Maxime Louzon

Benjamin Pauget

Frédéric Gimbert

Nadia Crini

Annette De

N Morin-Crini

A De Vaufleury
email: annette.devaufleury@univ-fcomte.fr

Ex situ environmental risk assessment of polluted soils using threshold guide values for the land snail Cantareus aspersus

Keywords: snails, metals, PAHs, PCBs, bioavailability, bioaccumulation

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

Introduction

Anthropogenic activities are at the origin of 2.5 million polluted fields in the European Union (EEA, 2019). Contaminated soils are still a subject of concern, as they can cause health problems [START_REF] Eeva | Recovery of breeding success in wild birds[END_REF], notably cancers for humans [START_REF] Nawrot | Environmental exposure to cadmium and risk of cancer: a prospective population-based study[END_REF] and the collapse of animal populations [START_REF] Laskowski | Accumulation of Zn, Cu, Pb and Cd in the garden snail (Helix aspersa): Implications for predators[END_REF] and disturb community structures (Miglioniri et al., 2004;[START_REF] Cébron | Biological functioning of PAH-polluted and thermal desorption-treated soils assessed by fauna and microbial bioindicators[END_REF]. For the environmental risk assessment (ERA) of polluted soils and sites, a combination of chemical, biological and ecological measurements can be used in situ and ex situ, as described in the TRIAD methodology (ISO 19204:2017). Understanding the relationship between soil pollution and environmental effects is essential for the establishment of management plans, and this relationship can only be assessed by measuring bioavailability through a combination of chemical and biological methods [START_REF] Lanno | The bioavailability of chemicals in soil for earthworms[END_REF]. Indeed, bioavailability is a dynamic three steps process (environmental availability, environmental bioavailability, and toxicological bioavailability, ISO 17402:2008). Thus, the measurement of bioavailability prevents the misinterpretation of the environmental risk level of contaminated soils. Therefore, the ERA of contaminated soils must take into account not only the total concentration of pollutants in soils but also the transfer of pollutants modulated by soil characteristics (e.g., pH, clay content, organic matter content) (Allen, 2002;[START_REF] Pauget | Use of chemical methods to assess Cd and Pb bioavailability to the snail Cantareus aspersus: a first attempt taking into account soil characteristics[END_REF][START_REF] Pauget | How contamination sources and soil properties can influence the Cd and Pb bioavailability to snails[END_REF].

The in situ methodology developed with the land snail Cantareus aspersus by Pauget and de [START_REF] De Vaufleury | Landsnail for ecotoxicological assessment of chemicals and soil contamination[END_REF] allows the assessment of environmental risk in the field of soil contaminated for 14 metallic elements (MEs). To accomplish this, the environmental bioavailability is estimated by the accumulation quotient (AQ) after 28 days of snail caging on a site and with a global index of the sum of excess transfers (SETs), which integrates the transfers of the 14 MEs to the snails. Based on the bioavailability assessment with the AQ, the toxicological risk can be estimated by weighting the AQ with the intrinsic toxicity of each transferred contaminant (toxicity point (TP) from ATSDR, 2015). This allows the evaluation of the environmental risks with the calculation of a risk coefficient (RC) for each contaminant. Then, the sum of each RC gives an index of the evaluation of the risk of transferred metallic elements (ERITME). The SET and ERITME index have been shown to be relevant for identifying the environmental risk of contaminated soils, e.g., resulting from past mining activities [START_REF] Mariet | Using bioindicators to assess the environmental risk of past mining activities in the Vosges Mountains (France)[END_REF]. Until now, it has not been possible to apply this methodology to organic compounds due to the lack of basic data, such as the usual internal concentration of these molecules in snail tissues. A total of 55.3% of identified soil contaminations in France are caused by MEs (44.7%), but polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) are also frequently found in polluted sites (8.1% and 2.6%, respectively) (MTES, 2019). The choice of C. aspersus was made for several reasons, among which are its worldwide repartition, its particular position at the soilair-plant interface [START_REF] Dallinger | Soil biology and ecotoxicology[END_REF], and it non-negligible ecological role [START_REF] Iglesias | Field observations on feeding of the land snail Helix aspersa Müller[END_REF]. Indeed, C. aspersus is a primary consumer phyto-and saprophagous which also ingest soil) [START_REF] Gomot | Influence of soils on the growth of the land snail Helix aspersa. An experimental study on the absorption route for the stimulating factors[END_REF][START_REF] Barker | The biology of terrestrial molluscs[END_REF][START_REF] Chevalier | Poaceae in the natural diet of the snail Helix aspersa Müller (Gastropoda, Pulmonata)[END_REF][START_REF] Dallinger | Soil biology and ecotoxicology[END_REF]. It is also usable as a bioindicator in ERAs of polluted soils (ISO 15952:2018;[START_REF] Larba | Use of the land snail Helix aspersa for monitoring heavy metal soil contamination in Northeast Algeria[END_REF][START_REF] Nica | Use of land snails (Pulmonata) for monitoring copper pollution in terrestrial ecosystems[END_REF][START_REF] De Vaufleury | Landsnail for ecotoxicological assessment of chemicals and soil contamination[END_REF]. Furthermore, due to the accumulation abilities of the land snail species, they can be involved in the trophic bioavailability of MEs, i.e., transfer in food webs [START_REF] Beeby | The role of Helix aspersa as a major herbivore in the transfer of lead through a polluted ecosystem[END_REF][START_REF] Laskowski | Accumulation of Zn, Cu, Pb and Cd in the garden snail (Helix aspersa): Implications for predators[END_REF][START_REF] Hispard | Comparison of transfer and effects of Cd on rats exposed in a short experimental snail-rat food chain or to CdCl2 dosed food[END_REF]. The in situ methodology enables snail exposure to various transfer sources, including not only the soil source but also, e.g., plants, humus, and air.

Currently, no ex situ assay with threshold guide values exists with the land snail to assess the environmental bioavailability of contaminants in soils. Ex situ exposure should facilitates the comparison of several soils by avoiding the influence of confounding factors, such as pedoclimatic conditions and site specific parameters, such as the diversity of plants. In the proposed ex situ bioassay, the soil constitute the main source of exposure of snails, which is relevant, in the context of the ERA of excavated soils, e.g., reused in embankments. Such ex situ approach will contribute to the ERA of destructured soils and guide management decisions for polluted soil stakeholders (e.g., the rehabilitation of excavated polluted soils, considering the transfer of pollutants to biota). Therefore, the in situ approach characterizes the risk of transferring contaminants from structured sites to biota, and the ex situ approach characterizes the risk of transferring contaminants from destructured soil to biota.

The assessment of the environmental risk of contaminated soils has to help stakeholders of polluted sites to elaborate appropriate decisions for sustainable soil management. For this purpose, some methodologies including the ratio called risk quotient (predicted environmental concentrations/predicted no effect concentrations; PEC/PNEC) exist (Ahlers, 2001).

Calculation of PNEC require disposing of toxicity data (NOEC and ECx values) to which assessment factors are then applied. Among other risk assessment methods, the species sensitivity distribution (SSD) can be applied but it requires a lot of ecotoxicity data for numerous species [START_REF] Jänsch | Derivation of soil values for the path 'Soil-Soil Organisms' for metals and selected organic compounds using species sensitivity distributions[END_REF]. As for many soils bioindicators, ecotoxicity metrics for snails are not available for all the contaminants considered in the present study, thus neither the ratio risk nor the SSD approaches can be applied. Furthermore these methods are applied to each contaminant separately and here the aim was to provide global indices of the fate and potential effect of several contaminants at the same time. Other methodologies are proposed for the risk assessment of contaminated soils, such as the TRIAD method (ISO 19204:2017). Among the parameters that are recommended in the TRIAD method, is the bioavailability that can be estimated notably with the snail, as bioindicator of the soil quality (ISO 17402:2008;ISO 19204:2017).

The aim of our study is to develop an ex situ approach for the main substances in contaminated soils, i.e.: 15 MEs (As, Cd, Co, Cr, Cu, Hg, Mo, Mn, Ni, Pb, Sb, Se, Sn, Sr and Zn), 16 PAHs and 7 PCBs. Almost all of these contaminants are ranked in the substance priority list (except for Mo and Sn;ATSDR, 2017). First, the present study determined the ex situ TGVs (threshold guide values) of the land snail for 15 MEs, 16 PAHs and 7 PCBs in a bio-assay with 68 soils from Europe. These soils have various physico-chemical properties that are representative of the natural soil variability. Then, this study presents the application of this ex situ assay to perform an ERA for various soils contaminated by MEs and PAHs, and one polluted soil by PCBs. Third, this study examines the relationships between the AQ of metal(loid)s and the influence of soil physico-chemical properties to better understand the role of soil parameters on contaminant transfers to snails.

Material and Methods

Soils

The soils used in this study originate from various European locations (France, Germany and Czech Republic) (Table S1). The soils were sieved at 4 mm, and the physico-chemical parameters were measured with the appropriate ISO standard protocols (granulometry, pHwater, etc.; Figure 1). To be applicable to various types of potentially contaminated soils from various soil locations, the TGV calculations are based on soil representing a wide range of physico-chemical properties (e.g., forest, farming or grassland), as presented in Table S1.

The min and max values for clay, silt, sand, pHwater, OM, Corg, CaCO3 contents, CEC, Alox and Feox were 6.8 and 63.0%, 7.5 and 73.4%, 2.9 and 83.7%, 3.64 and 8.30, 12 and 360 g.kg - 1 , 4.1 and 207 g.kg -1 , <0.5 and 277 g.kg -1 , 4.30 and 1218 cmol.kg -1 , 0.02 and 5.64 cmol.kg -1 and 0.005 and 5.14 cmol.kg -1 , respectively.

For the analysis of MEs in the soils, 500 mg of the soils (dry weight (dw)) was sampled, and after aqua regia digestion (HNO3:HCl 2:5, v:v ultratraces, Optima®, Fisher Scientific, Illkirch, France), the concentrations of MEs were measured by ICP-MS (inductively coupled plasma mass spectrometry, Thermo Scientific X Series II, Courtaboeuf, France) (Table S2). A certified reference material (loamy clay 1, CRM052, LGC Standards, Molsheim, France) was employed and gave recovery rates of 80% to 120% for all the MEs. The concentrations of the PAHs and PCBs in the soils were measured by GC-MS/MS (Tables S3 andS4) according to [START_REF] Morin-Crini | Towards a convenient procedure to characterize polycylic aromatic hydrocarbons in sediments receiving industrial effluents[END_REF]. Briefly, pressurized liquid extraction (PLE) was performed on 5 g dried samples with hexane using a Büchi (Rungis, France) SpeedExtractor E-914 system.

