The correlations of thesepredictions to biological surface adsorption index descriptors indicatedthat hydrophobicity-driven interactions contribute most to the overalladsorption affinity, while hydrogen-bond interactions and polarity/polarizability-driveninteractions differentiate the affinity to metal and carbon nanomaterials.Next, we use molecular dynamics (MD) simulations to obtain directmolecular information for a selection of vitamins that could not beassessed quantitatively using QSAR models. This showed how large andflexible metabolites can gain stability on the nanomaterial surfacevia conformational changes. Additionally, unconstrained MD simulationsprovided excellent support for the main interaction types identifiedby QSAR analysis. Combined, these results enable assessing the adsorptionaffinity for many enteric microbial metabolites quantitatively andsupport the qualitative assessment of an even larger set of complexand biologically relevant microbial metabolites to carbon and metalnanomaterials.Ingested nanomaterials are exposed to many metabolitesthat areproduced, modified, or regulated by members of the enteric microbiota.The adsorption of these metabolites potentially affects the identity,fate, and biodistribution of nanomaterials passing the gastrointestinaltract. Here, we explore these interactions using in silico methods,focusing on a concise overview of 170 unique enteric microbial metaboliteswhich we compiled from the literature. First, we construct quantitativestructure–activity relationship (QSAR) models to predict theiradsorption affinity to 13 metal nanomaterials, 5 carbon nanotubes,and 1 fullerene. The models could be applied to predict log  Many of thesemetabolites become available in the intestinal lumen, where they functionas antimicrobial agents, signaling molecules, and substrates.5The gastrointestinal tract harbors a densecommunity of viruses,archaea, bacteria, fungi, and protozoa, collectively termed the entericmicrobiota. In humans, the enteric microbiota constitute a similarorder of magnitude of cells as all host cells combined.7 Many biomolecules, and proteins in particular, have been found toassociate with the large surface area of ENMs, forming a shell ofbiomolecules referred to as the ‘biomolecular corona’8 or ‘ecological corona’9 from a biomedical or ecological perspective,respectively. By changing or masking the surface properties of ENMs,biocoronae can affect the colloidal stability10 and identity11 of ENMs. The principlesthat govern the biocorona-mediated recognition of ENMs are increasinglywell understood.12 Nevertheless, environmentalmetabolites, including many other metabolites than proteins, affectthe biodistribution and toxicity of ENMs in a yet unpredictable fashion.For over a decade, biomolecules have beenshown to play a key rolein the behavior and toxicity of engineered nanomaterials (ENMs).13 Furthermore, conjugation oflatex nanoparticles with invasin, a bacterial surface protein, hasbeen shown to facilitate the uptake of these particles across theintestinal epithelium of rats.14 Otherresearch investigating the interactions of microbial metabolites withENMs mostly focused on complex mixtures of environmentally relevantbiomolecules, such as extracellular polymeric substances,15 or employed the properties of specific microbialbiomolecules to develop ENMs that function as biosensors or nanocarriers.18 Less specific physisorption processes between enteric microbialmetabolites and ENMs, that do not concern specific interaction targets,like receptors, and include other metabolites than proteins, havebarely been investigated. Here, we focus on the potential contributionof this understudied set of enteric microbial metabolites to biocoronaeformation onto ingested ENMs in the intestinal lumen.When ENMs are ingested, they will be exposed to the myriad of entericmicrobial metabolites that are available in the intestinal lumen.Consequently, they may acquire enteric microbial metabolites in theirbiocoronae. Several specific interactions between microbial metabolites,the ENM surface, and biological membranes and receptors have alreadybeen found to affect the fate and biodistribution of ENMs. In bacterialcultures, for example, bacterial flagellin was found to reduce thecolloidal stability of nanosilver, thereby decreasing its antimicrobialactivity.In thepresent study, we construct models and generate data toinitiate the assessment of the role of enteric microbial metabolitesin biocorona formation onto ingested ENMs. First, we compile a conciseoverview and categorization of metabolites that are available in theintestinal lumen for biocorona formation. This is based on a literaturereview. Subsequently, we employ the biological surface adsorptionindex (BSAI) theory to construct a set of quantitative structure–activityrelationship (QSAR) models to predict adsorption affinities for entericmicrobial metabolites to various metal and carbon ENMs. In additionto this statistical approach to studying nano–bio interactionsat low computational cost, we perform a computationally demandingfree-energy analysis based on molecular dynamics (MD) simulations.For these investigations based on physical modeling, we focus on aselection of vitamins that cannot be assessed using current QSAR models,to obtain direct molecular information on characteristics of nano–biointeractions that need to be considered for these microbial metabolites.Ultimately, this could be used to improve current QSAR models. Additionally,through a combination of QSAR investigations and classical and unconstrainedMD simulations, we explore what interaction types are key to the adsorptionof enteric microbial metabolites to metal and carbon ENMs. Overall,we anticipate that the results of these investigations support thequalitative and quantitative assessment of biologically relevant adsorptioninteractions between enteric metabolites and ingested ENMs.28 following the procedure described in the We basethis study on a literature search, generating a concise overview ofmetabolites that are produced or regulated by gastrointestinal microbiota.Ten reviews on intestinal microbial metabolism were selected for thisinventory,2 theactual set of available enteric microbial metabolites is likely largeand diverse. In order to decide if the selected reviews representan adequate proportion of this diversity in available enteric microbialmetabolites, we determined the percentage of new metabolites thatwere identified with including increasing numbers of reviews in theinventory (25 reported 80.5% (137 metabolites) of the170 identified microbial metabolites. The next two reviews that wereincluded28 contributed 14.1% (24 metabolites) of thetotal number of unique metabolites, and the final five reviews27 contributed only 5.3% (9 metabolites) of the total number of identifiedmetabolites. This saturation in the total number of identified metabolitessuggests that sufficient reviews were included in the inventory. Moreover,the metabolites included in the first three reviews represented allof the 13 metabolite categories. This may result from the conservedfunctional capacity of the enteric metabolome29 and predicts that any metabolite that is not included in the inventorywill likely be functionally and structurally equivalent to the metabolitesincluded in our study. For this reason, we decided that the 170 consideredmetabolites represented sufficient diversity in enteric microbialmetabolites for our further analyses.Given the large metabolic potential and high intra-and interindividualvariation of the enteric metabolome,nventory 1. The fi30 including 13 metal ENMs, 5 carbon nanotubes,and 1 fullerene (4), silicondioxide (SiO2), titanium dioxide (TiO2), zincoxide (ZnO), and zirconium(IV)oxide (ZrO2).In the next two parts of our study, we investigate the adsorptionaffinity for the identified enteric microbial metabolites 1 to metaullerene 2. The cok values. The first model that we applyis the BSAI model established by Xia et al.,19 which uses Abraham’s molecule descriptors  and corresponding nanodescriptors to predict the adsorption affinity for biomolecules to ENMs following:c is the adsorptionconstant, Ei is the excessmolar refraction, Si isthe effective solute dipolarity and polarizability, Ai is the effective solute hydrogen-bondacidity, Bi is the effectivesolute hydrogen-bond basicity, Vi is the McGowan characteristic volume, and n is the number of biomolecules included. The nanodescriptors  weigh the contributions of interactions betweenbiomolecules and the ENM surface resulting from lone-pair electrons(Ei·r), polarity/polarizability (Si·p), hydrogen-bond acidity (Ai·a), hydrogen-bondbasicity (Bi·b), and hydrophobicity (Vi·v). We adopted the nanodescriptors derivedby Chen et al., which have been corrected for the effects of interactionsbetween probe molecules, using Langmuir model extrapolations.30We consecutively apply two QSAR models for each ofthe ENMs topredict log E,S,A,B,V] have been determined experimentally.31 However, these molecules only include 18 out of the 170 entericmicrobial metabolites. Because open-source toolkits for cheminformaticssuch as Chemistry Development Kit  and RDKit(https://www.rdkit.org) cannotderive Abraham’s molecule descriptors from the molecular structureof the metabolites, we used the log k predictionsfrom the BSAI model to build a second QSAR model for each of the ENMs.We exclusively used molecular descriptors from CDK as the descriptorsfor these second QSAR models. As a result, these models could be appliedto predict log k values based on the molecular structureof enteric microbial metabolites. In the remainder, we refer to thetwo QSAR models as ‘BSAI models’ (Tables S7 and S9). Furthermore, we refer to nanodescriptor‘r’ as ‘re’ and to nanodescriptor ‘p’ as ‘ps’, to avoid confusion with the Pearson correlation coefficient(r) and statistical p-values, respectively.The subscripts for these nanodescriptors were selected based on theircorresponding Abraham molecule’s descriptors E and S.We applied the BSAI model 1 to a se models’ 1 and ‘CDTable S2). Nevertheless,it is worth noting that the first descriptor in all models , explaining most of the variance in log k predictions, correlates with the Abraham’s molecule descriptor V . Thisis consistent with the large contribution of Abraham’s moleculedescriptor V in BSAI models19 and reflects the importance of interactions between hydrophobicsites of biomolecules and hydrophobic regions on the ENM surface.Xia et al. confirmed this experimentally for MWCNTs, obtaining a significantcorrelation between the log k measurements for probecompounds and their log Ko/w values.19Since the CDK models only function as a means to applyBSAI modelsto molecules without known Abraham’s molecule descriptors,we omit a detailed discussion of the descriptors that are includedin CDK models , as defined in the h* threshold todefine the AD of QSAR models.34 In this case, the ADderived using Insubria graphs shows high similarity to the AD basedon Mahalanobis distances .The set ofenteric microbial metabolites that can be analyzed using the QSARmodels depends on the chemical space that can be described by themolecules that were used to train the BSAI and CDK models. For allmodels, we determined this applicability domain (AD) using Insubriagraphs. Instead of cross-validated residuals, which are used to constructWilliams plots, these graphs present model predictions against thediagonal hat values of the model’s design matrix 2.32 All h* threshold and thresholds for the predicted log k value, defined by the mean (x) and standard deviation(σ) of log k predictions for probe molecules(x ± 3·σ) (30 which are present in the dataset with known Abraham descriptors31 (Table S3). For the second AD approach, we onlyapplied the h* threshold (h* and log k thresholds), 1525 molecules (h* threshold), and 1996 molecules (no thresholds) that couldbe used to build CDK models.The AD thresholds that are applied to BSAI models,determine howmany molecules are available for the construction of CDK models. Toinvestigate the effects thereof, we built CDK models