Claire Lafossas
email: claire.lafossas@gmail.com

Florence Benoît-Marquié
email: florence@chimie.ups-tlse.fr

Jean-Christophe Christophe Garrigues
email: jean-christophe.garrigues@chimie.ups-tlse.fr

Florence Benoit-Marquié

design of experiments and quantitative structure-property relationship (QSPR) study for interpretation and optimization

Keywords: Tetracyclines, Antibiotics, U-HPLC

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Introduction

The use of veterinary antibiotics to treat diseases, to protect animals from infections or to improve feed efficiency is constantly increasing. Aquaculture is also involved, with its heavy use of prophylactic antibiotics in the aquatic environment: rivers, lakes and oceans [START_REF] Cabello | Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment[END_REF]. All these treatments result in large amounts of antibiotics being released into various ecosystems [START_REF] Martinez | Environmental pollution by antibiotics and by antibiotic resistance determinants[END_REF]. The most used of the broad class of antibiotics are tetracyclines (TCs), followed by β-lactamines, sulfonamides, lincosamides, diaminopyrimidines and macrolides [START_REF] Sarmah | A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment[END_REF]. Since the late 1990s, the intense use and misuse of antibiotics has received particular attention due to correlations with the numbers of resistant pathogenic and commensal bacteria worldwide [START_REF] Barbosa | The impact of antibiotic use on resistance development and persistence[END_REF]. Studies of these effects on ecosystems and the potential risks of these compounds for human health have involved the development of extraction [START_REF] Subedi | Selective pressurized liquid extraction as a sample-preparation technique for persistent organic pollutants and contaminants of emerging concern[END_REF] and separation methods [START_REF] Díaz-Cruz | Recent advances in LC-MS residue analysis of veterinary medicines in the terrestrial environment[END_REF] for the identification and quantification of TCs. Because of the high polarity, water solubility and non-volatility of many antibiotics, liquid chromatography, with diodearray (DAD) [START_REF] Tuerk | Analysis of antibiotics in urine and wipe samples from environmental and biological monitoring -Comparison of HPLC with UV-, single MS-and tandem MS-detection[END_REF], mass spectrometry (MS) or tandem MS [START_REF] Zhou | Simultaneous determination of human and veterinary antibiotics in various environmental matrices by rapid resolution liquid chromatography-electrospray ionization tandem mass spectrometry[END_REF] detection, is widely used for their separation and quantification in complex matrices, such as surface water or wastewater [START_REF] Babić | Solid phase extraction and HPLC determination of veterinary pharmaceuticals in wastewater[END_REF]. In particular, TCs have been separated on a variety of HPLC Reversed-phase (RP) columns with isocratic or gradient elution. C18, C8, or phenyl selectivity has been used [START_REF] Sczesny | Residue analysis of tetracyclines and their metabolites in eggs and in the environment by HPLC coupled with a microbiological assay and tandem mass spectrometry[END_REF][START_REF] Spisso | Validation of a high-performance liquid chromatographic method with fluorescence detection for the simultaneous determination of tetracyclines residues in bovine milk[END_REF][START_REF] Schneider | Simultaneous determination of fluoroquinolones and tetracyclines in chicken muscle using HPLC with fluorescence detection[END_REF] with a great variety of aqueous mobile phases using methanol (MeOH) or acetonitrile (ACN) as organic modifier and buffers or acids such as oxalic acid, formic acid, oxalate buffers and citrate buffers. One of the problems encountered with the separation of TCs is the interaction of these molecules with the residual silanols of the stationary phases, leading to marked peak tailing. Zhu et al. [START_REF] Zhu | Analysis of oxytetracycline, tetracycline, and chlortetracycline in water using solid-phase extraction and liquid chromatography-tandem mass spectrometry[END_REF] proposed the addition of EDTA or oxalic acid to the mobile phase to limit this effect but the use of these agents decreases the sensitivity of MS detection. Cinquina et al. [START_REF] Cinquina | Short communication Validation of a high-performance liquid chromatography method for the determination of oxytetracycline , tetracycline , chlortetracycline and doxycycline in bovine milk and muscle[END_REF] used a ternary mixture of oxalic acid in water (60%), MeOH (25%) and ACN (15%). Skraskova et al. [START_REF] Škrášková | Fast and sensitive UHPLC methods with fluorescence and tandem mass spectrometry detection for the determination of tetracycline antibiotics in surface waters[END_REF] showed the influence of different column chemistries on the separation of TCs and used ACN or MeOH in optimized gradients. Nevertheless, it is commonly accepted that some chromatographic conditions have antagonistic effects on chromatographic quality. For example, conditions that lead to the narrowest peaks are sometimes very different from those that improve the resolution. From this observation, the choice of mean conditions with a mixture of organic solvents at the end of the gradient of elution are often used, without optimization of the quality of separation. Consequently, it seems interesting to study the different factors influencing the quality of TCs separation using chemometric tools and to connect these factors with molecular interactions between the analytes, the stationary phase and the elution solvents.

Various chemometric methods are already being developed and applied in order to study and optimize the selectivity of the separation of antibiotics by liquid chromatography [START_REF] Sczesny | Residue analysis of tetracyclines and their metabolites in eggs and in the environment by HPLC coupled with a microbiological assay and tandem mass spectrometry[END_REF]. Design of experiments, DoE, is one such mathematical tool, which uses a multivariate approach for an exhaustive study of the factors influencing a phenomenon and / or the determination of predictive models. In the field of chromatographic methods, parameters influencing the quality of the separation are all closely linked [START_REF] Siouffy | Optimization methods in chromatography and capillary electrophoresis[END_REF], making these tools particularly effective. Numerous authors have shown the interest of this type of strategy for the analysis of pharmaceutical compounds using liquid chromatography [START_REF] Fisher | chemometrics in pharmaceutical analysis[END_REF][START_REF] Hibbert | Experimental design in chromatography: A tutorial review[END_REF][START_REF] Glajch | Method development in high-performance liquid chromatography using retention mapping and experimental design techniques[END_REF][START_REF] Kumar | Chemometrics tools used in analytical chemistry: An overview[END_REF].

Moreover, thanks to the determination of predictive models, DoE appears to be a very suitable strategy for performing desirability analysis [START_REF] Ferey | An experimental design based strategy to optimize a capillary electrophoresis method for the separation of 19 polycyclic aromatic hydrocarbons[END_REF] or predicting robustness areas (RAs) and thus finding the analytic optimum [START_REF] Mbinze | Application of an innovative design space optimization strategy to the development of LC methods for the simultaneous screening of antibiotics to combat poor quality medicines[END_REF]. RAs are defined as "the multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality" [START_REF] Ich | Pharmaceutical Development Q8, ICH Harmon[END_REF]. They can therefore be considered as theoretical zones in which variations of the parameters of the analysis do not significantly alter separation quality.

In this context, the aim of the present work was to study and analyze the retention of four TCs: tetracycline (TC), doxycycline (DXC), chlortetracycline (CTC) and oxytetracycline (OTC) on six reversed phase U-HPLC columns by means of a methodological approach based on DoE and QSPR study. The chemical structures of the four TCs are given in Figure 1.

Two column technologies were evaluated: (i) the Core-Shell Technology, with three columns: ACQUITY CORTECS C18, 2.7 µm (Waters), KINETEX C18, 1.7 µm (Phenomenex) and NUCLEOSHELL RP18, 2.7 µm (Machery-Nagel) and (ii), for fully porous packings, different particle technologies and surface charges were evaluated with ACQUITY UPLC CSH C18, 1.7 µm (Waters), ACQUITY UPLC HSS T3, 1.8 µm (Waters), and ACQUITY UPLC BEH Shield RP18, 1.7 µm, (Waters).We analyzed the influence of: the selectivity of two organic solvents (ACN, MeOH) in gradient mode, the mobile phase flow rate, the mobile phase pH, and the temperature of the column, by means of experimental design. First of all, preliminary experiments were used to find the most selective of the six columns. Then, for each column chosen, the chromatographic conditions were analyzed in two stages. Firstly, a Fractional Factorial Matrix was used to screen the four chosen analytical parameters: mobile phase composition, gradient time, mobile phase pH, and column temperature, and determine the influence of each of them on: peak width at 5% of the height of each TCs (W i ), and resolution of critical pairs (Rs). Secondly, the two most effective factors were studied more precisely thanks to a Doehlert Design (DD). The use of this kind of DoE led to the determination of models predicting W i and Rs at any point of the experimental field. From these data, a robustness area was defined for each column and optimized chromatographic conditions were highlighted and tested. Finally, in order to understand the molecular mechanisms involved in the retention behavior of these TCs, a quantitative structure-property relationship (QSPR) study was carried out by using artificial neural networks (ANN). This QSPR study allowed a molecular-level analysis of the interactions related to the retention of TCs on the various stationary phases, according to the selectivity of the organic solvent used (ACN and MeOH) and the proportion of each one. This QSPR study enabled the modified order of elution of TCs to be analyzed according to the gradient conditions.

Materials and methods

Chemicals

Tetracycline hydrochloride, oxytetracycline, doxycycline and chlortetracycline were purchased from Sigma-Aldrich Co. (St. Quentin Fallavier, France). ACN and MeOH of HPLC grade and formic acid were also obtained from Sigma-Aldrich. All aqueous solutions were prepared using water purified with a milli-Q purification system (Millipore, St. Quentin Yvelines, France). The pH of the 0.1% (v:v) (pH = 2.2) or 0.01% (v:v) (pH = 3.3) aqueous solution of formic acid was measured with a pH-meter (Sartorius, Palaiseau, France). U-HPLC equipment and separation conditions U-HPLC analyses were performed using an Acquity ® H-Class UPLC system composed of a quaternary solvent manager, a sample FTN manager and a PDA detector (λ=254 nm) (Waters Corporation, Milford, MA, USA). Analyses were monitored by Empower 3 software (Waters Co.).