After the concentration of the samples, sample extracts were analysed in the GC-MS/MS apparatus (Agilent, Massy, France), which included a 7890A GC system, a 7000 GC triple quadrupole mass spectrometer and an 80 GC Combipal autosampler and a (5% phenyl)methylpolysiloxane (HP5MS) column (30 m x 0.25 mm i.d., 0.25 µm film thickness, Agilent 19091J-433). The injection volume was 1 µl, the injector temperature was 300°C, and injection was performed in splitless mode under a constant flow of He (purity of 99.9999%) at 1.5 mL.min -1 followed by a purge flow to the split vent after 0.5 min. The temperature of the transfer line, ion source and quadrupole were 300°C, 300°C, and 150°C, respectively. An electron-impact ionization source at 70eV was utilized. Nitrogen with a purity of 99.9% was produced by a NiGen LC-MS 40-1 Claind generator (Gengaz, Wasquehal, France) and used as the collision gas at 1.5 mL.min -1 . For PAH, the temperature gradient was from 45°C (held for 0.5 min) to 100°C at 30°C min -1 and then from 100°C to 325°C at 10°C min -1 . The total run time was 30 min. The abbreviations for the PAHs used in this study are as follows: NAP (naphthalene), ACY (acenaphthylene), ACE (acenaphthene), FLU (fluorene), PHE (phenanthrene), ANT (anthracene), FLT (fluoranthene), PYR (pyrene), BaANT (benzo(a)anthracene), CHY (chrysene), BbFLT (benzo(b)fluoranthene), BkFLT (benzo(k)fluoranthene), BaPYR (benzo(a)pyrene), dBahANT (dibenzo(a,h)anthracene), BghiPL (benzo(ghi)perylene), and IcdPYR (indeno [1,2,3-cd]pyrene). For PCB, the temperature gradient was from 120°C (held for 1 min) to 220°C at 3°C min -1 and then from 220°C to 280°C at 6°C min -1 . The total run time was 49 min. The PCBs analysed were indicators 28, 52, 101, 118, 138, 153 and 180. The soils were classified as contaminated on the basis of the soil guide values (SGVs) of MEs according to [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF] and ADEME (2018) for PAHs and PCBs (Tables 1,S2, S3 and S4).

Snails: Cantareus aspersus (Müller, 1774)

Snails were bred in our laboratory from individuals originating from a snail farm (CFPPA of Châteaufarine, 25000 Besançon, France). The species has been verified by DNA barcoding (Allgenetics, 15008 A Coruña, Spain). Juvenile land snails were reared in the laboratory under controlled conditions (20°C, 80% relative humidity, photoperiod of 18 h light and 6 h dark) until the sub-adult stage (7-9 weeks), as described by [START_REF] Vaufleury | Standardized Growth Toxicity Testing (Cu, Zn, Pb, and Pentachlorophenol) with Helix aspersa[END_REF]. The sub-adult stage was selected to avoid confounding factors related to growth and reproduction [START_REF] Gimbert | Long-term responses of snails exposed to cadmium-contaminated soils in a partial life-cycle experiment[END_REF][START_REF] Pauget | Ranking field site management priorities according to their metal transfer to snails[END_REF]. At the beginning of exposure (t0), the sub-adult snails weighed 4.98 ± 0.76 g and the internal concentrations of the MEs, PAHs and PCBs were measured in the visceral mass from twelve sub-adult snails (Table 1). The snails were fed during the growth period with uncontaminated commercial food, Helixal ® (Berthon S.A., France) and during the exposure period with uncontaminated fresh lettuce (from an organic farm, France) (Table 1).

Exposure

The snails were exposed to the soils, through digestive and cutaneous pathways for 28 days. For each soil, six snails were exposed in triplicate in transparent polystyrene containers of 24 x 21 x 8 cm. The exposure modalities are detailed in [START_REF] Pauget | Use of chemical methods to assess Cd and Pb bioavailability to the snail Cantareus aspersus: a first attempt taking into account soil characteristics[END_REF]. One week before exposure, 300 g of soil (dw) was introduced in containers and humidified (water holding capacity (WHC) adjusted to 50%) with osmotic water. The containers were cleaned every two days to remove faeces and renew the lettuce (1.5g.day -1 .snail -1 ). The lettuce was offered in a Petri dish left on the bottom of the container. At the end of the exposure, the snails were starved in clean containers (without soils) for two days (the faeces were removed every 12 h). Then, snails were frozen (-80°C).

Chemical analysis

After thawing, for the dissection of visceral mass from each snail, the whole soft body was removed from the shell, the foot and the anterior part of the body were separated to the visceral mass above the edge of the mantle [START_REF] Vaufleury | Methods for toxicity assessment of contaminated soil by oral or demal uptake in land snails: Metal bioavailability and bioaccumulation[END_REF]. The visceral mass then includes the digestive gland (main storage for contaminants), the kidney, the lung and the main part of the genital tract. Two visceral masses per replicate (three replicates per soil) were freeze dried for two days before the ME analysis. The lyophilized viscera were digested between 47°C and 98°C for 265 minutes (DigiPREP MS, SCP Science, Courtaboeuf, France) in 7 mL of nitric acid (HNO3 at 65%, ultratraces Optima®, Fisher Scientific, Illkirch, France) and the volume was brought to 50 mL with milliQ water. Then, the samples were filtred to 1 µm (DigiFILTER, SCP Science, Courtaboeuf, France) and diluted 10x for ICP-MS analysis (Thermo Scientific X Series II, Courtaboeuf, France) as for soil samples. The analysis was validated with a certified reference material (TORT-2 and DOLT, LGC Standards, Molsheim, France), which had recovery rates of 107% ± 15. For the PAHs and PCBs, the fresh visceral mass was dissected and pooled (two snails) from each replicate. The PAHs and PCBs were extracted with the QuECHERs (quick, easy, cheap, effective, rugged and safe) methodology (RESTEK, Lisse, France). Briefly, snail viscera samples (one pool of two animals per replicate) were weighted, mixed with equal parts of water and crushed with a ball mill until completely homogenized (25 ceramic sphere (FastPrep, MP Biomedicals, Illkirch, France) for two cycles (4.0 m/sec, 20 sec), with 180second breaks). Samples were transferred into 50 mL Teflon centrifuge tubes, acetonitrile was added. After a 15min-extraction by vortex, salts were added and samples were further vortexed for 15 min. Extracts were centrifuged and an aliquot of supernatant was transferred to d-SPE polypropylene tubes for cleanup. Concentrations were measured by GC-MS/MS (7890A GC system, 7000 GC triple quadrupole mass spectrometer, HP5MS column, Agilent, Massy, France) in the same conditions than for soil samples. When the concentrations of the MEs, PAHs or PCBs in the snails were below the quantification limit (QL), the values of the concentrations used to calculate the AQ were replaced by QL/2 as recommended [START_REF] Bergstrand | Handling data below the limit of quantification in mixed effect models[END_REF].

Ex situ threshold guide value calculation

The ex situ TGVs were calculated on the basis of the internal concentrations of contaminants in the visceral mass of snails exposed ex situ to uncontaminated soils, according to the methodology published by Pauget et al. (2013) (Eq. 1). The outliers were removed before calculation.

TGVs = 75 + 1.5 × (1)
Uncontaminated soils were identified on the basis of soil guide values (SGVs, Table 1) which correspond to the concentrations of contaminants beyond which a soil can be considered as contaminated (ADEME, 2018). SGVs are guidance concentrations in soils to assess if a soil is contaminated and TGVs are guidance concentrations in snails viscera after 28 days of exposure to assess if an excess of transfer of contaminants exist from the soil to the snail.

For the ex situ TGVs calculation of the PAHs and PCBs, the concentrations accumulated in snails exposed to 26 and 7 uncontaminated soils were used, respectively (Tables S1).

For the MEs, due to the strong differences between the concentrations of each ME in soils (Table S1),

the ex situ TGVs were calculated with the concentration accumulated in the snail viscera over 28 days from 44,26,66,64,37,24,51,48,66,34,37,30,40,67, and 18 soils for As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, Sn, Sr and Zn, respectively (a soil can be considered uncontaminated for As but contaminated for Pb).

Accumulation quotient, risk coefficient and index calculation

For each snail and each category of contaminant (ME, PAH, and PCB), the accumulation quotient (AQ), SET index, risk coefficient (RC) and ERIT-ME, -PAH, -PCB indexes were calculated to assess the sum of excess transfers (exposure) and the toxicological risk, according to the methodology described by [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF]. Briefly, the AQs for organic contaminants were calculated with the mean concentration in the snail tissue instead of the median concentration (Eq. 2) due to the fact that three measurements (one per replicate) were performed per soil (pool of two individuals per replicate). The SET indexes were calculated based on the sum of each AQ (Eq. 3). According to [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF], the RCs (risk coefficient = exposure x hazard) were calculated based on the AQ (that reflects the exposure of the snails) and the toxicity points (TPs) from ATSDR (2015) as a surrogate of the hazard of each contaminant (Table 1; Eq. 4). Indeed, many biomarkers of ecotoxicity have been studied in land snails, such as growth and reproduction rates, the acetylcholinesterase activities, the oxidative stress management, the (epi-)genomic stability and/or the histopathologic disorders [START_REF] Regoli | Use of the land snail Helix aspersa as sentinel organism for monitoring ecotoxicologic effects of urban pollution: an integrated approach[END_REF][START_REF] Radwan | Biomarkers of oxidative stress in the land snail, Theba pisana for assessing ecotoxicological effects of urban metal pollution[END_REF][START_REF] Itziou | Effects of organic contaminants in reactive oxygen species, protein carbonylation and DNA damage on digestive gland and haemolymph of land snails[END_REF]Angeletti et al., 2013;[START_REF] Baurand | Genotoxicity assessment of pesticides on terrestrial snail embryos by analysis of random amplified polymorphic DNA profiles[END_REF][START_REF] Mleiki | Effects of dietary Pb and Cd and their combination on glutathion-S-transferase and catalase enzyme activities in digestive gland and foot of the green garden snail, Cantareus apertus (Born, 1778)[END_REF][START_REF] Leomanni | Mercury induced haemocyte alterations in the terrestrial ansil Cantareus apertus as novel biomarker[END_REF][START_REF] Nica | High-level dietary cadmium exposure is associated with global DNA hypermethylation in the gastropod hepatopancreas[END_REF]ISO 15952:2018;[START_REF] Mleiki | Effects of dietary Pb and Cd and their combination on lysosomal and tissue level biomarkers and histopathology in digestive gland of thel and snail, Cantareus apertus (Born, 1778)[END_REF]. But these endpoints are not available for all the contaminants considered in the present study. Therefore, and according to Pauget and de [START_REF] De Vaufleury | Landsnail for ecotoxicological assessment of chemicals and soil contamination[END_REF], the use of TP was applied here. For the PAHs and PCBs, the ERITME indexes are respectively reappointed ERITPAH and ERITPCB, respectively (Eq. 5).