using BSAI modelpredictions that were selected using three different AD approaches,as exemplified in x ± 3·σ) 2a. Thesehreshold 2b. For threshold 2c. This k predictions from the BSAI models. According to theWilliams plots , over 93% of the training and validation compounds fell withinthe AD of CDK models for each of the AD thresholds . We noted some deviation from normalityof model residuals in Q-Q plots, potentially introducing bias to thestandard error of estimates.35 No issueswere identified for the remaining model assumptions.For all AD approaches, CDK modelsthat were built at the cross-validationratio of 80/20  explained most of thevariance in log k predictions for the training set and the validation set was obtainedfor CDK models that were built without AD thresholds for BSAI modelpredictions , followedby CDK models that were built with the h* thresholdonly , and CDK modelsthat were built with the h* and log k thresholds . Thesame trend was obtained for the AD of CDK models. The largest setof enteric microbial metabolites was within the AD of all CDK modelsthat were built without AD thresholds (60 metabolites), followed bythe AD of all CDK models that were built with the h* threshold only (51 metabolites), and the AD of all CDK models werebuilt with the h* and log k thresholds(38 metabolites) (Table S4). These trends show that boththe fit, in terms of R2 values, and theapplicability of CDK models, as determined using Insubria graphs,improve when these models are built based on a larger number of BSAIpredictions. Although this favors the application of CDK models thatare built without BSAI thresholds, this introduces the risk of basingCDK models on incorrect BSAI predictions. Nevertheless, given thestrong correlation (ρ > 0.96) between predictions of CDKmodelsfrom each of the BSAI AD approaches , we describe the results of CDK models that were built withoutapplying BSAI AD thresholds in the main text and include the resultsof the other CDK models in the Supporting Information. We only describe results that are supported by models from eachof the AD approaches, unless specifically stated otherwise.When comparingCDK models from each of the AD approaches, the bestfit between BSAI and CDK models in terms of log h*, shown in Insubria graph of Figures S7, S9, and S13, was dependent on metabolite category (h* threshold models), niacin, and linoleicacid. The CDK models could not be applied to any of the ‘MAMPs’,‘bile acids’, and ‘polyamines’. Thesecategories comprise large metabolites, which can adopt different spatialconformations, and molecules with rich surface functionalities, includingmany hydroxyl or amino groups per metabolite. This is in agreementwith the limitations of the BSAI model, which cannot successfullydescribe surface interactions of biomolecules with certain degreesof flexibility in bonds, cannot differentiate between the differentisomeric spatial conformations of biomolecules and are not applicableto biomolecules with diverse moieties and functional groups, likephosphate, thiophosphoryl groups, and nitrile bonds.36 For biomolecules with these characteristics, MD simulations canbe used to study ENM surface interactions at a higher computationalcost. This could potentially lead to the identification of descriptorsthat can increase the AD of QSAR models.36 In the finalpart of our study, we apply these simulations to investigate whatkind of interactions differentiate the adsorption behavior of vitaminsthat are within or outside the AD of QSAR models  to120 metabolites (‘FullrC60’ model) and can be foundin the Supporting Information for moredetailed investigations on specific ENMs (Table S4).In the following comparison between the adsorption affinities formicrobial metabolites to metal and carbon ENMs, we focus on the coreset of 60 metabolites that are included in the AD of QSAR models forall ENMs 4.The ADk predictions for the enteric microbialmetabolites. Moreover, we found a remarkable distance between log k predictions for the Buckminster fullerene (C60) and predictions for all other ENMs . This is in line with other unique interaction propertiesof C60 fullerenes, which may act like hydrophobic organicmolecules, by adsorbing to larger biomolecules, either individually,or in aggregated form, potentially changing properties of these largerbiomolecules.37 For this reason, log k predictions for the fullerene will be discussed separatelybelow. All nanodescriptors except for re correlated with log k-based distances between ENMs,as detected by distance-based redundancy analysis , b  and ps , this result was supported by the CDK modelsbuilt using the different AD approaches for the BSAI model . This indicates that in particular hydrogen-bond interactions andinteractions resulting from the polarity and polarizability of metabolitesdistinguish the adsorption affinities for enteric microbial metabolitesto ENMs. Although hydrophobicity-driven interactions contribute mostto the overall predicted adsorption affinity for enteric microbialmetabolites to ENM surfaces, these interactions explain less of thedifferences in log k predictions between metal andcarbon ENMs than the hydrogen-bond interactions and interactions driven by polarityand polarizability.Metal and carbon ENMs could clearly be distinguishedbased on log analysis 3a. For tk values were highest for carbon nanotubes, followed bymetal ENMsand fullerenes . By exception, predicted log k values for ‘cholinemetabolites’ were similar for metal ENMs and carbon nanotubes(median (interquartile range (IQR)) log k = 1.55(1.34–1.77) and 1.70 (0.30–2.03), respectively, p > 0.05) and predicted log k valuesfor‘SCFAs’ were higher for metal ENMs than for carbon nanotubes(median (IQR) log k = 2.61 (2.38–2.88) and2.19 (1.50–2.54), respectively, p < 0.001). This suggests that acidicgroups experience stronger interactions with metal ENMs than withcarbon nanotubes. This is consistent with the results of our dbRDAanalysis, identifying a highly significant contribution of nanodescriptor a to log k-based distances between metalENMs and carbon nanotubes . Accordingly, computational and experimental investigations forcitrate and other carboxylic acids showed that specifically the carboxylategroups of these molecules interact with Au and Fe3O4 ENMs.40 In contrast, and in line with our results, the QSARmodels developed by Roy et al. predict a negative impact of C–Ogroups and aliphatic primary alcohols on the adsorption affinity fororganic pollutants to carbon nanotubes.41 Notably, this did not result in higher log k estimatesfor ‘tryptophan precursors and metabolites’ and ‘phenolic,benzoyl, and phenyl derivatives’ to metal ENMs than to carbonnanotubes. Although both of these categories comprise biomoleculeswith acidic functional groups, the QSAR models predicted significantlyhigher log k values for these categories to carbonnanotubes (median (IQR) log k = 5.50 (4.75–6.43)and 4.64 (3.88–5.33), respectively) than to metal ENMs (median(IQR)) log k = 3.23 (2.91–3.48) and 3.05 (2.66–3.33),respectively) . Nevertheless,in contrast to ‘SCFAs’ and ‘choline metabolites’,which solely consist of small aliphatic biomolecules, ‘tryptophanprecursors and metabolites’, and ‘phenolic, benzoyl,and phenyl derivatives’ comprise unsaturated (poly)cyclic molecules.This suggests that π–π stacking interactions contributemore to the interaction between these molecules and ENMs than theinteractions of acidic functional groups. We further investigate therelative contributions of such different interaction types to theadsorption affinity for enteric metabolites to ENMs by way of unconstrainedMD simulations as discussed below.For metabolites of most categories, predicted log 42 Using acomputational protocol that is very similar to ours, the authors identifiedan excellent correlation (r ≥ 0.9) betweencalculated and measured values for the complete set of compounds.Rather than restricting ourselves to π–π stackinginteractions that are important for MWCNT, we also consider the extendedinteraction network between a metal substrate (SiO2) andbiologically relevant molecules like vitamins. We even go one stepbeyond a direct comparison between adsorption affinities and conducta proof of principle aimed at rationalizing which of the nanodescriptorsobtained by QSAR analysis contribute to key interactions identifiedusing unconstrained MD. The small set of vitamins, including thiamine,pyridoxine, biotin, and folate, was selected because of the significantspread in the predicted log k values by QSAR. Moreover,the set was selected to represent different structural properties,such as different numbers of aromatic rings (1–3), differencesin charge (0 or +1), and different numbers of acidic and basic functionalgroups. Finally, the set included vitamins that are outside the ADof QSAR models for SiO2 (biotin and folate) as well asvitamins that are within this AD  for comparison values, calculated using eq(kQSAR).In thefinal part of our study, we perform MD simulations to investigatewhat distinguishes ENM interactions of metabolites that are withinor outside of the AD of QSAR models. A recent study by Comer et al.that focuses on calculating the adsorption affinity of about 30 smallaromatic compounds to carbon nanotubes forms an inspiration and startingpoint for this investigation.mparison4a. All fmparison4b,whileusing eq2, are di2, log kQSAR and log kMD results should be compared with caution, because two out of thefour vitamins are outside of the AD of the QSAR models, and becauselog kQSAR predictions from models thatwere derived using the alternative AD approaches, correlate differentlywith the MD results . For the comparison of MWCNT results, we note that the presenceof a data point with high leverage (folate) results in high R2 values. Nonetheless, we find that the computedlog kMD and predicted log kQSAR values feature the same orders of magnitude and showa reasonable, but nonsignificant correlation . In both cases, we find thatexcluding thiamine improves the correlation between log kMD and log kQSAR results . While this discrepancy forthiamineis hard to pinpoint to a single cause, it may well be due to the usualchoice in our MD approach to exclude electronic polarizability43 since thiamine has an explicit +1 charge. Inparticular, a previous study of Wu et al. supports our suggestionthat polarizability effects are essential for this particular vitamin.44 The study focused on the controlled releaseof thiamine hydrochloride with mesoporous silica tablets and showedthat the pH of the medium affects thiamine release. For reasons ofcomputational efficiency, state of the art force fields in classicalMD only consider fixed atomic charges that are determined prior tosimulation via more resolved (and costly) methods like density functionaltheory. While polarizable force fields have been developed and appliedto study various phenomena, including adsorption on graphene surfaces,45 it is difficult to assess beforehand if thesubstantial computational cost of including polarizability will leadto greater accuracy. In our limited case study, the improved correlationbetween QSAR and MD methods in terms of log k valueswhen charged vitamins are omitted indicates that it merits includingpolarizable force fields in MD simulations for charged enteric microbialmetabolites. Next, we performed unconstrained MD to evaluate key interactionsfor vitamins inside of as compared to vitamins outside of the AD ofQSAR models.The results for the four vitamins can be foundin 2 and each vitaminmolecule were separated into Lennard- Jones (LJ) and electrostaticcontributions  has the highest LJ contribution, irrespective of its low log k value, while the smaller pyridoxine (rgyr = 0.24 nm) has the lowest LJ contribution but thehighest log k value. In the case of thiamine (rgyr = 0.36 nm), electrostatic (Coulomb) interactionsdominate, which can be due to the explicit +1 charge that is presenton the thiamine molecule.The interaction energies between SiOibutions 5a, wherek values, we additionally considered thehydrogen bonding between these molecules and the SiO2 slab.Using interatomic distances, we