Phenytoin and valproic acid, anticonvulsants, have a low therapeutic index and are highly plasma protein bound, mainly to albumin. Hypoalbuminaemia is common in critically ill patients and increases the unbound drug concentration. Thus, monitoring unbound rather than total plasma drug concentrations is recommended to optimise the dosing of these drugs.This retrospective study determined unbound plasma concentrations of phenytoin and valproic as a more accurate value of drug levels than total plasma drug concentrations.Total plasma concentrations were retrieved for 56 Intensive Care Unit patients for phenytoin and 93 for valproic acid. Total drug concentrations were converted to unbound concentrations using a serum albumin-based normalising equation.Total phenytoin plasma concentration was below (41.1% of patients), within (46.4%) or above (12.5%) the therapeutic range (10 μg/mL – 20 μg/mL). However, the predicted unbound plasma concentration of phenytoin was above the therapeutic range (1 μg/mL – 2 μg/mL) in the majority of patients (57.1%). For valproic acid, the total plasma concentration of most patients (87.1%) was below the therapeutic range (50 μg/mL – 100 μg/mL); among remaining patients (12.9%), it was within the therapeutic range. In the majority of patients (91.4%), the predicted unbound plasma concentration of valproic acid was between 2.5 μg/mL and 20 μg/mL.The usefulness of monitoring the total phenytoin or valproic acid levels for dose optimisation is limited as it is an inaccurate indicator of a patient’s drug therapeutic state. Thus, the unbound plasma drug concentrations should be quantified experimentally or predicted in resource-limited settings. Phenytoin and valproic acid are anticonvulsants frequently used to prophylax or treat seizures in critically ill patients.3 However, both drugs have a low therapeutic index; thus, drug monitoring is required to minimise the risk of toxicity and optimise therapeutic efficacy.6 Following oral administration, both drugs are well-absorbed, highly bound to plasma albumin and eliminated by metabolism.5 The cytochrome P450 2C9 and 2C19 enzymes metabolise phenytoin to the inactive hydroxy-phenytoin, following first-order kinetics. However, at therapeutic concentrations, the rate of phenytoin metabolism approaches saturation and hence shows capacity-limited elimination (zero-order kinetics).5 Metabolism of valproic acid proceeds mainly by glucuronidation (50.0%) and β- and ω-oxidation (40.0%) and, to a much lesser extent, by cytochrome P450-catalysed oxidation (10.0%).6 The β-oxidation of valproic acid has been reported to be saturable and subject to autoinduction.6The incidence of seizures in North American general intensive care units (ICUs) was reported in 2013 to range from 3.3% to 34.0%.9 As a result of this high interpatient variability, there is a poor correlation between the dose of these drugs and patient plasma concentrations. Hence, monitoring patients’ phenytoin or valproic acid plasma concentrations is common, even in critically ill patients.7The pharmacokinetic behaviour of phenytoin and valproic acid is complicated because their elimination kinetics are nonlinear. In addition, the pharmacokinetics of both drugs show a high degree of interindividual variability as a result of pharmacogenetic differences in enzyme and transporter activities, as well as high interpatient variability in plasma protein binding, in the case of valproic acid.10 Thus, hypoalbuminaemia, a condition in which concentrations of the plasma protein albumin are lower than 35 g/L, increases the unbound proportion of these drugs because of the decreased albumin (binding sites) concentrations.13 Hence, monitoring total plasma concentrations of phenytoin and valproic acid hypoalbuminaemia patients, such as critically ill patients, as the clinical indicator for therapeutic efficacy and safety, is not as reliable as utilising the unbound concentrations.15 Since it is only the unbound drug that can cross biological membranes and cause pharmacological action through interaction with the drug target, the therapeutic range and dosage individualisation in hypoalbuminaemia patients should be informed by the unbound plasma drug concentrations to prevent toxicity or therapeutic failure.17 Unbound plasma concentrations can be determined by equilibrium dialysis and ultrafiltration. However, the use of unbound plasma concentrations in clinical practice is limited because it is time consuming and costly. Therefore, equations have been formulated to estimate the unbound concentrations of phenytoin and valproic acid using a patient’s measured total drug plasma concentration and serum albumin level.22 The therapeutic range for the total phenytoin plasma concentration is 10 μg/mL to 20 μg/mL, and 1 μg/mL to 2 μg/mL for the unbound phenytoin plasma concentration.5 For valproic acid, the therapeutic range for the total plasma drug concentration is 50 μg/mL to 100 μg/mL; for the unbound plasma concentration, a therapeutic range of 2.5 μg/mL to 20 μg/mL has been suggested.5Phenytoin and valproic acid are highly bound to serum albumin (≥ 90%). The binding of these drugs depends on the drug plasma and albumin concentrations and the presence of other albumin-binding substances that may compete for binding sites with these drugs.The Windhoek Central Hospital in Windhoek (Namibia) conducts therapeutic phenytoin and valproic acid monitoring in critically ill patients in the ICU facility by measuring only total plasma concentrations of the drugs. This study aimed to retrospectively estimate unbound plasma concentrations as a more accurate measure of the therapeutic plasma levels of two drugs.The Research and Ethics committees of the Ministry of Health and Social Services, the Republic of Namibia, approved this study; reference numbers HM2019 and LNI2019. Data were retrieved from patient record archives; no patients were required to participate in the data collection procedure of this study, and the ethics committee waived the requirement to obtain consent from patients before accessing their records. Furthermore, names were excluded from the data collected for this study to protect patients’ privacy.® 6000 Analyser, Roche Diagnostics International, Rotkreuz, Switzerland). The total plasma concentrations of phenytoin and valproic acid were measured by fluorescence polarisation immunoassay .This retrospective analysis utilised data from the ICU of Windhoek Central Hospital, Namibia. Records of patients admitted between January 2013 and July 2019 were reviewed. Patients who received phenytoin or valproic acid while at the ICU and whose anticonvulsant plasma concentrations and serum albumin levels had been recorded were included in the study. The following information was extracted from the records: age, gender, diagnosis, total plasma concentrations of phenytoin or valproic acid, serum albumin concentration, and concomitant medication. Serum albumin concentrations were measured at the Namibia Institute of Pathology using the bromocresol purple method  was calculated based on the measured total plasma concentration of phenytoin (Cp PHT) and the serum albumin concentration (ALB), in grams/decilitre, by using the original Winter-Tozer equation5 . The theVPA) was calculated based on the serum albumin concentration (ALB) in μmol/L, by use of the hyperbolic equation proposed by Parent et al.17  was then estimated for each patient based on the measured total valproic acid plasma concentration (Cp VPA)  . The theDescriptive statistics were used to summarise the results. All statistical calculations were carried out using the Excel software package of Windows 10 .In total, 1661 files of patients hospitalised between January 2013 and July 2019 at the ICU of Windhoek Central Hospital were reviewed. Fifty-six patients, given phenytoin and 93 patients given valproic acid, who had data recorded for total plasma concentrations and serum albumin levels, were included in the study. Patients were admitted to the ICU for the following conditions: head trauma, hypoxic brain injury, infectious diseases such as meningitis and acute gastroenteritis, renal failure, and hepatic failure. The age of the patients ranged from two months to 66 years. The mean serum albumin levels were below 35 g/L in both sets of patients  . Forty-fThe total plasma concentration of phenytoin recorded for 56 patients ranged from 0.1 μg/mL to 43.0 μg/mL, with an average of 12.6 ± 8.20 μg/mL . In 23 pn = 85, 91.4%), the unbound plasma concentration of valproic acid was within the therapeutic range (2.5 μg/mL to 20 μg/mL). In seven patients (7.5%), the unbound plasma concentration was below the therapeutic range, and in only one patient (1.1%) was it above the therapeutic range.For valproic acid, in 81 patients (87.1%), the total plasma concentration was below the therapeutic range of 50 μg/mL to 100 μg/mL . BindingThis retrospective study determined the plasma concentrations of phenytoin and valproic acid in critically ill patients at the ICU of Windhoek Central Hospital and found that the estimated unbound plasma concentration is a more accurate measure of the therapeutic plasma levels for the two drugs compared to total plasma concentration. Using the total plasma concentration, most patients were within or below the phenytoin therapeutic range. In contrast, unbound plasma concentration showed that less than one-third of the patients were within the therapeutic range and over half were above. For valproic acid, total plasma concentrations placed most patients were below the therapeutic range. However, unbound plasma concentrations placed majority within the therapeutic range.23 In our study, the percentage of patients within therapeutic concentrations using total plasma concentrations reduced from 41.1% to 28.6% when an unbound phenytoin concentration was used. Thus, the use of total phenytoin concentrations suggests that dosing in these patients was at target therapeutic concentrations, suggesting no optimal patient dosage. The high variability in the binding of valproic acid to albumin observed in this study (9.0% – 39.0%) reflects reports in the literature, such as the 10.0% – 60.0% range reported in ICU patients by Lagneau et al.13 and the 15.0% – 89.0% range reported by Riker et al.24 This high variability in plasma protein binding explains the marked difference in the percentage of patients having concentrations that were within the therapeutic range when total valproic acid was considered (12.9%) compared to when the unbound valproic acid concentrations were taken into account (91.4%). Total valproate concentration is a poor predictor of the unbound drug concentration, even when correction is made for albumin.13Noval et al. reported a 2-fold increase in critical care patients having phenytoin concentrations falling within the therapeutic range when unbound concentrations instead of total phenytoin concentrations were considered for dose adjustment.22 Unfortunately, these equations are not very accurate and may underestimate the unbound plasma concentrations of phenytoin and valproic acid in critically ill patients.30 In addition, although the therapeutic plasma range for unbound phenytoin is relatively established (1 μg/mL – 2 μg/mL), various therapeutic ranges for the unbound plasma concentration of valproic acid have been proposed in the literature. However, more research is needed to determine the optimal unbound plasma concentration range for this anticonvulsant.31 Furthermore, recent investigations on the predictive performance of these equations led to the conclusion that more complex, multivariate predictive equations may be required, which, in addition to the total drug plasma concentration and serum albumin level, considering the ICU status of the patient, age, and blood urea nitrogen level.28Therapeutic drug monitoring of phenytoin and valproic acid by measuring unbound plasma concentrations is not widespread because of the additional time and cost implications. Thus, some therapeutic drug monitoring services measure only the total drug plasma concentrations or estimate the unbound plasma drug concentrations. These equations, including those used in this study, estimate the unbound concentrations of phenytoin and valproic acid based on the patient’s measured total drug plasma concentration and serum albumin level.Therapeutic drug monitoring of phenytoin and valproic acid should be based on unbound drug concentrations. The best way to monitor the unbound drug concentration would be by measuring it directly in ultrafiltrate obtained from serum to ensure that the accurate concentration of the free drug is determined and used to inform dosage adjustment.The main limitation of this retrospective study is that the unbound plasma concentrations of phenytoin and valproic acid were not determined experimentally. However, because the albumin-based normalising equations that were used underestimate the patient’s unbound plasma concentrations of phenytoin and valproic acid, the discordance between total and unbound concentrations is less pronounced than it would be in reality. In addition, there was no follow-up to adjust the dosage regimen of these two drugs in the individual patients based on the observed total plasma concentrations.In conclusion, for therapeutic drug monitoring of phenytoin and valproic acid in critically ill patients, direct measurement of the unbound phenytoin and valproic acid concentration in plasma would be the best approach.