Eluents were filtered with 47 mm, 0.45µm nylon membrane discs (Waters Co.).

The samples (10 -3 mol.L -1 ) were prepared in pure water and stored at 4°C, in the dark. Each TCs was injected (2 µL) separately, as well as in the mixture, for LC analysis and precise determination of the chromatographic parameters. For these analyses, a gradient mode was programmed with all initial conditions t 0 : 100% aqueous solution of formic acid (pH 2.2 or 3.2) and all final conditions t end : 100% organic solvent (MeOH or ACN), with a run time of 3 minutes, a column temperature of 40 °C when using MeOH or 30 °C with ACN as the organic solvent, and a flow rate of 0.6 mL.min -1 . These conditions were applied to 6 reversed phase columns: ACQUITY UPLC BEH Shield RP18, 1.7 µm, 2.1 x 50 mm (Waters); ACQUITY CORTECS C18, 2.7 µm, 2.1 x 100 mm (Waters); ACQUITY UPLC CSH C18, 1.7 µm, 2.1 x 50 mm (Waters); ACQUITY UPLC HSS T3, 1.8 µm, 2.1 x 50 mm; KINETEX C18, 1.7 µm, 3 x 50 mm (Phenomenex, Torrance, CA, USA); NUCLEOSHELL RP18, 2.7 µm, 2 x 100 mm (Macherey-Nagel GmbH, Düren, Germany).

Optimized separation conditions

The separations were carried out on the H-Class UPLC system described previously, on two reversed phase columns: ACQUITY CORTECS C18, 2.7 µm, 2.1 x 100 mm (Waters) and ACQUITY CSH C18, 1.7 µm, 2.1 x 50 mm (Waters). Each TCs (10 -3 mol.L -1 ) was injected (2 µL) separately as well as in the mixture, for LC analysis, in gradient mode with all initial conditions (% vol.) t 0 : 100% aqueous solution of formic acid (pH 2.2) and final conditions: UPLC a t 3min :25% MeOH, 75% ACN, flow 0.6 mL.min -1 ; UPLC b t 3min :88% MeOH, 12% ACN, flow 0.65 mL.min -1 ; UPLC c t 3min :5% MeOH, 95% ACN, flow 0.5 mL.min -1 . UPLC a was applied with the ACQUITY CORTECS C18 column, and UPLC b and UPLC c were applied with the CSH C18 column. For all conditions, the run time was 3 minutes and the column temperature was 40 °C.

Design of Experiments (DoE)

Selection of responses and experimental factors

The first step of a DoE approach is the selection of the chromatographic parameters to be optimized. These parameters are named responses. The present case focused on two responses:

• Peak width at 5% of the height (W i ) calculated for the TCs injected one by one, expressed in seconds • Resolution (Rs) of the TC pairs: TC and OTC on the one hand, and CTC and DXC on the other (eq. 1)

Rs=2

t r(i) -t r(j) W i + W j eq. 1 with t r(i) the retention time of the second eluted TCs of the pair and t r(j) the retention time of the first eluted TCs.

The second step consisted in evaluating the experimental parameters likely to significantly affect responses. These parameters are named factors and noted Ui. In the present study, the selected factors were:

• U 1 : Initial mobile phase pH (i.e. formic acid concentration): pH i = 2.2, i.e.

[HCOOH] = 0.1 % (v:v), or pH i = 3.2, [HCOOH] = 0.01 % (v:v) corresponding to low and high pH with values lower than and close to the smallest pKa of the 4 TCs [START_REF] Škrášková | Fast and sensitive UHPLC methods with fluorescence and tandem mass spectrometry detection for the determination of tetracycline antibiotics in surface waters[END_REF].

• U 2 : Type of organic solvent used as modifier in the gradient of elution: pure acetonitrile (ACN), pure methanol (MeOH) or different mixes of the two. These solvents were selected for their differences of polarity, selectivity, and hydrogen bond acidity or basicity, according to the Snyder and Rutan classifications [START_REF] Barwick | Strategies for solvent selection-a literature review[END_REF].

• U 3 : Mobile phase flow rate: from Q = 0.3 mL.min -1 to Q = 0.8 mL.min -1 , corresponding to a low and high linear velocity for all the column geometries tested.

• U 4 : Column temperature: T = 30 °C or T = 40 °C corresponding to a low and high value acting on the viscosity of the mobile phase [START_REF] Katz | Effect of pressure on solute diffusivity, solvent viscosity and column temperature in liquid chromatography[END_REF].

DoE selection

The most influential factors among the four mentioned above were selected by using a factorial fractional design. This well-known design was chosen to investigate the effect of each factor, and also had the advantage of highlighting the possible interactions between factors.

DoE construction

Factorial fractional design (FFD)

An FFD determines the most influential factors, that is to say, factors that have the highest effect on the selected response.

The domains of pH i (U 1 ); Solvent type (U 2 ); flow rate (U 3 ) and temperature (U 4 ) were 3.2-2.2;

Acetonitrile (ACN)-Methanol (MeOH); 0.3-0.8 mL.min -1 ; 30-40 °C, respectively.

The relation between the selected response Y and the coded variables X i that are related to the natural variables U i was expressed as eq. 2:

= + + + + eq. 2
where b i represents the estimation of the main effect of factor i, b ij the estimation of the effect of the first order interaction between factor i and factor j, and b ijk , b ijkl and b ijklm the estimation of the effect of the interactions of order two, three and four respectively. The aim is to calculate these effects in order to evaluate the influence of each parameter on the response.

Based on this modeled equation, an experimental matrix consisting of 16 different experiments could be generated. However, to reduce the number of experiments required, it was decided to fractionalize the experimental matrix, i.e. to perform only 8 of the 16 experiments. In such cases, it is assumed that some interactions are negligible and consequently can be confounded with significant factors or interactions, resulting in a reduction of the number of experiments required. By this operation, the results obtained are combinations of significant and negligible effects. These combinations are named aliases, noted l i . By analyzing the significance of each alias, it is possible to get back to the significance of main effects and first order interaction effects.

Here, it was unlikely that there would be an interaction between the initial pH (represented by X 1 ), the composition (represented by X 2 ) and the flow rate (represented by X 3 ) of the mobile phase.

Consequently, the interaction X 1 X 2 X 3 could be considered negligible and confounded with another factor or interaction. In our case, it was assumed that: X 4 = X 1 X 2 X 3 . From this equation, the defining relation could be identified as: I = 1234.

The defining relation was then used:

• To develop the complete alias structure (Table 1).

• To design the experimental matrix (Table 2)

Additional runs (1 a to 1 e ) were carried out to validate the statistical significance of the results through an Analysis of Variance model (ANOVA).

Doehlert design (DD)

A DD was performed to model the evolution of peak widths and peak resolutions as a function of the organic solvent in the gradient of elution (named U 1 here) and the mobile phase flow rate (named U 2 here ). The aim of this experimental design was to create a surface of response in order to capture the possible nonlinear effects and curvatures in the domain and consequently forecast the response at any point of the domain.

The postulated model is the quadratic equation given by eq. 4:

Y = b + b X + b X + b X X + b X X + b X X eq. 4
where Y represents the selected response, X 1 and X 2 the coded forms of the factors U 1 and U 2 respectively, b 1 and b 2 the estimation of the main effect of factors 1 and 2 respectively, b 12 the estimation of the effect of the first order interaction between factor U 1 and factor U 2 , b 11 and b 22 the estimation of the quadratic effect of the factors U 1 and U 2 respectively.

X 1 = (U 1 -U 1 0 )/ ∆U ; X 2 = (U 2 -U 2 0
)/ ∆U , where the value at the center of the experimental domain was 50% for U 1 0 and 0.55 mL.min -1 for U 2 0 , and the variation steps ∆U and ∆U were 25% and 0.25 mL.min -1 , respectively. It should be noted that the solvent composition is expressed in terms of %MeOH at the end of the gradient of elution (for example, 25%MeOH means that the solvent composition is MeOH 25% and ACN 75%).

The experimental matrix is given in Table 3. Three additional runs were performed to validate the statistical significance of the results through an Analysis of Variance (ANOVA). For these experiments, pH i was fixed at 3.2 and the column temperature at 40 °C.

DoE analysis

Experimental results obtained from the different designs were analyzed by the statistical graphic software NemrodW® [START_REF] Mathieu | new efficient methodology for research using optimal design (nemrod) software[END_REF], which was used for the estimation of regression coefficients, statistical tests, and graphical analysis. The statistical significance was calculated using a classical Analysis of Variance (ANOVA) model.

QSPR study

The concept of QSPR studies was proposed in the work of Hammett, Taft, Hanch, Fujita, Free and

Wilson [START_REF] Hammett | Some relations between reaction rates and equilibrium constants[END_REF][START_REF] Taft | Polar and Steric Substituent Constants for Aliphatic and o-Benzoate Groups from Rates of Esterification and Hydrolysis of Esters[END_REF][START_REF] Hansch | ρ-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure[END_REF][START_REF] Free | a Mathematical Contribution To Structure-Activity Studies[END_REF]. ANNs are widely used for variable selection and model construction in QSAR or QSPR studies [START_REF] Yousefinejad | Chemometrics tools in QSAR/QSPR studies: A historical perspective[END_REF]. The multilayer feed forward ANNs associated with a back propagation learning algorithm, proposed by Rumelhart et al. in 1986 [32], were used in this study, with independent input and output layers [START_REF] Pai | Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality[END_REF].