identified different chemical groupsfor each vitamin that are observed to form hydrogen bonds with theENM surface during the 500 ns simulation, considering a cutoff of0.24 nm to the SiO2 surface . Both pyridoxine and folate form onaverage 2–3 hydrogen bonds with the SiO2 slab.However, considering the increased size of folate (rgyr = 0.57 nm), it may also exhibit effects of sterichindrance while interacting with the slab. Different configurationsextracted from the unbiased MD simulation pathway (rgyr = 0.24 nm) to lie parallel to theslab without bending. As the smaller molecule does not need to adaptits conformation to the slab geometry, the hydrogen bonding gainsstability, rendering pyridoxine more probable of forming hydrogenbonds with SiO2 than folate. Overall, pyridoxine sits onthe slab, while folate undergoes several conformational changes tostabilize around the SiO2 slab; see some of the simulationsnapshots of folate and pyridoxine with SiO2 shown in 2.To further investigate the relation between dominantinteractionsand log  surface 5b. Time  surface S4. Both  surface S4. Both Figure S5 of the Supporting Information.Finally, to investigatethe conformational space sampled by eachmolecule, we performed cluster analysis over all 500 ns MD trajectories.As a condition for defining a new cluster, we considered a differenceof 0.25 nm in the root-mean-square displacement . As can be expected, only a single clusterwas identified for the small and rigid vitamins: thiamine, pyridoxine,and biotin. In contrast to this finding, we identified five differentclusters for the longest vitamin folate. Exemplary conformations takenfrom each cluster are shown in Overall, adsorption affinitiesdetermined using all-atom MD werefound to agree well with values predicted by QSAR modeling for severalcomplex molecules. The benefit of molecular simulation is that itprovides molecular insight into the nature of the principal interactionsbetween these molecules and a relevant ENM, enabling a more fundamentalunderstanding. Moreover, in silico determination of adsorption affinitiescan be useful for part of the materials spectrum where experimentalmeasurement is complicated, expensive or even ruled out, that is,to generate reliable training data for the computationally much moreefficient (nano)QSAR in that part of the spectrum. We particularlysee this limited case study as a showcase for the potential of physicalmodeling in this work field and for unraveling correlations that arenot clarified in the QSAR approach. We believe that a broader applicationof this approach will help experimentalists and nanotoxicologiststo further improve the applicability of QSAR and to better understandthe affinity of biologically relevant molecules on the various ENMsurfaces. In particular, although being computationally very costlycompared to QSAR, MD simulation is an ecofriendly and cost-effectivetechnique for performing affinity analysis prior to or even replacingin vitro experiments.For future perspectives,the combination of (1) the biological functions of enteric microbialmetabolites, (2) their predicted adsorption affinities to metal andcarbon ENMs, (3) key interaction types inferred from QSAR models andMD simulations, and (4) the direct molecular information obtainedfrom MD simulations can be used to rationalize what biologically relevantinteractions could occur between ingested ENMs and microbial metabolitesin the gastrointestinal tract. In this section, we present two relevantexamples to illustrate this rationale. We note that these examplesfocus on hypotheses that are based on the current understanding ofthe enteric microbial metabolome. Following the same principles, ourresults can be employed to rationalize what adsorption interactionsmay occur for enteric microbial metabolites that are yet to be discovered.46 Especially under these conditions, it is relevantto consider the potential adsorption of SCFAs to ENMs that are administeredorally to treat or prevent intestinal inflammation.49 In the caseof SCFAs, which are within the AD of QSAR models, our QSAR predictionscan readily be used to assess this. Log k predictionsfor SCFAs were significantly higher to metal ENMs than to carbon ENMs,indicating that the adsorption-driven sequestration of SCFAs formsa larger concern for metal ENMs than for carbon ENMs. Nevertheless,the results for more lipophilic metabolites put this into perspective,showing that the overall predicted adsorption affinities for SCFAsare relatively low to both carbon and metal ENMs.The first example focuses on the hypothesis that ingested ENMscan sequester essential SCFAs via the adsorption of these metabolitesto the ENM surface, thereby causing nutrient deficiencies. As presentedin 1218 but can also be expected forsecondary and conjugated bile acids . To this end, we either selected molecules that had been used inexperimental work to represent the concerning metabolite groups orselected molecules that had been identified in the gut lumen. Finally,we retrieved simplified molecular-input line entry-specifications(SMILES) from the PubChem database for each of the metabolites includedin the overview. In case both isomeric and canonical SMILES were availablefor the metabolites, isomeric SMILES were selected.In order to generate an overview of microbial metabolites that occurin the intestinal lumen, we retrieved names of enteric microbial metabolitesfrom reviews on gut microbial metabolism. The reviews were accessedthrough the Web of Science Core Collection database (1945–2020)via Leiden University’s library, by applying the search string:“(microbiome OR microbiota OR microflora) AND (gut OR *intestine*OR enteric) AND metabolite* AND )”. Reviews were added to the literaturesearch until no new categories of microbial metabolites were identifiedand until the total number of identified metabolites had saturated1. Metabok values for theidentified enteric microbial metabolites to 13 metal ENMs and 6 carbonENMs  were corrected using SMILESfrom the ChemSpider database (www.chemspider.com) (Table S1).Second, compounds with poor or suspicious data quality, or includingmetals or salts ,were excluded following the recommendations by the authors. Third,double, triplicate, and quadruplet entries of 431 compounds were removed,randomly selecting one of the references reporting Abraham descriptorsfor each of the concerning compounds. Similarly, isomers, which haveidentical values for each of the Abraham’s molecule descriptors,were removed from the data set. This resulted in a data set comprising1996 unique compounds with known Abraham descriptors.For BSAI predictions,we adopted the nanodescriptors derived by Chen et al.32 These graphs present the diagonal hat valuesof the design matrix  on the x-axis and QSAR predictions on the y-axis. Of the 25 probe compounds that wereused by Chen et al. to derive nanodescriptors,30 23 probe compounds were included in the data set with knownAbraham molecule’s descriptors. We used these compounds toderive the critical hat threshold (h*) as 3·(N + 1)/n, where N is thenumber of descriptors in the model, and n is thenumber of probe compounds included in the data set. For the predictedlog k values, we defined AD thresholds by the meanof the log k predictions for probe compounds and3 times the standard deviation of these predictions (x ± 3·σ). Subsequently, we selected compounds fromInsubria graphs by: (1) applying both the h* andlog k thresholds, (2) applying the h* threshold only, and (3) applying no thresholds. For the first approach,including log k thresholds, we only continued withcompounds that fell within the log k thresholds forall 19 ENMs. For comparison, we also derived the BSAI AD based onMahalanobis distance, as described below  to build QSAR models that can predict log k values using molecular descriptors from CDK. The moleculardescriptors were computed in R , accessingCDK functionality using the “rcdk” package (v. 3.5.0).50 To load molecules into the R environment, SMILESwere parsed, implicit hydrogen atoms were converted to explicit hydrogenatoms, and aromaticity was checked. Thereafter, molecular descriptorswere evaluated, and the data set was split into a training set anda validation set using the createDataPartition functionof the “caret” package (v. 6.0-86). The molecules withthe lowest and highest BSAI model prediction, calculated as the meanpredicted log k value for the 19 ENMs, were includedin the training set. These were keys ‘2924’ and ‘1700’(mean log k = 2.02 and 4.24), keys ‘2400’and ‘1253’ (mean log k = 0.98 and 5.53),and keys ‘518’ and ‘74’ (mean log k = 0.40 and 10.56), when applying the h* and log k threshold, the h* thresholdonly, and no thresholds, respectively. The remaining molecules weredivided into five quantiles, based on the predicted log k values from the BSAI model. Molecules of each of the quantiles wererandomly divided over the training set and validation set. We evaluatedthe performance of four different cross-validation ratios . Using the trainingset of each cross-validation ratio, MLR models were derived by forwardselection. A total of five molecular descriptors were selected forthe models, including the independent molecular descriptor explainingmost of the model variance at each of the consequent forward selectionsteps. To ensure the independence of descriptors, molecular descriptorswere only included if they did not result in variance-inflation factorslarger than two, as assessed using the vif functionfrom the ‘car’ package (v. 3.0-8).UsingBSAI log R2 value of models for all 19ENMs. Diagnostic plots of the models were inspected to identify outliers(Cook’s distance plot) and to evaluate the model assumptionsof linearity , normally distributedresiduals (Q-Q plots), and homoscedasticity .The AD of the models was assessed using Williams plots. Compoundswere considered to be outside of the AD of models if cross-validatedresiduals are smaller than −3 or larger than 3 or if the diagonalhat values are larger than 3·(N + 1)/n, where N is the number of descriptorsin the model, and n is the number of molecules inthe training set. Correlations between Molecular Descriptors fromCDK and Abraham’s molecule descriptors were assessed usingto the Spearman’s rank correlation coefficient, calculatedusing the cor.test function of the ‘stats’package (v. 3.6.3).We selected CDK models of the cross-validationratio with the best internal validation score, evaluated as the meanadjusted k predictions, SMILES were parsed, implicithydrogen atoms were converted to explicit hydrogen atoms, and aromaticitywas checked. Thereafter, molecular descriptors were evaluated using‘rcdk’. Metabolites that were assigned to the metabolitecategory ‘proteins/enzymes’ were excluded due to theirlarge size and three-dimensional conformations, which could not beaccounted for using this QSAR approach. The applicability of the modelsfor the other metabolites was assessed using Insubria graphs, by applyingthe h* threshold. Log k predictionsof metabolites that were considered to be within the AD of CDK modelswere compared between metal and carbon ENMs, for each metabolite categoryseparately. To this end, the Kruskal–Wallis rank sum test wasapplied in combination with the Dunn’s test from the ‘FSA’package (v. 0.8.32).51 For all Dunn’stests, Holm adjusted p-values are reported.To prepare the microbial metabolite datafor log k predictionsfrom QSAR models and to derive the AD of BSAI models based on Mahalonobisdistances. For both analyses, we used R functions that are availablein the package ‘vegan’ (v. 2.5-6).We used ordinationmethods to compare ENMs based on log k data was transformed to remove negative values by subtractingthe minimum log k value from all predicted log k values. Using the vegdist function, aBray–Curtis dissimilarity matrix was constructed for the transformeddata. The contribution of each of the five nanodescriptors to theselog k-based distances between ENMs was tested byway of dbRDA. To this end, the dbrda function wasused, assessing the marginal effects of the nanodescriptors.Log vegdist function. Subsequently,the metaMDS function was applied to