Lakshmi Y. SujeeunpersName, Nowsheen GoonoopersName, Honita RamphulpersName, Itisha ChummunpersName, Fanny GimiépersName, Shakuntala BaichoopersName, Archana Bhaw-LuximonpersName (2020date). “Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms.” Royal Society Open Sciencetitle·j.
Object of study — machine-learning prediction of fibroblast proliferation from physico-chemical features of electrospun nanofibrous skin scaffolds
Research question — whether in vitro data and MLterm·ai can reverse engineer scaffold performance and predict in vivo outcomes
Contribution — six supervised regression models compared on 182 scaffold observations, random forestterm·key best, checked against rat angiogenesisterm·dom
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | fibre and pore diameter as the dominant predictors of scaffold cell proliferation — “Among the three feature selection methods performed on the data, fibre diameter and pore diameter were identified as the most relevant features in the dataset.” | |
| claim-type | method — “After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%.” | |
| method — evidence | — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | |
| object of study — evidence | — “We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach.” | |
| research question — evidence | — “In this paper, we hypothesize that using in vitro cell culture data and ML approaches, we can reverse engineer scaffold performance and predict in vivo outcomes ( scheme 1 ).” | |
| contribution — evidence | — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | |
| mechanism | fibre as first contact Fibre diameter matters because the fibre surface is the cell's first point of contact for attachment and movement. — “The fibre surface is the first point of contact of cells as they first attach themselves to the fibre and exert the push–pull effect to be able to move and proliferate.” | mechanisms[0] |
| mechanism | contact angle outside the attachment range Water contact angle mattered least because fibroblasts attach at 60–80° and the scaffolds spanned 25–141.3°. — “Fibroblast cells are known to attach themselves on surfaces with water contact angle in the range of 60–80° [ 21 ]. This may explain the least impact exerted by this parameter as the scaffolds exhibited water contact angles in the range of 25–141.3°.” | mechanisms[1] |
| sense | accuracy - 100 minus the mean absolute percentage error of a model's predictions — “The MAPE is the most common statistical measure used to forecast error and is calculated as the average of the absolute percentage errors of predictions.” | senses[0] |
| relation | TE —same-as→ tissue engineering — (TE) — “Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions.” | concept_edges[0] |
| weight | TE —same-as→ tissue engineering — 0.95 — “Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions.” | concept_edges[0] |
| relation | ML —same-as→ machine learning — (ML) — “We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach.” | concept_edges[1] |
| weight | ML —same-as→ machine learning — 0.95 — “We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach.” | concept_edges[1] |
| relation | RFE —same-as→ recursive feature elimination — (RFE) — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concept_edges[2] |
| weight | RFE —same-as→ recursive feature elimination — 0.95 — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concept_edges[2] |
| relation | FFS —same-as→ forward feature selection — (FFS) — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concept_edges[3] |
| weight | FFS —same-as→ forward feature selection — 0.95 — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concept_edges[3] |
| relation | SVR —same-as→ support vector regression — (SVR) — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concept_edges[4] |
| weight | SVR —same-as→ support vector regression — 0.95 — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concept_edges[4] |
| relation | k-NN —same-as→ k-nearest neighbour — (k-NN) — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concept_edges[5] |
| weight | k-NN —same-as→ k-nearest neighbour — 0.95 — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concept_edges[5] |
| relation | MAPE —same-as→ mean absolute percentage error — (MAPE) — “Regression metrics such as accuracy measure, Spearman correlation and mean absolute percentage error (MAPE) were computed to evaluate the predictive accuracy of each ML model.” | concept_edges[6] |
| weight | MAPE —same-as→ mean absolute percentage error — 0.95 — “Regression metrics such as accuracy measure, Spearman correlation and mean absolute percentage error (MAPE) were computed to evaluate the predictive accuracy of each ML model.” | concept_edges[6] |
| evidential status | fibre diameter —causes→ angiogenesis — a direct correlation — “The results showed a direct correlation between fibre diameter and angiogenesis whereby a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis ( table 4 ).” | concept_edges[7] |
| relation | fibre diameter —causes→ angiogenesis — resulted in — “The results showed a direct correlation between fibre diameter and angiogenesis whereby a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis ( table 4 ).” — “a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis” | concept_edges[7] |
| weight | fibre diameter —causes→ angiogenesis — 0.6 — “The results showed a direct correlation between fibre diameter and angiogenesis whereby a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis ( table 4 ).” | concept_edges[7] |
| evidential status | mechanism — may explain — “Fibroblast cells are known to attach themselves on surfaces with water contact angle in the range of 60–80° [ 21 ]. This may explain the least impact exerted by this parameter as the scaffolds exhibited water contact angles in the range of 25–141.3°.” | mechanisms[1] |
| replication | original | |
| limitation | A first, preliminary study. — “This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.” | limitations[0] |
| limitation | More observations and features are expected to improve prediction accuracy. — “It is hypothesized that a better prediction accuracy will be seen by increasing the number of observations in the training set, and having more features in our dataset.” | limitations[1] |
| limitation | Feature influence may differ for another cell type. — “The influence of this feature may vary with another cell type.” | limitations[2] |
| limitation | Mechanical properties were measured at the macroscale while cells sense them at nano- and microscale. — “It is important to note that cells experience mechanical properties at the nanoscale and microscale, whereas mechanical properties of scaffolds are measured on the macroscale.” | limitations[3] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Random forest regression was the most accurate model, 62.74% with Spearman 0.64. — “Random forest regression gave the best performance with a decent accuracy of 62.74% and a high degree of Spearman correlation (0.64) implying that the actual and predicted values had similar directional movement, i.e. when the actual values increased, the predicted values also increased.” | content_tags.findings[0] |
| finding | Fibre diameter was the most important feature (0.39). — “The feature importance graph showed that fibre diameter was the most important feature (0.39) while water contact angle and Young's modulus were the least important features (0.14 and 0.1, respectively) ( figure 3 b ).” | content_tags.findings[1] |
| finding | Fibre and pore diameter were the most relevant features across all three selection methods. — “Among the three feature selection methods performed on the data, fibre diameter and pore diameter were identified as the most relevant features in the dataset.” | content_tags.findings[2] |
| finding | Physico-chemical parameters and in vitro results are not linearly related. — “Our experimental work indicated that there is no linear relationship between physico-chemical parameters and in vitro results.” | content_tags.findings[3] |
| finding | No two features were highly correlated (−0.19 to 0.42). — “Coefficient correlations were between −0.19 and 0.42, i.e. no variable was highly correlated to another one.” | content_tags.findings[4] |