Eukaryotic DNA replication is regulated by conserved mechanisms that bring about a spatial and temporal organization in which distinct genomic domains are copied at characteristic times during S phase. Although this replication program has been closely linked with genome architecture, we still do not understand key aspects of how chromosomal context modulates the activity of replication origins. To address this question, we have exploited models that combine engineered genomic rearrangements with the unique replication programs of post-quiescence and pre-meiotic S phases. Our results demonstrate that large-scale inversions surprisingly do not affect cell proliferation and meiotic progression, despite inducing a restructuring of replication domains on each rearranged chromosome. Remarkably, these alterations in the organization of DNA replication are entirely due to changes in the positions of existing origins along the chromosome, as their efficiencies remain virtually unaffected genome wide. However, we identified striking alterations in origin firing proximal to the fusion points of each inversion, suggesting that the immediate chromosomal neighborhood of an origin is a crucial determinant of its activity. Interestingly, the impact of genome reorganization on replication initiation is highly comparable in the post-quiescent and pre-meiotic S phases, despite the differences in DNA metabolism in these two physiological states. Our findings therefore shed new light on how origin selection and the replication program are governed by chromosomal architecture. DNA replication is an essential process that is tightly controlled to ensure the duplication and transmission of the genetic material. The initiation of DNA synthesis occurs at sites called origins of replication and relies on the ordered recruitment of conserved replication factors. The number of origins identified in eukaryotic organisms ranges from a few hundred to tens of thousands ,2,3, andRPD3 histone deacetylase induces late origins to fire earlier, and tethering the histone acetyltransferase Gcn5 near late origins has a similar effect [Despite the diversity in the sequences of origins identified in different organisms, a large body of work suggests that origin activity is governed by similar principles ,2,3. In r effect . Moreover effect ,15. Inder effect ,17. Thisr effect ,19,20. TSchizosaccharomyces pombe are duplicated early in S phase [Igh) was not relieved by insertion of a highly active transcription unit, despite inducing a local increase in marks of active chromatin [However, the interplay between chromatin structure and replication is far from straightforward. For example, a significant portion of origins in HeLa cells mapped in one study lack the histone marks for open chromatin , and dea S phase ,24. Furt S phase , and thehromatin . Altogetcis-acting features, known as Early Replication Control Elements (ERCEs), were subsequently shown to be responsible for promoting early replication and contributing to TAD structure [Importantly, a conserved characteristic of the replication program in eukaryotes is the organization of the genome in distinct domains that are duplicated at specific times in S phase. In fact, the observation of such reproducible patterns of origin usage in population studies, as well as the detection of clusters of origins in single-molecule and single-cell experiments, provided evidence that chromosomal context may be integral to the regulation of DNA replication ,30,31,32tructure . These iIgh TTR of mouse ES cells, where origins are silenced, insertion of normally constitutively active origins led to very few replication initiation events at these new sites [Lachancea yeast, where correlations in replication timing between species were, on average, weaker around synteny breakpoints [Although both the characteristics of origins and the organization of the chromosomes are involved in origin selection, the crosstalk between these two levels of regulation remains unclear. Intriguingly, studies that have assessed the activities of origins artificially integrated at ectopic loci suggested that individual initiation sites are subjected to modulation by genomic context. For example, in the ew sites . Positioew sites . While tew sites . In addiew sites . Interesew sites . Such deakpoints . CollectTo explore this question, we have taken advantage of the signature replication program used by fission yeast cells that are subjected to conditions of reduced nitrogen availability . Upon niS. pombe genetic procedures and media as in Moreno et al., 1991 were used [6 cells in EMM6S. Cells were then starved of nitrogen in minimal medium without NH4Cl (EMM-N) for 16 h at 25 °C, leading to cell cycle exit. Release from starvation was carried out by adding a nitrogen source (0.05 g/L NH4Cl). In addition, leucine (0.225 g/L) and uridine (0.225 g/L) were added at the time of release to allow the optimal growth of cells carrying the LEU2 and ura4+ markers associated with the loxP cassettes, as they do not fully complement the leu1-32 and ura4-D18 mutations. Cells were also shifted to 34 °C at this time; in strains carrying the pat1-114 mutation [Fission yeast strains used in this study are listed in ere used . Cells wmutation , this remutation .S. pombe strains with rearranged chromosomes, we used a Cre-loxP site-specific recombinase strategy in which recombination between loxP sites can be induced by transient expression of the Cre recombinase . Second, the LEU2 gene from S. cerevisiae was PCR amplified from pREP1 using primers containing the loxP sequence and integrated into pFA6a-kanMx6 via NcoI/PmeI digest, which results in the deletion of the kanMx6 ORF sequence (pJW4). The cassettes in pJW3 and pJW4 were then PCR amplified and sequentially integrated by homologous recombination in a strain auxotrophic for uracil and leucine. Integrations were selected on minimal medium plus supplements but lacking uracil or leucine, depending on the transformation, and then confirmed by PCR. Integration sites were directed by primers containing 80-bp homology to the targeted regions at the following positions: for the chromosome I inversion—3,810,766 and 4,785,016; for the chromosome II inversion—1,577,150 and 3,694,900.To generate ombinase . For thia-kanMx6  via PmeIloxP sites integrated in the two selected loci were transformed with pJW5 for conditional expression of the Cre recombinase under the control of the nmt41 promoter. pJW5 was generated by inserting the natMx6 cassette (EcoRV/BglII + blunt) from pFA6a-3HA-natMX6 [kanMx6 ORF, resulting in the expression of the kanr gene. Positive clones were re-streaked onto fresh EMM4S + G418 (100 µg/mL) plates. To repress Cre expression and permit loss of the plasmid carrying Cre, candidates were then grown in YE4S medium supplemented with thiamine (60 µM) for 2 days, followed by plating on YE4S + thiamine + G418. To check the loss of the plasmid expressing Cre recombinase, positive clones were re-streaked on YE4S + nourseothricin plates and screened for nourseothricin sensitivity.To generate the chromosomal rearrangements, the strains above containing A-natMX6  in PvuIIA-natMX6 . TransfoloxP sequences integrated at the target sites were crossed into a pat1-114 background, and rearrangements were subsequently induced as above. Clones were selected and then diploidized using a standard protocol. Briefly, cells were grown in YE4S at 25 °C to exponential phase, treated with the microtubule-destabilizing drug carbendazim (20 µg/mL) for 5 h at 25 °C, then plated on YE4S + Phloxin B. Colonies of diploid cells appear as dark red on these plates.For meiotic experiments, For DNA content analysis by flow cytometry, cells were fixed in 70% cold ethanol, washed in 50 mM sodium citrate and treated with RNase A (0.1 mg/mL) at 37 °C overnight. Samples were then stained using 2 mg/mL propidium iodide, sonicated , and analyzed using a BD Accuri C6 flow cytometer . Analysis was performed using the FlowJo analysis software. Note that the fission yeast cell cycle differs from that of a number of model systems, as the G1 phase is short and S phase occurs prior to cytokinesis. As a result, haploid cells have a 2C DNA content during most phases of their cell cycle. The appearance of a 1C peak in nitrogen-starved cells is the result of cell cycle exit and arrest in a quiescent state in these conditions: Cytokinesis occurs without an ensuing S phase.To assess meiotic progression, cells were ethanol fixed and stained with DAPI (2 mg/mL) to visualize nuclei. Samples were counted every 15 min to determine the kinetics of meiosis I and meiosis II: cells harboring two nuclei indicate completion of meiosis I, and four nuclei are observed upon completion of meiosis II. Microscopy was performed using an inverted Zeiss Axio Observer  equipped with a Lumencor Spectra X illumination system and an Orca Flash 4.0V2 sCMOS camera . Images were acquired using the VisiView software  and a Plan-Apochromat 63X/1.4 NA immersion lens .S. pombe arrays  as previously described [For origin mapping, competitive hybridizations of differentially-labelled samples were performed using Agilent 4 × 44 k escribed . The coppat1-114 diploids). Genomic DNA was extracted [For our analyses, cells were starved of nitrogen for 16 h, and 12 mM HU was added when cells were released upon addition of a nitrogen source. Samples were collected at a time where bulk S phase is complete in non-HU treated cells . Note that a consistent low outlier value at chII: 2.9838 × 106 was removed from the profiles of Control cI, Inversion cI, Inversion cII, and Pat1 Control cII.Next, for each experiment, the geometric means over five consecutive probes were determined across the genome for each reciprocally labelled dataset, and these datasets were averaged. Two biological repeats were performed for each strain, and we took the average of the data from these two experiments. For analysis and comparison of copy number, the baselines between different conditions, representing unreplicated DNA, were matched and set to 1 using a previously described approach . BrieflyControl and Inversion strains, we established a list of origins for post-quiescent S phase . For this, we retained origins with ratios greater than 1.05 in both the Control strains. Sites with ratios greater than 1.05 in either of the Inversion backgrounds were then added. To establish a similar list for pre-meiotic S phase , the same procedure was applied using the PatI Control and Pat1 Inversion datasets. Copy numbers were then converted into efficiency. These sets of origins were used to generate the profiles of replication domains in Figures 2B and 5B.To identify origins and determine their efficiencies, we applied LOESS regression (5000 bp 2nd radius) to the averaged corrected dataset for each strain. Sites were selected as origins if the ratio of HU vs. unreplicated was greater than 1.05, representing a clear increase over the background after smoothing. Origins that were within ~5 kb were considered as a single origin and assigned the highest efficiency value. Due to the low numbers of probes in centromeric regions, these sites were not considered in our analyses; we also did not consider probes related to mating type, as it was not the same for all of our strains. Origin selection was further confirmed through visual inspection of the data. For subsequent comparisons of origin usage between Control cI vs. cII and Pat1 Control cI vs. cII showed strong positive correlations . In addition, we estimated the experimental noise in our origin mapping assays, determining the differences in origin efficiencies between the pairs of control strains. The median and median absolute deviation (MAD) of these differences show that the compared datasets are virtually identical . Thus, sites with altered replication initiation due to the chromosomal