place each ofthe molecules in a two-dimensional space by way of nonmetric multidimensionalscaling. All compounds with equal or smaller distance to the centroidof all 23 probe compounds in this two-dimensional space were consideredto be within the BSAI model AD.To derive the distance-based AD for BSAI models, Mahalonobis distanceswere computed for the data set with known Abraham molecule’sdescriptors using the 2, and all the four vitamins, namely, pyridoxine,folate, thiamine, and biotin were built using the CHARMM-GUI builder.52 A realistic representation of the ENM structureis required for an accurate prediction of the interaction betweenthe nanoparticle surface and a vitamin. Hence, we considered a 5 ×5 × 4 nm3 SiO2 slab and a three-layeredgraphene sheet with an area of 6.5 × 6.5 nm2 and periodicboundary conditions, resulting in infinite surface along the Cartesian x–y direction. The SiO2 ENM used in the experiment usually occurs in a range of 20–200nm, while the carbon nanotube typically has an outer diameter between8 and 15 nm and a length of ∼50 μm. We postulated thatthe sizes of the vitamins examined in this study are tiny comparedto the considered ENMs, meaning that a flat surface representationis adequate. All the systems comprise ∼40,000 atoms, each varyinga little based on the size of the vitamins. All all-atom simulationswere performed with GROMACS 2020.53 TheCHARMM36 force field54 was used for allvitamins, while SiO2 and MWCNTs parameters were procuredfrom the INTERFACE force field,55 whichis integrated within the CHARMM force field. The water molecules weresimulated using the TIP3P force field.56 A Nosé–Hoover thermostat57 at 310 K and a Parrinello–Rahman barostat58 at 1 atm were considered. All hydrogen atoms were constrainedwith the LINCS algorithm,59 and long-rangeelectrostatics were evaluated with particle-mesh Ewald.60 A 1.4 nm cutoff was used for both the electrostaticsand LJ interactions. All MD simulations employed a 2 fs time stepin the standard Leap-Frog integrator,61 and periodic boundary conditions were considered throughout thestudy. The setup for biotin and both considered ENMs are visualizedin 62 was used for visualization.The initial structure of a solvated multiwalled carbon nanotube(MWCNTs), SiO63 as implemented in the Plumed plugin64 patched with GROMACS, at all-atom resolution with explicit solvent.The considered collective variable for the generation of PMFs is thedistance between the center of mass (COM) of the SiO2 orMWCNTs slab and the COM of the respective vitamin. Each system underwent5000 steps of energy minimization with the standard steepest descentmethod65 followed by 100 ps of standardequilibration. Consequently, a 300 ns production run was conductedto generate the free energy profile. Each run was performed on 48processors, resulting in 25–30 ns per day, that is, 10–12days per ENM and vitamin. The reduced performance compared to theunconstrained simulations can be attributed to the more frequent outputrequirement while performing free energy calculations. As previouslydiscussed by Comer et al.,42 the adsorptionaffinity (k) of any given vitamin with SiO2 surface can be calculated from the PMF asc is the cutoff distanceprovided by the onset of the (bulk) plateau region in the PMF, thatis, the adsorbed region, β = (kBT)−1 corresponds to the reciprocalof the thermal energy with the Boltzmann constant kB and temperature T (in Kelvin), and wicalc(z) is the PMF determinedby constrained MD. We have omitted the usual material dependent prefactorto the right-hand side of kcalc with log k predictions from QSAR models, the Pearson correlation coefficient(r) was calculated in R using the cor.test function of the stats package (v. 3.6.3).The potential of mean force(PMF) was determined using metadynamics2 slab setupas before was considered for each vitamin. Unconstrained simulationsfor MWCNTs were not considered because only LJ interactions betweenthe vitamins and this nanomaterial will play a role, meaning thata breakdown in other types of interactions is not meaningful. It canbe inferred that the interaction between MWCNTs and each respectivevitamin will purely be LJ interaction. Initially, we performed energyminimization, followed by 10 ns of NPT equilibration and a final productionrun of 500 ns. Each run was performed on 48 processors, resultingin ∼70 ns per day. For the purpose of analysis, a rerun ofthe MD trajectories was performed to extract the different contributionsto the interaction energies between the SiO2 and each vitaminmolecule. The number of hydrogen bonds formed as a function of timewas computed using the GROMACS built-in routine gmx hbond.Unconstrained simulationswere also required in order to differentiate between the several factors,including hydrogen bonding, π–π stacking, charged(electrostatic) interactions, and others that may play a role in adsorption.For the unconstrained simulation, the same SiOk values are availablefree of charge as Supporting Information. Data and results from MD simulations are available via Zenodo (DOI:10.5281/zenodo.6800734).The QSAR models, trainingand validation data sets, SMILES of entericmicrobial metabolites, calculated CDK descriptors, applicability domainsof QSAR models, and predicted log


E. coli and S. aureus, respectively). Of note, the excellent endogenous antibacterial activity of CTAB allowed the complete inactivation of bacteria after the assay process, thus effectively avoiding secondary contamination.The development of a convenient, sensitive, rapid and self-sterilizing biosensor for microbial detection is important for the prevention and control of foodborne diseases. Herein, we designed a surface-enhanced Raman scattering (SERS) sensing nanoplatform based on a capture–enrichment–enhancement strategy to detect bacteria. The gold−Azo@silver−cetyltrimethylammonium bromide (Au−Azo@Ag−CTAB) SERS nanotags were obtained by optimizing the synthesis process conditions. The results showed that the modification of CTAB enabled the nanotags to bind to different bacteria electrostatically. This SERS sensing nanoplatform was demonstrated to be fast (15 min), accurate and sensitive (limit of detection (LOD): 300 and 400 CFU/mL for  Foodborne illness caused by microorganisms has become a serious public health issue around the globe ,4,5,6,7.In recent years, Au-based SERS spectroscopy has received widespread attention in the detection of pathogenic bacteria because of its high sensitivity, high spectral resolution, rapid acquisition rate and spectral fingerprinting ,16,17,18E. coli and S. aureus. Furthermore, since CTAB leads to the destruction of the bacterial surface structure, resulting in significant collapse of the bacteria and leakage of the cytoplasm [Here, we proposed a universal SERS nanotag based on the electrostatic attraction between positively charged CTAB and bacteria . To avoid the intrinsic fluorescence interference of conventional Raman reporter molecules , based on the Azo-enhanced Raman scattering strategy ,28, Azo ytoplasm ,32, the 4∙4H2O), sodium citrate (C6H5Na3O7∙2H2O), silver nitrate (AgNO3), ascorbic acid (AA), dimethyl sulfoxide (DMSO), sodium chloride (NaCl), and CTAB were purchased from Aladdin Bio-Chem Technology Co., Ltd. . Azo reporters (E. coli (ATCC 8099) and S. aureus (ATCC 25923) were supplied by China Center for Culture Collection . Tryptone and yeast extract were purchased from Sigma-Aldrich . Lysogeny broth (LB) was prepared by dissolving tryptone (10 g/L), yeast extract (5 g/L), and NaCl (5 g/L) into the phosphate buffered saline solution , followed by autoclaving at 121 °C in an autoclave . LB solid medium was prepared by mixing the LB with agar . Deionized water was drawn from a Millipore Direct Q8 system with a resistivity of 18.2 MΩ cm . All chemicals were analytically pure and used without any further purification unless otherwise stated.Chloroauric acid tetrahydrate (HAuCleporters  were kin4 solution (0.29 mM) was added and heating continued for 10 min, followed by quick addition of 1.2 mL of sodium citrate solution (38.8 mM). Au NP was obtained after continuing stirring for 30 min. After cooling naturally to room temperature, the Au NP solution was stored in a refrigerator at 4 °C.Au NPs were synthesized by the classical reduction method of sodium citrate . 100 mL A total of 5 mL of the above Au NP solution was added into glass vials, followed by the addition of different volumes of Azo reporter solution  pre-dissolved in DMSO, respectively. The mixed solution was stirred (800 rpm) at room temperature for 4 h and then washed three times by centrifugation  using a benchtop centrifuge . Finally, the different Au−Azo NP solutions were stored in the refrigerator at 4 °C for further use.3 solutions  were added into the flasks, respectively. After continuing stirring for 2 h at room temperature, the reaction solution was washed three times by centrifugation  to remove the free Ag ions. Finally, the Au−Azo@Ag NPs were resuspended with deionized water and stored in the refrigerator at 4 °C for further use.A total of 5 mL of the as-prepared Au−Azo NP solution was added into a flask, and then 200 μL of sodium citrate solution (4.8 mM) was added and the mixed solution was stirred for 5 min (800 rpm), followed by rapid addition of 50 μL of AA solution (0.1 M). Then, different volumes of AgNOA total of 10 mL of the prepared Au−Azo@Ag NP solution was added into a flask. Then, different volumes of CTAB solutions  were added and the mixed solution was stirred for 5 min (800 rpm). The reaction solution was left to settle overnight at room temperature, followed by washing three times by centrifugation  to remove free CTAB. Finally, the Au−Azo@Ag−CTAB NPs were resuspended with deionized water and stored in the refrigerator at 4 °C.The UV-vis absorbance of the product in each step was recorded using a UV-3600 spectrophotometer (Shimadzu). UV-vis spectroscopic characterization provides a reference for NP stability. The UV-vis spectra were obtained as follows: after baseline calibration, the sample solution was added to the cuvette and then scanned in the selected wavelength range (300–800 nm).The hydrated size and zeta potential of NPs were determined by using the Malvern particle sizer . The DLS characterization showed the size distribution of NPs, while the zeta potential characterization showed the surface electrical properties of NPs. The details are as follows: the NP solution was added to a specific sample cell  and then the sample solution was subsequently detected by the corresponding mode.E. coli and Au−Azo@Ag−CTAB anchored S. aureus was characterized by FE-SEM . The specific experimental method was as follows: the appropriate concentration of sample solution was dropped onto a conductive single-crystal silicon wafer. After drying naturally, the samples sprayed with gold were characterized morphologically using an Inlens detector. The test voltage was 10 KV.The morphology of NPs, Au−Azo@Ag−CTAB anchored −1. The initial Raman spectrum in this study was calibrated and smoothed by WIRE 3.4 software , and the peak intensity at 1120 cm−1 (Ph-N) was used for quantitative analysis [To optimize the addition volume of Azo reporters, Ag ions and CTAB, the Au−Azo NPs, Au−Azo@Ag NPs and Au−Azo@Ag−CTAB NPs were characterized by Raman spectroscopy , respectively. The experimental procedure was as follows: a certain volume of sample solution was aspirated with a glass capillary tube (5 mm diameter), and then the capillary tube was placed under a microscope (20× objective) and the sample stage was adjusted so that the capillary tube was in focus under the laser, followed by Raman signal acquisition. Acquisition conditions: excitation wavelength of 633 nm, laser power of 10 mW, integration time of 1 s, and accumulation of 1 time. The Raman spectra were acquired in the spectral range from 950 to 1620 cmanalysis .E. coli (ATCC 8099) and S. aureus (ATCC 25923) was transferred into LB liquid medium (5 mL) respectively, followed by incubation  in a constant temperature shaking incubator  until the bacterial concentration reached 1 × 108 CFU/mL. Afterward, the