The 3D structures of the TCs were optimized using semi-empirical AM1 quantum mechanical calculations of the MOPAC application, in Chem 3D Pro version 14.0 software (CambridgeSoft). An RMS gradient of 0.100 was used to minimize energy for all compounds. The optimized structures were used to calculate 2D and 3D molecular descriptors in the Chem 3D Pro 14.0 and Chem 3D Ultra This ANN was structured with an input layer connected to each molecular descriptor and an output layer linked to the retention factor (k) of each TCs.

Results and discussion

U-HPLC separation of TCs on the 6 RP columns

The typical chromatograms obtained with the 6 RP columns are given in Figure S1 and the retention parameters calculated are given in Table S1.

For all the chromatographic conditions, the four TCs were separated into two groups: OTC and TC in a first group of peaks, followed by CTC and DXC. (Figure S1). Retention times were always increased by using MeOH as the organic solvent in the gradient of elution and the fastest separation was carried out in 1.58 min with an ACQUITY UPLC CSH C18 column using ACN as the organic solvent in the gradient of elution (Figure S1). In contrast, the results show that the peak width of all the TCs decreased when ACN was used. In fact, the lowest values of W i were obtained with MeOH on the ACQUITY UPLC BEH Shield RP18, ACQUITY CORTECS C18 and ACQUITY UPLC CSH C18 columns (Table S1). The tailing factor values were close to 1 for the ACQUITY CORTECS C18 and the ACQUITY UPLC BEH Shield RP18 columns for both ACN and MeOH.

The use of a linear gradient varying from 100% aqueous solution at initial conditions to 100% MeOH or ACN after 3 minutes allowed us to identify the column that satisfied the selected criteria of effectiveness with the smallest peak widths, the shortest retention time and a symmetry of peak close to 1. The polarity and similar chemical structures of TCs led to very different chromatographic developments, including simple linear gradients from 100% aqueous to 100% organic with MeOH and ACN [START_REF] Oka | Chromatographic analysis of tetracycline antibiotics in foods[END_REF]. On the basis of these criteria, we chose a fully porous column: ACQUITY UPLC CSH C18, which gave the lowest W i with ACN, and a Core-Shell column: ACQUITY CORTECS C18, which gave a small value for W i and a tailing factor close to 1 for ACN and MeOH. These two columns were used in a DoE optimization in order to identify the ideal conditions of retention for these TCs. The analysis of the retention parameters showed great variability of W i and Rs for the two selected columns, according to whether ACN or MeOH was used (Table S1). For tf, the variation was not representative when the values obtained with ACN or MeOH were compared for the two selected columns (Table S1). In the DoE phase, the selected criteria were thus W i and Rs.

For all the columns tested, the order of elution was identical when ACN was used as the organic solvent in the gradient of elution (OTC, TC, CTC then DXC) whereas this order was modified for all the tested columns when MeOH was used (TC eluted before OTC) (Figure S1). A study of the chromatograms showed that this peak inversion was accompanied by an increase in retention times for all the TCs and all the columns tested (Figure S1).

flow rate (b 23 ) and initial pH and temperature (b 14 ) seemed to have significant influence on the TCs peak widths. Nevertheless, as the temperature did not have significant influence, it can reasonably be considered that interactions including this factor were not significant. Thus, interactions b 24 and b 14 can be judged non-significant. The other two interactions b 13 and b 23 need to be studied more precisely through their interaction diagrams (Figure 2).

Each diagram represents the four different combinations of the two factors tested in the experimental plan (i.e. for the X 1 X 3 diagram: X 1 = +1 and X 3 = +1, X 1 = +1 and X 3 = -1, X 1 = -1 and X 3 = +1 and X 1

= -1 and X 3 = -1) and the average of the experimental responses obtained for each combination. From these diagrams, it is possible to find the combination that gives the best condition relative to the chosen response. In the case of the response W DXC , peak width has to be as small as possible, (red circle in Figure 2). Consequently, the best conditions for W DXC are: X 1 = +1, X 2 = -1 and X 3 = +1, i.e. pHi = 2.2, solvent = ACN and Q = 0.8 mL.min -1 .

Eventually, from the analysis of the Pareto graph and the interaction diagrams it is possible to conclude that (i) the two most influential factors are the type of solvent and the mobile phase flow rate and (ii) the best conditions to obtain the smallest DXC peak width are:

• Organic solvent in the gradient of elution : pure ACN

• Mobile phase flow rate: Q = 0.8 mL.min -1

• Initial mobile phase pH: pHi = 2.2 (i.e.

[HCOOH] = 0.1 % (v:v))

• Column temperature: N.S.

The same analysis was repeated for the other responses and for the two columns. It should be noted that the peak width needs to be as low as possible and the resolution between two peaks as close to two as possible. The best conditions obtained in each case are summarized in Table 4 for the two columns. From these results, it is possible to conclude, firstly, that the most influential factors were the type of solvent in the gradient of elution and the mobile phase flow rate for the two columns and the six different responses. In the same way, all the responses were enhanced when the mobile phase had a pH = 2.2. It appears that the effect of the temperature on W i was not significant, in the selected range, for either of the columns. For Rs, the temperature had a different effect according to the peak pairs TC/OTC and CTC/DXC. The effect was greater with the flow rate of 0.8 mL/min and with MeOH used as the organic solvent in the gradient (Table 4). The temperature applied for the optimized conditions will be 40 °C to take account of the viscosity of MeOH used as organic solvent. The conditions required to optimize the peak widths were the same for the four TCs and for both columns:

a mobile phase consisting of acetonitrile as solvent in the gradient of elution with a pH of 2.2, and a flow rate of 0.8 mL.min -1 . However, the conditions to obtain the best resolution of peaks presented some differences from one column to the other and from one critical pair to the other. Globally, the resolution of the two pairs was enhanced when the mobile phase was MeOH with a pH of 2.2, and a flow rate of 0.3 mL.min -1 .

These observations revealed that the two main factors, the type of solvent in the gradient of elution and the mobile phase flow rate, had opposite influences according to the response considered: peak width or peak resolution. In order to understand this behavior and find optimal conditions whatever the response, a Doehlert design was performed, modeling the evolution of the six responses as a function of the two main factors.

Doehlert design

The Doehlert design was performed to model the evolution of peak widths and peak resolutions as a function of organic solvent composition at the end of the gradient of elution, and the mobile phase flow rate. It should be noted that the organic solvent was a mix of ACN and MeOH. It is expressed as %MeOH. For example 25%MeOH means that the solvent composition was MeOH 25% and ACN 75%.

Experiments described in the experimental matrix (Table 3) were performed with the two columns Figure S2 shows the isoresponse curves for W TC (Figure S2a 1 ), W OTC (Figure S2b 1 ), W CTC (Figure S2c 1 ), W DXC (Figure S2d 1 ), Rs TC/OTC (Figure S2e 1 ) and Rs CTC/DXC (Figure S2f 1 ) on the ACQUITY CORTECS C18 column, depending on the organic solvent composition and the mobile phase flow rate. In order to find the optimized conditions, an area of interest was chosen for each response. This area fitted the smallest peak widths and the highest peak resolution. Consequently, only peak widths smaller than 1.5 (Figure S2a 2 , Figure S2b 2 , Figure S2c 2 and Figure S2d 2 ) and resolutions higher than 2 (Figure S2e 2 and Figure S2f 2 ) were considered.

In order to define the robustness area for the ACQUITY CORTECS C18 column, all areas of interest were superimposed in the same graph, resulting in Figure 3. The same analysis is shown for the ACQUITY UPLC CSH C18 column in Figure 4. In these graphs, the experimental area studied by the Doelhert design is represented by a red circle.

The hatched area is the overlap of the robustness areas of all responses, it indicates the best operating conditions for the separation of these four TCs in U-HPLC using a given column. Thus, the best separation conditions are obtained, for the ACQUITY CORTECS C18 column, with a mobile phase mainly composed of ACN at the end of the gradient of elution and a flow rate of around 0.6 mL.min -1 , whereas, for the ACQUITY UPLC CSH C18 column, the mobile phase at the end of the gradient needs to be either pure MeOH with a flow rate higher than 0.65 mL.min -1 or pure ACN with a flow rate around 0.5 to 0.6 mL.min -1 .

Optimized separation of the TCs

From these results, the optimized conditions were defined for the 2 columns. For the ACQUITY CORTECS C18 column, the robustness area shows only one optimized zone, close to 25% MeOH, 75% ACN at the end of the gradient of elution. For the ACQUITY UPLC CSH C18 column, two conditions can be applied, with either 88 % MeOH, 12 % ACN or 5 % MeOH, 95% ACN at the end of the gradient of elution. These three conditions were applied to the separation of the four TCs, as three test points. The chromatograms obtained with the two columns are given in Figure 5 and the calculated retention parameters in Table 5.

Table 5 : Retention parameters calculated from U-HPLC optimized chromatograms of TCs solutions: tetracycline (TC), oxytetracycline (OTC), chlortetracycline (CTC) and doxycycline (DXC) obtained on ACQUITY CORTECS C18, 2.7 µm, 2.1 x 100 mm column, with 0.6 mL.min-1, 25% MeOH, 75%

ACN at the end of the gradient (a1), and ACQUITY UPLC CSH C18, 1.7 µm, 2.1 x 50 mm with 0.5 mL.min-1, 5% MeOH, 95% ACN at the end of the gradient (a2) or with 0.65 mL.min-1, 88%

Organic Solvent (end of gradient): 25% MeOH, 75% ACN, Column: ACQUITY CORTECS C18, chromatogram a1, Figure 5. Two DoE tools were used to determine optimized conditions for the separation of TCs on two RP18 columns. For the three test points, the objective was achieved for the 4 TCs. The choice of a simple pH modifier (0.1% formic acid in water) and one or two organic solvents at the end of the gradient, revealed one or more robustness zones for the two selected columns. The resolution Rs>2 was obtained with a separation of less than two minutes for the four analytes. The peak widths were also optimized. The lowest values (≈ 2x10 -2 min) were obtained with the ACQUITY CORTECS C18 column and the gradient conditions UPLC a (25%MeOH, 75%ACN, end of gradient) and for the ACQUITY CSH C18 column with the gradient conditions UPLC c (5%MeOH, 95%ACN, end of gradient). For all the optimized conditions, tf factors were not controlled and varied from 0.60 to 1.28 (Table 5).