inversions correspond to those whose differences in efficiency between each Inversion/Control pair are significantly beyond the variability of the method, applying a stringent cutoff of Q3 + 3IQR and Q1 − 3IQR.To identify origins that showed changes in usage due to the engineered chromosomal inversions, we began by establishing the level of reproducibility between the experiments we performed for the control strains. The Pearson’s correlation coefficient (r) for pairwise comparisons of origin efficiencies in To validate the analyses above, we further demonstrated the reproducibility of our methodology for determining origin efficiencies. We established that individual biological repeats for a given experiment were highly reproducible, performing pairwise comparisons of origin efficiencies . For thiS. pombe strains containing rearrangements that disrupt and fuse distinct replication domains. Specifically, we exploited a context in which we previously observed striking differences in origin efficiencies between genomic regions [loxP site-specific recombinase technology [loxP sites that we integrated at selected genomic sites [loxP sites are located in intergenic regions , comparing them to wild type as well as their parental strains prior to rearrangement . Surprisingly, our results showed virtually identical doubling times for all tested strains , and we thus assessed the dynamics of cell cycle re-entry in this context. To this end, cells were grown to exponential phase, depleted of nitrogen for cell cycle exit, and then induced to re-enter the cell cycle upon addition of a nitrogen source . These differences are similar to the experimental variability determined using the two Control backgrounds (1.6% ± 1.1%). Collectively, these data indicate that the global effect described above is brought about by relocating origins that maintain their efficiencies.As our analyses suggest major changes in replication domains due to chromosomal inversions, we next ascertained the effects at the level of individual origins. Indeed, the observed alterations may result from widespread modifications of origin usage or from relocation of initiation sites across the inverted region. To facilitate the direct comparison of origin activities in the Inversion cI, we observed changes within a distance of ~100 kb of the disruptions in genomic context  or outside (Inversion cII) of the rearranged regions but do not appear to span the transition points. This “asymmetric” effect hints at the possibility that the architecture of longer fragments is imposed upon bordering sequences, while a smaller inversion is subject to regulation by the characteristics of its surrounding regions. Our findings thus suggest that while changes in the positions of initiation sites, rather than their activities, are responsible for the overall reorganization of the replication program brought about by gross chromosomal rearrangements, origins near the fusion points are exquisitely sensitive to their new genomic contexts.Taken together, our analyses indicate that large chromosomal rearrangements do not modify the activities of individual origins across the genome except in regions that adjoin new chromosomal neighborhoods. Surprisingly, the changes in origin efficiency that arise as a result of these inversions occur either inside  as well as that of their respective controls (Pat1 Control cI and Pat1 Control cII); see Materials and Methods for detailed protocols. Consistent with our previous observations [Pat1 Control backgrounds is very similar to that in cells exiting quiescence . This indicates that major alterations in chromosomal organization do not have more pronounced effects on sites of replication initiation, despite the signature behavior of meiotic chromosomes. In addition, a small subset of origins proximal to the inversion endpoints have significantly altered efficiencies  and large (100 kb–1 Mb) domains that are mediated by cohesin and condensin, respectively ,63. ChroOn the other hand, our findings also highlight the robustness of the mechanisms that determine origin activity. Although numerous origins change their positions following large-scale chromosomal inversions, their firing appears to be remarkably unaffected, as the efficiencies of individual sites are mostly preserved across the genome. This robustness may provide an explanation for the similarities in replication timing observed for syntenic regions of different species even in the presence of multiple rearrangements ,40,41. PTaken together, our study represents an important step in our understanding of the coupling between DNA replication and chromosomal architecture. The investigation of this intricate interplay will provide crucial insight into the remarkable robustness and flexibility of the replication program, with implications for the conservation of this feature of genome organization throughout eukaryotic evolution.


Listeria monocytogenes clones dominating in food processing environments in Norway, genome sequences from 769 L. monocytogenes isolates from food industry environments, foods, and raw materials (512 of which were sequenced in the present study) were subjected to whole-genome multilocus sequence typing (wgMLST), single-nucleotide polymorphism (SNP), and comparative genomic analyses. The data set comprised isolates from nine meat and six salmon processing facilities in Norway collected over a period of three decades. The most prevalent clonal complex (CC) was CC121, found in 10 factories, followed by CC7, CC8, and CC9, found in 7 factories each. Overall, 72% of the isolates were classified as persistent, showing 20 or fewer wgMLST allelic differences toward an isolate found in the same factory in a different calendar year. Moreover, over half of the isolates (56%) showed this level of genetic similarity toward an isolate collected from a different food processing facility. These were designated as pervasive strains, defined as clusters with the same level of genetic similarity as persistent strains but isolated from different factories. The prevalence of genetic determinants associated with increased survival in food processing environments, including heavy metal and biocide resistance determinants, stress response genes, and inlA truncation mutations, showed a highly significant increase among pervasive isolates but not among persistent isolates. Furthermore, these genes were significantly more prevalent among the isolates from food processing environments compared to in isolates from natural and rural environments (n = 218) and clinical isolates (n = 111) from Norway.To investigate the diversity, distribution, persistence, and prevalence of stress survival and resistance genes of IMPORTANCEListeria monocytogenes can persist in food processing environments for months to decades and spread through the food system by, e.g., contaminated raw materials. Knowledge of the distribution and diversity of L. monocytogenes is important in outbreak investigations and is essential to effectively track and control this pathogen in the food system. The present study presents a comprehensive overview of the prevalence of persistent clones and of the diversity of L. monocytogenes in Norwegian food processing facilities. The results demonstrate extensive spread of highly similar strains throughout the Norwegian food system, in that 56% of the 769 collected isolates from food processing factories belonged to clusters of L. monocytogenes identified in more than one facility. These strains were associated with an overall increase in the prevalence of plasmids and determinants of heavy metal and biocide resistance, as well as other genetic elements associated with stress survival mechanisms and persistence. Listeria monocytogenes is a foodborne pathogen responsible for the deadly disease listeriosis. Cross-contamination of food products with L. monocytogenes during processing is a major concern, especially with regard to ready-to-eat (RTE) products that support growth of the pathogen prior to consumption. Since the pathogen is widespread in natural and urban environments (L. monocytogenes that survive in the processing environment over an extended time-period (months or years) are referred to as persistent L. monocytogenes  and clonal complexes (CCs or clones) by multilocus sequence typing (MLST)  results in tolerance to low concentrations of quaternary ammonium compounds (QACs), biocides commonly used in the food industry  . Certaing (MLST) –21. The  (MLST) –, 22–29. ronments , 30–32. ments 30––37. For industry , 38–40. rnalin A , 42. Altormation –45 and irmation –.L. monocytogenes strains are more frequently isolated from food processing factories because of their increased ability to survive and multiply in niches that are difficult to keep clean , including 257 L. monocytogenes belonging to ST8 and ST9 subjected to WGS analysis as part of earlier studies  identify persistence, contamination routes, and cases where the same strain is present in more than one factory; (iii) evaluate these aspects in light of the presence of genetic determinants associated with stress survival, antimicrobial resistance, and persistence; and (iv) compare the prevalence of genetic determinants of stress survival in the isolates from food processing with that found in Norwegian clinical isolates and environmental isolates from urban and natural locations  were previously subjected to WGS analysis as part of earlier studies  belonged to lineage I and the remaining isolates to lineage II (n = 672), while lineage III or IV isolates were not detected. The isolates were assigned to 33 different STs and 28 different CCs  or group 2 (G2) . In addition, both G1 and G2 repA genes were identified in 10 CC5 isolates collected in factory M6 between 2016 and 2019, indicating that they harbored two plasmids. These isolates were related (13 to 45 wgMLST differences) to three CC5 isolates containing repA G1 but not repA G2 collected in the same factory during 2010 and 2012, suggesting that this strain had acquired a second plasmid harboring a repA G2 gene during the intervening years.A BLAST analysis was carried out to detect plasmids and genetic determinants associated with stress survival and antimicrobial resistance. Overall, plasmids were identified in 58% of the p 2 (G2) . In totalmo1702, fosX_2)  and arsenic resistance operons (arsA1D1R1D2R2A2B1B2 on Listeria Genomic Island 2 [LGI2] and arsCBADR on a Tn554-like transposon)  , which wnsposon) , QAC resd bcrABC , 62), va and tmr ), genes and inlL , 67), anand inlB . FurtherinlA PMSC mutations . However, it should be emphasized that the high prevalence among isolates from meat processing was associated with their high prevalence within CC9 isolates, which constituted the majority of isolates in this category . When CC9 isolates were excluded from the analysis, the occurrence of cadmium and arsenic resistance loci was significantly higher in isolates from salmon processing environments than from hygienic zones in meat processing factories (P ≤ 0.001). In contrast, the occurrence of biofilm-associated genes was significantly higher also when CC9 isolates were excluded in isolates from hygienic zones in meat processing factories than from salmon processing environments (P = 0.001) and low-hygienic zones associated with meat production (P = 0.02) . To assess the prevalence of each CC in food processing plants, we therefore did not count the raw number of collected isolates belonging to each CC, since this number would be heavily biased toward CCs present in the plants where the greatest number of samples were obtained. Instead, the prevalence of different CCs was assessed by counting the number of processing plants harboring each CC . The CC L. monocytogenes occurred in processing plants, and whether this was associated with certain CCs or the presence of plasmids or stress survival