two bacteria solutions were collected by centrifugation  and washed with PBS three times. Prior to the test of the bacterial sample, the concentrations of bacteria suspended in PBS were determined by spread plate method.The lyophilized powder of S. aureus and E. coli) were diluted separately with PBS to a concentration of 1 × 107 CFU/mL according to the results of plate counting. Then the diluted bacterial solution was used as mother liquor to prepare four groups of bacterial solutions with different concentrations . Afterwards, 20 μL of Au−Azo@Ag−CTAB NP solution was added to 200 μL of different concentrations of bacterial solution, respectively. The mixture was shaken for 10 min and then aspirated with a capillary tube and placed under a microscope  for Raman spectroscopy. The test conditions were the same as described in The two bacterial solutions , indicating a certain degree of aggregation of the NPs. In addition, the products were also characterized by Raman spectroscopy , the bacteria can induce the aggregation of the NPs through electrostatic effect, thereby enhancing the SERS signal of the detection system [E. coli and S. aureus, respectively, and the density of the nanotags on the surface of E. coli was slightly higher than that of S. aureus, which may be due to the relatively lower surface potential of E. coli . In addition, the relationship between the Raman intensity and the logarithm of the bacterial concentration was further explored using S. aureus as a model. Similar to E. coli, there was a positive correlation between them  of bacteria (S. aureus and E. coli) were incubated with nanotags for 2 h, and then characterized by the spread plate method. As shown in E. coli or S. aureus were observed in the Au−Azo@Ag−CTAB NPs group, demonstrating its good antimicrobial activity. Thus, the nanotags had the potential to prevent the secondary contamination of bacteria after detection.The Au−Azo@Ag−CTAB NPs were expected to possess excellent antibacterial ability due to the ability of CTAB to disrupt the cell surface structure . To veriS. aureus and E. coli) were mixed with the Au−Azo@Ag−CTAB nanotags, respectively, and then characterized by Raman spectroscopy under the same conditions as stated before. The bacterial concentration was calculated according to the calibration curve of the corresponding bacteria, respectively. As shown in E. coli and S. aureus detected by our method were comparable to that of the standard culture counting with the relative recovery ranging from 106% to 110%, which indicated that this method was suitable for the detection of microorganisms in tap water. The errors may be due to the interference from potential impurities such as particulate matters and ionic materials. Furthermore, the detected concentration of bacteria was relatively consistent for the repeated measurements with the RSD% less than 5%, demonstrating the reliability of our method for tap water monitoring. Compared to the time-consuming bacterial culture (2–3 days), the whole process of our method can be completed within 15 min, indicating the potential of point-of-care testing (POCT) in bacterial detection. The inactivation ability of the Au−Azo@Ag−CTAB nanotags to bacteria in tap water was characterized by the spread plate method. As shown in Due to the increasing popularity of tap water and its use as the main source of drinking water, the detection of microorganisms in tap water is the key to ensure the safety of drinking water. In order to verify the feasibility of this method in the detection of bacteria in tap water, the method of spiked bacteria in tap water was employed. Six groups of bacterial sample solutions with unknown concentrations (E. coli ranged from 108% to 110%, and the recoveries of S. aureus ranged from 106% to 107%, both of which demonstrated the feasibility of this method for the quantification of bacteria in tap water. Furthermore, the microorganisms in the tested solution can be killed completely and timely without any treatment, avoiding bacteria contamination after the bacterial detection. The anti-interference capability of the biosensor, which binds bacteria by electrostatic interaction, may need further improvement. Therefore, in the future, we plan to modify the nanotags with mercaptophenylboronic acid to enhance the recognition of bacteria. This SERS sensing nanoplatform has potential applications in the POCT of medical and environmental samples for which rapid in situ testing is required.In this work, we designed a novel SERS sensing nanoplatform that integrated broad-spectrum bacterial detection and self-sterilization. The Au−Azo@Ag−CTAB nanotags were prepared based on the rational configuration of the recognition unit, Raman reporter and SERS substrate. The positively charged Au−Azo@Ag−CTAB nanotags were anchored on the surface of bacteria through electrostatic action so as to induce the enhancement of the SERS signal. The results showed that the proposed SERS sensing nanoplatform was sensitive, rapid and reliable for bacterial detection. The recoveries of SERSSurface-enhanced Raman scatteringAu NPsGold nanoparticlesCTABCetyltrimethylammonium bromidePBSphosphate buffered saline solutionSEMScanning electron microscopyPOCTPoint-of-care testingRSDRelative standard deviationS. aureusStaphylococcus aureusE. coliEscherichia coliCFUColony-forming unitUV-visUltraviolet-visible3AgNOSilver nitrateLODLimit of detection


Several Ecuadorian farms use human test strips (cheaper than veterinary strips) to diagnose bovine ketosis; however, their reliability is unknown. This study aimed to determine the confidence level of human strips for the detection of ketosis in bovines by comparing two diagnostic methods for ketosis: one used in bovines (gold standard) to analyze blood samples and the other used in humans to analyze urine samples.® was used to measure acetoacetate (AcAc) in urine for the determination of ketosis.The study was conducted on an Ecuadorian farm using 50 animals, ten from each of five categories: heifers, 4 months pregnant (4MP), 15 days prepartum (15DPRE), 15 days postpartum (15DPOST), and 42 days postpartum (42DPOST). Blood samples were collected through coccygeal venipuncture and urine samples were collected during spontaneous urination. BHBCheck™ assay was used to measure b-hydroxybutyrate (BHB) in the blood, whereas Combur10Test®.BHB was detected in all animals. Based on a ketosis cutoff point of 0.8-1.2 mmol/L, 13 animals from the 15DPOST and 42DPOST categories had ketosis; AcAc was detected in the urine from nine animals originated from the two same categories. Metabolites, either BHB or AcAc, were not detected in heifers, 4MP, or 15DPRE individuals. Finally, the BHBCheck™ assay had better efficiency in detecting ketosis in animals (p<0.05) than the Combur10Test® urine strips reached 92% reliability for the detection of ketosis in dairy cattle, compared to BHBCheck™ assays.Combur10Test In dairy cattle, ketosis is a common metabolic disease that typically occurs during early lactation; it is characterized by a state of inappetence, and occasionally, signs of nerve dysfunction such as pica disorder, incoordination, and abnormal gait -4. KetosGenetic evolution has permitted the development of dairy breeds that produce high daily quantities of milk, with Holstein Friesians being the highest milk-producing breed (by volume). Nonetheless, high milk production levels can lead to several functional, reproductive, and metabolic problems, such as ketosis, in cattle ,11. KetoIn areas with limited access to ketosis diagnostic methods, the measurement and control of urinary ketones have been used as a diagnostic tool. Test strips developed for humans are commonly used because they are cheaper than those intended for animals; however, whether they truly work in animals in addition to their sensitivity and specificity remain unknown.®), to determine the reliability of both diagnostic methods in dairy cattle, such that veterinarians can obtain accurate results for the detection of ketosis.This study aimed to conduct a comparative study evaluating a specific diagnostic method for ketosis in cattle BHBCheck™ PortaCheck, Inc., Nueva Jersey, USA]), compared to the measurement of ketones in urine as assessed through test strips intended for humans  located in the province of Pichincha, Ecuador.Lolium perenne (30%), Lolium multiflorum (20%), Pennisetum clandestinum (30%), Dactylis glomerate (10%), and Trifolium repens (10%), whereas highly productive animals (average milk production of approximately 28 L/day) also receive 4 kg of Winavacas  food supplement. The cows are protected from diseases by vaccination and regularly inspected by veterinarians.The present study was conducted on an Ecuadorian farm (Hacienda Miraflores Bajo #4) located in the province of Pichincha, in Ecuador. The farm has 281 dairy cows from different breeds, with the main breeds being Holstein, Jersey, and Brown Swiss. Animals are managed using a grazing system based on In the present investigation, cows were classified into six groups according to their physiological status: veal (n=47), heifer (n=73), 4 months pregnant , 15 days prepartum , 15 days postpartum , and 42 days postpartum . From these six groups, ten animals were randomly selected from each physiological group based on inclusion and exclusion criteria , resultiFor each sample, 1 mL of fresh blood was drawn through venipuncture of the coccygeal vein by lifting the cow’s tail and using non-sterile examination gloves. From the collected sample, 20 mL of blood was placed in the BHBCheck™ device (Portacheck - USA) to determine the ketosis result (either positive or negative), whereby the range of 0.35-0.8 mmol/L was considered a negative result, and values above 0.8 mmol/L were considered a positive result.® strip  to evaluate the ketosis status, wherein ≤0.5 mmol/L was considered a negative result and any value greater than this cutoff was considered a positive result . Both procedures were repeated for each of the 50 cows belonging to the five different categories.A urine sample was collected from the same animal by spontaneous urination after cleaning the vulva area to prevent feces contamination of the sample. Once the urine sample was obtained, 1 mL of urine from the respective sterile container was placed drop-by-drop on a Combur10 Test® for urine). The results of the BHBCheck™ test were produced immediately after placing the blood sample, whereas the results of the Combur10 Test® strips were verified against the corresponding colorimetric scale.The ketone body results were interpreted according to the protocol guidelines of the diagnostic tests . Descriptive statistics were compiled, wherein measures of central tendency were determined for the values obtained from the blood samples, and a Shapiro–Wilk test was applied to verify a normal distribution for each category. Subsequently, the BHB results from the blood of all study groups were compared using an analysis of variance at a 95% confidence threshold. Afterward, Duncan’s test was applied, which works with a changing mean threshold. Due to the mostly non-normally distributed data, a Kruskal–Wallis test was applied to compare the AcAc levels across the five categories of sampled animals, to verify whether there was a significant difference in the distribution of the values in urine; statistical significance was set at p<0.05. With respect to the entire population comparison of BHB in blood and AcAc in urine, a Wilcoxon signed-rank test with a 95% confidence level was performed to determine whether the two tests were equally effective in detecting ketosis. The specificity and sensitivity of the Combur10 Test® were also calculated [The obtained results were statistically analyzed using the IBM statistical package for the social scienceslculated .With respect to the blood sample results, the BHB levels of heifers, 4MP, and 15 DPRE cows followed a normal distribution forming a suitable Gaussian bell, illustrating a negative diagnosis for ketosis. Conversely, in the 15DPOST group, five cows were positive for ketosis (5/10), whereas in the 42DPOST group, eight cows were positive for ketosis 8/10; . There wFor the urine samples, 41 (82%) of all animals were negative for ketosis, with the remaining 9 (18%) being positive for the disease. Among the animals with positive results, 8 (16%) produced urine with 1 mmol/L of AcAc and 1 (2%) produced urine with 5 mmol/L of AcAc. In terms of their AcAc values, all heifers, 4MP, and 15DPRE animals (100%) were negative for ketosis. Conversely, in the 15DPOST category, 70% (7/10) of the animals were negative for ketosis, whereas 30% (3/10) were positive, with a value of 1 mmol/L of AcAc in their urine. In the 42DPOST category, 40% (4/10) of the animals were negative for ketosis, whereas 50% (5/10) were positive with 1 mmol/L of AcAc in their urine and 10% (1/10) were positive with 5 mmol/L of AcAc in their urine .