Compound tr (min) k' W i (x10

The chromatogram for the optimized conditions with 88% MeOH, 12%ACN at the end of gradient, used with the ACQUITY CSH C18 column, showed a modified order of elution for the TCs (Figure 5, a3). This inversion in elution order of TCs was also observed in the screening phase of the six reversed phase columns, when MeOH was used as the organic solvent to replace ACN (Figure S1). With a majority of ACN at the end of the gradient, the order of the first pair of TCs was OTC-TC (Figure 5, a2). In majority presence of MeOH at the end of the gradient, this order changed to TC-OTC (Figure 5, a3). For the ACQUITY CORTECS C18 column, the mixture used at the end of the gradient, 75%

ACN, 25% MeOH, showed the OTC-TC order of elution (Figure 5, a1). To study these retention mechanisms and this selectivity related to the solvent used in the end of gradient, we undertook a QSPR study with ANNs, in order to connect the molecular descriptors of each TC to its retention factor, k'. The weights with absolute value >1 of nodes linked to molecular descriptors of the TCs are given in Figure 6. It appears that, in the presence of ACN-rich mixture (75% ACN; 25% MeOH or 95% ACN; 5% MeOH), only two molecular descriptors have a significant weight: log P, linked to hydrophobicity; and total valence connectivity (Tvc), related to the steric structure of the molecule.

With ACN-rich mixture, k' is not directly linked to the calculated log P for all TCs and the elution order is: OTC, log P: -4.53; TC, log P: -3.76; CTC, log P: -3.24; DXC, log P: -3.41. DXC with a lower log P is eluted after CTC. The steric parameter Tvc shows an important influence in the elution of CTC/DXC, and shows less influence in the elution of OTC/TC. Koopmans et al. [START_REF] Koopmans | High-performance liquid chromatography of alkylbenzenes: Relationship with lipophilicities as determined from octanol-water partition coefficients or calculated from hydrophobic fragmental data and connectivity indices; lipophilicity predictions for polyaromatics[END_REF] showed the importance of calculated log P in the analysis of the retention of polyaromatics using ACN as organic modifier. In their study, the parameter of connectivity chosen (Tcn) did not bring extra information. In the case of TCs, calculated log P does not predict or explain the order of elution of the compounds.

For this, it is necessary to add a steric parameter Tvc or Tcn.

With MeOH-rich mixture, log P is the descriptor of strongest weight (5.64) and 10 other molecular descriptors are linked to the retention factor of the TCs (Figure 6). Four descriptors have very significant weights >3: log P, Balaban and Tcn; two topological parameters; and a thermodynamic descriptor, critical pressure. With a MeOH-rich mixture, the influence of the steric parameters is very marked for the elution of OTC/TC. The QSPR study showed that the retention of the four TCs was controlled by log P and one steric parameter when ACN was mainly used on all the stationary phases retained in our work. In this case, the retention factors, k', were smaller and lay between 3 and 4.71 under the optimized conditions (Table 5, a1, a2). In presence of a majority of MeOH, the retention was controlled by 10 molecular descriptors, including log P, 2 steric descriptors and 1 thermodynamic descriptor for the four having very strong influence (Figure 6). The mechanisms of retention were then significantly modified, with an increase of k' for the four TCs (range 5.41-7.21, Table 5, a3). This diversity of molecular interactions was also connected with an inversion of the OTC/TC elution order (Figure 5, a3) and no modification of the elution order for CTC/DXC. The stationary phases using charged surface hybrid (CSH) technology showed optimized separation with Rs >2 in 2 different elution systems: using 95% ACN and 5% MeOH or 88% MeOH and 12% ACN at the end of the gradient. With CSH columns, the silica particles are covered with positive charges, allowing an electrostatic repulsion of TCs from free silanols, which are separated by hydrophobic and steric interactions (Figure 6). This type of column was used by K. Skraskova et al. for the separation of TCs in surface water [START_REF] Škrášková | Fast and sensitive UHPLC methods with fluorescence and tandem mass spectrometry detection for the determination of tetracycline antibiotics in surface waters[END_REF]. The authors used ACN or MeOH alone to optimize the retentions, without evaluating the optimization of MeOH/ACN mixtures. A low flow of 0.2 mL/min. was applied by Jia et al. for the separation of TCs on a BEH C18 U-HPLC column by using ACN as organic solvent in a gradient mode [START_REF] Jia | Simultaneous determination of tetracyclines and their degradation products in environmental waters by liquid chromatography-electrospray tandem mass spectrometry[END_REF]. MeOH was used in gradient mode for the separation of TCs on a Luna C18 column [START_REF] Pamreddy | Determination of antibiotics (tetracyclines and sulfonamides) in biosolids by pressurized liquid extraction and liquid chromatography-tandem mass spectrometry[END_REF]. In a recent review of analytical methods used for the separation of TCs, Kim et al. have

shown that ACN used in gradient mode with aqueous formic acid is the preferred mobile phase [START_REF] Kim | A review of analytical procedures for the simultaneous determination of medically important veterinary antibiotics in environmental water: Sample preparation, liquid chromatography, and mass spectrometry[END_REF].

However, the influence of each of the organic solvents, ACN and MeOH or mixture, is not analyzed on the separation performances. In this study, we show that it is necessary to carry out 7 experiments (Table 3) to identify the flow and the proportion of ACN/MeOH mixture at the end of gradient to obtain a Rs>2 for the 4 TCs on a U-HPLC RP18 column. It is the chemistry of the stationary phase that determines the optimal proportions of the organic solvent of the gradient mobile phase. For the ACQUITY CORTECS C18 column, the mixture at the end of the gradient must contain less than 30%

of MeOH and between 90% and 70% of ACN (Figure 3). For the CSH C18 column, 2 different areas allowing optimized separation are identified: one containing 100% ACN or a mixture of more than 90% of ACN with less than 10% of MeOH at the end of the gradient, and the other containing between 88% and 95% of MeOH and less than 5% of ACN at the end of the gradient. It is important to note that, when using MeOH as organic solvent, it is necessary to mix it with between 2% and 5% of ACN to optimize the resolution of the 4 TCs (Figure 4). The QSRR study shows that the mechanisms of retention are equivalent on both ACQUITY CORTECS C18 and ACQUITY CSH C18 columns and implies hydrophobic and steric interactions (Figure 6, a1, a2) when ACN is used as the predominant organic solvent at the end of the gradient. The selectivity, solvent strength, acidity or basicity of ACN and MeOH used as the mobile phase was studied and modeled by Snyder [START_REF] Snyder | Solvent-Strength Selectivity in Reversed-Phase HPLC[END_REF] and Glajch [START_REF] Glajch | Method development in high-performance liquid chromatography using retention mapping and experimental design techniques[END_REF]. For the 4 TCs, the QSRR study did not show any influence of a molecular parameter linked to proton exchange, or electronic or polar structure of the analytes (Figure 6). The elution order of the 4 TCs was not directly correlated to log P. The mechanism of retention involved hydrophobic and steric interactions as shown in Figure 6. By molecular simulation, Rafferty et al. [START_REF] Rafferty | Mobile phase effects in reversed-phase liquid chromatography: A comparison of acetonitrile/water and methanol/water solvents as studied by molecular simulation[END_REF] showed that ACN/water mixture could interact with the alkyl chains of the column at different levels. ACN largely penetrates the alkyl chains, close to the silica substrate. The result is an increase in the alkyl chain order with the use of ACN. With the use of MeOH, the structure of the alkyl chains is more flexible, in connection with an increased weight of the steric parameters, controlling the retention of the TCs in association with hydrophobic interactions.

Conclusion.

This study has shown that the robustness area of some particular columns, such as the ACQUITY UPLC CSH C18, is located in different specific areas corresponding to the use of pure organic modifier or a mixture, depending on the chemistry and type of reversed phase column. These optimal conditions are very different from those more currently used in the literature since most of those studies are performed with average conditions, without robustness optimization. In the case presented here, the antagonistic effects of the retention parameter quality are avoided. Moreover, the optimal areas highlighted in this study are very thin and thus could not be found without the methodology used

Table 1 :

 1 Structure of aliases (FFD) l 0 = b 0 + b 1234 l 4 = b 4 + b 123 l 1 = b 1 + b 234 l 5 = b 12 + b 34 l 2 = b 2 + b 134 l 6 = b 13 + b 24 l 3 = b 3 + b 124 l 7 = b 23 + b 14

(

  ACQUITY CORTECS C18 and ACQUITY UPLC CSH C18). Model coefficients b 1 , b 2 , b 11 , b 22 , and b 12 were calculated by the NemrodW software and validated through ANOVA tests for the six responses and the two columns. Finally, model equations were expressed in the form of surface responses where the evolution of one response was plotted as a function of the two selected factors.

  here, and particularly without the use of a Doelhert network. With this optimization methodology, four Tcs could be separated in less than 2 min. with a resolution Rs>2. Simultaneously with the optimization step, it was possible to study the mechanisms of retention at the molecular level. The QSPR study showed the deep modification of the mechanisms involved with the use of ACN or MeOH as the organic modifier. The increase in the factors of retention, k', observed with the use of MeOH is connected with the number and the diversity of the molecular descriptors involved in the chromatographic retention. The mechanism of retention involves hydrophobic and steric interactions and is not directly correlated with oil/water partitioning (log P).