| finding | Smaller fibre diameter went with more blood vessels near the implant. — “The results showed a direct correlation between fibre diameter and angiogenesis whereby a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis ( table 4 ).” | content_tags.findings[5] |
| finding | The biopolymer changed fibroblast morphology from elongated to dendritic to clustered. — “For instance, changing the biopolymer from KCG to PSuc to cellulose caused a change in L929 morphology from elongated to dendritic to flat dense clusters, respectively.” | content_tags.findings[6] |
| quantity | 182 — — “The dataset used for this study contained 182 observations, four numeric features characterizing the scaffolds—fibre diameter, pore diameter, water contact angle and Young's modulus—and the target variable: number of cells.” | content_tags.quantities[0] |
| quantity | 37.26% — — “Compared to the other models, the random forest method has shown the lowest MAPE (37.26%), indicating a reasonable prediction of our model [ 22 ].” | content_tags.quantities[1] |
| quantity | 0.3073 — — “In our case, R 2 (0.3073) showed that more than 30% of the variance in the data is explained by the model.” | content_tags.quantities[2] |
| variable | fibre diameter — “The dataset used for this study contained 182 observations, four numeric features characterizing the scaffolds—fibre diameter, pore diameter, water contact angle and Young's modulus—and the target variable: number of cells.” | content_tags.variables[0] |
| variable | number of cells — “The dataset used for this study contained 182 observations, four numeric features characterizing the scaffolds—fibre diameter, pore diameter, water contact angle and Young's modulus—and the target variable: number of cells.” | content_tags.variables[1] |
| definition | Hyperparameters — “Hyperparameters are predefined parameters fixed before the training process.” | content_tags.definitions[0] |
| definition | MAPE — “The MAPE is the most common statistical measure used to forecast error and is calculated as the average of the absolute percentage errors of predictions.” | content_tags.definitions[1] |
| definition | predictive modelling — “Predictive modelling is a probabilistic process that allows us to forecast outcomes, on the basis of predictors.” | content_tags.definitions[2] |
| key-terms | nanofibrous scaffolds, electrospinning, tissue engineering, random forest, feature selection, fibre diameter, pore diameter, angiogenesis, MTT |
| concept | tissue engineering — not defined in this paper's own words in this sentence — “Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions.” | concepts[0] |
| concept | TE — tissue engineering — “Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions.” | concepts[1] |
| concept | machine learning — not defined in this paper's own words in this sentence — “We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach.” | concepts[2] |
| concept | ML — machine learning — “We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach.” | concepts[3] |
| concept | MTT — 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide — “We have used in vitro and physico-chemical characterization data, namely number of cells (quantified during 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay), fibre diameter, pore diameter, water contact angle and Young's modulus, generated from a series of experiments using biopolymer-based scaffolds, to build a predictive model for scaffold performance.” | concepts[4] |
| concept | predictive modelling — a probabilistic process that allows us to forecast outcomes, on the basis of predictors — “Predictive modelling is a probabilistic process that allows us to forecast outcomes, on the basis of predictors.” | concepts[5] |
| concept | recursive feature elimination — not defined in this paper's own words in this sentence — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concepts[6] |
| concept | RFE — recursive feature elimination — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concepts[7] |
| concept | forward feature selection — not defined in this paper's own words in this sentence — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concepts[8] |
| concept | FFS — forward feature selection — “Feature selection was performed with random forest regressor, recursive feature elimination (RFE) and forward feature selection (FFS) [ 18 ].” | concepts[9] |
| concept | support vector regression — not defined in this paper's own words in this sentence — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concepts[10] |
| concept | SVR — support vector regression — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concepts[11] |
| concept | k-nearest neighbour — not defined in this paper's own words in this sentence — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concepts[12] |
| concept | k-NN — k-nearest neighbour — “Six regression algorithms, namely linear regression, support vector regression (SVR), random forest regression, lasso regression, decision tree regression and k-nearest neighbour (k-NN) regression were applied to the data and compared to obtain the best performance.” | concepts[13] |
| concept | mean absolute percentage error — not defined in this paper's own words; the paper says the MAPE “is calculated as the average of the absolute percentage errors of predictions” — “Regression metrics such as accuracy measure, Spearman correlation and mean absolute percentage error (MAPE) were computed to evaluate the predictive accuracy of each ML model.” | concepts[14] |
| concept | MAPE — mean absolute percentage error — “Regression metrics such as accuracy measure, Spearman correlation and mean absolute percentage error (MAPE) were computed to evaluate the predictive accuracy of each ML model.” | concepts[15] |
| concept | Low variance filter — calculates the variance of each variable in our dataset and removes low variances — “Low variance filter calculates the variance of each variable in our dataset and removes low variances (below a given threshold, set at 10 −3 in our case) that would be of no significant contribution to the ML model.” | concepts[16] |
| concept | High correlation filter — calculates the correlation between independent scaled numerical variables — “High correlation filter calculates the correlation between independent scaled numerical variables.” | concepts[17] |
| concept | bagging — not defined in this paper's own words; the paper names as bagging the averaging where “the predictions of each decision tree are averaged to make an overall prediction as final output” — “In the case of a regression problem, the predictions of each decision tree are averaged to make an overall prediction as final output ( figure 3 a ). This technique is commonly known as bagging.” | concepts[18] |
| concept | Hyperparameters — predefined parameters fixed before the training process — “Hyperparameters are predefined parameters fixed before the training process.” | concepts[19] |
| concept | fibre diameter — not defined in this paper's own words in this sentence — “The results showed a direct correlation between fibre diameter and angiogenesis whereby a decrease in fibre diameter resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesis ( table 4 ).” | concepts[20] |
| concept | angiogenesis — not defined in this paper's own words; the paper calls it “an important phenomenon in the wound healing process” — “Angiogenesis is an important phenomenon in the wound healing process.” | concepts[21] |
Full text — tagged
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Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffoldsterm·key for skin tissue engineeringterm·dom using supervised machine learningterm·ai algorithms Abstract The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineeringterm·dom (TEterm·dom) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TEterm·dom, to model scaffold performance using machine learningterm·ai (MLterm·ai) approach. Fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTTterm·mth) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameterterm·mth tuning, random forestterm·key regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on MLterm·ai methods for the prediction of cell–material interactions on nanofibrous scaffoldsterm·key.