=

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/01

(2)

234 = ∑( -1) (3)
9: = ( -1) × 4;

(4) 39<4=3 > 39<4; ? > 39<4;:@ = ∑ 9:

(5)

The global SET index (GSET) and global evaluation of the risk of toxicological elements (GERITOXE) were integrative indexes of SET and ERIT-for all the families of compounds and were calculated with the sum of SET or ERITME, ERITPAH and ERITPCB (Eqs. 6 and 7).

A234 = ∑(234 =3 + 234 ; ? + 234 ;:@ ) (6) A39<4BC3 = ∑(39<4=3 + 39<4; ? + 39<4;:@) (7)

Statistical analysis

The TGV calculation and all the statistical analysis were performed with R (version [START_REF] Core | R: A language and environment for statistical computing[END_REF]. The relationships between the total concentrations of MEs in the soils, the SET or ERITME, ERITPAH and ERITPCB indexes, were assessed by univariate linear regressions, with data transformed by log10 (x+1). The assessment of the relationships between the total concentrations of the MEs in soil and the excess of transfers (AQ>1) was performed by univariate linear regressions. The influence of soil physico-chemical parameters on AQ>1 was modelled with multiple linear regressions. For each set of regressions, the best model (i.e., the one providing the best adjusted coefficient of determination with the lowest number of independent variables) was chosen using the corrected Akaike criterion (AICc) [START_REF] Burnham | Multimodel inference understanding AIC and BIC in model selection[END_REF]. For statistical robustness, these assessments of the influence of soil physico-chemical parameters on anomalous transfers have been modelled only for compounds that have at least 10 soils for which AQ>1.

3.4.2.) (R

Results and Discussion

Ex situ threshold guide values

The ex situ TGV based on the concentrations in the snails viscera exposed to uncontaminated soils (Figure 2) are presented in Table 1. The ex situ TGVs are benchmarks for assessing the environmental bioavailability of contaminants in soils to snails. The ex situ TGVs for the MEs proposed in this study have the same order of magnitude as those of the CiRefs in situ developed by [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF] for 14 MEs. There are some differences for a few MEs, such as Zn (in situ and ex situ: 1490 and 581 mg.kg -1 dw, respectively), which may be the result of the influence of the vegetation or humus available for snails in situ and the relatively low content of this element in the commercial food (Helixal®) used for the growth of the snails (182 and 51.6 mg Zn.kg -1 dw in 2013 and in 2017, respectively). These results underline the influence of ME content in the food used for snail rearing [START_REF] Gomot | Influence of soils on the growth of the land snail Helix aspersa. An experimental study on the absorption route for the stimulating factors[END_REF] and the need to check the concentrations in the control snails before exposure (Table 1,Ct0). These new ex situ TGVs, developed with the same methodology as those used for the CiRefs in situ for land snails [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF], characterize two additional MEs, Se and Mn. Having information on these elements is essential, as Se can be responsible for soil pollution caused by the coal industry [START_REF] Huang | Assessment of selenium pollution in agricultural soils in the Xuzhou District, Northwest Jiangsu, China[END_REF], and Mn pollution frequently occurs as a result of past and present activities at mining and smelting sites [START_REF] Petitjean | From iron ore to ecotoxicological impacts: One thousand years of manganese history[END_REF]. This study provided also new data for organic compounds frequently found in contaminated soils, but for which no guidance values for gastropods were known due to the difficulty of extracting and analysing PAHs and PCBs with low QLs in such complex biological matrices. Concerning the PAHs (except for NAP) and PCBs, the uncontaminated soils contained only traces of these compounds, some of which were not quantifiable.

Therefore, snails exposed to these soils did not accumulate enough contaminants to allow the calculation of the ex situ TGVs in the same way as for the MEs. Thus, the proposed ex situ

TGVs for the PAHs (except for NAP) and PCBs were assimilated to the QL to avoid false positives, even though the concentrations of contaminants could potentially be weaker.

Further technical improvements would allow a decrease in the QL and thus a more refined value of these TGVs.

Application to contaminated soils

The ex situ TGVs were used to assess the environmental risk of soils contaminated by MEs with the SET index and ERITME index (Table 2). The AQ calculated with the ex situ TGVs identifies the contaminants that are accumulated in excess in snails [START_REF] Pauget | Ranking field site management priorities according to their metal transfer to snails[END_REF]. The highest AQs were found for As, Cd, Hg, Pb, Sb, and Sn, with maximal values of 343, 59.5, 15.2, 360, 211 and 39.2, respectively (Tables 2A andS5). Consequently 2B andS5). The relationships between the sum of the 15 MEs in the soils and the SET or ERITME indexes are presented in figure 3 and have r² adj. values for the linear regression models of 0.560 and 0.430, respectively. Half of the variance in the AQ is explained by the total concentration of MEs in the soils. The other part of the variance can be explained by the physicochemical properties of the soils and biotic factors [START_REF] Pauget | Ranking field site management priorities according to their metal transfer to snails[END_REF]. The coefficient of determination (r² adj.) better explains (+0.13) the relations of the sum of the 15 MEs in the soils with the SET index values than with the ERITME index values. This is in accordance with the low accumulation (high excretion) and the low toxicity (low TP) of essential elements and with the high accumulation (sequestration and detoxification) and the high toxicity (high TP) of non-essential elements, such as Cd [START_REF] Betzer | Copper uptake and excretion by Busycon canaliculatum L[END_REF][START_REF] Berger | Accumulation of cadmium and copper by the terrestrial snail Arianta arbustorum L.: kinetecs and budgets[END_REF][START_REF] Gomot | Influence of soils on the growth of the land snail Helix aspersa. An experimental study on the absorption route for the stimulating factors[END_REF][START_REF] Pauget | Soil parameter are key factors to predict metal bioavailability to snails based on chemical extractant data[END_REF].

These results highlighted that the commonly used rule based on " the highest contaminated soil presents the highest risk" can lead to misinterpretation of risks, notably when considering the numerous SET/ERITME values that were null or close to 0 for effectively contaminated soils (Figure 3). For each soil, the values of SET and ERITME were not driven by the same ME; for example, for O15, the SET is mainly influenced by Cr and the ERITME, mainly by Cd and Pb, due to the value of their TP. The AQ, SET index and risk index (ERITME)

showed the environmental bioavailability to snails of the contaminants in the soils investigated.

The new ex situ TGVs for PAHs and PCBs have been used to assess ex situ the environmental risk of soils moderately contaminated by PAHs and PCBs with the SET PAH and ERITPAH indexes (Table 3, data presented only for soils causing a SET PAH and ERITPAH > 0) and SET PCB and ERITPCB indexes (Table 4). With the studied contaminated soils by PAHs, the results mainly evidenced a CHY bioavailability to snails. No PAH transfers have been detected for the G1 soil, which contains the highest PAH concentrations, possibly because of its high organic matter content (175 mg.kg -1 ) and the ageing of contamination, as described by [START_REF] Vasseur | Bioavailability of chemical pollutants in contaminated soils and pitfalls of chemical analyses in hazard assessment[END_REF] and Spasojevic et al. (2017). This could explain the highest value reached by the SET-PAH index for the K1 soil (i.e., 4.6) whose OM content is six times lower than the G1 soil. For the PCBs, the N2 soil is highly contaminated (the sum of the PCBs in the soil is 34.9 mg.kg -1 and the SGV is 1 mg.kg -1 ), and the PCB with the highest transference is PCB153 (AQ = 5.05). This result reveals the bioavailability of PCBs in this soil, notably PCB153, which is often bioaccumulated in biota (Atuma et al., 1996;[START_REF] Danis | Bioaccumulation and effects of PCBs and heavy metals in sea stars (Asteria rubens, L.) from the North Sea: A small scale perspective[END_REF].

These results also showed that the ex situ TGVs developed in this study are relevant for assessing the environmental bioavailability by bioaccumulation measurements of MEs, PAHs and PCBs over 28 days in low contaminated soils.

Contaminated soils are often polycontaminated; therefore, this study developed GSET and GERITOXE indexes for an integrated risk assessment. GSET and GERITOXE provide an overall picture of the risk resulting of the contamination of the environment. On the basis of the SET and ERIT-indexes calculated for each soil and each family of contaminants analysed, GSET and GERITOXE were determined (Table 5). These calculations are presented for the soils for which data were available for ME and PAH (Table 5). The GSET and GERITOXE values vary according to the bioavailable compounds to snails as found for example for soils G2 and H2. For these soils the total concentrations of MEs (and PAHs)

were 1007 -(3.56) and 985 -(0.204 mg.kg -1 ) respectively, whereas their GSET equal 2.1 vs 14.6 and their GERITOXE equal 374 vs 4751 showing that similar ME-PAH soil contamination leads to contrasted level of risk. In term of the total contamination G2 soil was the most contaminated of the two soils, but in term of risk, according to the values of GSET and GERITOXE, H2 soils was the most risked soils (Table 5). These findings evidence reflects that the highest contaminated soils are not necessarily those showing the highest environmental risk.

Our original approach also provides global indexes useful for multicontaminated sites (SET and GERITOXE) which is not possible with the ratio risk (PEC/PNEC) or the SSD methods.

Finally, these indexes are the same than those used in human health risk assessments and pave the way to develop integrative methodologies that conciliate ERA and human health risk assessment [START_REF] Louzon | Bioaccessibility of metal(loid)s in soils to humans and their bioavailability to snails: A way to associate human health and ecotoxicological risk assessment?[END_REF], such as the ISS (interpretation of the state of soils or IEM for the French acronym), which combines the assessments of exposure and the toxicological risk [START_REF] Mossmann | Base de données BASOL sur les sites et sols pollués (ou potentiellement pollués) appelant une action des pouvoirs publics, à titre préventif ou curatif[END_REF]. Results of the "TGV-AQ-GSET-GERITOXE-ex situ method" will be helpful to orientate the further use of the excavated soils (depending on the level of risk identified: eliminated as a waste, or need to be covred, or submitted to soil remediation techniques, or used for renaturation of sites…). In the latter case, this can, for example, limit the environmental disturbances and the cost associated to the excavation of soils.