® intended for humans. To obtain the true positive and true negative results between the two diagnostic methods for ketosis, the values in urine that did not concur with the values in blood were counted to provide a level of confidence for the use of test strips intended for humans for the diagnosis of ketosis in dairy cattle . Moreover, the blood BHB and urinary AcAc levels differed between the two diagnostic methods when applied to the 50 bovines in this study (p<0.05). The results for the BHB (mmol/L) levels in blood were obtained using the BHBCheck™ gold standard test, intended for animal use, whereas the results of the AcAc (mmol/L) levels in urine were obtained using the Combur10Testy cattle .®, 8% produced different results from that achieved based on the measurement of BHB, whereas the results of the remaining 92% were reliable. The Combur10 Test® showed a test specificity of 100% and sensitivity of 69.23%.Of the 50 animals sampled, the results of four animals were false negatives according to the urine test. Therefore, of the animals that were subjected to ketosis diagnosis using the Combur10 Testet al. [In this study, two diagnostic methods were evaluated to detect ketosis based on blood and urine samples. Consequently, the results depict that, across categories, the BHB values in blood (mmol/L) differed across groups, producing three mean subsets: Group A encompassed averages of 0.430 mmol/L (4MP), 0.450 mmol/L (heifers), and 0.530 mmol/L 15DPRE); Group B had an average of 0.860 mmol/L (15DPOST); and Group C had an average of 1.070 mmol/L (42DPOST). These results agree with those of a study published by Oetzel [5DPRE; Gret al.  suggesteIn the present investigation, there were differences in the measurements of ketone bodies in the samples analyzed using the two diagnostic methods. BHB was evaluated in blood and AcAc was evaluated in urine. This difference may be explained by the concentration and circulation of this metabolite in the corporal fluids of cows ,18.et al. [Horber et al.  conducteet al. . Accordiet al. , which iTyöppönen and Kauppinen  stated tet al. [Sailer et al.  comparedet al.  investiget al.  noted th® kit for humans was used, which had reliability of 92%, with the remaining 8% of the results not coinciding with those of the gold standard test; this may have occurred because the kit is intended for human use. Vuljanic et al. [® strips had a sensitivity of 97% and a specificity of 81% for glucose; these values may be similar for ketone bodies, since the test’s sensitivity and specificity have not been studied. However, in relation to ketone bodies, Stodulska [® tool for humans can be extrapolated to veterinary medicine for the diagnosis of ketosis in bovines, given that its reliability level was 92%, with an 8% error range , compared with the BHBCheck™ gold standard test.Herein, the Combur10 Testc et al.  noted thtodulska  indicatetodulska  affirmedIn this study, the circulation and volatility of ketone bodies were different for BHB in blood and AcAc in urine; this is a consequence of the stability of each ketone body, with BHB being more stable in blood than AcAc and AcAc being more stable in urine than BHB ,27. We uOne of the main limitations of this study was the population distribution across the different categories according to their physiological state; this made it impossible to have animals available for sampling at the same time and place, particularly given the farm’s low pregnancy rate and the lack of farm workers. Moreover, with respect to urine sample collection, certain animals showed discomfort at the time of stimulation to induce spontaneous urination and were reluctant to urinate, making harvesting their urine samples complicated.®, finding that both were suitable for ketosis detection in 15DPOST and 42DPOST cows, illustrating that their physiological statuses are in a good period for ketosis diagnosis. Nonetheless, the Combur 10 Test® had a 92% reliability for detecting ketonic bodies through the AcAc detection method, wherein it indicated false negatives. Additional studies that aim to detect ketonic bodies in a herd suspected of ketosis before and after applying treatment are needed to evaluate trends in the formation and elimination of these metabolites using different diagnostic methods [This study compared the BHBCheck™ and Combur 10 Test methods -31; thisKVB and AMA: Participated in the experimental work. KVB, LFN, SHSP, and CFC: Analyzed data and results. KVB: Wrote the manuscript. SHSP: Revised manuscript. CFC: Conceptualized and designed the study. LFN: Edited and finalized the manuscript. All authors read and approved the final manuscript.


Subjects with CAC showed a significantly higher FIB4 index score compared to subjects without , whereas the FIB4 index was significantly elevated with a higher grade of AAC . Linear regression analysis adjusted for clinical characteristics indicated that the FIB4 index was positively associated with IMT, Suita score, CAC, and AAC grade . FIB4 index is closely associated with arterial damage and future risk of CHD in type 2 diabetes.This study evaluated the association between fibrosis-4 (FIB 4) index and arterial damage or future risk of coronary heart disease (CHD) in type 2 diabetes. The study subjects were 253 patients with type 2 diabetes. The FIB4 index, as a marker of hepatic fibrosis based on age, aspartate aminotransferase and alanine aminotransferase levels, and platelet count, was calculated for all subjects. Carotid intima-media thickness (IMT), carotid artery calcification (CAC), and aortic arch calcification (AAC) grade (0–2) were assessed as atherosclerotic variables. The Suita score was calculated as the future risk of coronary heart disease (CHD). We assessed whether the FIB4 index was associated with both atherosclerotic variables and the Suita score. FIB4 index was significantly associated with IMT ( Subjects with the traditional risk factors of cardiovascular disease (CVD), such as dyslipidemia, hypertension, diabetes mellitus, and smoking, have a high risk of developing CVD . ParticuWe designed a cross-sectional study to determine the relationship between the FIB4 index and both atherosclerosis and future CVD risk in Japanese subjects with type 2 diabetes. In this study, we used carotid intima-media thickness (IMT) and aortic arterial calcification to evaluate arterial damage, and the Suita score of study subjects was calculated to estimate future coronary heart disease (CHD) risk. Carotid IMT  and aortTwo hundred fifty-three patients with type 2 diabetes were enrolled in our study, comprised of 163 men and 90 women with an average age of 65.6 ± 10.9 years. All subjects were ambulatory and treated at either the Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology at the Yamagata University Faculty of Medicine or the Division of Diabetes and Metabolic Diseases, Department of Internal Medicine at Nihon University School of Medicine. All subjects had no history of hepatic cirrhosis, viral hepatitis, collagen diseases, including hepatic arteritis and primary biliary cirrhosis, autoimmune hepatitis, and malignant diseases. Patients with chest and cardiac diseases that affect the AAC evaluation, malignant diseases, collagen diseases, and acute and chronic inflammatory diseases and receiving immunosuppressants and steroid hormone therapy were excluded from our study.The study was designed on the principles of the Declaration of Helsinki. We conducted a cross-sectional study using a clinical database. The study protocol was approved by the Ethics Committee of Yamagata University Faculty of Medicine and Nihon University School of Medicine. The protocol of this study is found on the web page of the Ethics Committee, and we provided the opportunity for participants to opt out of the study. The requirement for written informed consent was waived because of the retrospective nature of the study by our Ethics Committee.2) = 194 × serum creatinine−1.094 × age−0.287 × 0.739  [We evaluated the clinical characteristics of our study cohort, including sex, age, body mass index (BMI), smoking habit, systolic and diastolic blood pressure, hypertension, statin use, platelet (PLT) count, and biochemical variables. PLT and biochemical variables including lipid metabolism, uric acid, and HbA1c were measured after an overnight fast. PLT count was measured using a hematology analyzer, whereas low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, triglycerides, uric acid, creatinine, and HbA1c were measured using an automated analyzer. We used the estimated glomerular filtration rate (eGFR) as an indicator of renal function, which was determined by the following formula: eGFR  . Blood p female) .The FIB4 index was calculated to evaluate hepatic fibrosis in our study subjects. It is a useful variable to assess hepatic fibrosis in Japanese patients with NAFLD , althougA FIB4 index with a cutoff of 1.45 provides suitable diagnostic accuracy to predict hepatic fibrosis in the Japanese population .IMT was measured as a marker to evaluate arterial injury in our study subjects. Carotid IMT is a suitable surrogate marker for future CVD risk . A totaln = 141) or absence (n = 112) of CAC on both sides was recorded using B-mode ultrasonography. Posteroanterior chest X-ray films were performed for study subjects, and AAC grade was reviewed in a manner as previously reported [n = 95, n = 73, n = 83, Figures Carotid artery calcification (CAC) and the grade of AAC were evaluated as measures of arterial calcification. CAC and AAC are closely related to future CVD risk , 21. Thereported , 23. SubThe Suita score was estimated to predict the risk of future CHD in study subjects. It is an established cardiovascular risk score using risk factor categories for predicting CHD in the Japanese population based on age, gender, smoking, diabetes, hypertension stage, LDL and HDL cholesterol, and chronic kidney disease (CKD) stage . The SuiU test was performed to compare the mean value of the FIB4 index in subjects between the presence and absence of CAC. Bonferroni's multiple comparison test was used to compare the mean value of the FIB4 index between AAC grades. Univariate and multivariate linear regression analysis adjusted for clinical characteristics was performed to certify whether IMT and Suita scores were significantly associated with the FIB4 index. In this univariate and multivariate analysis, the FIB4 index was assumed as an independent variable, and IMT, Suita score, CAC, and AAC grade were assumed as dependent variables. Data are presented as means ± standard deviation (SD), number (%), coefficient covariation (r), or β coefficients. Statistical significance was defined as P < 0.05. All analyses were performed with IBM SPSS Statistics for Windows Version 25 J .Pearson's coefficient correlation and univariate linear regression analysis were used to identify whether clinical characteristics, IMT, and Suita score were significantly associated with the FIB4 index. In this univariate analysis, we assumed the FIB4 index as a dependent variable and sex (men), smoking habit (current), hypertension, and statin use as independent variables. The Mann–Whitney 2, and 7.95 ± 1.88%, respectively.r = −0.189, P < 0.001), eGFR , and HbA1c  were significantly