subjected to multidirectional loading; further studies will be performed to investigate torsion, tensile and shear force on the ligament.

Frontiers in Physics, 2023

Introduction: Electrospinning is a manufacturing technique that creates a net of nano and microfi... more Introduction: Electrospinning is a manufacturing technique that creates a net of
nano and microfibres able to mimic the natural extracellular matrix (ECM) of
biological tissue. Electrospun scaffolds' morphology and mechanical behaviour
can be tailored by modifying the environmental, solution and process parameters.
This study aims to produce biomimetic vascular implants optimising the
manufacturing set up through two machine learning techniques and statistical
approaches.
Methods: Polyvinyl alcohol (PVA) based scaffolds were produced by modifying the
concentration of the polymer, flow rate, voltage, type of collector, diameter of the
needle, distance between needle and collector and revolutions of the mandrel. The
scaffolds were morphologically and mechanically characterised using scanning
electron microscopy and mechanical testing respectively to inform the
morphological model (simultaneously predicting diameter of the fibres and interfibre
separation) and mechanical model (predicting strain at rupture and ultimate
tensile strength).
Results: Prediction and traditional techniques led to an optimum set up of: 12% PVA,
1 ml/h flow rate, 20 kV, 8 cm between the needle, 18 G gauge needle, rotational
mandrel of 15 cm and 2000 rpm. Optimised PVA scaffolds replicated the mechanical
properties and morphology of the vascular tissue with an ultimate tensile strength of
6.17 ± 0.18 MPa, a strain at break of 97.39 ± 5.06, fibre diameters of 126 ± 6.11 nm and
inter-fibre separation of 1488 ± 91.99 nm.
Discussion: This work revealed for the first time that machine learning Chi-squared
Automatic Interaction Detection (CHAID) models are a novel and visual route to elect
the optimum manufacturing set up to develop biomimetic vascular implants. Novel
two-output Artificial Neural Networks (ANN) and multivariate analysis of variance and
covariance (MANOVA, MANCOVA) models presented comparable prediction results
(R2=0.91); however, two-output ANN predicted models demonstrated to be the
most powerful tool for non-parametric conditions, showing cross-validation mean
squared errors (MSE) of 0.0001943. Multi Linear Regression models (MLR) exhibited
the lowest accuracy in their predictions (R2=0.6). Machine learning, statistical
approaches and traditional characterisation methods were studied to successfully
achieve vascular substitutes with analogous biomechanical behaviour and physical
structure to the native vascular tissue.