Influence of soil physico-chemical properties on the accumulation quotient

The influence of soil physico-chemical properties on the accumulation quotient (AQ>1), which characterized the excess of transfer of contaminants to snails, was assessed by i) univariate linear regressions with concentrations of the compounds in the soils and by ii) multivariate linear regressions, by taking into consideration all of the physico-chemical properties that have been summarized in Figure 1. First, the results presented in Table 6 showed that anomalous transfers were mainly related to the total concentrations of metal(loid)s in the soils, with r² adj. for significant models of 0.56, 0.78, 0.63, 0.72 and 0.56 for As, Cd, Cr, Pb and Sb, respectively. The regressions were improved by taking account the physico-chemical characteristics of soils, only for Cd and Zn with r² adj. increased up to 0.88 and 0.34, respectively. The models underline the modulating influence of Al/Fe oxides and clay in relation with their impact on ME mobility in soils (adsorptions/desorptions) [START_REF] Gadde | Heavy metal adsorption by hydrous iron and manganese oxides[END_REF][START_REF] Violante | Adsorption of heavy metals on mixed Fe-Al oxides in the absence or presence of organic ligands[END_REF][START_REF] Hamid | An explanation of soil amendments to reduce cadmium phytoavailability and transfer to food chain[END_REF] and transfers to snails [START_REF] Pauget | Ranking field site management priorities according to their metal transfer to snails[END_REF]. When the concentrations of the exposure were quite low, i.e., close to those of the pedogeochemical background, the influence of soil properties (pH, OM content, oxides) has been identified by multiple linear regressions for non-anomalous transfers of MEs (AQ<1) (Table S5). However, when the concentration of MEs increases in the soil, the influence of the soil physico-chemical properties is less significant. The anomalous transfers (AQ>1) were consequently driven by the total concentration of MEs in the soils (Table 6).

Finally, the interest of the methodology developed in this paper with ex situ-TGV to assess the bioavailability is to consider the influence of physico-chemical properties of the soils and the specificity of each sites (e.g., ageing of the contamination) on the exposure of organisms, to estimate real risks. To estimate the exposure of organisms and consequently to take in consideration the specificity of each site, bioavailability can be assessed with the method presented here and the determination of accumulation quotient (AQ). Then using toxicity point (TP) allows to estimate the resulting risk coefficient (RC) for each compounds [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF].

Conclusion

Studies based on threshold values and bioaccumulation measurements used to assess the fate and impact of contamination have been performed by in situ caging of invertebrates in terrestrial (Cantareus aspersus: [START_REF] Pauget | The SET and ERITME indices: Integrative tools for the management of polluted sites[END_REF] and aquatic (Gammarus fossarum: [START_REF] Besse | Caged Gammarus fossarum (Crustacea) as a robust tool for the characterization of bioavailable contamination levels in continental waters: towards the determination of threshold values[END_REF][START_REF] Ciliberti | Caged Gammarus as biomonitors identifying thresholds of toxic metal bioavailability that affect gammarid densities at the French national scale[END_REF] environments. Although these studies are relevant in biomonitoring, they are currently subjected to the influence of pedoclimatic conditions and specificity of sites (e.g., plants and humus). Currently, the use of guide values and indexes of risk, such as TGV, SET and GERITOXE (ERITME, ERITPAH and ERITPCB), in ecotoxicology, initially developed in situ for snails, is of increasing interest, as evidenced by the recent application to earthworms [START_REF] Coehlo | Bioavailability and bioaccumulation of heavy metals of several soils and sediments (from industrialized urban areas) for Eisenia fetida[END_REF][START_REF] Coehlo | Transfers et [les] effets des retardateurs de flamme bromés (RFBs) sur trois espèces végétales et une espèce de lombric dans des anthroposols[END_REF]. The interest of the present study is to propose guidance values for an ex situ bioassay (TGVs) using snails to assess the environmental risks, e.g., of excavated contaminated soils. The ex situ TGVs determined in this study provide new and essential data (the GSET and GERITOXE indexes) for assessing the ex situ environmental risks of soil contamination by 15

MEs, 16 PAHs and 7 PCBs and preventing misinterpretation during ERAs according to the bioavailability concept. Considering AQ instead of raw/absolute internal concentrations in organisms account for the specific ability of an organism to concentrate some compounds.

Thus, misinterpretation of the transfer of the compounds from soil to biota is limited. This study provides original knowledge on the influence of the physico-chemical properties of the soil on contamination transfer to biota which was found to be variable depending on the level of the bioavailable concentration of metal(loid)s in soils. Indeed, when considering AQ>1, a low incidence of physico-chemical properties of soils has been demonstrated on the excess of transfers to organisms. Our objective was to propose the first TGVs for organic pollutants in a terrestrial organism that is currently used mainly as a bioindicator of ME accumulation. These TGVs showed bioavailability of PAHs and PCBs (mainly CHY and PCB153 in the panel of soils in this study) in contaminated soils. To help the stakeholders of polluted soils in their make management, ex situ TGVs for snails are a basis for interpreting contamination levels:

when ex situ TGVs are exceeded, the contaminant is bioavailable, and therefore, soil contamination should be managed. The presented results also provide a basis for prioritizing the management of contaminated soils for limiting the impact on the environment and by considering the risk of subsequent management measures (e.g., soil excavation and embankment use).

Acknowledgements

The authors warmly thank Aurélie Pelfrêne, Jörge Römbke, Jakub Hofman and Maly

Stanislav who sent us soil samples. The authors also thank Dominique Rieffel, Quentin Devalloir and Caroline Amiot for their technical assistance. The authors thank the reviewers for their constructive remarks.

Funding sources

This work was supported by the Agence de l'environnement et de la maîtrise de l'énergie (ADEME) [grant number 1572C0310, COMBINE].

Conflict of interest

The authors declare no conflict of interest.

Supplementary data

The supplementary data associated with this article can be found in the online version. Tables Table 1:

Figures:Figure 1 :

 1 Figures:Figure1: Diversity of the physico-chemical parameters of the soils (clay, silt, sand, OM, Corg, and CaCO3 are in g.kg -1 ; CEC, Alox and Feox are in cmol.kg -1 ).

Figure 2 :

 2 Figure 2: Concentrations of the MEs in the viscera of snails exposed to uncontaminated soils for the determination of the ex situ TGVs (in mg.kg -1 dw; TGVs are shown by the dotted red lines).

Figure 3 :

 3 Figure 3: Relations between the sum of the concentration of the fifteen MEs in the soils (mg.kg -1 dw) and SET or ERITME index values (data are transformed by log10(x+1)).

Figure 1 :

 1 Figure 1: Diversity of the physico-chemical parameters of the soils (clay, silt, sand, OM, Corg, and CaCO3 are in g.kg -1 ; CEC, Alox and Feox are in cmol.kg -1 ).

Figure 2 :

 2 Figure 2: Concentrations of the MEs in the viscera of snails exposed to uncontaminated soils for the determination of the ex situ TGVs (in mg.kg -1 dw; TGVs are shown by the dotted red lines).

Figure 3 :

 3 Figure 3: Relations between the sum of the concentration of the fifteen MEs in the soils (mg.kg -1 dw) and SET or ERITME index values (data are transformed by log10(x+1)).

  Soil guide values (SGVs) in the soils; toxicity points (TPs) for each contaminant; concentrations of the contaminants in the visceral mass before exposure (Ct0, n=12), in lettuce, and in Helixal®; and transfer guide values (TGVs) calculated from the visceral mass of Cantareus aspersus sub-adults after 28 days of exposure to uncontaminated soils (Sources for SGVs are 1: Pauget and de Vaufleury (2015), 2: Baize (2000), 3: Villanneau et al.(2008( ), 4: ADEME (2018)). The toxicity points used are from ATSDR(2015). The Ct0 are median (min|max) for MEs and mean ± standard deviation pour PAHs and PCBs. The concentrations and TGVs for the ME are in mg.kg -1 dry weight (dw) and those for the PAHs and the PCBs are in µg.kg -1 fresh mass (fm) pooled from two individuals).

  , these contaminants are the principal drivers of the SET index in this study, with a maximal value of 608 (L3 soil). Associating the AQ with the TP of each ME allows the estimation of environmental risk (RC) according to the toxicity of the transferred ME. The highest RCs were again for As, Pb, Cd, Hg, Sb and Sn, with maximal values of 205200, 143600, 23400, 8520, 2100, and 2025, respectively, and a maximal value of the ERITME index of 230664

(L3 soil) (Tables

  ADEME, 2018. Méthodologie de détermination des valeurs de fonds dans les sols : Echelle territoriale. Groupe de travail sur les valeurs de fonds, France, 112 p.

	Terrestrial gastropods (Helix spp.) as sentinels of primary DNA damage for biomonitoring
	purposes:	A	validation	study.	Environ.	Molec.	Mutat.	54,	204-212.
	https://doi.org/10.1002/em.21766
	ATSDR, 2015. Detailed Data Table for the 2015 Priority List of Hazardous Substances that
	will be the subject of toxicological profile.
	ATSDR, 2017. Support Document to the 2017 Substance Priority List (Candidates For
	Toxicological Profiles).
	Atuma, S.S., Linder, C.E., Andersson, Ö., Bergh, A., Hansson, L., Wicklund-Glynn, A., 1996.
	CB153 as indicator for congener specific determination of PCBS in diverse fish species
	from Swedish waters. Chemosphere 33(8), 1459-1464. https://doi.org/10.1016/0045-
	6535(96)00284-6

Alhers, J., 2001. Strategies for risk assessment of existing chemicals in soil. J. Soil. Sed. 1, 168-174. https://doi.org/10.1007/BF02986481 Allen, H.E., 2002. Bioavailability of metals in terrestrial ecosystems: importance of partitioning for bioavailability to invertebrates, microbes and plants. Society of Environmental Toxicology and Chemistry (SETAC), Pensacola. Angeletti, D., Sebbio, C., Carere C., Cimmaruta, R., Nascetti, G., Pepe, G., Mosesso, P., 2013.

Table 2 : (A)-Accumulation quotient (AQ) and SET index and (B)-Risk coefficient (RC) and ERITME index based on the concentration of metallic elements (MEs) in the visceral mass of snails after 28 days of exposure to soils. Color gradient from green to red as function of the levels of the transfers and risks.