inversely correlated with the FIB4 index in our cohort  was significantly higher than that in subjects without (n = 112)  or 2 (n = 83) was significantly higher than that found in patients with grade 0 (n = 95)  , Suita score , CAC , and AAC grade  (Positive correlations were found between the FIB4 index and IMT and Suita score (< 0.001) . Similar< 0.001) .In this study, we found that the FIB4 index was closely associated with arterial damage, such as carotid intimal thickening and arterial calcification in two major vessel beds (carotid and aortic arch). In particular, the FIB4 index was significantly increased according to the higher grade of calcification in the aortic arch. Further, we found a significantly positive correlation between the FIB4 index and the Suita score, which is a predictive marker of future CHD development. The results of this study support the new insight that noninvasive hepatic fibrosis score is significantly related to arterial damage and future risk of CHD in Japanese patients with type 2 diabetes. The clinical utility of the results of our study is that arterial damage can be easily estimated using some biochemical data  and age in type 2 diabetes patients. Evaluation of arterial damage in patients using computed tomography, magnetic resonance imaging, carotid artery ultrasonography, pulse wave velocity, and endothelial function, among others, cannot be frequently performed. In contrast, the FIB4 index can be calculated instantly using the results of blood sampling in general practice or medical checkups. As a result, physicians can easily and frequently estimate arterial damage in patients with type 2 diabetes during general consultations or medical checkups using the FIB4 index. We conclude that the FIB4 index may be a useful tool for identifying patients with type 2 diabetes who are at high risk of CHD.The effect of NAFLD and NASH on the future risk of CVD and mortality remains unclear. A meta-analysis study reported that cardiovascular mortality was not significantly related to NAFLD, although NAFLD increased the risk of cardiovascular events . HoweverWe considered some reasons why hepatic fibrosis becomes a risk of arterial damage and future CVD based on the results of past studies. First, we considered whether the worsening of glucose metabolism accompanied by the progression of hepatic fibrosis had an important role. Interestingly, overall mortality and death from CVD in patients with NAFLD without diabetes were not significantly elevated compared to those in control patients . TherefoSecond, we conclude that systematic metabolic abnormalities in patients with NAFLD and NASN also contribute to the progression of arterial damage and CVD. NAFLD-induced production of pathological factors promotes multiple aspects of atherosclerosis progression. Increase of lipid particles  , excessiFinally, we must also raise the point that MetS contributes to the progression of arterial damage and CVD in diabetes with NAFLD and NASH. NAFLD and NASH are closely associated with obesity, diabetes, hypertension, and dyslipidemia, all of which are components of MetS . MetS, pr = −0.256 and −0.169, respectively, P < 0.01). Since the numerator of the FIB4 index formula contains age, the FIB4 index shows a positive association with age . It is reasonable to speculate that the negative associations between age and both HbA1c and diastolic blood pressure in our study account for the significant inverse association between FIB4 index and either HbA1c or diastolic blood pressure. We believe that younger subjects in our study had a more difficult time improving their glycemic control. Indeed, our study subjects who received insulin therapy were younger and had relatively high HbA1c levels (data not shown). Furthermore, diastolic blood pressure is generally decreased according to aging [Interestingly, we demonstrated HbA1c and diastolic blood pressure to be inversely associated with the FIB4 index. The reasons for these inverse associations remain unknown. We conclude that there may be a negative relationship between the FIB4 index and HbA1c or diastolic blood pressure with age. HbA1c and diastolic blood pressure showed significant inverse associations with age in our study subjects (to aging .There were several possible limitations in our study. First, we calculated the FIB4 index in subjects including patients at low risk of NAFLD or NASH. In principle, the FIB4 index is recommended for evaluating hepatic fibrosis in patients with NAFLD/NASH, not in patients without NAFLD/NASH. We considered that the FIB4 index was linked to the risk of hepatic fibrosis because the prevalence of actual hepatic fibrosis in patients with type 2 diabetes rose in tandem with an increase in the FIB4 index underlying liver disease . As a reThis study provides evidence that noninvasive assessment of hepatic fibrosis using the FIB4 index is closely associated with arterial damage and future risk of CHD in Japanese patients with type 2 diabetes. The results of our study also indicate that the FIB4 index is a suitable tool to identify those patients with diabetes at a higher risk of CVD.


Hip or knee osteoarthritis (OA) is one of the main causes of disability worldwide and occurs mostly in the older adults. Total hip or knee arthroplasty is the most effective method to treat OA. However, severe postsurgical pain leading to a poor prognosis. So, investigating the population genetics and genes related to severe chronic pain in older adult patients after lower extremity arthroplasty is helpful to improve the quality of treatment.We collected blood samples from elderly patients who underwent lower extremity arthroplasty from September 2020 to February 2021 at the Drum Tower Hospital Affiliated to Nanjing University Medical School. The enrolled patients provided measures of pain intensity using the numerical rating scale on the 90th day after surgery. Patients were divided into the case group (Group A) and the control group (Group B) including 10 patients respectively by the numerical rating scale. DNA was isolated from the blood samples of the two groups for whole-exome sequencing.P < 0.05), including CASP5, RASGEF1A, CYP4B1, etc. These genes are mainly involved in biological processes, including cell–cell adhesion, ECM–receptor interaction, metabolism, secretion of bioactive substances, ion binding and transport, regulation of DNA methylation, and chromatin assembly.In total, 661 variants were identified in the 507 gene regions that were significantly different between both groups (The current study shows some variants within genes are significantly associated with severe postsurgical chronic pain in older adult patients after lower extremity arthroplasty, indicating a genetic predisposition for chronic postsurgical pain. The study was registered according to ICMJE guidelines. The trial registration number is ChiCTR2000031655 and registration date is April 6th, 2020.The online version contains supplementary material available at 10.1186/s13018-023-03683-y. Hip or knee osteoarthritis (OA) is one of the main causes of disability worldwide and occurs mostly in the older adults. With an aging global population and the increase in the incidence of obesity, the incidence of OA is expected to further increase . Total hPrevious studies have shown that the development of chronic pain has complex genetic characteristics . To explChronic pain after general hip or knee arthroplasty is a difficult postsurgical complication that affects patients' surgical satisfaction, recovery, and quality of life. When patients with medium to severe pain cannot relieve their pain their physical and mental health and daily life are severely and negatively impacted. The variation in pain between patients suggests that a genetic component is involved. However, to date few studies have been conducted on the relationship between lower arthroplasty and CPSP. Therefore, the current study sequenced whole genome exon variants of older adult patients with severe chronic pain after lower extremity arthroplasty. The susceptibility genes related to CPSP were investigated to provide a direction for the development of new clinical treatment methods and identify potential susceptibility biomarkers for patients with chronic postoperative pain.Elderly patients who underwent lower extremity arthroplasty in Drum Tower Hospital Affiliated to Nanjing University Medical School from September 2020 to March 2021 were selected and followed up for postoperative pain assessment using the numerical rating scale NRS. The elderly osteoarthritis patients selected were all caused by joint degeneration rather than fractures or necrosis caused by other reasons. They were performed operations by the same group of doctors and operation method. All patients signed informed consent and were authorized by the ethics committee of Drum Tower Hospital Affiliated to Nanjing University Medical School . Inclusion criteria: (1) Age ≥ 65 years old; (2) American Society of Anesthesiologists (ASA) classification II–III; (3) Patients undergoing lower extremity arthroplasty; (4) Operation duration ≥ 60 min; (5) Patients recorded in electronic medical record system; (6) Patients agreed to participate in the study and signed the informed consent. Exclusion criteria: (1) With gene deficiency disease; (2) Having history of opioid abuse; (3) Using drugs that induce or inhibit liver isoenzymes  in 4 weeks before operation; (4) Combined with peripheral neuropathy and psychiatric history, chronic pain and long-term opioid use history; (5) With poor body conditions affecting the perioperative pain evaluation; (6) Patients can’t cooperate and communicate with.The patient's NRS score being ≥ 4 on the 90th day after operation was identified as having severe CPSP. A total of 10 patients were judged to have severe CPSP (group A). 10 patients hospitalized in the same period without chronic postsurgical pain (NRS score = 0 on the 90th day after operation) were randomly selected as the control group (group B).5 ml peripheral blood of the patients was collected from the artery, then the DNA was extracted using the Magnetic Universal Genomic DNA Kit . The degree of DNA degradation and the presence of RNA and protein contamination were analyzed by agarose gel electrophoresis. We used Qubit 3.0 (Life Technologies) to accurately quantify DNA concentration. DNA samples containing more than 0.6 μg were used to build the database.Full exon sequencing (WES 1000 g) was performed by Agilent's liquid chip capture system. Genomic DNA extracted from peripheral blood for each sample was fragmented to an average size of 180–280 bp and subjected to DNA library creation using established Illumina paired-end protocols. The Agilent SureSelect Human All ExonV6 Kit  was used for exome capture according to the manufacturer’s instructions. The Illumina Novaseq 6000 platform  was utilized for genomic DNA sequencing in Genechem Bioinformatics Technology Co., Ltd  to generate 150-bp paired-end reads with a minimum coverage of 10 × for ~ 99% of the genome (mean coverage of 100 ×). After sequencing, base call files conversion and demultiplexing were performed with bcl2fastq software (Illumina). The resulting fastq data were submitted to in-house quality control software for removing low quality reads, and then were aligned to the reference human genome (hs37d5) using the Burrows-Wheeler Aligner (bwa), and duplicate reads were marked using Sambamba tools. ANNOVAR software was used to annotate the variants.Filtering of rare variants was performed as follows: (1) variants with a MAF less than 0.01 in 1000 genomic data , esp6500siv2_all, gnomAD data (gnomAD_ALL and gnomAD_EAS) and in house Genechem-Zhonghua exome database from Genechem; (2) Only SNVs occurring in exons or splice sites (splicing junction 10 bp) are further analyzed since we are interested in amino acid changes. (3) Then synonymous SNVs which are not relevant to the amino acid alternation predicted by dbscSNV are discarded; The small fragment non-frameshift (< 10 bp) indel in the repeat region defined by RepeatMasker are