Figure 1 :X 3 X 2 X 3 Figure 2 :

 13232 Figure 1: Molecular structures of the TCs analyzed by RP U-HPLC

Figure 3 :

 3 Figure 3: CORTECS C18 column: Overlap of areas of interest showing Rs TC/OTC , Rs CTC/DXC , W TC , W OTC , W CTC , and W DXC in relation with the flow rate and organic mixture composition (%MeOH and %ACN) at the end of the gradient. The typical point C 1 corresponds to a gradient: t0: 100% aqueous solution of formic acid (pH 2.2) and final conditions: t 3min :25% MeOH, 75% ACN, flow 0.6 mL.min -1 .W: peak width at 5%, Rs: resolution.

Figure 4 :Figure 5 :

 45 Figure 4: ACQUITY UPLC CSH C18 column: Overlap of areas of interest showing Rs TC/OTC , Rs CTC/DXC , W TC , W OTC , W CTC , and W DXC in relation with the flow rate and organic mixture composition (%MeOH and %ACN) at the end of the gradient. The typical points correspond to a gradient: t0: 100% aqueous solution of formic acid (pH 2.2) and final conditions for C 2 : t 3min :5% MeOH, 95% ACN, flow 0.5 mL.min -1 , and for C 3 : t 3min :88% MeOH, 12% ACN, flow 0.65 mL.min -1 .W: peak width at 5%, Rs: resolution.

Figure 6 :

 6 Figure 6: Weight (absolute value) of the key molecular parameters connecting the structures of TCs to their retention factor (k') for the CORTECS column with 25%MeOH, 75%ACN end of gradient (a1), CSH C18 column with 95% ACN, 5% MeOH end of gradient (a2) and 88% MeOH, 12% ACN end of gradient (a3)

Table 2 :

 2 Experimental matrix with the pH of the aqueous solution (pH i, ), organic solvent used in the gradient, flow rate of the mobile phase, column temperature and effect matrix (FFD)

		Experimental plan
		U 1	U 2	U 3	U 4
	Run
		pH i	Solvent	Flow rate	Temperature
				mL.min -1	°C
	1 a	3.2 (-1)	ACN (-1)	0.3 (-1)	30 (-1)
	1 b	3.2 (-1)	ACN (-1)	0.3 (-1)	30 (-1)
	1 c	3.2 (-1)	ACN (-1)	0.3 (-1)	30 (-1)
	1 d	3.2 (-1)	ACN (-1)	0.3 (-1)	30 (-1)
	1 e	3.2 (-1)	ACN (-1)	0.3 (-1)	30 (-1)
	2	2.2 (+1)	ACN (-1)	0.3 (-1)	40 (+1)
	3	3.2 (-1)	MeOH (+1) 0.3 (-1)	40 (+1)
	4	2.2 (+1)	MeOH (+1) 0.3 (-1)	30 (-1)
	5	3.2 (-1)	ACN (-1)	0.8 (+1)	40 (+1)
	6	2.2 (+1)	ACN (-1)	0.8 (+1)	30 (-1)
	7	3.2 (-1)	MeOH (+1) 0.8 (+1)	30 (-1)
	8	2.2 (+1)	MeOH (+1) 0.8 (+1)	40 (+1)

Table 3 :

 3 Experimental matrix with the organic mixture composition at the end of the gradient (%MeOH), flow rate and effect matrix (DD). The mixture is composed of %MeOH and 100 -% MeOH for the ACN proportion.

		Experimental plan
		U 1	U 2
	Run
		% MeOH	Flow rate
			mL.min -1
	1	100 (+1)	0.55 (0)
	2	0 (-1)	0.55 (0)
	3	75 (+0.5)	0.799 (+0.87)
	4 a	25 (-0.5)	0.301 (-0.87)
	4 b	25 (-0.5)	0.301 (-0.87)
	4 c	25 (-0.5)	0.301 (-0.87)
	5	75 (+0.5)	0.301 (-0.87)
	6	25 (-0.5)	0.799 (+0.87)
	7	50 (0)	0.55 (0)

Table 4 :

 4 Results from analysis of Student test and interaction diagrams for the 6 responses and the two

	different columns
		Results
		ACQUITY UPLC CSH C18 column		ACQUITY CORTECS C18 column
	Factors	Peak width			Peak resolution	Peak width			Peak resolution
		W TC W DXC W CTC W OTC	Rs	Rs	W TC W DXC W CTC W OTC	Rs	Rs
						TC/OTC	CTC/DXC					TC/OTC	CTC/DXC
	pH i	2.2	2.2	2.2	2.2	N.S.	2.2	2.2	2.2	2.2	2.2	2.2	2.2
	Solvent	ACN ACN ACN ACN MeOH MeOH	ACN ACN ACN ACN MeOH MeOH
	Flow rate	0.8	0.8	0.8	0.8	0.8	0.3	0.8	0.8	0.8	0.8	0.3	0.3

Temperature N.S. N.S. N.S. N.S.

[START_REF] Glajch | Method development in high-performance liquid chromatography using retention mapping and experimental design techniques[END_REF]

30 N.S. N.S. N.S. N.S.

40 30

Fractional factorial design

First, a factorial fractional design was performed to highlight the chromatographic factors that had the most influence, for the two columns selected (ACQUITY CORTECS C18 and ACQUITY UPLC CSH C18), that is to say, the factors that had the greatest effect on the selected responses. Responses were peak widths (W TC , W OTC , W CTC , W DXC ) and peak resolutions (Rs TC/OTC , Rs CTC/DXC ). The choice of these two complementary chromatographic parameters allowed the width of each chromatographic peak to be optimized, firstly by considering the effect of the analytical conditions over this width. The resolution calculation improved the separation analysis of the four peaks. The chosen factors and their values are detailed in part 2.4.1. In each case, the two levels had to be sufficiently different to induce response variations.

The experiments described in the experimental matrix (Table 2) were performed with the two columns. The chromatographic results: peak widths and resolutions, are given in the complementary data for each column (Table S1). Aliases l 1 , l 2 , l 3 , l 4 , l 5 l 6 and l 7 , were calculated by least squares linear regressions of the experimental results, together with the margin of error (by means of a Student test), by the NemrodW software, for the 6 responses of each column.

For all the responses studied, according to the structure of the aliases (Table 1), and considering that interactions between three or more factors were irrelevant, (i) aliases l 1 , l 2 , l 3 , l The study of the interaction effects contained in the l 6 and l 7 aliases revealed that interactions between the initial pH of the mobile phase and the solvent type in the gradient of elution (b 12 ), and between the mobile phase flow rate and temperature (b 34 ) did not have significance influence. In contrast, interactions between initial pH and flow rate (b 13 ), solvent type and temperature (b 24 ), solvent type and


Dr Nicolas Golse
email: nicolasgolse@me.com

Cyril Cossé

MD M.-A A Allard

MD A Laurenzi

MD M Tedeschi

MD N Guglielmo

MD E Fernandez-Sevilla

PhD M Robert

B Trechot

MD D Pietrasz

MD C Cosse

MD G Pittau

MD O Ciacio

MD A Sa Cunha

MD D Castaing

MD D Cherqui

PhD R Adam

PhD D Samuel

PhD E Vibert

PhD From

Evaluation of a micro-spectrometer for the real-time assessment of liver graft with mild-to-moderate macrosteatosis: A proof of concept study

Keywords: liver transplantation, steatosis, near infrared spectrometer, histopathology, biopsy CAP: controlled attenuation parameter F-TIR: Fourier-transform infrared spectroscopy LS: liver steatosis pocket sized micro-spectrometer R2: coefficient of determination

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Sa Cunha, Castaing, Cherqui, Adam: performed surgeries, study supervision Sebagh: Pathological analysis Samuel: supervision and correction of the manuscript Vibert: Supervisor of the work, manuscript correction ABSTRACT Background: Liver macrosteatosis (MS) is a major predictor of graft dysfunction after transplantation. However, frozen section techniques to quantify steatosis are often unavailable in the context of procurements, and the findings of preoperative imaging techniques correlate poorly with those of permanent sections, so that the surgeon is ultimately responsible for the decision. Our aim was to assess the accuracy of a non-invasive pocket spectrometer (PSM) for the extemporaneous estimation of MS.

Methods: We prospectively evaluated a commercial PSM by scanning the liver capsule. A double pathological quantification of MS was performed on permanent sections. Initial calibration (training cohort) was performed on 35 livers (MS≤60%) and an algorithm was created to correlate the estimated (PSM) and known (pathological) MS values. A second assessment (validation cohort) was then performed on 154 grafts.

Results: Our algorithm achieved a coefficient of determination R2=0.81. Its validation on the second cohort demonstrated a Lin's concordance coefficient of 0.78. Accuracy reached 0.91%, with reproducibility of 86.3%. The sensitivity, specificity, positive and negative predictive values for MS≥30% were 66.7%, 100%, 100% and 98%, respectively. The PSM could predict the absence (<30%) / presence (≥30%) of MS with a kappa coefficient of 0.79.

Neither graft weight nor height, donor body mass index nor the CT-scan liver-to-spleen attenuation ratio could accurately predict MS.

Conclusion:

We demonstrated that a PSM can reliably and reproducibly assess mild-tomoderate MS. Its low cost and the immediacy of results may offer considerable added-value decision support. This tool could avoid the detrimental and prolonged ischaemia required by the pathological examination of (potentially) marginal grafts. This device now needs to be assessed in the context of a large-scale multicentre study.

LAY SUMMARY

The macro-vacuolar liver steatosis is a major prognostic factor for outcomes after liver transplantation. However, it is often difficult for logistical reasons to get this estimation during a procurement. In this perspective, we developed an algorithm for a commercial, portable and affordable spectrometer to accurately estimate this content in a real-time fashion. This device could be of great interest for clinical decision-making to accept or discard a potential human liver graft.