 2 Detailed results for all soils are presented in tableS5.

Table 3 : Accumulation quotient (AQ)│risk coefficient (RC) and SET│ERITPAH indexes based on the visceral mass of snails after 28 days of exposure to polycyclic aromatic hydrocarbons (PAHs

 3 ) (the bold values are >1 for AQ, >5 for SET, >0 for RC and > 1000 for ERITPAH, only data for soils for which a SET PAH and ERITPAH > 0 are presented in this table ).

Table 4 : Accumulation quotient (AQ)│risk coefficient (RC) and SET│ERITPCB indexes based on the visceral mass of snails after 28 days of exposure to polychlorinated biphenyl indicators (PCBs)

 4 (the bold values are >1 for AQ, >5 for SET, >0 for RC and > 1000 for ERITPCB).

Table 5 : Total concentrations of the contaminants in the soils (mg.kg -1 dw), global sum of the excess of transfer (GSET) and global evaluation of the risk of the toxicological elements (GERITOXE) in soils

 5 (na: data not available. The color gradient varies from green to red according to the value of the ME and GERITOXE indices).

Table 6 : Influence of total metal(loid) concentrations and soil parameters on the accumulation quotient (AQ>1)

 6

	(significant variables are ranked with

Table 1 : Soil guide values (SGVs) in the soils; toxicity points (TPs) for each contaminant; concentrations of the contaminants in the visceral mass before exposure (Ct0, n=12), in lettuce, and in Helixal®; and transfer guide values (TGVs) calculated from the visceral mass of Cantareus aspersus sub-adults after 28 days of exposure to uncontaminated soils

 1 (Sources for SGVs are 1: Pauget and de[START_REF] De Vaufleury | Landsnail for ecotoxicological assessment of chemicals and soil contamination[END_REF], 2:[START_REF] Baize | Teneurs totales en "métaux lourds" dans les sols français. Résultats généraux du programme ASPITET[END_REF]), 3: Villanneau et al. (2008), 4: ADEME (2018). The toxicity points used are from ATSDR(2015). The Ct0 are median (min|max) for MEs and mean ± standard deviation pour PAHs and PCBs. The concentrations and TGVs for the ME are in mg.kg -1 dry weight (dw) and those for the PAHs and the PCBs are in µg.kg -1 fresh mass (fm) pooled from two individuals).

		SGV		Concentrations	Concentrations	Concentrations
	Contaminants		TP				TGV
		(mg.kg -1 dw)		in snails (Ct0)	in lettuce	in Helixal®
	As	25 2	600	0.11 (0.09|0.13)	0.13	0.72	0.364
	Cd	0.45 2	400	1.49 (1.08|1.62)	0.65	0.43	5.60
	Co	23 2	400	1.22 (0.90|1.76)	0.07	0.66	8.12
	Cr	90 2	10	0.93 (0.66|3.66)	0.05	7.77	0.653
	Cu	20 2	10	55.9 (34.9|84.4)	10.1	7.99	173
	ME
	Hg	0.1 2	600	0.02 (0.01|0.03)	0.01	0.49	0.089
	Mn	754 2	10	208 (80.7|348)	46.4	69.5	1355
	Mo	1.59 3	53	2.76 (1.85|3.38)	0.21	1.01	2.31
	Ni	60 2	178	5.11 (3.63|7.42)	0.37	3.10	6.10
	Pb	62.3 3	400	0.42 (0.26|1.21)	0.11	0.78	9.06

Table 3 : Accumulation quotient (AQ)│risk coefficient (RC) and SET│ERITPAH indexes based on the visceral mass of snails after 28 days of exposure to polycyclic aromatic hydrocarbons (PAHs

 3 ) (the bold values are >1 for AQ, >5 for SET, >0 for RC and > 1000 for ERITPAH, only data for soils for which a SET PAH and ERITPAH > 0 are presented in this table ).

	AQ│RC

Table 4 : Accumulation quotient (AQ)│risk coefficient (RC) and SET│ERITPCB indexes based on the visceral mass of snails after 28 days of exposure to polychlorinated biphenyl indicators (PCBs

 4 ) (the bold values are >1 for AQ, >5 for SET, >0 for RC and > 1000 for ERITPCB).

	AQ│RC

Table 5 : Total concentrations of the contaminants in the soils (mg.kg -1 dw), global sum of the excess of transfer (GSET) and global evaluation of the 63 risk of the toxicological elements (GERITOXE) in soils

 5 (na: data not available. The color gradient varies from green to red according to the value of the ME 64 and GERITOXE indices).

	65
	66
	Soils	[ME]soils [PAH]soils [PCB]soils SET ME SET PAH SET PCB	GSET	ERITME ERITPAH ERITPCB GERITOXE
	J1	112	0.044	na	0	0	na	0	0	na	0
	I1	192	0.427	na	0	0	na	0	0	na	0
	E2	586	0	na	0	0	na	0	0	na	0
	F1	688	0.745	na	0	0	na	0	0	na	0
	A1	810	0.205	na	0	0	na	0	0	na	0
	A2	844	0.270	na	0	0	na	0	0	na	0
	A3	871	0.502	na	0	0	na	0	0	na	0
	A4	880	0.572	na	0	0	na	0	0	na	0
	C5	1003	0.059	0.001	0	0	0	0	0	0	0
	C7	1089	0.315	0.001	0	0	0	0	0	0	0
	C6	1184	2.54	0.001	0	0	0	0	0	0	0
	D2	1280	0.622	na	0	0	na	0	0	na	0
	C4	1432	0.099	0.002	0	0	0	0	0	0	0
	C2	1493	0.063	0.001	0	0	0	0	0	0	0
	C1	2640	0.190	0.001	0	0	0	0	0	0	0
	B3	1194	0.081	na	0.02	0	na	0.02	0.20	na	0.20
	H1	643	0.121	na	0.02	0	na	0.02	1.06	na	1.06
	F3	1036	0.275	na	0.45	0	na	0.45	4.50	na	4.50
	E1	614	0.049	na	0.61	0	na	0.61	6.10	na	6.10
	B1	854	1.43	na	0.79	0	na	0.79	7.90	na	7.90
	D1	1302	0.518	na	1.38	0	na	1.38	13.8	na	13.8
	B2	1078	0.058	na	1.60	0	na	1.60	16.0	na	16.0
	I2	718	0.154	na	0.89	0	na	0.89	22.2	na	22.2
	F2	753	1.30	na	2.24	0	na	2.24	66.0	na	66.0

Table 6 : Influence of total metal(loid) concentrations and soil parameters on the accumulation quotient (AQ>1

 6 ) (significant variables are ranked with 79 stars as a function of the p-value obtained: * < 0.05, ** < 0.01, *** < 0.001; the data are transformed by log 10 (x +1); N=number of soils assessed in this study 80 with AQ>1).

	81
	Elements	Equations	N	adj. r²	p-value
	As	AQ = -0.333 + 0.700[As] soil ***	23	0.564	< 0.001
		AQ = no influence of soil parameters
	Cd	AQ = 0.034 + 0.678[Cd] soil ***	24	0.782	< 0.001
		AQ = -0.563* + 0.626[Cd] soil *** -2.15[Alox]** + 0.567[clay]**		0.877	< 0.001
	Cr	AQ = -1.03* + 0.895[Cr] soil *	11	0.631	0.002
		AQ = no influence of soil parameters
	Hg	AQ = 0.458*** + 0.266[Hg] soil	19	0.083	0.123
		AQ = no influence of soil parameters
	Pb	AQ = -0.922*** + 0.746[Pb] soil ***	34	0.715	< 0.001
		AQ = no influence of soil parameters
	Sb	AQ = 0.243* + 0.393[Sb] soil ***	26	0.558	< 0.001
		AQ = no influence of soil parameters
	Sn	AQ = 0.590*** + 0.393[Sn] soil	25	0.022	0.498
		AQ = no influence of soil parameters
	Zn	AQ = 0.034 + 0.151[Zn] soil	21	0.133	0.058
		AQ = -0.166 + 0.214[Zn] soil ** + 18.6[Alox]* -72.6[Feox]*		0.341	0.018


Nicolas Garcelon

Anita Burgun

Rémi Salomon

Antoine Neuraz

Electronic health records for the diagnosis of rare diseases

Keywords:

With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many locks for the repurposing of clinical data. The development of decision support systems depends on the ability: to recruit patients; then to extract and integrate the patients' data; to mine and stratify these data; and to integrate the decision support algorithm into the patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review we examine the research that provide solutions to unlock these barriers and accelerate translational research: structured electronic health records and free text search engine to find patients, data warehouses and natural language processing to extract the phenotypes, machine learning algorithms to classify patients, similarity metrics to diagnose patients etc. Medical informatics is experiencing an impellent request to develop decision support systems and this requires ethical considerations for clinicians and patients to ensure a good usage of health data.

Introduction:

Since the rise of the electronic health record (EHR), clinicians spend an increasing number of hours at their computers, from 49% of their total time 1 up to 66% [START_REF] Sinsky | Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties[END_REF] . Moreover, hospital adoption of EHRs with advanced functionality has increased in the last decade [START_REF] Jamoom | Physician adoption of electronic health record systems: United States, 2011[END_REF] . This is why "in the current digital age, the electronic health record represents a massive repository of electronic data points representing a diverse array of clinical information" [START_REF] Carter | Electronic Health Records: A Guide for Clinicians and Administrators[END_REF] . Such data repositories can be used for research purposes, to accelerate the recruitment of patients, and discover new knowledge. Moreover, being able to leverage this massive amount of latent knowledge to support doctors' decision making is essential.

In 2011, Frankovitch et al. reported on how they used the clinical data warehouse of Stanford hospital to make a clinical decision for a young patient with systemic lupus erythematosus complicated by nephrotic-range proteinuria, antiphospholipid antibodies, and pancreatitis [START_REF] Frankovich | Evidence-based medicine in the EMR era[END_REF] . There were neither guidelines nor publications which could help to make this decision. They queried the data warehouse for similar cases and made "the decision on the basis of the best data available." This paper opened the gates to a new utilization of EHRs: repurposing the clinical data to develop decision support systems inside the EHR. The virtuous circle [START_REF] Wartman | Toward a virtuous cycle: the changing face of academic health centers[END_REF] of translational research could then be closed: from routine care data to research, and from research to clinical care. In the context of rare diseases, the capacity of reusing EHR data to support diagnosis is even more critical. Indeed, given the rarity of these diseases, the diagnosis journey of the patients often last for years [START_REF]The Shire Rare Disease Impact Report[END_REF] . Tools taking advantage of the data of already diagnosed patients to help the decisions for the new cases could save a lot of time.