discarded. (4) Variations are screened according to scores of SIFT, Polyphen, MutationTaster and CADD software. The potentially deleterious variations are reserved if the score of more than half of these four software support harmfulness of variations. Sites (> 2 bp) did not affect alternative splicing were removed.t-test or Mann Whitney U test was used to compare continuous data of the two groups , and categorical data of the two groups  was compared using chi-squared test or Fisher test. Binary logistic regression analysis was used to evaluate the correlation between each variant loci and the risk of chronic pain in patients after lower extremity arthroplasty. Level of P values less than 0.05 (two-sided) was regarded as statistically significant in all tests. The FDR correction method was used to test multiple hypotheses on the data, but the sample size was small, so the corrected P value had no reference significance. The cluster profiler package of R language was used for gene ontology (go) enrichment analysis and Kyoto Encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The significantly different target genes were selected for annotation (P < 0.05), to get the terms and pathways of relevant genes enriched in.SPSS 22.0 and R software were used for statistical analysis. Normal distribution of continuous data was assessed, which conformed to normal distribution was expressed as mean ± standard deviation (As shown in Table P < 0.05). Synonymous variants and variants in introns and intergenic regions were removed from further analysis as they typically have little impact on function. Thereafter, 661 variants in 507 genes with potential functional impacts remained [After filtering all loci for quality control, the correlation between 339,767 variants and severe chronic pain in patients after lower extremity arthroplasty was analyzed using binary logistic regression  enrichment. Considering a significance threshold cutoff of In the current study, we identified 661 variants in 507 genes associated with CPSP. These results were obtained after bioinformatics analysis and require further functional validation. These genes are associated with numerous functions; however, many are related to neuroinflammation, cognitive function, nervous system development, and key metabolic enzymatic activities.CASP5), which encodes for a proinflammatory caspase related to inflammasome formation, was significantly associated with CPSP. Recently, Islam et al. found that blood CASP5 levels were markedly increased in patients with neuropathic pain, suggesting that inflammation is involved in the occurrence and development of neuropathic pain [CASP5 variants in the patients with severe CPSP was significantly higher than that in the control group. According to other studies, the upregulation of CASP5 can promote the progress of OA and increase the sensory input to the central nervous system, thus aggravating pain [CASP5 variants in patients with OA may lead to increased CASP5 expression levels after arthroplasty, which subsequently increases the risk of severe CPSP. CASP1 of the caspase family was also significantly related to CPSP in our study. Some studies have shown that NLRP3-CASP1 mediates the promotion of microglia in neuroinflammation. However, whether it regulates the occurrence or development of pain remains to be further verified [Caspase 5 gene , which is widely expressed in the nervous system, was also significantly associated with CPSP. ASIC1 is a proton-gated cation channel that is activated by extracellular acidification. It has been reported that MiR-485-5p is involved in the development of OA and that reducing the expression of MiR-485-5p can cause inflammatory pain by upregulating ASIC1expression. Therefore, MiR-485-5p/ASIC1 may be a potential target for the treatment of inflammatory pain in patients with OA [ASIC1 is also expressed in microglia. In inflammation, ASIC1 promotes microglia migration and releases inflammatory cytokines, such as NOS and COX-2; this indicates that ASIC1 is involved in neuroinflammatory response [ASIC1 variant was observed in the control group. It can be speculated that this variant interferes with the correct transformation of ASIC1 into a protein with normal physiological function, thus reducing the risk of CPSP in the control group. The above hypothesis can be evaluated through further functional verification. These findings suggest that blocking ASIC1 may be a powerful method for the prevention and treatment of chronic pain after lower extremity arthroplasty.Acid-sensitive ion channel 1 gene , is expressed in T cells in the brain. As a costimulatory molecule for T-helper 1 cell activation, ADAM12 mediates tissue inflammation and may play a role in the formation and development of pain. The polymorphism of ADAM12 has also been significantly associated with knee OA [BCAP29, IDO2, IGFBP1, TLR10, and TNC. How these genes regulate and affect chronic pain after arthroplasty requires further exploration [Another significantly associated gene, A disintegrin and metalloproteinase 12  and is expressed in the spinal cord. A study demonstrated that peripheral inflammation significantly increases the level of eLOX3 in the spinal cord and the metabolites of eLOX3. These metabolites promote inflammatory pain via inflammatory cytokines [ytokines .PLCE1 encodes phospholipase C ϵ-1, which can stimulate the expression of many inflammatory cytokines, including TNF-α, IL-4, and IFN-γ. It can also activate the NF-κB signaling pathway to promote inflammation and may play an important role in inflammatory pain [ory pain . The aboMZF1 encodes for a myeloid zinc-finger transcription factor that regulates the transcription process of related proteins. Many studies have shown that MZF1 plays an important role in neuropathic pain. MZF1 can regulate the expression of voltage-gated K+ channel and TRPV1 genes, thus enhancing the excitability of dorsal root ganglion (DRG) neurons, thereby strengthening the transmission of pain signals and promoting the development of neuropathic pain [ZNF382 encodes for zinc-finger protein 382, which is a transcription factor highly expressed in the nervous system. ZNF382 is persistently downregulated in injured DRG neurons, losing its binding to the silencer upstream of the Cxcl13 promoter, which promotes the transcription of Cxcl13. This eventually contributes to the development and maintenance of neuropathic pain [CPSP is usually multifactorial and is associated with not only postoperative inflammation but also neuropathic factors, such as peripheral nerve injury, and pain regulation disorder in the central nervous system during operation . AKAP12/hic pain , 50, 56.hic pain . These gTET1 from the current study is of particular interest. The TET enzyme encoded by TET1 is essential for brain function. TET1 participates in the regulation of DNA demethylation, gene expression, synaptic transmission, and memory formation. Its mutation is related to human cognitive dysfunction. After injury, TET1 is upregulated so that DNA demethylation at the CpG site of BDNF and mGluR5 promoters is mediated by the TET1 increase, which increases the transcription of BDNF and mGluR5 and promotes the development of abnormal neuropathic pain. TET1 also regulates the function of astrocytes through Ca2+ signaling, thus affecting neuronal development and cognitive function [Combined with the above-related factors leading to CPSP, function , 19, 52.DCC, which encodes the DCC receptor, is highly expressed in dopamine cells and interacts with Netrin-1, which plays an important role in regulating synaptic development. Its polymorphism is closely related to the susceptibility to emotional disorders, psychosis, and addiction [DCC showed a significant correlation with chronic pain in another study, further supporting the correlation between nervous system development and pain [LASP1 plays an important role in formation and maintenance of synaptic in the hippocampus in rats [LASP1 have also been associated with many neurological diseases, such as schizophrenia, autism, and bipolar disorder [AP1S1 encodes an adaptor protein complex that is related to synaptophysin and the vesicular acetylcholine transporter, which is very important for spinal cord development; Slitrk2 mediates excitatory synapse formation and transmission; and GPR50 encodes the G protein coupled receptor 50 that is associated with synaptic plasticity [CLIP3 regulates astrocyte proliferation and myelination and participates in regeneration after nerve injury [Although many of our findings are not specifically associated with pain, these genes are involved in the development of the nervous system or the repair process after injury, which is likely to play a role in neuropathic pain. For example, ddiction . Similarand pain . Similar in rats . Geneticdisorder . In patiasticity , 26, 39.e injury , 7. In pAMACR, encoding α-methylacyl-CoA racemase, catalyzes key steps in ibuprofen metabolism [AOX1 encodes xanthine dehydrogenase, which metabolizes aza- and oxo-heterocycles representing the scaffold of many drugs like morphine and fentanyl [CYP1A1 encodes cytochrome oxidase and participates in steroid catabolism [NAAA encodes for the hydrolase of PEA, and variants in NAAA may affect the level of PEA, thus affecting the activation of corresponding receptors, causing the biological transformation from acute pain to chronic pain [Polymorphisms not only affect patients’ sensitivity to pain, but also the large variation in individual efficacy of analgesic drugs. Some genes may encode for the key metabolic enzyme for these drugs. Variants in these genes alter enzyme activity, preventing drug metabolism or transformation into active forms in the body, leading to great differences in the demand for analgesic drugs in different patients. For example, tabolism , while Afentanyl  and CYP1tabolism . In addinic pain .PIP2) is located at the key convergence point of multiple receptors, ion channels, and signal pathways that promote chronic pain. Downregulating PIP2 in neurons can weaken receptor signals, which is a potential new method for the treatment of pain [PI3K and its downstream Akt are widely expressed in the spinal cord, especially in the lamina I–IV of the dorsal horn, where the primary afferent nerve fibers mostly terminate. At present, many studies have confirmed that the PI3K/Akt pathway plays a key role in the development and maintenance of chronic pain [As mentioned in the results, the enrichment analysis found many pathways, molecular functions, and biological processes associated with CPSP. The most significant findings are discussed in this study. Cell adhesion molecules mediate the migration of leukocytes to the injured tissues and the release of opioids locally (mainly opioids β-endorphins), which produce an analgesic effect, and block adhesion molecules . Other s of pain . In OA,  of pain . The sti of pain  found thCASP5, ASIC1, and ADAM12 are of particular interest as these nervous system genes are closely related to chronic pain and immunity in patients who undergo hip and knee arthroplasty. Reduction of the occurrence or degree of postoperative chronic pain by regulating these genes would greatly improve the therapeutic effect and the quality of life of older adult patients after lower extremity arthroplasty. Furthermore, the study may provide a basis and reference for further research on CPSP and gene susceptibility. Additionally, certain biomarkers can be explored and new intervention targets predicted to identify high-risk patients with CPSP.Pain after total hip or knee arthroplasty has always been of clinical concern as it greatly decreases the quality of life of the patients and increases the consumption of family and social resources. At present, clinicians mostly use multimode analgesia, combining nerve blocks and a variety of analgesic drugs to reduce pain. However, the prevalence of CPSP remains high , 32, 49.Additional file 1. S1. General table of groupAB SNP.Additional file 2. S2. IDs of SNP.Additional file 3. S3. Genotypes of each individual.Additional file 4. S4. Metascape result.