genes. In total, 551 isolates (72%), belonging to 15 different CCs, were linked to persistence. The proportion of persistent isolates was significantly higher in lineage II (74%) than in lineage I (53%) (P < 0.001). A persistent isolate was here defined as an isolate that showed 20 or fewer wgMLST allelic differences toward an isolate collected from the same factory in a different calendar year. A persistent strain was defined as a clonal population showing this level of similarity toward at least one other isolate in a cluster found across more than one calendar year in the same factory. The definition of persistence is irrespective of whether the isolates originated from, e.g., an established house strain, reintroduction from raw materials or external environment, or from house strains present at a supplier’s factory. Clonal clusters of isolates collected within the same calendar year and thus not designated as persistent included 23 isolates belonging to CC3 (0 to 8 wgMLST differences) collected from factory S6 between January and April of 2020, and 55 CC9 isolates collected during an 8-week period in 2014 at factory M4 in connection with the previously described event related to installation of a contaminated second-hand slicer line   was obtained through sampling in the period from February 2019 to April 2020. The emergence of CC121 was followed by extensive sampling in S6 in this period, and the CC121 strain was detected in the slaughterhouse processing equipment, machines, and environment. Interestingly, the CC121 strain was also found onboard a salmon slaughter ship supplying S6 with fresh salmon for further processing and on 26 samples from fresh salmon sampled upon arrival in the factory. This indicated that fresh slaughtered salmon contaminated with L. monocytogenes was a likely source for the introduction and subsequent persistence of this particular CC121 strain in the plant. Salmon slaughtered in other slaughterhouses and subsequently further processed in factory S2 was likely the source of a persistent CC1 strain repeatedly and extensively isolated in samples from production environment, equipment and product in S2 over a 2-year period: In a cluster of 30 isolates differing by 0 to 16 wgMLST alleles, two isolates were obtained from samples of supplied slaughtered salmon. For salmon processing plant S1, we previously reported isolation of the same CC8 strain 10 years apart , persistent strains were identified in 10 CCs 11 STs; . Six of For the meat processing plants, repeated isolation of the same strain over at least two different years was observed for nine CCs . The domL. monocytogenes found in more than one factory as pervasion. The definition does not differentiate between the mode of dissemination between factories and includes isolates with a common source and ancestor. In total, 433 of the isolates (56%) were pervasive, showing 20 or fewer wgMLST allelic differences toward an isolate found in a different factory. A pervasive strain was defined as isolates belonging to such a cluster. The proportion of pervasive isolates differed between food source, with 70% of isolates from meat processing environments designated as pervasive, in contrast to only 40% of isolates from salmon processing industry. The proportion of pervasive isolates was significantly higher in lineage II (59%) than in lineage I (34%) (P < 0.001).Phylogenetic analysis revealed that many isolates were closely related despite being isolated from different processing plants . We hereWith two exceptions, pervasive strains were either shared between meat processing factories or between salmon processing factories  and B. BFactories S2 and S6 were the two most heavily sampled salmon slaughterhouses. The two factories are located in different geographic regions in Norway and belong to different companies. WGS was performed for 68 isolates collected during 2011 to 2012 and during 2018 to 2019 from S2, and for 188 isolates collected between June 2017 and April 2020 from S6. We identified three different CCs  in which closely related isolates  were found in both factories. In the case of CC1, one isolate from a product sample collected in October 2019 from factory S6 (MF7739) differed by 7 to 15 wgMLST alleles from the previously mentioned persistent strain comprising 30 environmental isolates from factory S2, for which the likely source was slaughtered salmon. These were obtained between July 2018 and May 2019. Also, one CC121 isolate collected from a drain at factory S2 in 2012 (MF4804) showed 5 to 11 wgMLST allelic differences toward the previously mentioned cluster of 59 persistent CC121 isolates collected in factory S6 during 2019 to 2020, which included one isolate collected onboard the slaughter ship. However, the slaughter ship had not been in operation in 2012 when the S2 isolate was detected. The third case linking these two factories were two isolates separated by 17 wgMLST alleles belonging to CC88, obtained from the boots of a factory worker at S2 in November 2018 and from salmon from an external supplier at factory S6 one year later, in November 2019. Genetically similar isolates were also found at factories S2 and S3, where two CC14 isolates collected in S2 from 2011 to 2012 showed 12 to 28 wgMLST allelic differences toward a cluster of 13 isolates from S3 collected from 2018 to 2019.L. monocytogenes from four meat processing plants  collected from 2009 to 2017   was also significantly higher in isolates classified as pervaders compared to nonpervaders (P ≤ 0.004) (see Table S6).In contrast to persistent strains , isolateOf the pervasive isolates, 81% (351/433) were also persistent. The 82 pervaders classified as nonpersistent  included the 55 CC9 isolates associated with the previously described contaminated second-hand slicer line collected from factory M4 during an 8-week period in 2014 (10). Only 63% (351/551) of the persistent isolates were also pervaders. If the CC9 isolates were excluded from the analysis, 92% of pervaders were also persistent, only 51% of persistent isolates were also pervasive, and only 3% of pervasive isolates were classified as nonpersistent.P < 0.001; see Table S7), with 26 and 8% of clinical and environmental isolates harboring plasmids, respectively, compared to 41% for the isolates from food processing environments (see Table S3). The lowest prevalence of plasmids was found in the 87 isolates from dairy farms (5%). In addition to repA G1 and repA G2 plasmids, one repA group 4 (G4) plasmid and two small non-repA plasmids were identified among the clinical isolates  tetracycline resistance gene, identified through search against the ResFinder database  (P < 0.006), with the exception of biofilm associated (P = 0.688) and arsenic resistance genes (P = 0.131). Of note, none of the 218 isolates collected from rural, urban, and farm environments harbored the bcrABC or qacH QAC resistance genes or inlA PMSC mutations.The clinical and natural environment isolates harbored the same core and accessory stress genes as the isolates from food processing environments with three exceptions: (i) one clinical isolate harbored a chromosomally encoded database , (ii) onsistance , and (iiable S7) . CompariL. monocytogenes genomes, the prevalence of inlA PMSC truncation mutations was 9 and 41% in lineages I and II, respectively. In contrast, the 3CD inlA genotype associated with increased invasion  to an isolate collected from a different factory. These isolates were designated as “pervasive,” a term which has previously been used to describe subpopulations of bacteria with enhanced ability to spread or migrate to new geographical locations , where initially cucumbers imported from Spain were erroneously implicated as the source of the infections  tetracycline resistance gene in a clinical isolate]. This is lower than that recently reported among isolates from other countries  between persistence and QAC resistance determinants and biofilm-associated genes, and none between persistence and plasmids, heavy metal resistance genes, SSIs, or inlA PMSC mutations. In contrast, we found that pervasive isolates, belonging to strains present in more than one factory, showed strong statistically significant association (P < 0.001) with all examined categories of stress survival and persistence genes, as well as toward plasmids and several individual genes or gene loci. Pervasive strains were identified in eight CCs: one belonging to lineage I and seven belonging to lineage II. Notably, only a small proportion of pervaders were classified as nonpersistent . It thus appeared that strains that occurred at several factories and were repeatedly isolated over time in one or more of these facilities were more likely to show problematic properties, i.e., carry genetic determinants that enable them to establish as house strains and disperse to new environments. Our results further support the hypothesis that there is not one single genetic determinant responsible for survival in food processing environments but rather an accumulation of stress resistance genes, biofilm-associated genes, and inlA PMSC mutations.It is likely that the genetic basis behind increased prevalence of certain ronments , 6. Thern agents , 39, 101n agents , 100. Hoytogenes , 103 or ytogenes , 103–105L. monocytogenes in the food system, which in turn could guide improvements in control strategies for this important pathogen.The identity of the isolates classified as nonpersistent pervaders in the present study exemplify one of the difficulties in separating between “true” persistent and “true” transient isolates using operational definitions. These included 82 isolates, 55 of which were from the previously described contaminated second-hand slicer line isolated from factory M4 . These wL. monocytogenes strain collection at Nofima, Norway. A total of 305 isolates from both meat and salmon industry were collected from 2011 to 2015 as part of a previous study . Contigs with sizes of <500 bp and k-mer coverage of <5 were removed from the assemblies (a coverage cutoff of 15 was used for MF7896). The average coverage for the genome assemblies was calculated using BBmap v36.92 , and genomic DNA was isolated using the DNeasy blood and tissue kit (Qiagen). Libraries were prepared using the Nextera XT DNA sample preparation kit or the Nextera DNA Flex Library prep kit (both from Illumina) and sequenced on a MiSeq platform with 300-bp paired-end reads. Raw reads were filtered on q15 and trimmed of adaptors before v.3.13.0  with thep v36.92 . The quap v36.92 .L. monocytogenes online MLST repository (https://bigsdb.pasteur.fr/listeria/). CC14 and CC91 were defined as previously described .Classical MLST analysis followed the MLST scheme described by Ragon et al.  and the escribed . The wgMMinimum spanning trees were constructed using BioNumerics based on the categorical differences in the allelic wgMLST profiles for each isolate. Loci with no allele calls were not considered in the pairwise comparison between two genomes. The number of allelic differences between isolates was read from genetic distance matrices computed from the absolute number of categorical differences between genomes.Calculation of pairwise wgMLST distances for neighbor-joining (NJ) trees was performed using the daisy function  from theRead mapping based SNP analysis was performed separately for each CC. An internal reference genome was selected from each CC (listed in Table S1) using the following criteria: centrally positioned in CC clusters by wgMLST analysis, from larger subclusters if more than one cluster was present in a CC, older isolates preferred to more recent isolates, and higher-quality assemblies  preferred. The reference-based SNP analysis was performed using the CFSAN SNP pipeline v2.1.1  and defaListeria strains, 63 of which were assigned to 19 groups . In the current analysis, one plasmid