We designed a query (Figure 1) to find references in Medline on how the EHR may be used to diagnose rare diseases. This query returned 52 references as of February 14 2019. We performed a manual review and removed 7 references that were false positives (acronyms with other meaning). Among the remaining 45 publications (categories are not exclusive):

• 20 publications report the results of clinical studies (epidemiological studies, clinical trials, natural history of diseases). The authors used EHRs to recruit the patients and/or to describe the natural history of the diseases. • 5 publications present structured EHRs tailored for the reuse of the data for a specific rare disease.

• 6 publications describe rare disease registries.

• 5 publications provide description of methods to recruit patients based on EHR data.

• 4 publications explain how they used EHRs to extract gene/phenotype association or disease/environment association.

• 1 publication provides a global overview of the repurposing of the EHR.

• 3 publications correspond to online applications for the diagnosis of rare diseases.

• Only 4 publications describe methods to diagnose rare diseases by repurposing the EHR data, but none of them are integrated in any EHR system. The paucity of publications found may be explained by several issues, and at least 6 steps must be performed for developing a clinical decision support system (CDSS) and integrating it into the EHR. These steps include (Figure 2):

1. The identification and recruitment of patients (which may require manual annotation/review of the patient records), 2. The extraction of information from heterogeneous clinical data (e.g. narrative reports, imaging system, lab exams), 3. Data integration, enrichment and data auditing to assess data quality, 4. Development of machine learning methods, 5. The evaluation of the CDSS, 6. The integration of the CDSS in the EHR system, and its usability, so that it can be used by the doctor during patient care process. In addition, Shortliffe et al. highlighted six characteristics a decision support system must have in the clinical field: understandability, time efficiency, ergonomics, adaptability for the clinical domain, respect for users, and solid scientific reliability [START_REF] Shortliffe | Clinical Decision Support in the Era of Artificial Intelligence[END_REF] .

Similarly to the publication by Jensen et al. in 2012 [START_REF] Jensen | Mining electronic health records: towards better research applications and clinical care[END_REF] , we expanded our review on approaches proposed to address these issues, but we prioritized the publications related to rare diseases or genetic diseases. Concretely we selected publications related to the recruitment of patients through the EHR: (EHR or electronic health record) and ("cohort identification" or "cohort selection" or "patient identification" or "recruitment").We then focused on the technologies used to improve this selection by adding "data warehouse," then "natural language processing", "Knowledge extraction", "deep phenotyping", "rare disease and diagnosis", "learning health system" and "Genomics".

Identification and recruitment of patients

Several approaches to help identifying patients for clinical studies have been explored. Most of the time investigators used legacy terminologies like the International Classification of Diseases (ICD) to identify the patients of interest [START_REF] Kirby | PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability[END_REF] . This method is barely applicable for rare diseases. Fung et al. estimated the coverage of the 6,500 rare diseases up to 11% with ICD-9-CM and 21% with ICD-10 11 . EURORDIS was even more pessimistic and estimated that only 500 rare diseases have a specific code in the ICD10 [START_REF] Bearryman | Does Your Rare Disease Have a Code? EURORDIS[END_REF] . Since the coverage of ICD codes for rare diseases is very low, it is necessary to explore other data sources.

EHR for recruitment and clinical research

The repurposing of EHRs performed better for patient recruitment than using data from registries [START_REF] Geva | A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry[END_REF] . Schreiweis et al. compared the abilities of five EHRs to recruit patients in clinical trials [START_REF] Schreiweis | Comparison of electronic health record system functionalities to support the patient recruitment process in clinical trials[END_REF] . They did not find any functional components to support the research requirement of patient recruitment. For this reason, several authors developed dedicated EHRs tailored for specific rare diseases and data repurposing for research. Vawdrey et al. proposed a model based on Common Data Elements to structure the EHR for supporting clinical research [START_REF] Vawdrey | Enhancing electronic health records to support clinical research[END_REF] . The OpenEHR initiative integrated genomics archetypes in their EHR model and showed their applicability to clinical practice with rare genetic diseases [START_REF] Mascia | OpenEHR modeling for genomics in clinical practice[END_REF] . Other solutions propose to map the EHR's local logical schema onto a common external schema to facilitate data mining and extraction [START_REF] Abrahão | A method for cohort selection of cardiovascular disease records from an electronic health record system[END_REF] . This last example introduces the necessity of integrating data into a new data model to clean and enhance it for efficient repurposing. EHR data models are designed for care purposes not for research. They are optimized to efficiently enter and retrieve all data for one patient at a time. Therefore, they are composed of hundreds to thousands of different tables which make it difficult to make transversal queries (i.e. query specific data for a large number of patients). The strategies that were implemented to overcome this issue: (i) the creation of structured EHRs dedicated to translational research and (ii) the creation of copies of the clinical production databases in translational data warehouses, are described below.

Structured EHRs for translational research

The first strategy developed to enhance the repurposing of clinical data produced during patient care was to adapt the EHR for this objective. One possible way is to develop specialized EHRs for specific disciplines or diseases. Bremond-Gignac et al. showed the benefit of an ophthalmology-specific EMR for collecting a comprehensive ocular visual phenotype useful for clinical research [START_REF] Bremond-Gignac | Contribution of Electronic Medical Records to the Management of Rare Diseases[END_REF] . Other approaches consisted in adding disease oriented predefined templates to existing EHRs, for example "infantile spasms template" [START_REF] Santoro | Use of electronic medical record templates improves quality of care for patients with infantile spasms[END_REF] . On the other hand, a horizontal approach would consist in finding a way to explore all the diseases, including rare diseases, in a common format. The French consortium for rare diseases (BNDMR) defined a minimum data set common to all rare diseases [START_REF] Choquet | A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research[END_REF] . They used international terminologies such as the Human Phenotype Ontology (HPO) [START_REF] Köhler | The Human Phenotype Ontology in 2017[END_REF] , the Online Mendelian Inheritance in Man (OMIM) 22 and Orphanet [START_REF]Orphadata: Free access data from Orphanet[END_REF] , as well as a standard data exchange format, the Fast Healthcare Interoperability Resources (FHIR) to structure and transfer the data [START_REF] Bender | HL7 FHIR: An Agile and RESTful approach to healthcare information exchange[END_REF] . Semantic integration of EHRs and other resources is another way to improve data reuse. Chelsom et al. enhanced the open source EHR cityEHR with semantic web technologies OWL (Web Ontology Language) to map the data onto knowledge databases in order to display relevant information to the clinician [START_REF] Chelsom | Linking Health Records with Knowledge Sources Using OWL and RDF[END_REF] . OSSE [START_REF] Storf | Registries for rare diseases : OSSE -An open-source framework for technical implementation[END_REF] or FluxMed 27 also implement structured EHRs specialized for rare diseases. Asking clinicians to tick hundred of boxes is, however, not efficient: it is time consuming and in the context of rare diseases checkboxes do not qualify diagnoses or phenotypic descriptions. We will detail the importance of free text later in the article. Nevertheless, if a structured specialized-EHR is required for a rare disease, it is no longer acceptable not to use an international thesaurus like HPO or SNOMED-CT [START_REF] Wang | The SNOMED clinical terms development process: refinement and analysis of content[END_REF] . Working on several databases is not an issue if they are able to interoperate through a common thesaurus.

Translational data warehouses

The common EHR system is rarely the sole source of information in a hospital. Most of the time the Hospital Information System consists of a combination of several software and databases dedicated to specific tasks or specialties (EHR, PACS, Biological Results, pathology, etc.). These databases are more or less interoperable. In addition, dozens of decommissioned databases are stored on local hard drives or servers inside the hospital and are generally not available for clinical care or research. Until recently, physicians and researchers have built registries to serve specific goals. Wen et al. compared the efficiency (time, cost and number of participants) of registry versus data warehouse methods to recruit patients. They demonstrated that the data warehouse method was more efficient with more participants recruited in less time and at a lower cost [START_REF] Weng | Comparing the effectiveness of a clinical registry and a clinical data warehouse for supporting clinical trial recruitment: a case study[END_REF] . Built on top of EHRs, Clinical Data Warehouses enable collection and secondary use of clinical data for many purposes, including research (Phenome-wide analysis, record mining, epidemiological surveillance, pharmacovigilance, etc.). Most major hospitals built their own data warehouses to aggregate all the clinical data produced [START_REF] Lowe | STRIDE--An integrated standards-based translational research informatics platform[END_REF][START_REF] Zhou | Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support[END_REF][START_REF] Krasowski | Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research[END_REF][START_REF] Kortüm | Using electronic health records to build an ophthalmological data warehouse and visualize patients' data[END_REF] . For example, the multidisciplinary consortium NACI (NephCure Accelerating Cures Institute) developed a data warehouse specifically to improve care for nephrotic syndrome [START_REF] Gipson | NephCure Accelerating Cures Institute: A Multidisciplinary Consortium to Improve Care for Nephrotic Syndrome[END_REF] . Several research projects have developed open source or free license data warehouses. The most widespread and commonly used is i2b2 developed by the NIH-funded National Center for Biomedical Computing based at Partners HealthCare System in the US [START_REF] Murphy | Integration of clinical and genetic data in the i2b2 architecture[END_REF][START_REF] Murphy | Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)[END_REF] . i2b2 is primarily oriented toward structured data related to thesauri. Another open source tool, Dr Warehouse was developed at Necker hospital, a pediatric hospital specializing in rare diseases, and was oriented toward clinical documents such as narrative reports [START_REF] Garcelon | A clinician friendly data warehouse oriented toward narrative reports: Dr Warehouse[END_REF] . It integrates 26 different sources of clinical data, over 500 000 patients and 5 millions clinical reports. These data warehouses aim at accelerating recruitment but also the mining of data by integrating pre-and post-processing methods. For example, Davis et al. developed natural language processing methods to extract clinical traits from the Vanderbilt data warehouse in order to recruit patients with multiple sclerosis [START_REF] Davis | Automated extraction of clinical traits of multiple sclerosis in electronic medical records[END_REF] . Their algorithm was highly specific to cases of multiple sclerosis but it highlights the potentiality of biomedical data warehouses to recruit patients with complex phenotypes. OHDSI 39 (formerly the Observational Medical Outcomes Partnership -OMOP) initiative contributes to standardize data warehouses by building and maintaining the OMOP CDM (Common Data Model) [START_REF] Hripcsak | Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers[END_REF] . This standard would allow interoperability and reproducibility of studies on different data warehouses.