Introduction Tissue engineeringterm·dom (TEterm·dom) scaffolds are primarily engineered to: (i) stimulate cell–material interactions and adhesion, and extracellular matrix (ECM) deposition, (ii) allow adequate transport of biological factors to enable cell survival, proliferation and differentiation, and (iii) promote processes such as angiogenesisterm·dom and reduce inflammation [ 1 ]. Scaffolds have been developed for the engineering of a range of tissues such as bone, skin, muscle, vascular, neural, cartilage and ligament [ 2 ]. Regardless of the tissue type, a number of key considerations are important when designing or determining the suitability of a scaffold for tissue regeneration, namely biocompatibility, biodegradability, sufficient mechanical strength to ensure integrity and interconnected porous structures and high porosity to allow cell penetration and tissue integration, vascularization for the transport of gases, diffusion of nutrient and growth factors [ 3 – 5 ].
Polymeric TEterm·dom nanoscaffolds have experienced tremendous progress with a number of scaffolds being used in clinical setting. Materials scientists are now faced with more sophisticated challenges such as being able to match materials and scaffold design with wound healing requirements. For instance, a preliminary matching of in vitro data with scaffold's physico-chemical characteristics may offer a better comprehension of the cell behaviour and bring evidence-based data to scaffold design. The use of computational methods in three-dimensional printing techniques for scaffold design, fabrication and simulation has been studied [ 6 ]. To the best of our knowledge, no study in the literature, up to now, has specifically looked at modelling of cell–material behaviour on electrospun scaffolds. Scaffold material, structure and fabrication technique are the three main parameters which determine scaffold properties. Thus, designing a computational modelterm·mth which can integrate both material and structure data is a challenging task. It has been argued that machine learningterm·ai (MLterm·ai) can offer an indispensable tool and overcome challenges in the biomedical field involving complex heterogeneous data when conventional statistical tools have failed [ 7 – 10 ]. Predictive modellingterm·mth is a probabilistic process that allows us to forecast outcomes, on the basis of predictors. The latter are features that come into play in the determination of the required result, i.e. the outcome of the model [ 11 ]. However, the huge potential of MLterm·ai for predictive modellingterm·mth in TEterm·dom still remains mostly unexplored.
In this paper, we hypothesize that using in vitro cell culture data and MLterm·ai approaches, we can reverse engineer scaffold performance and predict in vivo outcomes ( scheme 1 ). We have used in vitro and physico-chemical characterization data, namely number of cellsterm·var (quantified during 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTTterm·mth) assay), fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus, generated from a series of experiments using biopolymer-based scaffolds, to build a predictive model for scaffold performance. Our goal is to predict the in vitro outcome, given the physico-chemical features. Six simplified regression models were used and their metrics were compared to determine the most accurate algorithm. The accuracy of the MLterm·ai model was then assessed against in vivo experimental results. Scheme 1. Hypothesis.
Material and methods Polymer blends Polyhydroxybutyrate (PHB) (Sigma-Aldrich), poly(hydroxybutyrate- co -valerate) (PHBV) (HV content 12 mol%, Sigma-Aldrich), kappa-carrageenan (KCG) (Sigma-Aldrich), polydioxanone (PDX) (Resomer X 206S, inherent viscosity 2.0, Evonik), fucoidan (FUC) (Fucoidan from Fucus vesiculosus greater than or equal to 95%, Sigma-Aldrich), polysucrose (PSuc) and poly- l -lactic acid (PLLA) (PURASORB PL18, IV 1.8 g dl −1 , Purac) were used as purchased. Cellulose was extracted from locally available sugarcane bagasse using a combination of mercerization and bleaching treatments to generate an average % yield of 40 (±2). Cellulose acetate (CA) was synthesized from sugarcane bagasse-derived cellulose using an optimized acetylation method with an average % yield of 62 (±2). Nanosilica was generated through the acid hydrolysis of extracted silica (% yield 30 (±2)). The silica used for the preparation of nanosilica was extracted from sugarcane bagasse ash using the sol-gel process.
Thirteen scaffold families were included in this study: PHB/KCG, PHBV/KCG, PDX/FUC, PDX/KCG, PDX/PHBV, PDX/PSuc, PLLA/PSuc, PLLA/CA, PLLA/cellulose, PDX/CA, PLLA/CA 1% nanosilica, PLLA/cellulose 1% nanosilica and PDX/CA 1% nanosilica. Each family consisted of at least four blend compositions in triplicate results (of different polymer contents—100% of polymer A and 0% of polymer B; 90% of polymer A and 10% of polymer B; 80% of polymer A and 20% of polymer B; 70% of polymer A and 30% of polymer B; 60% of polymer A and 40% of polymer B; and 50% of polymer A and 50% of polymer B).
Scaffold fabrication Scaffolds were fabricated using the electrospinningterm·key method (bottom-up NE300 Laboratory scale electrospinner, Inovenso Company, Turkey). Parameters for electrospinningterm·key were varied depending on the polymers within the blend and on the blend composition so as to produce random bead-free fibres. PHB/KCG and PHBV/KCG fibres were produced as reported [ 12 ]. Electrospinningterm·key parameters for PSuc-based and cellulose-based fibres have been reported, respectively, by ChummunpersName et al . [ 13 ] and RamphulpersName et al. [ 14 ]. The fabrication of PDX/KCG and PDX/FUC has been detailed in GoonoopersName et al. [ 15 ].
Most blend solutions were prepared by mixing two solutions (solution A and solution B) with the exception of PDX/PHBV, PDX/PSuc and PLLA/PSuc. Table 1 summarizes the mass of polymers as well as the volume of solvents used for the preparation of different blend solutions. PLLA/cellulose and PLLA/cellulose 1% nanosilica mats were obtained following the deacetylation of PLLA/CA and PLLA/CA 1% nanosilica mats. Briefly, the acetylated mats were immersed in 0.05 M NaOH solution for 48 h at room temperature followed by washing with distilled water. Table 1. Summary of the mass of polymers and volume of solvents used for 80/20 blend ratio for various blend families. blend family solution A solution B polyhydroxybutyrate (PHB)/kappa-carrageenan (KCG) 400 mg of PHB in 8 mlterm·ai of HFIP 100 mg of KCG in 2 mlterm·ai of CHCl 3 poly(hydroxybutyrate- co -valerate) (PHBV)/KCG 1200 mg of PHBV in 8 mlterm·ai of HFIP 300 mg of KCG in 2 mlterm·ai of CHCl 3 polydioxanone (PDX)/fucoidan (FUC) 1400 mg of PDX in 9 mlterm·ai of HFIP 350 mg of FUC in 1 mlterm·ai of DMF PDX/KCG 1200 mg of PDX in 7 mlterm·ai of HFIP 300 mg of KCG in 3 mlterm·ai of CHCl 3 PDX/PHBV 800 mg of PDX and 200 mg of PHBV were dissolved in 10 mlterm·ai of HFIP PDX/polysucrose (PSuc) 960 mg of PDX and 240 mg of PSuc were dissolved in 10 mlterm·ai of HFIP poly- l -lactic acid (PLLA)/PSuc 800 mg of PLLA and 200 mg of PSuc were dissolved in 10 mlterm·ai of HFIP PLLA/cellulose acetate (CA) 800 mg of PLLA in 8 mlterm·ai of HFIP 200 mg of CA in 2 mlterm·ai of HFIP PDX/CA 800 mg of PDX in 6.7 mlterm·ai of HFIP 200 mg of CA in 1.7 mlterm·ai of HFIP PLLA/CA 1% nanosilica to a 80/20 solution of PLLA/cellulose acetate, 1% nanosilica (10 mg) was added and allowed to stir overnight PDX/CA 1% nanosilica to a 80/20 solution of PDX/cellulose acetate, 1% nanosilica (10 mg) was added and allowed to stir overnight Physico-chemical characterization Fibre and pore diametersterm·key The average fibre diametersterm·dom of the electrospun mats were determined by scanning electron microscope (SEM) as reported previously [ 13 , 14 , 16 ]. Average pore diametersterm·key were determined by measuring the diameter of at least 50 different pores using ImageJ software. Pores were identified as areas of void space bounded by fibres on all sides at or near the same depth of field. The shortest diagonal axes were measured and averaged together to obtain pore diameterterm·key. SEM images were taken using a Tescan Vega 3 LMU microscope (CBBR, Mauritius) with accelerating voltage of 30 kV.
Mechanical properties A Universal Instron Tester 3344 (Instron, USA) was used to measure the tensile properties of the electrospun mats at 25°C. The electrospun mats cut in rectangular shapes (4 × 1 cm) were clamped with gauge length set at 1 cm and strained at a rate of 10 mm min −1 using a load cell of 2 kN.
Water contact angle The contact angles of the electrospun mats were measured using Milli-Q water as a probe liquid with a Krüss Drop Shape Analyzer DSA 25 (Advanced Lab GmbH, Germany). Static contact angle data based on the sessile drop method were acquired immediately after deposition of a 2 µl drop on at least three positions for each sample and are stated as the arithmetic mean.
In vitro evaluation by MTTterm·mth assay The MTTterm·mth assay was conducted using L929 mouse fibroblasts (Sigma-Aldrich, ECACC certified) after 7 days as reported earlier [ 12 ] and absorbance values were measured at a wavelength of 540 nm using a Thermo Scientific Varioskan LUX Multimode Microplate Reader.
In vivo biocompatibility tests Surgical procedures were approved by the Animal Ethics Committee of CYROI, La Réunion, France (APAFIS Number: 2018052311219125V3). Wistar albino rats (8–10 weeks old), both males (308–407 g) and females (189–252 g), were used for in vivo studies ( n = 5). Prior to implantation, the scaffolds were disinfected in 70% ethanol solution overnight. The rats were anaesthetized by isoflurane inhalation and the dorsal region was shaved followed by 1–2 cm dorsal incisions (two on each side of the spine). All the scaffolds were implanted as stacks of three each with dimensions 0.5 × 1 cm. The rats were provided with regular supply of food and water throughout the study period. They were also monitored daily for any signs of inflammation. The scaffolds were removed and the neighbouring skin tissues were harvested for histology analysis after two and four weeks, respectively. Histological analysis of tissues was performed using Masson's Trichrome staining. The stained slides were examined using light microscopy (Nanozoomer 560 digital slide scanner, Hamamatsu). For quantitative analysis of angiogenesisterm·dom, the number of blood vessels were counted within a fixed distance of 100 µm from the explanted scaffold and the results were expressed as the number of blood vessels per mm 2 for each scaffold [ 17 ].
Data preparation and supervised machine learningterm·ai procedure Before applying statistical learning to scaffold data, standard pre-processing was performed. Data were collected, cleaned and rearranged in a format appropriate for meaningful statistical analyses. The dataset used for this study contained 182 observations, four numeric features characterizing the scaffolds—fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus—and the target variable: number of cellsterm·var. The variables were scaled by the MinMaxScaler method in order to bring the data on a relatively similar scale and close to the normal distribution. Low variance filterterm·mth and high correlation filterterm·mth using correlation matrix with Pearson correlation were applied on the features. Feature selectionterm·key was performed with random forestterm·key regressor, recursive feature eliminationterm·mth (RFEterm·mth) and forward feature selectionterm·mth (FFSterm·mth) [ 18 ].
For predictive modellingterm·mth settings, a data matrix denoted X is needed, and y as the target variable (to predict). We used 80% of the data for training, and the remaining 20% was used for testing. Six regression algorithms, namely linear regression, support vector regressionterm·mth (SVRterm·mth), random forestterm·key regression, lasso regression, decision tree regression and k-nearest neighbourterm·mth (k-NNterm·mth) regression were applied to the data and compared to obtain the best performance. The six MLterm·ai algorithms have undergone a training process to find patterns in the training data that map the input parameters to the target. Each algorithm learned from the training dataset containing both inputs and outputs to generate a MLterm·ai model. Each MLterm·ai model was then tested using the test dataset. This metric has allowed us to evaluate how the model might perform against unseen data. To optimize the training phase, hyperparameterterm·mth tuning was performed for each model in order to improve its accuracy. All codes were implemented in Python 3 using Seaborn/Scikit-learn libraries [ 19 ].
Importing Python libraries and loading the dataset into a data frame Required libraries numpy and pandas were imported, and the read_csv method of pandas was used to load the dataset ‘scaffold_data.csv’ into a pandas data frame df , as shown below. import pandas as pd import numpy as np df = pd.read_csv('scaffold_data.csv') Splitting the dataset into training and test sets Before feeding data to a regression model, some pre-processing is needed. A variable X was created from the initial dataset df to contain the four scaffold parameters (i.e. features): fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus. A variable y was created to represent the target variable, number of cellsterm·var. The train_test_split function of Scikit-learn was used to split the data into training and test sets. test_size = 0.2 indicates that 20% of the initial dataset was used for testing, and 80% for training. random_state ensures reproducibility and X_train, X_test, y_train and y_test were defined for the ouput of train_test_split. from sklearn.model_selection import train_test_split X = df ['fibre_diameter','pore_diameter', 'contact_angle','youngs_modulus'] Y = df ['nb_cells’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) As variance is range dependent, feature scaling is required on the variable X before training the data. The MinMaxScaler method was used to scale and translate each feature individually such that it is in the given range on the training set, i.e. between zero and one. from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() scaler.fit(X) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) Creating a random forestterm·key regression model and fitting it to the training data X_train and y_train obtained above were used to train a random forestterm·key model from RandomForestRegressor. The fit method was used to pass the parameters defined below. The output of this step describes a large number of parameters for the random forestterm·key model. These parameters are tuneable, and can be adjusted to optimize the accuracy of the model. from sklearn.ensemble import RandomForestRegressor model= RandomForestRegressor(n_estimators = 100,random_state = 0, max_depth =4) model.fit(X_train, y_train) Prediction Once the model is trained, it is ready to make predictions. The predict method was used on the model and X_test was passed as a parameter to get the output as y_pred. y_pred=model.predict(X_test) y_pred Statistical analysis All group data were reported as the arithmetic mean ± standard error of mean. Statistical analysis was performed with GraphPad Prism software. Statistical differences were analysed using unpaired Student t -test. Differences were considered statistically significant for p < 0.05 (*significant at p < 0.05, **significant at p < 0.01, ***significant at p < 0.001, ****significant at p < 0.0001). Regression metrics such as accuracy measure, Spearman correlation and mean absolute percentage errorterm·mth (MAPEterm·mth) were computed to evaluate the predictive accuracy of each MLterm·ai model. Correlation between the actual and predicted output data was performed to assess the adequacy of each MLterm·ai model.