INTRODUCTION

The current organ shortage has led most liver transplant teams to use marginal grafts that modify the benefit-risk ratio for recipients and imposes a heavy responsibility on the surgical teams [START_REF] Graham | Resuscitation" of marginal liver allografts for transplantation with machine perfusion technology[END_REF].

Liver steatosis (LS) remains a major concern in liver transplantation (LT) because nonalcoholic fatty liver disease can affect up to 30% of individuals (potential donors) in western countries, as confirmed by the reported incidence of LS during procurement [START_REF] Angele | Effect of graft steatosis on liver function and organ survival after liver transplantation[END_REF]. LS involves two types of steatosis: macrosteatosis (MS) characterised by a single, bulky fat vacuole in hepatocytes that displaces the nucleus to the edge of the cell, and microsteatosis (mS) when the cytoplasm of hepatocytes contains tiny lipid vesicles without nuclear dislocation. In almost all reports, only MS has negatively impacted outcomes after LT, while the low or negligible impact of mS is accepted [START_REF] Zamboni | Effect of macrovescicular steatosis and other donor and recipient characteristics on the outcome of liver transplantation[END_REF][START_REF] Sharkey | High-grade microsteatosis and delay in hepatic function after orthotopic liver transplantation[END_REF][START_REF] Salizzoni | Marginal grafts: finding the correct treatment for fatty livers[END_REF][START_REF] Fishbein | Use of livers with microvesicular fat safely expands the donor pool[END_REF]. If steatotic (MS) grafts are used, there is general consensus regarding a higher incidence of primary non function and biliary complications, increased costs and longer stays in hospital, associated with poorer patient and graft survivals [START_REF] Mccormack | Liver transplantation using fatty livers: always feasible?[END_REF][START_REF] Dutkowski | The use of fatty liver grafts in modern allocation systems: risk assessment by the balance of risk (BAR) score[END_REF][START_REF] Adam | The outcome of steatotic grafts in liver transplantation[END_REF][START_REF] Baccarani | Steatosis of the hepatic graft as a risk factor for post-transplant biliary complications[END_REF][START_REF] Mccormack | Use of severely steatotic grafts in liver transplantation: a matched case-control study[END_REF]. These grafts are more susceptible to cold ischemia [START_REF] Spitzer | The biopsied donor liver: incorporating macrosteatosis into high-risk donor assessment[END_REF], which explains why there is a growing body of literature on the normothermic preservation of fatty livers in order to limit ischemia-reperfusion disorders and induce "defatting" [START_REF] Graham | Resuscitation" of marginal liver allografts for transplantation with machine perfusion technology[END_REF][START_REF] Jamieson | Hepatic steatosis and normothermic perfusion-preliminary experiments in a porcine model[END_REF][START_REF] Nagrath | Metabolic preconditioning of donor organs: defatting fatty livers by normothermic perfusion ex vivo[END_REF].

The principal issue regarding LS in the LT setting is the diagnosis and quantification of MS. There is a global agreement that mild MS (<30%) causes little or no graft injury, while a moderate (30-60%) or high (>60%) degree of MS constitute a significantly higher risk [START_REF] Mccormack | Liver transplantation using fatty livers: always feasible?[END_REF][START_REF] Adam | The outcome of steatotic grafts in liver transplantation[END_REF][START_REF] Ureña | Assessing risk of the use of livers with macro and microsteatosis in a liver transplant program[END_REF][START_REF] Nocito | When is steatosis too much for transplantation?[END_REF]. However, the preoperative diagnosis of MS remains a challenge. Many noninvasive techniques have been described but their accuracy remains a matter of debate [START_REF] Schwenzer | Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance[END_REF]: 1/ ultrasonography is able to detect the presence of severe steatosis but remains a little accurate (non-quantitative) and operator-dependent procedure [START_REF] Hernaez | Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a metaanalysis[END_REF]; 2/ despite many liver attenuation indices published, a diagnosis of mild to moderate LS remains insufficient using a CT scan [START_REF] Park | Macrovesicular hepatic steatosis in living liver donors: use of CT for quantitative and qualitative assessment[END_REF][START_REF] Rogier | Noninvasive assessment of macrovesicular liver steatosis in cadaveric donors based on computed tomography liver-to-spleen attenuation ratio[END_REF]; 3/ the magnetic resonance spectroscopy examinations are accurate but costly and unavailable before organ retrieval [START_REF] Zheng | Accuracy of MR Imaging and MR Spectroscopy for Detection and Quantification of Hepatic Steatosis in Living Liver Donors: A Meta-Analysis[END_REF][START_REF] Bohte | The diagnostic accuracy of US, CT, MRI and 1H-MRS for the evaluation of hepatic steatosis compared with liver biopsy: a meta-analysis[END_REF]; 4/ the use of percutaneous ultrasonic controlled attenuation parameter (CAP) is a promising technique but we are still awaiting cheap machines with reliable and consensual cut-off values for the distinction of moderate/high LS content [START_REF] Sasso | Controlled attenuation parameter (CAP): a novel VCTE™ guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes[END_REF].

Practically, the final decision often relies on the macroscopic appearance of the graft, even though it is well known that an evaluation performed by the surgeon is poorly correlated to pathological estimations [START_REF] Rey | Hepatic steatosis in organ donors: disparity between surgery and histology?[END_REF][START_REF] Yersiz | Assessment of hepatic steatosis by transplant surgeon and expert pathologist: A prospective, doubleblind evaluation of 201 donor livers[END_REF]. It must also be remembered that the results of frozen sections are not aligned with those of permanent sections, with MS being underestimated in 75% cases [START_REF] Lo | Utility of liver allograft biopsy obtained at procurement[END_REF]. Moreover, a frozen section is not always technically feasible (organisational issues) as its accuracy depends on the hospital where the retrieval is performed (often peripheral), and it frequently results in a longer cold ischemia time if the biopsy needs to be transferred from the hospital where the procurement takes place to the transplant centre.

Recent publications in the LT setting on infrared spectroscopy have produced some very promising results as it enabled an accurate quantification of LS [START_REF] Naour | Chemical imaging on liver steatosis using synchrotron infrared and ToF-SIMS microspectroscopies[END_REF][START_REF] Naour | Quantitative assessment of liver steatosis on tissue section using infrared spectroscopy[END_REF][START_REF] Peng | Vibrational signatures to discriminate liver steatosis grades[END_REF]. Spectroscopy is based on determining the absorption of infrared light due to resonance with vibrational motions of functional molecular groups. Clinical studies have already demonstrated the feasibility and reliability of this concept [START_REF] Evers | Diffuse reflectance spectroscopy: toward real-time quantification of steatosis in liver[END_REF][START_REF] Westerkamp | Diffuse reflectance spectroscopy accurately quantifies various degrees of liver steatosis in murine models of fatty liver disease[END_REF]. However, the outstanding issue is that this technique requires expensive and non-transportable equipment. Until now, clinical experiments required contact between a probe and the liver (introduction of a needle into the organ), this being an invasive technique with theoretical complications.

We aimed to prospectively evaluate the feasibility and accuracy of a new "contact-free" portable pocket spectrometer to quantify MS in liver grafts. The main goal of this study was to correlate the estimated MS with the one obtained from the pathologists (continuous and categorical correlations).

MATERIALS AND METHODS

Spectrometer

We used a pocket-sized micro-spectrometer (PSM) commercialised by SCIO -Consumer Physics (http://www.consumerphysics.com). This near infrared (700-1100nm) PSM is granted CE and FCC labelling and is sold for both the general public and professionals. It is provided with an application that can quantify the composition of foods, as well as estimating body fat levels and identifying analgesics. At present it is mainly used by professionals to test animal feeds, grains or raw materials, in manufacturing and in the pharmaceutical industry. For these purposes, it requires specific applications and algorithms which can test the desired variable and are developed by the users themselves after the creation of a dataset and models. To this end, a correlation between the spectra and known quantifications of the studied variable is necessary and obtained by creating a specific algorithm.

The PSM is portable, small (68x40mm), light (35 grams), rapid (scan time <4 seconds) and affordable (Fig 1). It delivers real-time results and requires three components: 1/ the SCIO spectrometer itself, 2/ a smartphone (iOS or Android) connected by Bluetooth to operate the SCIO application, and 3/ a secured internet connection to the SCIO Cloud in order to build a database (stage 1) and then query the pre-established algorithm (stage 2).

Rationale for the present study (stage 0)

Before starting this study, our first aim was to determine whether the application supplied (SCIO application -not developed for human organ assessment) could be used to quantify the fat content in human liver. Between September 2016 and November 2016, we tested several modules of the app ("dairy", "raw fat", "raw poultry", "raw pork", "raw fish" and "other raw meats") on 25 livers. Compared with the definitive pathological results, none of the modules was able to quantify micro-, macro-or total steatosis (data not shown) and the values obtained by the PSM never correlated with the findings of pathological analysis. We concluded that in order to obtain an accurate PSM, we would have to create our own algorithm, calibrated on human livers.

Design of the study (Flow chart, Fig 2)

This study was performed in a tertiary centre with extensive experience of LT (>150 LT per year). Because the PSM is a new diagnostic tool requiring specific calibration, this study was therefore carried out in two stages:

1/ Between December 2016 and February 2017, the first stage (S1, training cohort) enabled us to define the optimum conditions of use. We developed an algorithm that could determine the MS content from the liver scans.

2/ Between March 2017 and August 2018, we tested the algorithm (created during stage 1) on a new cohort of patients (validation cohort), mainly in LT setting. This second stage (S2) was necessary to evaluate the accuracy of our algorithm.