Natural Language Processing methods

Several studies showed that narrative reports contain more information than coded data [START_REF] Cuggia | A full-text information retrieval system for an epidemiological registry[END_REF][START_REF] Raghavan | How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?[END_REF][START_REF] Escudié | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease[END_REF] . Especially for rare diseases or undiagnosed patients, free text is the only way to describe the fine-grained signs and symptoms of the patients without nosological guidelines. An important element of the text may reference the patient's family history or note the absence of clinical signs. Indeed, we showed that a query on rare disease records may return 70% false positive results due to sentences contained negated terms (e.g. "the patient does not have renal insufficiency") and family history (e.g. "The father has Crohn's disease") [START_REF] Garcelon | Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse[END_REF] . Several methods were developed to detect negation, experiencer, and temporality in clinical narrative, including Negex, Context, DEEPEN, and NegAIT [START_REF] Garcelon | Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse[END_REF][START_REF] Friedlin | Using A Natural Language Processing System to Extract and Code Family History Data from Admission Reports[END_REF][START_REF] Goryachev | Implementation and evaluation of four different methods of negation detection[END_REF][START_REF] Chapman | Extending the NegEx lexicon for multiple languages[END_REF][START_REF] Bill | Automated extraction of family history information from clinical notes[END_REF][START_REF] Mukherjee | NegAIT: A new parser for medical text simplification using morphological, sentential and double negation[END_REF] . Most of these methods are based on regular expressions, thus not requiring a machine-learning phase on an annotated corpus of texts. They show high levels of accuracy, recall and precision. One main objective of natural language processing methods is to reduce the noise of the search engine results and to make pre-screening work easier and less time consuming [START_REF] Ford | Extracting information from the text of electronic medical records to improve case detection: a systematic review[END_REF] . A second purpose is to structure free text to facilitate data mining and knowledge extraction. It will be presented in the "Knowledge discovery on rare diseases" section.

Search engine on EHR data

The first use case of biomedical data warehouses is to find patients eligible for biomedical studies (feasibility study, pre-screening) or multidisciplinary consultation meetings (patients like mine). The amount of time required to find patients without a suitable tool is untenable, and even impossible for some criteria combining multiple sources of clinical data (e.g. creatininemia > 2 mg/dl AND Rituximab AND Uveitis AND Cataract). The strength of data warehouses is to make this type of task possible and fast by integrating all of the data available within the hospital. In Nephrology, Schmidt et al. developed a method based on faceted searches allowing a user to narrow down searches on items that co-occurred in the same document [START_REF] Schmidt | Towards Integrated Information Extraction and Facetted Search Applications in Nephrology[END_REF][START_REF] Sonntag | An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation[END_REF] . i2b2 provides a search engine through a thesaurus allowing the user to build complex queries on structured data [START_REF] Murphy | Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)[END_REF] . Other search engines, like in Dr Warehouse [START_REF] Garcelon | A clinician friendly data warehouse oriented toward narrative reports: Dr Warehouse[END_REF] and Emerse [START_REF] Hanauer | Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE)[END_REF] , are oriented toward free text and provide an intuitive user interface to find patients. Dr Warehouse combines queries on free text, coded data, and temporal relations between the search criteria. PheKB is a platform that proposed a catalog of queries including criteria to find patients based on phenotypes (combination of ICD codes, medications, laboratories and natural language processing) [START_REF] Kirby | PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability[END_REF] . For example, Mayo clinic share a flowchart to recruit patient called "Electronic Health Record-based Phenotyping Algorithm for Familial Hypercholesterolemia".

Knowledge extraction from EHR

Our second category of use cases regards the extraction of information and/or knowledge from EHRs. With focus on rare diseases, two specific tasks will be explored: the extraction of phenotypes and their associations, and the analysis of clinical pathways.

Phenotype extraction and association test

Kohane introduced the EHR-Driven Genomic Research (EDGR) as the repurposing of the EHR for genomic characterization [START_REF] Kohane | Using electronic health records to drive discovery in disease genomics[END_REF] . Once clinicians are able to create a cohort of patients, the next challenge is to mine the available data. The difficulty lies in extracting information from free text. Indeed, narrative clinical reports hide relevant information, which can be automatically detected by machine learning algorithms within NLP pipelines [START_REF] Savova | Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications[END_REF][START_REF] Métivier | Automatic Symptom Extraction from Texts to Enhance Knowledge Discovery on Rare Diseases[END_REF][START_REF] Wei | Extracting research-quality phenotypes from electronic health records to support precision medicine[END_REF][START_REF] Friedman | Towards a comprehensive medical language processing system: methods and issues[END_REF][START_REF] Wu | A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text[END_REF][START_REF] Adamusiak | Next Generation Phenotyping Using the Unified Medical Language System[END_REF] . The extracted medical concepts are then mapped to the Unified Medical Language System (UMLS) Metathesaurus® developed by the US National Library of Medicine. This concept extraction pipeline can be used to generate high throughput phenotyping on a cohort of patients. Comparing the result to the data available in the literature (Orphanet, Wikipedia, PubMed), researchers can identify novel associations and early phenotypes [START_REF] Geva | A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry[END_REF][START_REF] Escudié | A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease[END_REF][START_REF] Métivier | Automatic Symptom Extraction from Texts to Enhance Knowledge Discovery on Rare Diseases[END_REF][START_REF] Holmes | Discovering disease associations by integrating electronic clinical data and medical literature[END_REF][START_REF] Garcelon | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse[END_REF][START_REF] Shen | Rare disease knowledge enrichment through a data-driven approach[END_REF] . Pairing with control, phenome-wide association studies (PheWAS) can be automatically processed to test association between SNPs and extracted phenotypes [START_REF] Namjou | Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis[END_REF][START_REF] Hebbring | Application of clinical text data for phenome-wide association studies (PheWASs)[END_REF] . Photographs of patients can also be used to identify phenotypes by using deep learning [START_REF] Gurovich | Identifying facial phenotypes of genetic disorders using deep learning[END_REF] . The application of artificial intelligence to the analysis of patients' images can facilitate the discovery of new genetic disorders [START_REF] Marbach | The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping[END_REF] .

Clinical Pathways analysis

Another dimension to explore is the sequence of the interactions between the patient and the hospital or the health system in general, also known as clinical pathway. Clinical pathways of the patients inside the hospital may also be of interest to describe a cohort of patients [START_REF] Perer | Mining and exploring care pathways from electronic medical records with visual analytics[END_REF][START_REF] Dagliati | Temporal electronic phenotyping by mining careflows of breast cancer patients[END_REF][START_REF] Hurt | Understanding and improving the care pathway for children with autism[END_REF] . In the context of chronic kidney disease (CKD), Zhang and Patman identified seven subgroups of patients by using data-driven clinical pathway learning [START_REF] Zhang | Innovations in chronic care delivery using data-driven clinical pathways[END_REF] and developed a prediction algorithm to predict the most probable clinical pathway given a patient's characteristics.

Diagnosis support system tools

Machine Learning

Machine learning can be used in each step of the pipeline of creation of a CDSS: to recruit the patients and to extract knowledge from the narrative records, and to classify the patient with the right diagnosis. Machine learning algorithm can be divided into 4 categories :

- Neverthless, machine learning algorithms require a large quantity of data to learn the expected outcome. In the context of rare diseases, this number is generally not reachable. The best approach is to combined rule-based method and machine learning algorithms.

Internet tools and diagnosis

The web has become a primary source of information about rare diseases. Several websites offer search engines specializing in rare diseases and use scientific literature to allow users to find diagnoses by querying phenotypes (FindZebra 72 , Phenolyzer 73 , PhenoDis [START_REF] Adler | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases[END_REF] , Rare disease discovery [START_REF] Müller | Computer-assisted diagnosis of rare diseases[END_REF] , GDDP [START_REF] Chen | Novel phenotype-disease matching tool for rare genetic diseases[END_REF] ). They extract and curate information found in publicly available information on rare diseases such PubMed, Wikipedia or Orphanet. And they enriched them with ontologies. HPO is widely used as a standard ontology to annotate diseases with phenotypes, to calculate the information content of phenotypes, or to provide common pivot semantics to heterogeneous databases. Other knowledge resources are used to enrich existing data, for example rare disease description or gene-phenotype annotations: Orphanet 23 , UMLS [START_REF] Lindberg | The Unified Medical Language System[END_REF] , eRAM [START_REF] Jia | eRAM: encyclopedia of rare disease annotations for precision medicine[END_REF] , Uberpheno ontology [START_REF] Köhler | Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research[END_REF] , Gene Ontology 80 , ClinVar 81 etc. Machine learning methods integrate those databases in the learning phase to compute similarity distances, phenotype scores, and to provide a list of prioritization of causal variants (xRare [START_REF] Li | Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis[END_REF] , RDAD [START_REF] Jia | RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis[END_REF] and phenotype risk score [START_REF] Bastarache | Phenotype risk scores identify patients with unrecognized Mendelian disease patterns[END_REF] ).

EHR and diagnosis

The CDSS integration may either rely on the EHR structured data alone or exploit the whole spectrum of EHR data. Wulf et al integrated, in openEHR, a rule based algorithm using clinical structured data within the EHR to early detect systemic inflammatory response syndrome [START_REF] Wulff | An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR[END_REF] .

Text mining is needed to exploit narrative clinical data [START_REF] Simmons | Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health[END_REF] . EHR-Phenolyzer, developed by Son et al., integrates HPO phenolyzer [START_REF] Yang | Phenolyzer: phenotype-based prioritization of candidate genes for human diseases[END_REF] and EHR text mining to provide prioritization of causal genes for a patient [START_REF] Son | Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes[END_REF] . This framework extracts phenotypes from the patient's narrative reports, sends them automatically to Phenolyzer and displays a list of potential causal genes.

Creating machine learning methods on EHRs data to perform general predictive models is difficult because of the quantity, high dimensionality, sparsity and quality of data [START_REF] Liang | Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence[END_REF] . Most of the time, machine-learning algorithms are centered on a specific pathological condition. They can be trained on literature and knowledge extracted from unstructured EHRs (text mining and PheWAS) then applied to EHRs' data to help clinicians diagnose a specific disease.