each from the 19 groups (selecting the largest plasmid in each group) and the 50 plasmids that did not belong to a group were included for a total of 69 different plasmids (see Table S8). All contigs >2,000 bp from the L. monocytogenes genomes were used as queries in a BLAST search (blastn v2.10.0+) against a local nucleotide BLAST database created for the 69 Listeria plasmids. A contig was viewed as a plasmid contig if the query coverage (qcov) was ≥85%, and the percent identity of the best alignment was >90%. Hits were inspected manually.The selection of plasmids included in the current analysis was based on a study by Chmielowska et al. , which irepA gene selected from each of the 11 RepA groups (G1 to G11) identified by Chmielowska et al.  or lineage and the source of the isolates  or classification as persistent or pervasive. All tests were performed separately for pairs of categories.D) was converted to a similarity score: S = 21 – D. Thus, a distance of 20 wgMLST alleles corresponds to a similarity score of 1, and a distance of 0 wgMLST alleles corresponds to a similarity score of 21. Of note, the lowest genetic distance separating two isolates from different factories was 2. Then, all similarity scores were grouped by CC and factory and summed to generate the final scores, which are represented by the thickness of each arc in Genetic associations between pervasive isolates from different factories, weighted by their similarity, and illustrated in the chord diagram in PRJNA689484. For GenBank and Sequence Read Archive (SRA) accession numbers for all 769 genomes from food processing industry, see Table S1 in the supplemental material. The assemblies were annotated using the NCBI Prokaryotic Genomes Annotation Pipeline (PGAP) server.The raw data and assembled genomes for the 512 genomes sequenced in the present study has been submitted to NCBI as BioProject


Catalyst developmentfor biorefining applications involves manychallenges. Mathematical modeling can be seen as an essential toolin assisting to explain catalyst performance. This paper presentsstudies on several machine learning (ML) methods that can model theperformance of heterogeneous catalysts with relevant descriptors.A systematic approach for selecting the most appropriate ML methodis taken with focus on the variable selection. Regularization algorithmswere applied to variable selection. Several different candidate modelstructures were compared in modeling with interpretation of results.The systematic modeling approach presented aims to highlight the necessarytools and aspects to unexperienced users of ML. Literature datasetsfor the hydrogenation of 5-ethoxymethylfurfural with simple bimetalcatalysts, including main metals and promoters, were studied withthe addition of catalyst descriptors found in the literature. Goodresults were obtained with the best models for estimating conversion,selectivity, and yield with correlations between 0.90 and 0.98. Thebest identified model structures were support vector regression, Gaussianprocess regression, and decision tree methods. In general, the useof variable selection procedures was found to improve the performanceof models. The modeling methods applied thus seem to exhibit a strongpotential in aiding catalyst development based mainly on the informationcontent of descriptor datasets. Additionally, for biorefineries to thriveand be able to financially compete with fossil-based manufacturingand ensure sustainability of the processes, it is necessary to minimizethe use of precious metals and metals from conflict areas. In contrastto traditional catalysis development approaches, the methodologiesfollowed in BioSPRINT,6 an EU funded project,involve judicious design of catalyst formulations through explorationof wider experimental spaces combining multimetal chemistries to findout nontrivial synergies and enhanced formulations that will mostlyrely on affordable and sustainable metals. The mathematical modelingapproach presented in this study could help to explain the complexdependencies between catalyst formulation and performance. Hence,modeling can be an essential tool in catalyst development.8Replacing fossil-based fuels and products with bio-based counterpartsproduced from renewable resources is of great interest due to climatechange and depleting fossil-based feedstocks. Fossil-based feedstocksconsist of hydrocarbons, which makes catalysis relatively easy. Incontrast, biomass has overfunctionalized composition, where alcohols,ethers, esters, and carboxylic acids are present, thus making theconversion of biomass challenging.8 The empiricalapproach aims to find correlations between the catalyst descriptors,in other words computational features describing the physicochemicalproperties of the catalysts, and figures of merit (FOM), for example,product selectivity, yield, turnover number and frequency, cost perkg, or a combination of these.9 It is basedon data without assumptions on reaction mechanisms and reactor configurations.Empirical models are based on statistical analysis and are often combinedwith stochastic optimization methods.10Modeling in catalysis can be fundamental, empirical (data-driven),or a combination thereof.8 and Raset al.,11 catalyst performance could bepredicted using data-driven modeling with simple descriptors. Thesemodels can be less interpretable but more practical than methods thatrequire high computational power and use complex algorithms with quantumand classical mechanics.8 However, Madaanet al.8 point out that both fundamentaland empirical approaches are needed for finding new catalysts andoptimizing the existing ones. The lack of universal databases forcatalytic FOMs and descriptors hinders the use of data-driven methodsin catalysis.4 Empirical approaches arefast, but these adapt poorly to new factors and interpretability canbe weak.8 In combination of fundamentaland empirical approaches, descriptors based on chemical principlescan also be combined with statistical modeling.8 This approach is preferable for predicting catalyst performance.10 Examples of different modeling approaches are listed in Madaan etal.’s8 study.Accordingto Madaan et al.5 With combined computational modeling and/or experiments,catalysts can be rapidly screened, descriptors of catalyst performancecan be found, and catalyst synthesis can be enhanced.7 ML can also be used in formulating new descriptors usedin combination with quantum mechanical methods and to formulate interatomicpotentials.7Machine learning(ML) enhances ways to discover catalysts, generatesknowledge about catalysis, and establishes a deeper understandingof the relationship between material properties and their catalyticFOMs.3 Goldsmith et al.7 listedseveral examples, where integration of ML and high-throughput (HTP)screening for heterogeneous catalysts was used to predict catalystFOM for large catalyst spaces. However, it has been concluded thatthe predictions of catalytic FOM are still in their early stages.7 Synthesis conditions and compositions have beenused as model input variables for predictive models, which can beseen, for example, in Baumes et al.13 These ML approachescan guide the synthesis toward better catalysts, although data fromexperiments are often incomplete and can result in poorly generalizedmodels for large chemical spaces.7 Hence,usually multiple iterations and experimentations are required forthe successful application of ML in catalyst development.9The use of ML in computational catalysisresearch and integrationwith experimental research programs has been increasing.9 The same main principles can be applied for heterogeneous catalysistoo. From the ML point of view, the model structures encountered inthe published studies have involved, for example, genetic algorithms(GAs) and artificial neural networks (ANNs),15 Gaussian process regression (GPR), radial basis function network(RBFN), support vector regression (SVR),16 orthogonal partial least squares (OPLS),11 and random forest (ensemble of decision trees).18 In comparison, modeling methods used in this study are linear regression,decision tree regression, SVR, ensemble tree regression, and GPR.Also, partial least squares regression (PLSR) and regularization regressionare used to identify reference models.The early work in applying ML with focuson homogeneous catalysishas been reviewed by Maldonado and Rothenberg.Despite the fact thatseveral studies can be found where ML isused in catalysis, only in a few studies, a systematic approach for selecting the ML method is taken. In addition, the variable selectionstep has not gained significant focus in most of the earlier works,although it is an important step in identifying low-dimensional MLmodels applicable on small datasets.19 differentvariable selection methods were tested to find the relevant descriptorsfor predicting solid catalyst performance in the propene oxidationreaction. Also, Goldsmith et al.7 listedsome references, where variable selection was studied; in particular,the use of the sure independence screening and sparsifying operator(SISSO)20 algorithm was mentioned. In bothof these studies, data-based variable selection was found to be apromising method to find important variables from a high-dimensionalfeature space, while reducing the dimensionality of the identifiedmodels. In a similar manner, in this study, regularization algorithmsare used as variable selection methods. Researchers have used regularizedalgorithms to study catalysis, such as Lasso.22Earlier research has provedthat it is possible to describe catalystperformance well with mathematical models developed via variable selection.In the study of Procelewska et al.,In this paper, a systematic approach for testing and comparingdifferent ML approaches with variable selection is taken. The aimin this study is to identify a model structure that is as incomplexas possible but still able to model the outputs accurately. A variableselection procedure is applied to find a subset of relevant inputvariables and thus make the model structure simpler and to avoid overfittingproblems often encountered with small datasets. Different ML modelingapproaches are tested to increase knowledge of the suitability ofthese methods to the task. The systematic approach implemented withreadily available and easily approachable methods also aims to givean insightful example of the utilization of ML to chemical engineerswith limited experience in ML.24 The desired product is 5-ethoxymethylfurfuryl alcohol, which isa potential additive for diesel fuel.1 5-EMFcan be easily obtained via acid-catalyzed dehydration of C6 sugarswith ethanol as a solvent.1 According tothe best knowledge of the authors, the studies of Ras et al.25 are the only openly availablestudies that focus specifically on this reaction with the aim of modelingthe catalyst performance. Studies that focus on the synthesis of 5-EMFcan be found, for example, in refs , which is an important reaction related to the conversionof biomass into biofuels. in refs , 24. Thi in refs , 24.Thisarticle is structured as follows: 2The systematic model identificationapproach of this study is illustratedin 2.11 was utilized.The dataset consists of conversion and selectivity as responses, namely,FOMs. Also, their product, namely, yield, was selected in this studyas response. Literature data were available from multiple experiments,with 48 different catalyst combinations in three different temperatures and with two solvents . Eight different main metals  and six promoters  had been usedas catalysts. Al2O3 support had been appliedin all observations. According to the original paper by Ras et al.,1 the main metal had a loading of 1 wt% and thepromoter loading was 10 mol% in relation to the main metal. Feedstockcomposition remained constant in the studied dataset. In contrastto the approach by Ras et al.,11 whereyield is predicted, also conversion and selectivity with two solventswere predicted separately in this study, to evaluate the most importantdescriptors for them. This led to six different models having eachan individual response variable: conversion with diethyl carbonatesolvent (C1), selectivity with diethyl carbonate solvent (S1), conversionwith 1,4-dioxane solvent (C2), selectivity with 1,4-dioxane solvent(S2), yield with diethyl carbonate solvent (Y1), and yield with 1,4-dioxanesolvent (Y2).The experimental data aspublished by Ras et al.26 were calculated according to the methodology presented by Ras etal.11 Calculations resulted in four variablesto describe the wave function of the valence electrons in each metal:rAPEX, R(r)APEX, FWHH, and SKEW. Ras et al.11 described these variables in the following way: rAPEX is definedas the “distance of maximum probability of encountering a valenceelectron”, R(r)APEX is the “maximum value of the probabilitydistribution”, FWHH is the “width of the probabilitydistribution at the half-height”, and SKEW is the “measurefor the asymmetry of the distribution”. Interaction and quadraticterms for the STOs were calculated and added to the descriptor dataas well. In addition, descriptors based on the periodic table wereadded to the dataset.30 The database consisted thus 61 input variables and six responsevariables at this stage. The analyzed data are available in the Supporting Information.Slater orbitals (STO)2.2Based on the exploratoryanalysis of the experimental dataset see 2, it is Tables S15–S19 for a full list of used descriptors). Also, continuous variableswere standardized so that each variable was set to mean value of 0and standard deviation 1.Ir/W observations wereunavailable for the first solvent, and becauseof that, these were removed in the preprocessing stage. Variableswith missing values were also removed leading to 57 variables in total.Categorical variables were converted into dummy variables so thatthese were able to be utilized as input variables with the appliedmethods. A dummy variable is created for each category of each categoricalvariable. For example, there are eight different main metals. Accordingly,the same number of dummy variables were determined for categoricalvariable “main metal”. Each dummy variable defines eachcategory with binary values 0 or 1: value 1 means true, and value0 is false . Separate variables were obtained for main metalsand promoters. The final dataset consisted of 147 variables afterpreprocessing , and Gaussianprocess regression (GPR) were used in the modeling. These methodsare introduced, for example, in the works of Witten, Mitchell, andWilliams.2.5Tables S20–S23). Cross-validation (CV) was appliedto model validation in the training phase. The CV subsets were usedfor selecting the relevant input variables and estimating the performanceof the models with unseen data. Fivefold CV was used in both variableselection and modeling. The fivefold data split procedure was repeatedwith random resampling 94  times to avoid possible chance correlations.The focus of this studywas to study the interpolation capabilities of the models rather thanextrapolation. The observations  were first split intotraining set (2/3 of the data) and test set (1/3 of the data) by performinga static data split. Stratified sampling was applied to ensure equalnumber of observations from each category  in the test set. Detailed information on the data splitcan be found in the Supporting Information (see 2.6The rootmean square error (RMSE) values were calculated for the training data, the CV data, andthe test set to validate models’ performance in this study. The predictionuncertainties  were evaluated by calculating the mean value of error± two times its standard deviation, and then indicating the interval,where approximately 95% of the errors are expected to be within thisrange. The Pearson correlation coefficient  between measured and predicted values was also calculatedto evaluate model performance.39 wasapplied to test if the residuals follow normal distribution. An αvalue of 0.01 was set to the significance level. The results can befound in A combination of Shapiro–Wilkand Shapiro–Francia tests2.7fitrlinear functionwas used to perform variable selection with ridge and lasso regularization.A vector for different λ values was created, and the λvalue that minimizes the mean square error (MSE) was chosen. The fitrlinear function with CV trains a model for each fold.Therefore, the variables chosen were determined for each model . After this, variables that occurred at least in halfof the folds  were chosen into the finalsubset. This procedure was executed 20 times, and the variables thatoccurred in half of the iterations were chosen in the final subset.This was done to minimize the amount of randomness due to CV, whilekeeping the computation times relatively short. Variable selectionwith fitrlinear function and ridge regressionwas performed with Stochastic Gradient Descent solver. With lasso,in contrast, Sparse Reconstruction by Separable Approximation wasused. The least squares learner was used for both methods. MATLABalgorithms are described more in detail in the MATLAB documentation.Preprocessing, variable selection, modeling,model validation, and statistical analysis were performed in thisstudy with MATLAB. The lasso function was also used in MATLABto perform lasso variable selection and elastic net variable selection.Elastic net variable selection was performed with an α valueof 0.5. For both methods, variable selection was performed with twodifferent λ values: a λ value that gives the minimum meansquare error value (minMSE) and a λ value that is the largestλ value, one standard error away from the minMSE λ value(1SE). Thus, the 1SE λ value will give a smaller variable subsetwith a slightly larger MSE value.The Table S8, variable subset VII). The readily available MATLAB functions forthe RLA models include different ways to tackle overfitting (regularization),which differs for each modeling approach. However, this paper focuseson the regularization used in the variable selection. More detailedinformation about the possibilities with the various model structurescan be found from the MATLAB documentation and introductory ML studies.37The Statistics and MachineLearning Toolbox in MATLAB was used,which includes the Regression Learner App (RLA). All model structuresfound in the RLA were used in the study with some exceptions; stepwiselinear regression was excluded because variable selection was carriedout with embedded methods. In addition, coarse Gaussian SVR gave poorresults, and the calculation times were long. Hence, the results withthis method were also omitted. Variable subsets had to be restrictedin some cases; the interactions linear model was only used with variablesubsets smaller than 30 due to the increasing number of interactionterms. With linear, quadratic, and cubic SVR models, the kernel scalevalue was set to 1 instead of an automatically chosen value. Thiswas done because with automatically chosen kernel scale values, thecalculation times were longer, and in some cases, the RMSE valueswere extremely high. The applied methods included linear regression,decision trees, SVR, ensemble trees, and GPR. In addition, PLSR  and linearregression models with regularization were identified as referencemodel structures. Finally, the modeling was performed with candidatesubsets selected by the variable selection methods 2.3 and t3This sectionis structured as follows: 3.1Tables S1–S6 (see the SupportingInformation), the results of reference models in terms of model performancemetrics . Compared to the results in Tables S1–S6, it can be noticed that significantly better results are obtainedwith quadratic SVR, fine tree, and boosted ensemble tree methods identifiedwith the RLA . This highlights the applicability of RLAmethods over the basic/regular modeling approaches . However, themodels are highly complex without variable selection procedures.In 3.2Table S7. The used variable subsetsare depicted in Table S8, and the definitionsfor all of the used variables can be found in Tables S15–S19. According to Table S7, the best results in general were obtained with GPR, cubicSVR, and fine tree models . Fourdifferent kernel functions  were used for GPR, which all gave almost equalresults for RMSEP. Only the best one was involved in Table S7 for each response. In most cases, the fitrlinear function with ridge regularization workedbest in variable selection. As mentioned in Afteridentification of the reference models, modeling was performedwith variable selection. The best results for each response can beseen in Tables S1–S6, the use of variable selection methods and themodels from RLA here seem to have a beneficial impact on the results.For the best models (excluding the best results without variable selection),the RP value ranges are 0.90–0.90 for C1, 0.97–0.97for S1, 0.96–0.98 for Y1, 0.95–0.96 for C2, 0.65–0.66for S2, and 0.84–0.94 for Y2. In general, significant improvementis obtained using variable selection methods in combination with theRLA models, which can be noticed by comparing the range of valuesof RMSEP, RP, and PUP between the reference models and the best modelsseen in Table S7. The best RMSEP valuesare obtained for the response C1 with the reference models. Poor results(RMSEP > 30%) were obtained for response S2 with all of the methods.Even though responses C2, Y1, and Y2 had also a challenging data structure,good  results were stillobtained (see Table S7). When comparingthe results with and without variable selection, it can be seen thatwith quadratic SVR, fine tree and boosted ensemble tree models almostequally good  results are obtained withoutvariable selection except for response Y2. Hence, similar (or evenslightly better) model performance can be obtained with a simplermodel structure using the studied regularization methods in variableselection. The results obtained show that variable selection is animportant step when building models for catalyst performance.In comparison to the reference results in The best modeling results are summarized in 1 More detailedinformation about the models can be found in the Supporting Information.Scatter plots for each of thebest test set predictions for eachresponse can be seen in 3.311 However, thereare some differences between the used experimental datasets and thedescriptor datasets. Modeling results for predicting yield (productof conversion and selectivity) with the used experimental datasetcan be found in the study of Ras et al.11 with one solvent (diethyl carbonate). In that same study, with bimetalliccatalyst dataset, an R2 value equal to0.79 (with the test set) was obtained by modeling the yield of unsaturatedalcohol and diether together. The yield of the unsaturated alcoholfollowed by the hydrogenation of the carbonyl group was also predictedseparately (R2 = 0.90 with the test set).In that study, only data corresponding to 80 °C temperature experimentswere used. In addition to the bimetallic catalyst dataset results,modeling results for combined yield predictions (four reactions) withmonometallic catalyst dataset can be found in the paper of Ras etal.11 with an R2 value equal to 0.76 (with test set). With the same catalyst dataset,the yield of the diether was predicted with training set (R2 = 0.80 without removing outliers and R2 = 0.91 after removing two outliers). It seemsthat similar results were obtained when comparing the best RP valuesin this study for responses C1, S1, C2, Y1, and Y2 (see Table S7) and the results for Ras et al.’s11 models.Direct comparison of model performance metrics in data-driven modelingis challenging, as different datasets or data divisions are oftenused, and the modeling performance is inherently tied to the underlyingdata. The models identified in this study can be compared to the modelsproposed by Ras et al.Table S7  varies between 2 and13. The models with only two input variables (subset XVII) performedwell in comparison to more complex model structures. However, someof the studied models may be highly complex. The complexity couldbe further reduced by increasing the value of λ in the regularizationpart and/or by lowering the coefficient threshold value with ridgeand elastic net regularization, with potential small accuracy lossin the model performance. In comparison, Ras et al.10 have used stepwise elimination of redundant variables toreduce the number of variables for a case with a monometallic dataset.Other studies in heterogeneous catalysis have also demonstrated thatlow-dimensional models can be achieved via the use of principal componentanalysis (PCA) and partial least squares (PLS).40The number of variables differsfrom the studied subsets. When evaluating the complexity of the models,it should be noted that the presence of continuous variables makesthe model more complex than the presence of binary valued dummy variables.Thus, the number of variables in the studied subset cannot be solelyconsidered. The number of input variables in the models seen in 3.439 to test if the residuals of the best modelsfor test set follow normal distribution. The Shapiro–Franciatest was used with residual vectors where kurtosis was leptokurtic(kurtosis > 3). In other cases, the Shapiro–Wilk test wasperformed.An α value of 0.01 was used. The test was executed without removingthe outliers and after removing outliers. Observations that differedmore than 3 times the scaled mean absolute deviation (MAD) from themedian were removed (see MATLAB documentation for function rmoutliers). It was noticed that after removing the outliers,more normal distributed residuals were identified according to thetest. When performing the test after removing outliers, normal distributedresiduals were found almost for all of the good models, when consideringthe calculated error metrics. Although some exceptions exist, forexample, the Quadratic SVR model for response S1 had good metric valueswith variable selection method ridge (fitrlinear) but failed to produce normally distributed residuals accordingto the test. An example of the normal probability plot of Fine Treemodel’s residuals for the test set for response S1 with ridge(fitrlinear) variable selection method canbe seen in A combination ofShapiro–Wilk and Shapiro–Francia tests was usedTable S7). Correlations of models for response S2 were not analyzed.In general, low correlations were obtained for most of the variables with the studied methods.The highest correlations in general were obtained with the test setin comparison to the correlations with training set and CV set. Thelargest absolute correlation value (R = 0.30) withtest set was obtained between the boiling point of the main metaland yield with diethyl carbonate solvent (Y1). Absolute correlationvalues between 0.25 and 0.35 were obtained for a few variables, whenconsidering the correlations with training, CV, and test sets. Overall,the correlations were relatively low. In The correlation betweenresiduals and input variables was alsocalculated for the best models (models in 3.5R = −0.62), and also has a moderately high correlationvalue for Y1 (R = −0.51). Several STO variableswith the addition of Brinell hardness and resistivity (Resistivity_M)for main metal have moderate correlation (|R| ≥0.40) for conversion responses (C1 and C2). Low correlation values(|R| ≤ 0.25) were obtained for both Brinellhardness for main metal and temperature in comparison to responsesS1, Y1, and Y2 even though good results  were obtained by only using these variables in the predictions.Correlationsbetween input variables and responses were calculated. Only the correlationsfor variables in the best models are considered here. AbbreviationsM and P are used for main metal and promoter, respectively. Temperatureseems to have the highest correlation in contrast to response S1  are discussed in more detail.The abundance of each variable with different methods was evaluatedwith four different regularization methods:lasso functionwith α value 0.5 and minMSE λ value,elastic net (EN) with lasso function withminMSE λ value (L1),lasso with fitrlinear function(L2) andlasso with fitrlinear function(RR).ridge with In contrast to the previous datadivision with separate test data,here, the whole dataset was used in combination with CV to evaluatethe variable importance. Variable selection algorithms were executedin multiple iterations to minimize the effect of the randomness ofthe CV in the data division. Each algorithm was executed 20 times,resulting in a total number of 80 iterations. For the elastic netvariable selection, the following threshold values see 2.3 for cTables S9–S14 (cf. SupportingInformation), occurrences for the most often selected variables foreach response are represented with four different regularization methodsand their sum. As seen in Table S9, temperatureand Brinell hardness (M) occur in every chosen subset with responseC1. Temperature logically affects the reaction rates, and therefore,the conversion observed. On the other hand, the correlation with materialshardness is not directly obvious. However, hardness has been correlatedthrough first principles with the microstructure and chemical bondingin the materials such as in refs (In  in refs −43. BothTable S10 that four promoter variables (dummyvariable for Fe (P), dummy variable for group 8 in the periodic table(P), volume magnetic susceptibility (P), and electrical conductivity(P)) are present for response S1. This somehow indicates that promotersare more likely to affect selectivity than conversion.In contrast to the impact on C1, it can be noticed from Slaterinteraction and quadratic terms are in the absence of responseS1. Temperature, boiling point (M), and bulk modulus (M) occur inevery studied subset. Electron affinity (M) and density (M) seem tobe also important variables in modeling. This finding suggests thatfor this reaction, selectivity is affected by molecular-level electronicinteractions with the catalyst more than by its structure and chemicalbonding.Table S11. It can be noticed that only one promoter variableis present (speedof sound (P)) for response C2. One Slater interaction term (for RAPEXand FWHH (M)) and a quadratic term (for rAPEX (M)) can be seen inthe table. Brinell hardness (M) and electron affinity (M) seem tobe also strong variables in addition to temperature.Similar trends were observed from Table S12, it can be seen thattwo promoter variables are chosen (dummy variable for Cr (P) and firstionization energy (P) for response S2). Slater interaction or quadraticterms are not seen. Volume magnetic susceptibility (M), Brinell hardness(M), and electronegativity (M) are the most often occurring variablesafter temperature.From Table S13, temperature andboiling point (M) occur in every subset with yield Y1. Also, density(M) Brinell hardness (M), bulk modulus (M), and electron affinity(M) seem to be strong variables for predicting Y1. In addition, thepresence of main metals Pt, Ir, and Pd has a great impact on the yieldresults with diethyl carbonate solvent according to the chosen subsets.Promoter variables or STO variables cannot be found from the tableof the most influential descriptors.As seen in Table S14, it can be seen thatin addition to temperature, Brinell hardness occurs in almost everysubset. Also, the interaction term between RAPEX and FWHH (M), electronaffinity (M), first ionization energy (M), and neutron cross section(M) seem to be strong descriptors for predicting yield with 1,4-dioxanesolvent (Y2). The presence of main metals Ir and Pd seem to be mostinfluential according to the chosen variable subsets . Once again,promoter variables cannot be found from the table.From p-values from the ANOVA also show thatthe group means based on promoter metals do not have a statisticallysignificant difference . (5) Itisalso notable that the fitrlinear function rarelychooses dummy variables in the variable subset. Therefore, the chosenvariable subsets with fitrlinear function and lasso function differ significantly.From theresults for the occurrence of variables, the followingconclusions can be made: (1) promoter variables seem to be more relevantin the prediction of selectivity than conversion. (2) STO variablesand their interactions and quadratic terms seem to be more relevantfor predicting conversion than selectivity. (3) Temperature was presentin every variable subset with all responses. Also, several descriptorswere found important, including Brinell hardness (M), electron affinity(M), and Slater interaction term for RAPEX and FWHH (M). (4) In general,main metal variables are much more relevant than promoter variables,which is also the case with the studied datasets. Analysis of variance(ANOVA) shows that the variation is clearly due to main metal variables whilepromotervariables seem to have less effect on the variation (mean squares< 80). The Table S7 in the last rows for each responsevariable (cf. variable subset VII). The RMSEP values are only slightlyworse or even better for responses C1, S1, and Y1 than with the variablesubsets chosen by variable selection algorithms. As discussed earlier,the Brinell hardness was proven to be a strong descriptor. With thestudied dataset, these two variables explain most of the variancein the response data. It was surprising that with the responses, whichhad low correlation  against Brinell hardness andtemperature, good modeling results were still obtained.For comparison,the modeling was also performed with the two mostimportant variables identified based on variable selection results3.2, name4The application of ML in catalyst developmenthas shown great promise.In this work, a systematic approach for testing different variableselection algorithms and model structures was considered for modelingcatalyst performance . For thestudied case of hydrogenation of 5-ethoxymethylfurfural with simplebimetal catalysts, it was shown that relatively high modeling accuracycan be achieved (correlation varying between 0.90 and 0.98) throughthe utilization of regularization algorithms, RLA models, and descriptordataset of STO parameters with the addition of variables found inthe literature.The importance of systematic variable selectionis supported bythe results as it seems to have a beneficial impact on the models’performance. It was also shown that fairly good results can be obtainedwith only two input variables in this case. Brinell hardness for mainmetal was found to have high predictive power. Promoter variableswere considered unimportant with variable selection algorithms thatcan be an issue when deriving optimal catalyst formulations, whereboth the main metal and promoter need to be selected.In general,the best results were obtained with GPR, SVR, and finedecision tree methods. From the studied variable selection algorithms,different model structures perform best with different responses.Even though the modeling results were good, the variable selectionmethods were almost purely data-driven, and the physical interpretationof all of the variables remains unclear. Also, some of the valuesin the descriptor dataset were obtained from compiled lists from multipleexperimental and simulated studies. Therefore, these are likely tocontain a certain amount of inaccuracy .The lasso algorithm was introduced with datasets consisting of highlycorrelated variables, which can lead to the algorithm picking onevariable and ignoring the remaining ones, resulting in loss of potentiallysignificant variables see 2.3. DespIn the future work, model-based optimization is tobe studied withthe goal of finding catalysts that give the maximum FOM values. Also,the model extrapolation capabilities could be further studied. Moreover,also other relevant descriptors can be identified and added to thedataset .