Results and discussion Physico-chemical characterization of nanoscaffolds: fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus In this study, we have used nanofibrous scaffoldsterm·key generated through the electrospinningterm·key process. We have previously reported on the synthesis of these scaffolds which have been assessed for skin TEterm·dom both in vitro and in vivo [ 12 – 15 , 17 ]. These scaffolds are engineered using blend solutions of various biopolymer combinations ( table 2 ) and we have shown that the cell-scaffold response depended on the combination of materials as well as the physico-chemical properties of the scaffolds namely pore diameterterm·key, fibre diameterterm·dom, water contact angle and Young's modulus ( table 2 ). Fibre diameterterm·dom and pore diameterterm·key were determined using ImageJ software. The data are presented as arithmetic mean ± standard error of mean. The different combinations of biomaterials used in this study will allow the testing of the robustness of the MLterm·ai models to relate biological data with the physico-chemical characteristics of the scaffolds. Table 2. Physico-chemical characteristics of scaffolds and in vitro MTTterm·mth assay of L929 fibroblasts after 7 days. nanoscaffolds pore diameterterm·key (µm) fibre diameterterm·dom (µm) water contact angle (°) Young's modulus (MPa) number of cellsterm·var as determined from MTTterm·mth assay polyhydroxybutyrate (PHB)/kappa-carrageenan (KCG) 100/0 1.9 ± 0.7 1.3 ± 0.4 126 ± 1 518.2 ± 36.5 56 068 ± 3498 90/10 1.2 ± 0.6 0.9 ± 0.5 120 ± 1.9 271.7 ± 58.9 49 515 ± 10 347 80/20 0.9 ± 0.3 0.7 ± 0.5 107 ± 1.3 160.5 ± 32.8 35 661 ± 3079 70/30 1.1 ± 0.5 0.7 ± 0.5 104 ± 0.9 90.6 ± 10.9 38 083 ± 11 082 poly(hydroxybutyrate- co -valerate) (PHBV)/KCG 100/0 0.8 ± 0.3 0.4 ± 0.1 112 ± 1 197.2 ± 44.6 73 421 ± 3834 90/10 1.0 ± 0.5 0.7 ± 0.2 73.6 ± 2.8 160 ± 14.8 59 800 ± 17 584 80/20 0.9 ± 0.3 0.6 ± 0.2 62.4 ± 0.5 111.7 ± 22.2 45 060 ± 12 110 70/30 1.0 ± 0.5 0.5 ± 0.2 57.8 ± 1.7 108.5 ± 6.8 46 109 ± 10 857 polydioxanone (PDX)/fucoidan (FUC) 100/0 0.2 ± 0.06 0.3 ± 0.1 32.1 ± 0.0 73.8 ± 7.6 32 372 ± 6793 90/10 0.2 ± 0.08 0.2 ± 0.07 32.1 ± 0.0 69.6 ± 8.4 7510 ± 4328 80/20 0.2 ± 0.1 0.2 ± 0.05 32.1 ± 0.0 38.1 ± 3.9 13 691 ± 4154 70/30 0.2 ± 0.1 0.2 ± 0.05 32.1 ± 0.0 35 ± 11.3 21 154 ± 6873 PDX/KCG 100/0 2.0 ± 0.7 1.1 ± 0.3 32.1 ± 0.0 73.8 ± 7.6 32 372 ± 6793 90/10 1.6 ± 0.5 1.0 ± 0.2 32.1 ± 0.0 72 ± 6.5 21 831 ± 6967 80/20 1.5 ± 0.6 0.9 ± 0.2 32.1 ± 0.0 42.6 ± 6.3 16 606 ± 3756 70/30 0.9 ± 0.3 0.5 ± 0.2 32.1 ± 0.0 38.2 ± 5.5 30 576 ± 3825 PDX/PHBV 100/0 1.1 ± 0.4 0.4 ± 0.1 32.1 ± 0.0 73.8 ± 7.6 24 956 ± 1969 90/10 1.7 ± 0.6 1.0 ± 0.3 32.1 ± 0.0 95.6 ± 11.6 25 072 ± 4328 80/20 1.2 ± 0.4 0.7 ± 0.3 105.1 ± 2.2 72.9 ± 6.9 23 136 ± 6132 70/30 1.2 ± 0.5 0.6 ± 0.2 119.6 ± 2.5 100.4 ± 18.3 25 912 ± 9796 PDX/PSuc 100/0 8.9 ± 5.4 1.0 ± 0.04 32.1 ± 0.0 51.8 ± 10.3 35 708 ± 6551 90/10 6.2 ± 4.2 0.8 ± 0.1 32.1 ± 0.0 43.0 ± 10.4 33 721 ± 17 362 80/20 5.1 ± 2.8 0.8 ± 0.03 32.1 ± 0.0 31.5 ± 5.2 57 370 ± 31 770 70/30 4.1 ± 3.2 0.7 ± 0.06 32.1 ± 0.0 83.2 ± 23.9 41 445 ± 14 515 60/40 3.6 ± 2.3 0.7 ± 0.03 32.1 ± 0.0 58.2 ± 27.5 10 1115 ± 73 374 50/50 3.4 ± 2.5 0.6 ± 0.04 32.1 ± 0.0 33.0 ± 2.0 18 131 ± 13 203 PLLA/PSuc 100/0 5.7 ± 3.3 1.0 ± 0.05 141.3 ± 2.0 235 ± 15 51 408 ± 17 250 90/10 4.2 ± 2.5 0.9 ± 0.1 135.1 ± 1.6 109 ± 8.7 53 312 ± 13 995 80/20 3.9 ± 2.4 0.8 ± 0.03 134.0 ± 0.7 114.3 ± 4.0 56 111 ± 14 113 70/30 2.9 ± 2.0 0.7 ± 0.04 126.3 ± 4.7 85.7 ± 5.9 51 717 ± 31 051 60/40 2.01 ± 0.8 0.7 ± 0.01 132.9 ± 1.1 90.7 ± 5.5 49 338 ± 10 401 50/50 2.4 ± 1.5 0.6 ± 0.02 81.6 ± 9.5 99.3 ± 31.6 41 473 ± 12 501 PLLA/CA 0/100 0.83 ± 0.3 0.7 ± 0.08 134.4 ± 1.8 90.4 ± 15.3 10 043 ± 6267 100/0 5.7 ± 3.3 1.0 ± 0.3 140.7 ± 1.8 235.4 ± 14.8 51 408 ± 17 450 90/10 2.3 ± 0.4 0.9 ± 0.3 138.4 ± 0.8 251.8 ± 23.4 53 228 ± 25 665 80/20 2.7 ± 0.5 0.8 ± 0.4 139.6 ± 0.7 173.3 ± 29.2 18 719 ± 4141 70/30 3.5 ± 1.1 0.6 ± 0.2 119.5 ± 0.9 58.9 ± 3.3 17 040 ± 7452 60/40 2.5 ± 0.7 0.5 ± 0.3 127.7 ± 0.7 41.2 ± 17.6 22 442 ± 5173 50/50 2.0 ± 0.6 0.5 ± 0.2 117.3 ± 1.3 104.4 ± 13.8 7552 ± 3618 PLLA/cellulose 0/100 1.5 ± 0.6 0.3 ± 0.1 25.0 ± 0.0 162.3 ± 21.3 31 678 ± 14 856 100/0 5.7 ± 3.3 1.0 ± 0.3 140.7 ± 1.8 235.4 ± 14.8 51 408 ± 17 450 90/10 2.3 ± 0.4 0.8 ± 0.3 97.2 ± 0.4 275.0 ± 36.8 34 028 ± 17 333 80/20 2.7 ± 0.3 0.7 ± 0.2 25.0 ± 0.0 146.5 ± 10.6 34 420 ± 20 453 70/30 3.5 ± 1.0 0.6 ± 0.2 25.0 ± 0.0 95.7 ± 6.7 33 161 ± 23 589 60/40 3.0 ± 0.5 0.4 ± 0.2 25.0 ± 0.0 27.6 ± 12.4 30 444 ± 21 740 50/50 2.3 ± 0.5 0.3 ± 0.07 25.0 ± 0.0 102.6 ± 72.8 32 237 ± 11 258 PDX/CA 100/0 8.9 ± 5.4 1.2 ± 0.5 32.1 ± 0.0 51.8 ± 10.3 35 708 ± 6551 90/10 3.2 ± 1.5 1.0 ± 0.3 32.1 ± 0.0 45.8 ± 4.6 45 280 ± 3130 80/20 2.6 ± 1.0 1.0 ± 0.5 32.1 ± 0.0 90.9 ± 16.0 35 904 ± 3914 70/30 4.8 ± 2.1 0.8 ± 0.3 32.1 ± 0.0 134.5 ± 15.5 33 665 ± 24 790 60/40 1.7 ± 0.6 0.6 ± 0.3 32.1 ± 0.0 191.8 ± 24.0 16 620 ± 8480 50/50 2.2 ± 0.5 0.6 ± 0.2 32.1 ± 0.0 65.7 ± 14.0 30 446 ± 10 504 PLLA/CA 1% nanosilica 100/0 5.7 ± 3.3 1.0 ± 0.1 128.6 ± 0.8 168.3 ± 16.4 58 350 ± 9922 90/10 2.3 ± 0.4 0.6 ± 0.1 129.8 ± 1.3 129.4 ± 8.1 30 530 ± 12 091 70/30 2.9 ± 0.9 0.4 ± 0.1 130.4 ± 1.3 66.9 ± 8.9 15 333 ± 6890 50/50 2.4 ± 0.5 0.4 ± 0.1 123.6 ± 0.4 89.2 ± 10.3 9623 ± 9606 PLLA/cellulose 1% nanosilica 100/0 5.7 ± 3.3 1.0 ± 0.1 128.6 ± 0.8 168.3 ± 16.4 58 350 ± 9922 90/10 2.3 ± 0.5 0.7 ± 0.1 108.7 ± 7.7 76.0 ± 14.0 41 473 ± 9204 70/30 2.2 ± 0.4 0.4 ± 0.1 25.0 ± 0.0 48.4 ± 20.8 41 165 ± 18 776 50/50 1.9 ± 0.5 0.3 ± 0.1 25.0 ± 0.0 135.1 ± 41.7 30 726 ± 11 732 PDX/CA 1% nanosilica 100/0 1.4 ± 0.4 0.8 ± 0.1 32.1 ± 0.0 60.4 ± 8.4 50 485 ± 12 176 90/10 2.5 ± 0.9 1.0 ± 0.1 32.1 ± 0.0 68.1 ± 7.9 67 026 ± 15 791 60/40 2.5 ± 0.9 0.6 ± 0.1 32.1 ± 0.0 155.2 ± 5.0 35 036 ± 21 101 L929 fibroblasts cells were grown on the scaffolds and cell proliferation was assessed using MTTterm·mth after 7 days. Fibroblasts were chosen due to their crucial role in tissue regeneration in general across all tissue types. They are responsible for the proliferative phase in skin TEterm·dom and are the major producer of ECM for the growth of keratinocytes and endothelial cells. Fibroblasts and macrophages are the first cells which interact with the surface of TEterm·dom scaffolds in wounds and thus, determine the fate of the scaffolds as an efficient medical device.
Our experimental work indicated that there is no linear relationship between physico-chemical parameters and in vitro results. For instance, an increase in pore diameterterm·key did not translate directly to an increase in cell number but would influence contact angle and mechanical property, and together these will regulate cell–material interaction. The right balance between these properties needs to be determined to predict scaffold performance in vitro . Another observation that we made concerned the fibroblast morphology and behaviour ( figure 1 ). For instance, changing the biopolymer from KCG to PSuc to cellulose caused a change in L929 morphology from elongated to dendritic to flat dense clusters, respectively. Each morphology change is an indication of a different type of cell–material interaction. Thus, feature selectionterm·key was performed to select the predominant physico-chemical parameter which will impact not only cell behaviour but also the other physico-chemical parameters. Figure 1. SEM images of L929 fibroblasts growing on 70/30 ( a ) PDX/KCG, ( b ) PDX/PSuc and ( c ) PDX/CA.