Examples include Hunter syndrome [START_REF] Ehsani-Moghaddam | Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network[END_REF] , cardiac amyloidosis [START_REF] Garg | A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records[END_REF] , and Allergic Bronchopulmonary Aspergillosis [START_REF] Maguire | Identifying rare diseases using electronic medical records: the example of allergic bronchopulmonary aspergillosis[END_REF] . Liang et al. developed a machine-learning model to predict the primary diagnosis of a patient's encounter among a set of 50 diseases 88 by using the clinical data produced during the encounter. Their model was trained on a set of health reports manually annotated by pediatricians and obtained high diagnostic accuracy across multiple organ systems. However none of those diseases were rare.

Imaging and diagnosis

Regarding common diseases, several studies demonstrated strong performance in imagebased diagnoses, notably in imaging [START_REF] Zimmer | Learning and combining image neighborhoods using random forests for neonatal brain disease classification[END_REF] , dermatology [START_REF] Esteva | Dermatologist-level classification of skin cancer with deep neural networks[END_REF] , and ophthalmology [START_REF] Gulshan | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[END_REF] .

In the context of rare diseases, Gurovich et al. published deep learning methods on pictures of patients to detect facial dysmorphism associated with syndromic conditions 66 . Their system, called DeepGestalt, was trained on a dataset of over 17,000 images representing more than 200 syndromes. DeepGestalt is a form of next-generation phenotyping technology no longer based on the text but now on photographs.

Artificial Intelligence for care and management

The figure 3 detailes the different steps for developing and implementing a CDSS in CKD. Machine learning methods are also used to optimize care processes, for example anemia management in end-stage renal disease [START_REF] Brier | Artificial intelligence for optimal anemia management in endstage renal disease[END_REF][START_REF] Barbieri | An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients[END_REF] , prediction of response after neo-adjuvant chemoradiation [START_REF] Bibault | Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer[END_REF] , automated alerts on acute kidney injury [START_REF] Kashani | Automated acute kidney injury alerts[END_REF] or outcome predictions of chronic kidney disease with random forest regression [START_REF] Zhao | Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression[END_REF] or Support Vector Machine 100 . For this random forest regression, authors built the model using the data of 61,740 EHRs to predict kidney function and provide clinical decision support with high macro-averaged and microaveraged metrics. Ennis et al showed that CDSS improves primary care physicians adherence to guidelines for laboratory monitoring of CKD 101 . Wang et al. decribed a protocol from the development of a regional CKD surveillance system to a CDSS that will be implemented into the hospital information system 102 .

CDSS must be evaluated in a clinical context such as medical devices. A research in clinicaltrial.org with these keywords "kidney failure" and " machine learning" finds 8 clinical trials (October 2019). Among these 8 trials, two are completed, two are active but not recruiting and four are recruiting patients. The query "kidney failure" and "clinical decision support" and "EHR" displays 9 studies which six were published after January 2018. The query ("rare diseases" or "rare disease") and ("clinical decision support" or "machine learning") and "EHR" does not find accurate study. These numbers illustrate how new is this technology in the clinician practice.

Clinical and omics data

Chen et al. 103 developed an algorithm to identify patient with ciliopathies. This group of rare and severe diseases are cause by ciliary dysfunction, associated to over 200 genes.

Ciliopathies can affect all organs, and are divided into 30 rare syndromes whose clinical signs overlap each other. The authors addressed two tasks: (1) identifying patient with ciliopathies from patients with other nephropaties, and (2) phenotypic subtyping ciliopathies. For the first task, they compute similarity metrics on the phenotypes extracted from the EHR of Necker hospital. They evaluate several similarity metrics on 77 patients with ciliopathies amongst 10,462 patients with nephropathies. Unsupervised machine learning methods were applied to select the relevant phenotypes to accomplish this task: linear support vector selection with l2 penalty, tree-based selection and random forest selection. The linear support vector selection gave the best accuracy to find patients with ciliopathies with a precision of 76% in the 30 patients most similar to an index patient. For the second task they considered the clinical research data collected from 1,031 patients mutated on genes associated to ciliar dysfunction. The unsupervised clustering method showed strong concordance with expert knowledge. Similarity metrics applied to rare genetic disease in nephrology offer new perspective to recruit patients for research and reduce the diagnostic journey. The phenotypic similarity associated with omics data will address precision medicine, especially in the context of complex rare diseases like ciliopathies. It is crucial to diagnose the patient before onset of renal failure, so that the patient can benefit from a drug as soon as possible.

EHRs and the hospital's collective memory

Several situations may require having access to the hospital memory to make an informed decision for a patient: (i) complex patients with rare conditions for whom the published treatment guidelines do not provide a clear recommendation, (ii) undiagnosed patients, (iii) finding undiagnosed patients eligible for diagnoses based on the presentation of index patients with new mutations. At Stanford, a specialty consultation service composed by a team of medical and informatics experts mine EHRs data and other health databases to "learn from patients like mine". They provide a summary of what happens to patients similar to the patient proposed to the staff 104 .

Even with a search engine that enables the mining of historical data, it can be a complex task to find patients similar to an index patient. It may be difficult to choose which phenotypes must be used in the query. At the Imagine institute and Necker Hospital, a "patient like mine" tool is integrated in the data warehouse user interface. It computes a similarity distance between an index patient and all the patients of the data warehouse based on medical concepts extracted from the clinical text reports in the EHR. This tool provides the top20 most similar patients. If the index patient is undiagnosed, the clinician may use the information from the similar patients to reorient their clinical investigation. If the index patient is diagnosed, the clinician may find similar undiagnosed patients eligible for the same mutation 105 . The absence of a learning step is an advantage of this unsupervised method. In the particular context of rare diseases the low number of cases limits the usage of methods like deep learning approaches which require a large number of examples during the training phase.

Figure 4 summarizes how decision support systems for diagnosis are developed.

Toward a Learning Health System

The Institute of Medicine defined the learning health system in 2007 106

Issues and potential solutions

Several obstacles must be overcome to make routine data suitable for research, including data fragmentation 114 , data quality, and data privacy 115 . As seen before, a solution commonly adopted to address data fragmentation is the development of data warehouses. Other obstacles are related to the quality of routine care data. While some clinicians complain about spending too much time entering data in legacy systems 116 , they believe that EHR is essential for the quality of care, 117 and for personalized medicine 118 . Data quality can be assessed against a number of dimensions: completeness, validity, coherence and comparability, accessibility, usefulness, timeliness, prevention of duplicate records 119 . Research data are expected to be high quality data with respect to coherence and validity but with narrow scope and limited follow up. Conversely, EHR data exhibit strengths like extremely broad coverage and long follow-up; in addition, redundancy among data types can be exploited to check the consistency of phenotypic data. 120,121 . However, data processing steps are required when relevant information is present only in unstructured format, e.g., images or text reports, 122 to transform the entire EHR into a form suitable as input for a learning health system 123 . As for accessibility, another dimension of data quality, data sharing for research purposes has raised many concerns about privacy. Regarding data governance, some authors have claimed that some ambitious overly theoretical frameworks may fit neither practical needs 124 nor patients' vision since patients who have rare diseases, and life-threatening conditions may be more open to the concept of data reuse for research 125 . Part of the solution for trust-fabric is technical: all institutions have been leveraging their IT infrastructure to implement state-ofthe-art functionalities regarding data access and security. The other part of the solution relies on ethics.

Ethics

After explaining the benefits of repurposing the EHR, and discussing building solidarity in the community ("the good of all of us is good for each of us"), Lee listed three recommendations to minimize risk and maximize benefits: (i) ensuring that data and results are valid, (ii) implementing data protection to reduce the risk of unauthorized disclosure of personal data, (iii) applying severe repercussions to bad actors responsible for data breaches 126 .

In addition, she insists on the importance of training clinicians and researchers on proper usage of health data. Segal et al. adds the adoption of professional guidelines that will explain the decision support system in diagnosis 127 . Some physicians are also concerned about the use of their narratives, some of them use "shadow charts" to represent their semiological reasoning without it being used against them in a lawsuit.

Conclusion

Artificial intelligence based on EHRs to diagnose rare diseases is not yet well developed. Several obstacles must be overcome to accelerate the development of these very promising algorithms to reduce diagnostic wandering and improve patient care management. For example, many efforts must be made to efficiently analyze the free text in order to extract reliable and accurate information. To achieve this goal, we have to decompartmentalize care and research in order to accelerate the repurposing of clinical data for new knowledge and the repurposing of new knowledge for better care. We have described several algorithms developed during research projects but rarely integrated into the hospital information system for use by clinicians. Expectations of patients with rare disease and clinicians are high; learning health systems must strive to meet them.

DISCLOSURE:

The authors declare no conflict of interest.

Figures: Figure 1 : 2 :

 12 Figures:Figure1: Query designed for PubMed review Figure2: Challenges to developing a clinical decision support system, and knocking down the technical barriers Figure3: Steps to develop and implement CDSS in nephrology department.

Figure 4 :

 4 Figure 4: Decision support system on EHR. From the patient EHR to the different results available

  . The aim is to conduct effective translational research by accelerating the repurposing of clinical data for research and integrating the new knowledge into patient care. This convergence of the deployment of EHRs and the desire to accelerate translational research and reduce costs has allowed for the development of new areas of expertise in medical informatics107 . The core objective is to learn from the collective experience of care delivery as recorded in the observational data. As seen previously in this article, much recent research has explored machine learning, data mining and data visualization methodologies to enhance personalized medicine with collective experience 108 . Such insights are referred to as Practice Based Evidence, or Real World Evidence 109 .Several initiatives are trying to move out of the local hospital setting to provide inter-hospital interoperability for learning health systems. The TRANSFoRm project promoted an architecture for the learning health system in Europe by using ontologies and archetypes 110 . A Learning Health System is particularly important in the context of rare diseases where each patient is a source of new knowledge and can help to reduce the diagnosis odyssey of the other patients. PEDSnet is a national US network for children with inflammatory bowel disease and embeds several tools to accelerate translational research and precision medicine. In addition to clinicians and researchers, they extend the learning health system to communities of patients for the "common purpose of improving the health and lives of children" 111 . The Undiagnosed Disease Network 112 is also a national US network for undiagnosed patients. It provides a platform to share data, tools, and common protocols to accelerate the diagnosis of rare or previously unrecognized diseases. Furthermore Blizinsky et al. developed a framework to take into account the variability of population, in particular the ancestral, social and environmental factors that must be included in the genomic-enabled learning health system. They highlight the potentiality of learning health system to improve equity of care in the age of precision medicine 113 .

Acknowledgments

We deeply thank Jan Niemira for his proofreading of the manuscript. Nicolas Garcelon was partially financed by The French National Research Agency, under the C'IL-LICO project (17-RHUS-0002).