Study population

During S1, we included different categories of patients and scanned various types of livers, namely grafts during procurement (deceased donors only) or after implantation into recipients, and also organs obtained during elective liver surgery (benign or malignant tumours). In the LT setting, no specific consent could be obtained because only deceased donor LT procedures were included in this study. However, livers from donors whose families refused the conduct of research were not scanned (information systematically given by the French agency regulating the transplantations). In the context of elective liver surgery all patients gave their informed and signed consent for medical research on their liver specimens. This study was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization of Good Clinical Practices.

No clinical decisions were based on the PSM findings in the context of this exploratory study.

Exclusion criteria defined at the end of S1: age <18 years old; liver with underlying pathology: fibrosis ≥F2 Metavir score (definitive pathological assessment); cholestasis; patient having received chemotherapy; livers with malignant lesions. In fact, cholestasis (yellow colour), sinusoidal obstruction syndrome (blue colour) and fibrosis could have biased the spectral analysis. These criteria were established in accordance with our ultimate goal to assess the steatosis of liver grafts (not in an elective surgery setting) and for this purpose we excluded all clearly pathological organs (namely cirrhotic, cholestatic and post-chemotherapy livers).

Liver scanning protocol

Surgeons (juniors and seniors operators) belonging to the Paul Brousse team performed all the scans. The median time to scan the livers was less than 2 minutes, and the impact on the surgical procedure (stop of the procedure) was almost negligible.

Operating conditions: the scan was performed at a distance ≤1cm from the liver capsule, in a well-lit operating room (OR) with the surgical light switched off. At least three scans per liver were performed on the left lobe, near to the site of the surgical biopsy. All scans were performed during the beating heart phase, at 37°C.

All the data of this study were anonymized before sending to the secured server.

Pathological analysis

All the grafts analysed underwent a surgical biopsy in the left lobe (no needle biopsy), before clamping and/or after revascularisation. In the non-LT group, the non-tumour parenchyma was scanned and analysed for steatosis while the tumour content was not assessed for this study.

The samples were fixed in alcohol-formalin-acetic acid, embedded in paraffin and stained with standard haematoxylin eosin safran and picrosirius stain. The samples were blindly analysed by the team of the pathological unit. Secondarily, a single experienced pathologist (M. S.) reviewed the samples, specifying the mS and MS content. In case of discrepancy, we noted the mean values between operators.

Spectral analysis

Spectral analysis was performed online using the developer's website (SCIO ® ) in order to determine a correlation between known and estimated values. The only parameters modulated were: 1/ the pre-processing method: processed, normalised, processed and normalised, or (log(R))" + normalised, and 2/ the wavelength filtering. The analytical algorithm was not communicated but the performance of the models was shown by means of two indicators: the coefficient of determination (R2: measuring the degree of replication by the model of observed outcomes) and the root-mean-square deviation (RMSE: sample standard deviation of the differences between predicted values and observed values). A perfect model would have values of R2=1 and RMSE = 0. In the sections below, the estimation of MS by the algorithm is referred to as the "calculated macrosteatosis".

Sample size calculation

To obtain a correlation (ρc) of at least 0.35 (i.e. at least a fair correlation) between the two assessments of macrosteatosis, and based on an alpha risk of 5% and a power of 90%, the calculated sample size was of (at least) 80. We enlarged this required cohort to obtain a higher proportion of steatotic specimens.

Statistical analysis

Epidemiological, pathological and clinical data were all collected by one operator (NG).

Quantitative continuous variables were expressed as mean ± standard deviation (SD), while qualitative variables were expressed as numbers (n) and percentages (%).

Correlations were presented as scatterplots generated using Lin's concordance correlation coefficient (ρc). These scatterplots were obtained by averaging 1,000 bootstrap samples (sampling with replacements) from the original population (n = 154). This method was able to limit the impact of outliers and enable more robust representations.

To provide a clearer information, the Kappa coefficient (κ) was also calculated to assess the consistency between pathological MS (gold standard) and calculated MS. To achieve this second analysis, the MS values were placed within two ranges (<30, ≥30).

Overall uncertainty was presented as accuracy and systematic bias with a 95% confidence interval. The reproducibility represented the variability between measures obtained from the same sample with the same method; it was an approximation of the R2.

The systematic bias corresponded to the systematic error that was introduced in all calculations, i.e. the precision of the values we obtained.

The results of this series were presented according to the STARD guidelines. P values ≤ 0.05 were considered to be statistically significant. All statistical analyses were performed using PASW software, version 22 (SPSS Inc., Chicago, Ill).

RESULTS

Stage 1: Calibration = creation of the algorithm

During the first step of the methodology, we included 67 livers (270 scans) from donors (n=18), recipients (n=25) and patients undergoing elective liver surgery (n=24). The characteristics of the population are shown in Table 1.

For the whole group, we failed to create a reliable algorithm that could predict liver steatosis (Figs 3A). Whatever the pre-processing method and wavelength analysed, we were not able to achieve a R2 value >0.4.

We applied selection criteria (proposed after iterative approach) on this cohort and finally analysed only 35 livers (138 scans). Known MS values ranged from 0% to 60%, while those for mS were between 0% and 50%. By filtering the spectral wavelength between 895 nm and 945 nm, we were able to create an algorithm, and this model predicted MS with R2 = 0.811 and RMSE = 5.26 (Fig 3B and4). However, we failed to create an accurate model that could predict mS or global steatosis.

Stage 2: Validation of the pre-established algorithm

The algorithm obtained during stage 1 was then tested on a new prospective cohort of organs (n=154). We performed 4.1 ± 1 scans / liver analysed. Nine livers reached a definitive MS ≥ 30%.

Lin's correlation between the estimated MS values (using PSM) and known MS values was 0.78 (0.73-0.83; p<0.0001). The accuracy of our algorithm was of 0.91% (0.84 -0.98); its reproducibility was 86.3% with a systematic bias of 1.12 % (0.24-1.99).

After applying the MS ranges (<30%; ≥30%), the kappa agreement index between the PSM and the pathological results was 0.79. The sensitivity, specificity, positive and negative predictive values for MS ≥30% were 66.7%, 100%, 100% and 98%, respectively.

As shown in Table 2 and Fig 5, none of the potential preoperative predictors of MS

available for acceptance decision (body mass index, liver height, liver-to-spleen attenuation ratio) achieved a reliable correlation with the pathological results because the correlation was <0.3. Although statistically significant (p<0.05), graft weight and BMI generated a poor correlation with the MS.

In the validation cohort, 10 patients presented at definitive pathological analysis a mS ≥ 30%. Among these patients, the mean (definitive) MS was of 5.1 ± 4.3% and the maximum MS was of 10%. All the MS estimations by the PSM were < 17%, meaning that the algorithm was able to differentiate the MS from mS.

During this validation stage, two (junior) surgeons refused a graft (n=2) because of their steatotic gross appearance. No frozen sections were performed because no pathologist was available during the night and the hospital was too distant from our transplant centre to send a biopsy specimen. These procurements were stopped. In both cases, the permanent section analysis contradicted the visual fat estimation, and showed 5% MS only. The PSM also confirmed the low-fat content of these refused grafts (respectively 3% and 5% MS). If the surgeons had taken account of the PSM values into their decision algorithm, these two grafts would have been transplanted.

Performance of the frozen section analysis versus PSM estimation

During the study time, a frozen section analysis was only performed on seven donor grafts for MS and fibrosis assessment. The Lin's correlation between the frozen section and pathological definitive assessments was of 0.59, whereas it was of 0.73 between the PSM and definitive pathological assessment (MS analysis). The median difference between frozen section and definitive analysis was of 10.8% ± 16, whereas it was of 6.5% ± 9 for the PSM (p=0.43).

DISCUSSION

Statement of principal findings

Using a commercial PSM for macrosteatosis quantification, we created our own algorithm that could correlate the liver spectra with the definitive pathological assessment, and we confirmed its accuracy on a second independent cohort. The good coefficient of correlation (0.78) between the estimated and known MS confirmed the relevance of our algorithm. The specificity and negative predictive value were particularly high, thus confirming that the actual algorithm is able to determine with a great confidence the low steatotic livers. Moreover, we did not observe any false positive cases, meaning that the operator should be very vigilant before accepting a graft in case of PSM value ≥30%. The PSM was more accurate than frozen section to estimate MS content. This non-invasive device, which is both transportable and affordable, could become part of the standard surgical equipment necessary during liver procurement and, with the view of clinical use, the PSM will provide the raw MS percentage and the clinician will be able to accept or not the organ (multiparametric decision).

Strengths and weaknesses of the study

The principal strength of this study was its prospective design involving two independent cohorts (one for calibration, one for validation). Thanks to a large sample size, we were able to define the optimum conditions for use, and we obtained a highly accurate algorithm.

Another advantage of this device is the potential upgradability of the algorithm during the coming months/years, when we will have included hundreds of patients.

However, this study had certain limitations. First, we did not scan any livers with MS over 60%. This was a surprising finding, mainly explained by the fact that the 2 livers rejected because MS>60% (visual assessment confirmed at definitive pathology) were not scanned due to the absence of network during procurement. This limits the scope of our algorithm because we cannot know if such a high fat content might have been assessed correctly by the PSM. One might argue that such steatotic livers do not present difficulties for macroscopic assessment and the PSM would probably have been of no added-value in such obvious cases. However, in order to achieve a rigorous evaluation, it will be necessary to study such grafts. It is worth noting that three patients had a BMI over 40 kg/m 2 (max: 53 kg/m 2 ) but none of them presented with MS over 15% (range: [5-15%]). This confirms the lack of correlation between BMI and MS. This low incidence of highly steatotic grafts could be explained because a surgical team is not generally dispatched for procurement when a highly steatotic liver is suspected. This limitation will be avoided in any future prospective and multicentric study when all grafts will be scanned, whether the organ is transplanted or not.