Wnt5a, β-catenin, and Nrf-2 expression were significantly increased following genistein and lycopene treatment (p < 0.0001), thus confirming their antioxidant activity as well as their ability in stimulating osteoblast function, mostly when genistein and lycopene were used in association. The combined treatment of genistein and lycopene improved the bone damage induced by glucocorticoids and significantly restored the normal architecture of bones as well as adequate interconnectivity of bone trabeculae, thus increasing bone mineral density parameters. The obtained data demonstrated that genistein and lycopene but in particular their association might prevent GC’s adverse effects, thus stimulating bone formation and reducing bone resorption, improving bone structure and microarchitecture, through different molecular pathways, such as the Wnt/β-catenin and the Nrf-2 signaling.Chronic glucocorticoid (GC) therapy is the most common cause of iatrogenic osteoporosis and represents an important risk factor for osteoporosis and bone fractures. New therapeutic approaches are required in order to treat osteoporosis and reduce the side effects related to the use of anti-osteoporotic drugs. In this context, previous studies reported the efficacy of some isoflavones and carotenoids, such as lycopene and genistein, on the reduction of the risk of fracture related to osteoporosis. The aim of this study was to investigate the effects of a combined oral treatment, consisting of genistein and lycopene, in an experimental model of glucocorticoid-induced osteoporosis (GIO). GIO was induced by subcutaneous injection of methylprednisolone  for 60 days, whereas the control group (Sham) received saline solution only. Following induction, MP animals randomly were assigned to receive alendronate, genistein, lycopene, or the association of genistein and lycopene or saline solution for additional 60 days together with MP. Femurs obtained from the Sham group were used for osteoblasts extraction; they were then incubated with dexamethasone (DEX) for 24 h to be then treated with lycopene or genistein or the association of lycopene and genistein for an additional 24 h. Treatments with lycopene and genistein restored the impaired mineralization of cells observed following DEX treatment and stimulated osteoblast differentiation by increasing the depressed expression of bALP and RUNX2 ( The dynamic balance of bone metabolism is a physiologic process throughout the lives of humans ; the sopTherefore, on the basis of these scientific assumptions and clinical evidence, the aim of the present study was to comprehensively investigate the effects of a combined oral treatment, consisting of genistein and lycopene, in both in vitro and in vivo models of glucocorticoid-induced osteoporosis.n = 60; Charles River, Calco, Italy) 5 months of age and weighing between 250–275 g were used in this study. Upon arrival, animals were housed (3–4 rats per cage) in the Animal Facility of the Department of Clinical and Experimental Medicine of the University of Messina and provided with a standard diet ad libitum as well as free access to water. Rats were monitored daily and maintained in a 12 h light/darkness cycle and a temperature of 24 °C. The present study was approved by the Ethics Committee of Messina University and by the National Ethics Committee for Research Animal Welfare of the Italian Ministry of Health (authorization no. 679/2019-PR on 7 October 2019). All the experimental procedures complied with the ARRIVE guidelines [Female Sprague–Dawley rats (idelines  and weren = 50) were randomly assigned to receive Alendronate 1 mg/kg , genistein 5 mg/kg , lycopene 10 mg/kg , or the association of genistein and lycopene   or saline solution . All treatments were daily administered per os through gavage and lasted additional 60 days simultaneously with MP (s.c.). The Sham group  received saline throughout the experimental protocol (120 days). At the end of the experimental procedure, all animals were sacrificed by cervical dislocation, and femurs were collected for the analyses described below. The in vivo model of glucocorticoid-induced osteoporosis (GIO) was induced by subcutaneous (s.c.) injection of methylprednisolone  for 60 days at the dose of 30 mg/kg which represents the human equivalent dose (7.5 mg). Following induction, MP animals  diluted in culture medium (DMEM) at 37 °C for 2 h on a shaker to promote all soft tissue removal. After additional washes with culture medium  bone pieces were cultured and incubated in Petri dishes at 37 °C of temperature and 5% of COThe obtained primary osteoblasts were incubated with 1 μM of dexamethasone  for 24 h to induce an in vitro model of osteoporosis, as previously described . After iOnce collected, osteoblasts were fixed with paraformaldehyde (4%) for 30 min at 37 °C in the dark and then stained with ARS at the concentration of 1%  for 5 min at room temperature. The formed calcification nodules were detected under an inverted light microscope and images were captured at ×10 magnification.cDNA was reverse transcribed  from 2 μg RNA extracted from cells using TRIzol LS reagent  according to the manufacturer’s protocols. ® Select Master Mix , primers (10 µM) and cDNA were run in duplicate in 96-well plates using QuantStudio 6 Flex Real-Time PCR System . Data were reported using the 2−ΔΔCT relative quantification method and GAPDH was used as a control. Graphs represent the values as fold changes relative to the control cells [Real-time PCR analysis was used to study the mRNA expression obtained from the different experimental groups, as previously described, by using specific primers reported in ol cells ,17. v/v) concentrate and 10% formic acid from 89% (v/v) concentrate dissolved in PBS was used for ∼24 h at 37 °C and later samples were dehydrated in graded ethanol to be embedded in paraffin. Hematoxylin–eosin staining was performed on 5-μm thick bone sections and observed under light microscopy. Bone tissue was collected by leg disarticulation at the hip, knee, and ankle. Once femurs were removed, they were immediately cleaned to remove soft tissues and fixed in 10% neutral-buffered formalin. A decalcifying solution composed of 8% HCl from 37% . The femoral bones were extracted for ex-vivo µCT at the end of the experimental procedure (120 days) and stored at −20 °C until scanning. The excised femurs were defrosted at room temperature and then scanned parallel to the sagittal and coronal plane.Scanning parameters were set as follows: camera pixel size (15.45 µm); tube voltage (50 kV), tube current (800 µA); image pixel size (11.1 µm); exposure (500 ms); rotation step (0.8°); 180° tomographic rotation with random movement; filter (Al 0.5 mm). Image reconstruction was performed using NRecon software , through Hamming-filtered back projection, with the following parameters: smoothing (4); smoothing kernel (2—Gaussian); ring artifact correction (4); beam hardening correction (41%); grey thresholds (82–255). µCT images were resliced parallel to the axial plane of the femoral neck, and the cortical and trabecular bone microarchitecture analysis was performed on 350 consecutive slices.CTAn Skyscan 1275 software  was preliminarily used to separate the cortical and trabecular volumes. To analyze the trabecular bone microarchitecture the bone volume fraction , trabecular thickness , trabecular separation  were measured by means of Batman tool. Structure model index (SMI), which assesses the change in surface curvature occurring when a structure varies from spherical (SMI  =  4) to cylindrical (SMI  =  3) to planar (SMI  =  0), was also considered to measure rods and plates morphology in trabecular bone.3) was determined from the trabecular volume, using hydroxyapatite phantom rods of 4 mm in diameter with known BMD (0.25 gHA/cm3 and 0.75 gHA/cm3) as reference. Subsequently, a separate analysis and quantification of the cortical bone microstructure were performed by evaluation of cortical bone fraction  [Bone mineral density  ,19,20.Results of bone microarchitecture parameters from the right and left sides were averaged to determine differences amongst groups.p value of less than 0.05 was considered significant. SPSS Statistics for Windows v22.0 was used for statistical analysis  and GraphPad Prism was used to create the graphs .All data are expressed as means ± Standard Deviation (SD). Both for in vitro and in vivo experiments the differences between the groups were evaluated and analyzed using one-way ANOVA with the Tukey post-test. A Bone ALP and Runx2 expression was investigated as hallmarks of osteoblast differentiation. Dexamethasone treatment reduced both bALP and Runx2 expression compared to control cells (not treated with DEX). Both treatments with genistein or lycopene significantly increased bALP and Runx2, however, an even more significant increase was observed following osteoblasts incubation with the association of genistein and lycopene A,B. Alizarin Red S staining confirmed that DEX weakened mineralized matrix formation in osteoblasts, as observed by the red color attenuation F. TreatmThe antioxidant Nrf-2 transcription factor was significantly increased following genistein and lycopene treatment but the adjunction of the combination of genistein and lycopene further augmented the hampered reduction of Nrf-2 in DEX-challenged osteoblasts C.Osteoblast activity is also regulated by other molecular pathways, such as the Wnt/β-catenin signaling. Additionally, Wnt5a and β-catenin expression were significantly increased following genistein and lycopene treatment; the concomitant administration of genistein and lycopene significantly increased the expression of both targets compared to the treatments alone. Dexamethasone increased sclerostin expression, which is an inhibitor signal for osteoblast activity, whereas the treatments alone but in particular the association of genistein and lycopene significantly decreased sclerostin levels compared to DEX-treated cells. These results suggested that both genistein or lycopene treatment and even more the association of genistein and lycopene may stimulate osteoblast activity in depressed conditions like that reproduced in the present in vitro model of osteoporosis induced by dexamethasone administration . The analysis of 3D µCT parameters of bone structure at femoral proximal epiphysis showed significant differences in bone microarchitecture among the different experimental groups .A significant bone deterioration was detected in osteoporotic rats (MP group) compared to controls (Sham), which showed looser trabecular bone microarchitecture, reduced cortical bone fraction, and endosteal cortical erosion . Femoral neck analysis  showed tAll 3D morphometric parameters did not show any significant difference when nutraceuticals were used alone compared to the MP group, either for the trabecular and cortical bone compartments, whereas a significant improvement was observed following the administration of the association of genistein and lycopene.Bone tissue samples obtained from the Sham group showed a normal compact bone structure of diaphysis composed of parallel columns of Haversian system with concentric rings of calcified matrix and spongy component, thus exhibiting a homogeneous trabecular network with regular lamellar thick A. In conGlucocorticoid-induced osteoporosis is a well-known cause of iatrogenic osteoporosis, in particular following long-term GC use. Glucocorticoids may influence osteoblasts viability and differentiation, thus inducing osteoclastogenesis and producing a significant imbalance of antiresorptive molecules, such as OPG and RANKL . To switSince the positive effects of genistein and lycopene were already demonstrated in osteoporosis experimental models as well as in clinical trials ,31, the The data described so far confirm that glucocorticoids may affect bone metabolism, thus inducing bone resorption and suppressing bone formation, as demonstrated by µCT and histological analyses. Genistein and lycopene but in particular the association of both might prevent GCs’ adverse effects, thus stimulating bone formation and reducing bone resorption, improving bone structure and microarchitecture. These results are supported by in vitro experiments that showed that both nutraceuticals were able to stimulate osteoblasts activity through different molecular pathways, such as the Wnt/β-catenin and the Nrf-2 signaling. Recently the attention of researchers has focused on the clinical development of osteoanabolic agents for the management of osteoporosis; the final goal is to ameliorate bone strength by positively modulating bone modeling and remodeling. Indeed, the rational therapeutic strategy aims at normalizing bone strength by replacing bone mass loss and by improving the disturbed skeletal architecture. Bisphosphonates and denosumab are the most used anti-resorptive drugs which reduce both bone resorption and partial bone formation, thus causing a positive bone balance and ameliorating, in turn, bone mineral density, and bone strength parameters. The weakness of this therapeutic strategy stems from the lack of effects on the bone formation that does not allow a restoration of the altered microarchitecture of the trabecular or cortical bone. Only the osteoanabolic and bone-forming drugs teriparatide (the 1–24 PTH fragment) and romosozumab (an antibody against sclerostin) may accomplish this task, thus they are currently subjected to intense clinical investigation in patients with osteoporosis, especially those with a high risk of fracture. We are tempted to speculate that the genistein-lycopene combination falls in the category of osteoanabolic agents and may represent a natural and safe alternative therapy to both teriparatide and romosozumab. However, this hypothesis needs to be deeply explored and confirmed in randomized clinical trials.