The main feature of MLterm·ai is learning from experience. In the first step of supervised learning, a labelled training input dataset is fed to a MLterm·ai algorithm. With the training dataset, the system adjusts itself by modifying parameters to create a logical model. The performance of the built model is then tested with a test dataset which the model has never seen before and the accuracy of the MLterm·ai model is evaluated. Six supervised MLterm·ai algorithms were used to predict scaffold performance with in vitro cell culture data.
Data preparation, exploration and feature selectionterm·key A supervised learning problem is defined as inferring a functional mapping y = f ( x ), based on a labelled training dataset D = {( x 1 , y 1 ), ( x 2 , y 2 ), … ,( x n , y n )}. The present study is a regression problem: the inputs x i are d-dimensional vectors, x i = ( x i 1 , x i 2 , … , x i d ) ∈ R d and the output variable y takes continuous values ( y ∈ R ) . The goal of supervised learning algorithms is to analyse the training data and produce a function in order to predict the correct outcome for newly presented input data. The function used to connect input values/features x to a predicted output y , is created by the MLterm·ai model during the training phase. The obtained function is then tested by how accurately it performs on unseen data assumed to follow the same distribution as the training data [ 20 ].
Data exploration and feature selectionterm·key were performed on the 13 scaffold families ( table 2 ), representing a dataset of 182 observations and four features (pore diameterterm·key, fibre diameterterm·dom, water contact angle and Young's modulus). Data were collected and regrouped to create an initial dataset df where each row is an observation (scaffold) and each column represents one feature (scaffold parameter) (electronic supplementary material, table S1). The initial dataset was loaded as a data frame and a variable X was created, including the four scaffold parameters (fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus). A variable y was also created and represented the target variable number of cellsterm·var.
Low variance and high correlation filtersterm·mth In MLterm·ai, variances are calculated to make generalizations about a dataset, aiding in the understanding of data distribution. The variance of a variable is a measure of the average variation of values in the distribution with respect to the mean, given by 3.1 σ 2 = 1 N Σ ( X − μ ) 2 , where σ 2 is the variance, N is the total number of observations, X is the value of an individual observation and µ is the mean.
Low variance filterterm·mth calculates the variance of each variable in our dataset and removes low variances (below a given threshold, set at 10 −3 in our case) that would be of no significant contribution to the MLterm·ai model. Variance calculations with scaled data was 0.017 for fibre diameterterm·dom, 0.017 for pore diameterterm·key, 0.023 for Young's modulus and 0.149 for water contact angle.
High correlation filterterm·mth calculates the correlation between independent scaled numerical variables. If the correlation between two variables is high (value closer to 1), it indicates that the variables have similar trends and are likely to carry similar information which can bring down the performance of some MLterm·ai models. Figure 2 shows the Pearson correlation matrix performed on the four features (fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus). Coefficient correlations were between −0.19 and 0.42, i.e. no variable was highly correlated to another one. Figure 2. Pearson correlation matrix performed on four features: fibre diameterterm·dom, pore diameterterm·key, water contact angle and Young's modulus.
Feature selectionterm·key with random forestterm·key Random forestterm·key is one of the most widely used algorithms for feature selectionterm·key and allows us to compute the importance of each variable on the decision tree. The latter is a set of tests that are hierarchically organized and consists of nodes for testing features, edges to represent the outcome of each test and leaf/terminal nodes indicating the decision taken after computing all features. Random forestterm·key algorithm generates a large set of randomized decision trees to predict the target, and each feature's usage statistics are used to find the most informative subset of features in the dataset. Every decision tree has high variance, but when combined together and run in parallel, the resultant variance is low as random foreststerm·key train each individual decision tree independently on different bootstrapped samples of the training data and hence, the output does not depend on one decision tree but multiple decision trees. In the case of a regression problem, the predictions of each decision tree are averaged to make an overall prediction as final output ( figure 3 a ). This technique is commonly known as baggingterm·mth. Figure 3. ( a ) Simplified structure of random forestterm·key regression algorithm. ( b ) Feature importance graph computed using random forestterm·key regressor, ranking the features based on their importance.
A score was calculated for each feature and the most predictive features were those with the highest scores. The feature importance graph showed that fibre diameterterm·dom was the most important feature (0.39) while water contact angle and Young's modulus were the least important features (0.14 and 0.1, respectively) ( figure 3 b ). This indicated that fibre diameterterm·dom played an important role in dictating cell growth. The fibre surface is the first point of contact of cells as they first attach themselves to the fibre and exert the push–pull effect to be able to move and proliferate. Pore diameterterm·key, the second important feature, is responsible for cell penetration in scaffolds as well as the circulation of nutrients for cell growth. It is important to note that cells experience mechanical properties at the nanoscale and microscale, whereas mechanical properties of scaffolds are measured on the macroscale. Fibroblast cells are known to attach themselves on surfaces with water contact angle in the range of 60–80° [ 21 ]. This may explain the least impact exerted by this parameter as the scaffolds exhibited water contact angles in the range of 25–141.3°. The influence of this feature may vary with another cell type.
Recursive feature eliminationterm·mth and forward feature selectionterm·mth RFEterm·mth and FFSterm·mth are applied to datasets with small number of input variables as is the case in this present study. RFEterm·mth uses an accuracy metric to rank the features according to their importance. Linear regression was selected as model with four features and RFEterm·mth gave ‘1’ and ‘True’ for all four features. Three optimum features were found by the RFEterm·mth method: fibre diameterterm·dom, pore diameterterm·key and Young's modulus (score = 0.063). FFSterm·mth is the opposite process of RFEterm·mth as it tries to find the best features which can improve the performance of the model. In this study, the variables fibre diameterterm·dom ( p = 0.003) and pore diameterterm·key ( p = 0.004) were retained as the most important parameters.
RFEterm·mth and FFSterm·mth scores confirmed the scores computed with random forestterm·key regressor. Among the three feature selectionterm·key methods performed on the data, fibre diameterterm·dom and pore diameterterm·key were identified as the most relevant features in the dataset. These two parameters could contribute most to the construction of our predictive model.
Evaluation and selection of the predictive model After the training process, model testing is performed to determine the accuracy of each MLterm·ai model. In general, for a selected MLterm·ai model, the scores generated on the different test sets are not enough to establish a quantitative score to select and assess the robustness of the model. Hyperparametersterm·mth are predefined parameters fixed before the training process. They can be tuned and optimized to achieve the maximal performance of a model. Thus, model selection is performed by maximizing the cross-validation score. Hyperparametersterm·mth are tweaked in a principled way and a selection between different models is available.
Six regression models, namely linear regression, SVRterm·mth, lasso regression, random forestterm·key regression, decision tree regression and k-NNterm·mth regression, were employed to predict scaffold performance with in vitro cell culture data. Each MLterm·ai algorithm was trained on an 80% random split of the dataset, while the remaining 20% of the data was used to test the cross-validated model and evaluate the accuracy of the MLterm·ai algorithm. For the MLterm·ai models to provide optimal prediction performance, hyperparameterterm·mth tuning was performed with the GridSearchCV estimator. For each MLterm·ai model, cross-validation scores were computed for all hyperparameterterm·mth combinations to find the best one. While selecting a regression model, computing metrics like accuracy measures and error rates are important to evaluate the prediction accuracy of the model. The MAPEterm·mth is the most common statistical measure used to forecast error and is calculated as the average of the absolute percentage errors of predictions. MAPEterm·mth can be formalized by the following mathematical expression 3.2 MAPEterm·mth = 1 n ∑ t = 1 n | A t − P t A t | × 100 , where n is the size of the sample, P t is the value predicted by the model for time point t and A t is the value observed at time point t . The measure of the accuracy of the model is then calculated as accuracy = 100 − MAPEterm·mth.
Table 3 shows the accuracy score, MAPEterm·mth and Spearman correlation coefficient to the corresponding regression method after hyperparameterterm·mth tuning. Random forestterm·key regression gave the best performance with a decent accuracy of 62.74% and a high degree of Spearman correlation (0.64) implying that the actual and predicted values had similar directional movement, i.e. when the actual values increased, the predicted values also increased. Compared to the other models, the random forestterm·key method has shown the lowest MAPEterm·mth (37.26%), indicating a reasonable prediction of our model [ 22 ]. The following set of hyperparametersterm·mth were adjusted for the random forestterm·key method: n_estimators (number of trees in the forests, set at 200), max_features (number of features to consider for splitting a node, set to sqrt(n_features)), max_depth (maximum number of levels in each decision tree, set at 40), min_samples_split (minimum number of samples required to split an internal node, set at 2), min_samples_leaf (minimum number of samples required to be at a leaf node, set at 4) and bootstrap (using bootstrap samples when building trees, set to True). The five other MLterm·ai models yielded accuracy scores lower than 60% indicating that these statistical models were not presenting accurate mapping between the inputs and predicted output ( table 3 ). Table 3. Model accuracy scores obtained after performing hyperparameterterm·mth tuning with GridSearch. regression methods accuracy (%) MAPEterm·mth (%) Spearman correlation coefficient ( p -value) linear 54.95 45.05 0.42 (**) SVRterm·mth 55.63 44.37 0.42 (**) lasso 54.98 45.02 0.42 (**) random forestterm·key 62.74 37.26 0.64 (****) decision tree 53.91 46.09 0.39 (**) k-NNterm·mth 54.46 45.44 0.42 (**) The correlation between the actual and predicted output data is another important factor when designing a predictive model. We evaluated the adequacy of the model by plotting the actual values from the test dataset against predicted values from the random forestterm·key model ( figure 4 ). The plot displayed a statistically significant decent fit ( p < 3.7 × 10 −4 ), inferring a potential relationship between the predictors and the outcome that can be improved with more values closer to the fitted regression line. R -squared ( R 2 ) represents the percentage of variance explained by covariates in the model and measures the proportion of variability in the outcome that has been explained by the model. It is ranged between zero and one. Usually, the higher the value, the better the model is able to explain the variability in the outcome, depending on the quality of the data. In our case, R 2 (0.3073) showed that more than 30% of the variance in the data is explained by the model. It is hypothesized that a better prediction accuracy will be seen by increasing the number of observations in the training set, and having more features in our dataset. Figure 4. Actual versus predicted plot showing the actual targets from the test dataset (y_test) against the predicted values by the random forestterm·key model (y_pred).
Fibre diameterterm·dom values between 0.13 and 1.35 µm (including standard error of mean) were on/closest to the fitted line. An analysis of the experimental data indicated that these fibre diametersterm·dom corresponded to the presence of biopolymers in the ratio range 10–30% in the polymeric blends with PDX, PHBV, PHB or PLLA (electronic supplementary material, table S2).
Preliminary correlation with in vivo data: impact of fibre diameterterm·dom on angiogenesisterm·dom The MLterm·ai findings were correlated with in vivo biocompatibility experiments conducted on Wistar rats. Two families of scaffolds (PDX/CA and PLLA/cellulose) were implanted in the dorsal region of the rats and after two/four weeks, the scaffolds and surrounding tissues were removed and analysed. Angiogenesisterm·dom is an important phenomenon in the wound healing process. The number of blood vessels was counted within a fixed distance of 100 µm from the explanted scaffold in a measured surface area (electronic supplementary material, figure S1). In this quantification study, a blood vessel was identified as one with a well-defined lumen consisting of red blood cells and an intact wall. The results showed a direct correlation between fibre diameterterm·dom and angiogenesisterm·dom whereby a decrease in fibre diameterterm·dom resulted in an increase in number of blood vessels adjacent to the scaffolds, indicating enhanced angiogenesisterm·dom ( table 4 ). Table 4. In vivo estimation of number of blood vessels around explanted scaffolds. nanoscaffolds fibre diameterterm·dom (μm) number of blood vessels (mm 2 ) two weeks four weeks PDX 100% 1.28 20 ± 2 — PDX/CA 90/10% 0.97 44 ± 2 — PDX/CA 60/40% 0.64 247 ± 2 357 ± 3 PLLA 100% 0.97 298 ± 9 400 ± 14 PLLA/cellulose 70/30% 0.60 375 ± 7 444 ± 13 PLLA/cellulose 50/50% 0.28 409 ± 11 476 ± 10 cellulose 100% 0.25 449 ± 13 517 ± 6 Conclusion We have shown for the first time the implications of incorporating MLterm·ai methods to nanoscaffolds performance. A simplified predictive model was constructed and six regression algorithms were tested using the Seaborn/Scikit-learn Python toolkit. Hyperparameterterm·mth tuning was performed to choose a set of optimal hyperparametersterm·mth for each model in view of obtaining the best performance of each MLterm·ai model, and random forestterm·key regression was found to be the model with the highest accuracy. Fibre diameterterm·dom and pore diameterterm·key emerged as the two physico-chemical parameters which impacted more on the MTTterm·mth values. Algorithms from transfer learning and reinforcement learning for increased accuracy are now being considered to increase the robustness of our model in predicting in vitro outcome. The final outcome would be a generalizable model that uses physico-chemical parameters of scaffolds as input and generates predictions of cells proliferation during in vitro cell culture in new scaffolds. The impact of different cell types on the model will also be studied.
Supplementary Material