Because of the high prevalence of livers with low/intermediate fat content, the most reasonable conclusion to be drawn from our study is that the PSM never overestimates the MS of "good livers", and probably does not underestimate high values for "marginal livers". This means that use of the PSM will not cause the incorrect refusal of acceptable grafts. We have to confirm these promising results.

Secondly, neither mS nor global steatosis could be predicted by the PSM. In fact, our algorithm was exclusively calibrated to quantify MS.

Another limitation was the use of pathological assessment when calibrating our PSM. It would probably have been more accurate to use Fourier-transform infrared spectroscopy (F-TIR) or an automated software [START_REF] Marsman | Assessment of donor liver steatosis: pathologist or automated software?[END_REF][START_REF] Fiorini | Development of an unbiased method for the estimation of liver steatosis[END_REF], even though these costly techniques are not yet established as the gold standard and necessitate specific equipment. More importantly, F-TIR requires preparation of the sample that is not appropriate in routine practice and large data collection. We are aware of the potential biases of pathological analysis, even for expert pathologists [START_REF] El-Badry | Assessment of hepatic steatosis by expert pathologists: the end of a gold standard[END_REF]. For this reason, all the samples were analysed a second time by a single blinded operator (M.S.) and we reached an excellent inter-observer reproducibility.

The lack of external validation could also appear as a strong limitation of our study. In that aim, we will soon implement a prospective multicentric study to validate (or improve if required) our algorithm. The last limitation of this device that has prevailed until now is the need for a GSM network (or WIFI) while scanning the liver in order to access the online algorithm. For this reason, 40% of the procurements performed during the study were not scanned, thus justifying a scan in the recipients. With the application that we are currently developing for the future, it will be possible to scan offline and then transmit the data as soon as a network is available.

Interpretation with reference to other studies

MS is a major concern in the LT and re-LT settings [START_REF] Adam | The outcome of steatotic grafts in liver transplantation[END_REF][START_REF] Kulik | Primary non-function is frequently associated with fatty liver allografts and high mortality after re-transplantation[END_REF] and its importance will increase as larger numbers of marginal grafts are used during the decades to come. To the best of our knowledge, no other tool is validated to quantify MS during procurement and there is unanimous agreement that imaging techniques cannot predict fat content [START_REF] Schwenzer | Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance[END_REF]. Biochemical data on the donors are also well known to correlate poorly with steatosis [START_REF] Adani | The role of hepatic biopsy to detect macrovacuolar steatosis during liver procurement[END_REF][START_REF] Barbois | Benefit-risk of intraoperative liver biopsy during bariatric surgery: review and perspectives[END_REF], and complex scores/biomarkers have failed to differentiate moderate vs severe steatosis [START_REF] Cucchetti | Assessment of donor steatosis in liver transplantation: is it possible without liver biopsy?[END_REF][START_REF] Stern | Non-invasive diagnosis of hepatic steatosis[END_REF]. Some authors recently reported their preliminary experience with diffuse reflectance spectroscopy in the OR [START_REF] Evers | Diffuse reflectance spectroscopy: toward real-time quantification of steatosis in liver[END_REF][START_REF] Westerkamp | Diffuse reflectance spectroscopy accurately quantifies various degrees of liver steatosis in murine models of fatty liver disease[END_REF] but it is not cost efficient to equip every OR with such expensive devices.

Interestingly, the correlation between pathological results and those of their device was 0.8-0.9, which was as good as ours.

The assessment of MS in liver allograft biopsies using smartphone add-on lenses has recently been reported [START_REF] Cesaretti | Testing feasibility of an accurate microscopic assessment of macrovesicular steatosis in liver allograft biopsies by smartphone add-on lenses[END_REF]. This device does not appear to be as useful as our PSM because it requires a biopsy and a prepared slide (3µm tissue section). Another pilot-study has been published by a French group, assessing the performance of a smartphone camera to quantify the liver steatosis [START_REF] Cesaretti | Noninvasive assessment of liver steatosis in deceased donors: A pilot study[END_REF]. Although promising, this work only evaluated 12 livers and the algorithm did not differentiate the mS from the MS.

When considering the potential advantages (or disadvantages) of PSM versus a liver biopsy (frozen or permanent section), account must be taken of the heterogeneity of steatosis within the liver. For this reason, and particularly in borderline cases, at least two biopsies (from two sites) need to be performed [START_REF] Frankel | The optimal number of donor biopsy sites to evaluate liver histology for transplantation[END_REF][START_REF] Ratziu | Sampling variability of liver biopsy in nonalcoholic fatty liver disease[END_REF]. This increases the time required for interpretation and the risk of potential liver injury. Conversely, it is possible to scan many times both sides of the liver with the PSM and to obtain a MS estimation within just a few seconds.

Opinions vary considerably regarding the assessment and management of graft steatosis across countries and teams [START_REF] Imber | Current practice regarding the use of fatty livers: a trans-Atlantic survey[END_REF]. No consensus has been reached as to a decision-support algorithm. We are proposing a new, user-friendly and accurate tool that could homogenise practices and enable systematic testing for MS in the OR. We believe that this device offers a new surgical technology that meets all requirements and could be adopted rapidly by the surgical community: there is high clinical demand, it is easy to use, inexpensive and compatible with current practices (no need to reorganise procedures) [START_REF] Wilson | Adoption of new surgical technology[END_REF]. Moreover, new policies have just been introduced in France regarding liver retrieval performed by urologists or a local team. In this case, the PSM could offer a valuable decision-support tool for these "non-HPB" surgeons.

In the near future, the role of PSM will be challenged by transient elastography and the CAP, a tool that can be used at the bedside and that provides estimates of both fibrosis (elastography) and steatosis [START_REF] Sasso | Controlled attenuation parameter (CAP): a novel VCTE™ guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes[END_REF]. However, compared to the PSM, the CAP remains operatordependent and the cut-off points for different grades of steatosis remain ill defined [START_REF] Stern | Non-invasive diagnosis of hepatic steatosis[END_REF].

In terms of perspectives regarding new uses for liver spectroscopy, we propose to use the PSM to help the clinician for the selection of marginal graft for normothermic preservation CONCLUSION In these preliminary results, we have shown that our algorithm, calibrated and validated on human livers, was well correlated to the pathological assessment for prediction of the MS content of liver grafts. This is the first tool specifically calibrated to assess graft MS, as other devices only estimate the global steatosis. Its low cost and the immediacy of its results may offer considerable value-added decision support and avoid the detrimental and prolonged ischemia required for pathological examination in the event of a (potentially) marginal graft.

However, the PSM algorithm now needs to be evaluated (and upgraded in a more sensitive way) in a larger scale multicentric study so as to definitively validate its utility and its impact on the organisation of LT.

2 Tables + 5 Figures

 5 Words count= 5503 (5974 for the previous version) AUTHOR'S CONTRIBUTION: Golse: Writing paper, design and data collection manager Cosse: Statistical analysis Golse, Allard, Laurenzi, Tedeschi, Pietrasz, Guglielmo, Fernandez-Sevilla, Robert, Tréchot, Pittau, Ciacio: Data acquisition during procurements. Reviewed the manuscript.

Fig 2 :

 2 Fig 2: Flow chart of the present study.

Fig 3 :

 3 Fig 3: Testing of the model on the whole cohort (A, n=67) or after selection (B, n=35) during stage 1.

Fig 4 :

 4 Fig 4: Liver scans during stage 1. The analysis was restricted to wavelengths between 895nm and 945nm.

Fig 5 : 29 Fig 1 Fig 2 Stage 1

 529121 Fig 5: Correlations between the pathological MS analysis and the estimated MS. (Validation cohort)

  Fig 5 R=0.78

Table 1 : Epidemiological, radiological, clinical and pathological features of the livers scanned during stages 1 (calibration cohort) and 2 (validation cohort)

 1 Intraoperative view showing the spectrometer near the Glisson capsule and communicating with a smartphone via Bluetooth connexion.

		Stage 1, before selection n=67	Stage 1, after selection n=35	Stage 2 n=154
	Age	55.6 ± 17	57 ± 18	58.1 ± 19
	Male/Female (%)	35 (53.7) / 31 (46.3)	19 (54.3) / 16 (45.7)	73 (47.4) / 81 (52.6)
	BMI (kg/m 2 )	24.8 ± 5	25.5 ± 4.5	26.4 ± 6.3
	Alcohol consumption, n (%)	11 (16.4)	5 (14.3)	26 (16.8)
	Max. liver height (mm)	161 ± 29	163 ± 25	159 ± 25
	Liver weight* (g)	1380 ±416	1342 ± 432	1381 ± 334
	Liver/Spleen attenuation ratio*	1.33 ± 0.57	1.33 ± 0.57	1.24 ± 0.59
	METAVIR fibrosis score F0/F1/F2/F3/F4 (%)	47.8/26.9/3/7.4/14.9 71.4/25.7/2.9/0/0 75/22.5/2.5/0/0
	Scans in LT setting vs elective liver surgery, n (%)	43 (64.2) / 24 (35.8)	35 (100) / 0 (0)	138 (89.6) / 16 (10.4)
	If LT, scan in donor vs recipient, n (%)	18 (41.9) / 25 (58.1)	13 (37.1)	83 (60.1) / 55 (39.9)
	HBV or HCV carriers, n (%)	11 (16.4)	2 (5.7)	4 (2.6)
	Percentage of
	Macrosteatosis	9.3 / 5 / 13.1	7.5 / 2 / 12.6	6 / 2 / 9.9
	Mean/Median/SD
	Percentage of
	Microsteatosis	9.2 / 5 / 12.4	11.5 / 5 / 13.9	9 / 0 / 13.3
	Mean/Median/SD

*: data only available for liver grafts; SD: standard deviation; LT: liver transplantation LEGENDS Fig 1: