Lactobacillus delbrueckii strain A01, Lactobacillus delbrueckii subsp. bulgaricus strain D01, Lactobacillus delbrueckii subsp. bulgaricus strain E01, Lactococcus lactis strain G01, Lactobacillus delbrueckii strain C01, and Lactobacillus delbrueckii subsp. bulgaricus strain F01 were identified using 16S rDNA sequencing, morphological and biochemical traits. All strains have been registered in the National Center for Biotechnology Information (NCBI) with accession numbers MN611241.1, MN611300.1, MN611301.1, MN611303.1, MN611241.1, and MN611299.1, respectively. Having found ε-Poly-L-Lysine (ε-PL) in all strains isolated, Lactobacillus delbrueckii strain A01 was identified as an active producer of ε-Poly-L-Lysine (ε-PL). The one-factor-at-a-time method and central composite design were applied to optimize ε-Poly-L-Lysine (ε-PL). A predicted 200 ppm of ε-PL was obtained in the medium containing the lowest level of glucose, 25 g/l, and yeast extract, 6 g/l.New strains of lactic acid bacteria (LAB) were isolated from different traditional dairy products. Six new strains named  Lactobacillus acidophilus reduced the cholesterol and triglycerides of rabbit blood , w, wA) andppm ε-PL , consideLactobacillus delbrueckii strain A01, in optimal condition, could be regarded as a promising source of ε-PL. The highest content of ε-PL was attained in the medium containing the lowest content of protein and glucose. To conclude, a preserving activity of LAB in fermented dairy products could be somehow related to the ε-PL produced by the wild LAB in traditional dairy products.The first attempt has been made to isolate and characterize LAB from the traditional fermented milk of the rural area near Shahrood in Iran. Six strains of LAB isolated from dairy products were identified and published with GenBank accession numbers. The result indicated that isolated strains named


ADORA2A gene, coding for adenosine receptor type 2A (A2AR), has been involved in aberrant immune activation. Here we aimed to assess the prevalence of this SNP in 279 MCS patients and 238 healthy subjects, and its influence on ADORA2A, IFNG and IL4 transcript amounts in peripheral blood mononuclear cells of randomly selected patients (n = 70) and controls (n = 66) having different ADORA2A genotypes. The ADORA2A rs2298383 TT mutated genotype, significantly more frequent in MCS patients than in controls, was associated with a three-fold increased risk for MCS , while the CT genotype, highly prevalent among controls, resulted to be protective . Notably, ADORA2A mRNA levels were significantly lower, while IFNG, but not IL4, mRNA levels were significantly higher in TT MCS patients compared with controls. A significant negative correlation was found between ADORA2A and both IFNG and IL4, while a significant positive correlation was found between IFNG and IL4. These findings suggest that A2AR defective signaling may play a relevant role in PBMC shift towards a pro-inflammatory phenotype in MCS patients.Systemic inflammation and immune activation are striking features of multiple chemical sensitivity (MCS). The rs2298383 SNP of  Multiple chemical sensitivity (MCS), also called environmental sensitivity illness (ESI) or toxicant-induced loss of tolerance (TILT), involves an aberrant susceptibility response to a broad range of chemical substances present in daily life . In the Despite the absence of validated diagnostic biomarkers, the epidemiological evidence has led the individual countries to at least partially recognize MCS as a pathological state. In Europe, in particular, Germany and Austria classified MCS under the ICD-10 code T78.4  [d-aspartate (NMDA)-nitric oxide/peroxynitrite triggered by various chemical agents, that ultimately lead to oxidative stress and activation of pro-inflammatory transcription factors [To date the proposed etiopathogenetic mechanism involves an aberrant activation of the vicious cycle N-methyl- factors . Indeed, factors ,12,13,14 factors ,19,20,21ADORA2A gene, encoding for A2AR, is a functional variant that may affect the rate of gene transcription due to its location within a regulatory sequence of the gene [The release of pro-inflammatory cytokines, as well as the activation and proliferation of T cells, can be inhibited by adenosine receptor activation. Adenosine receptor 2A (A2AR), one of four adenosine receptor sub-types, is present in almost all immune cells, including lymphocytes, monocytes, macrophages and dendritic cells, and its activation increases the production of anti-inflammatory cytokines . The sinthe gene ,24. Notathe gene .ADORA2A gene, and the consequent effects on the pro-inflammatory phenotype shift of peripheral blood mononuclear cells (PBMC) isolated from both MCS patients and healthy controls.We here aimed to assess the prevalence of ADORA2A rs2298383 polymorphism in MCS patients as well as age- and gender-matched healthy subjects, in order to establish a possible association of this SNP with MCS syndrome. Moreover, we evaluated the influence of the rs2298383 SNP on the transcription levels of ADORA2A rs2298383 SNP, and results are shown in p = 0.928), and with those reported for Italian population in the 1000 Genomes project (http://asia.ensembl.org/Homo_sapiens/Variation/Population?db=core), while they were found to deviate from the expected value by HWE (p = 0.000000) in the group of MCS patients. The T mutated allele was more frequent among MCS patients than among healthy subjects, and was predominant in homozygous state, while in controls it was more frequently found in heterozygous state (Both patients and controls were genotyped for us state . The mutus state . The wilp < 0.0001). Instead, a negative association with MCS was observed for the heterozygous CT genotype, that was significantly more frequent among healthy subjects than in MCS group. Individuals that were carriers of this genotype had a three fold-decreased risk for MCS .The mutated TT genotype resulted to be associated with MCS disorder, since the Odds Ratio calculation showed that the risk of developing MCS syndrome increases by about three folds in individuals who have this genotype , and in comparison with MCS patients having other genotypes . Significant differences were also found when MCS having either CC or CT genotype were compared with healthy subjects carriers of the same genotypes . No statistically significant differences were found among controls having different ADORA2A genotypes , while no statistically significant differences were found in comparison with MCS patients having other genotypes. No statistically significant differences in IFNG mRNA levels were found when comparing the different genotypes within the healthy cohort. Interestingly, statistically significant differences were observed between either CC or CT MCS subjects and their healthy counterpart .The IL4 mRNA levels were observed between MCS cases and controls or between different genotype subgroups within the two cohorts , while IL-4 concentrations were 1.5 fold-increased in MCS cases compared with controls . However, only IFN-γ protein levels resulted to be significantly different between the two groups, likely due to the small number of subjects examined.The assessment of secreted cytokine amounts showed that the mean IFN-γ serum concentrations were three-fold increased in MCS patients compared with healthy subjects  and IL4  mRNA expression levels, while a significant positive correlation was found between IFNG and IL4 mRNA expression levels .Notably, a significant negative correlation was found between In the last decade, literature data provided evidence for the association of MCS with increased oxidative/nitrosative stress and inflammation ,12,13,14ADORA2A rs2298383 SNP, that has been shown to affect A2AR signaling [ADORA2A rs2298383 TT mutated genotype, being largely prevalent in MCS patients, is associated with MCS and represents an unfavorable genetic background for this disorder. Instead, the heterozygous CT genotype, more frequent among healthy subjects, is negatively associated with MCS and displays a protective effect due to a three-fold decreased disease risk. The ADORA2A rs2298383 genotype distributions observed in our control group agree with those previously reported for Caucasian population [http://asia.ensembl.org/Homo_sapiens/Variation/Population?db=core), an optimal reference population which contains allelic frequencies for a sample of 107 subjects from Tuscany, Italy.In this study, we first investigated the prevalence of ignaling ,30, in Cpulation  and in 1ADORA2A transcript levels in MCS cases. These findings are apparently in contrast with previously made assumptions. Indeed, on the basis of in silico analysis it has been hypothesized that the wild-type CC genotype is associated with a drastic decrease in ADORA2A transcription levels and amount of A2AR molecules, with functional consequences comparable to the receptor blocking by antagonists [ADORA2A haplotype including the rs2298383 CC genotype, showed higher amounts of ADORA2A mRNA and A2AR protein compared with those having different haplotypes [Also interestingly, the TT genotype was associated with a dramatic reduction in agonists ,32. Howeplotypes . The preplotypes , and sugInterestingly, given that A2AR is abundantly present in olfactory bulb , it is pIFNG and IL4 were higher in subjects carriers of the ADORA2A rs2298383 TT mutated genotype than in subjects having either CC or CT genotype. Moreover, the highest serum concentrations of these cytokines were found in TT MCS patients that also had the lowest amount of ADORA mRNA transcripts. These observations are supported by the significant negative correlation found between ADORA2A mRNA and both IFNG and IL4. Our data partially replicated the findings of Dantoft et al. [We here also first provided a confirmation of this hypothesis by showing that mRNA levels of t et al. , that deADORA2A expression in PBMC of MCS patients.IFN-γ plays a key role in macrophage activation, inflammation, and host defense against intracellular pathogens. The inhibition of expression and function of anti-inflammatory molecules represents a key mechanism of IFN-γ-mediated priming of enhanced innate immune responses . The higIL-4 is a cytokine associated with the activation of Th2 cells, and has long been considered as one of the tolerogenic cytokines in many autoimmune animal models and clinical settings; however, its overproduction has mainly been associated with the development of allergic reactions and atopy . NotablyADORA2A homozygous mutated genotypes were shown to have lower habitual caffeine consumption compared with wild-type ones, likely due to a mechanism of negative feedback [ADORA2A heterozygous genotype in the general Caucasian population supports the hypothesis that this genetic trait was selected as a result of adaptive evolution to the daily intake of caffeine, the excess of which may display well known toxic side effects for the body. Thus, it is possible to speculate that the ADORA2A rs2298383 TT mutated genotype has been subjected to positive selection because it is protective against the long lasting effects of caffeine resulting from its slowed metabolism. A side effect of this natural selection is the increased susceptibility to the onset of inflammatory processes and dysregulation of immune system, as observed in MCS patients that have a defective metabolism of xenobiotics.It is well known that adenosine effects are reduced by caffeine and its metabolites, acting as competitive inhibitors at adenosine receptors throughout the body . Individfeedback ,31,42,43ADORA2A mRNA expression in a dose- and time-dependent manner [These observations suggest that recommendations of family practitioners to MCS patients should include drastic reduction or even elimination of caffeine, that has been shown to reduce t manner . Moreovet manner ,28, coulOur preliminary findings suggest the important role of A2AR signaling in the chronic inflammatory process associated with MCS. Further replication studies will be useful to confirm the present observations.This study conforms with the Declaration of Helsinki (amend. 2013). All subjects recruited for this study provided written informed consent to the collection of anamnestic data and peripheral blood sampling in EDTA tubes aimed to carry out research investigations.This study was carried out using a biobank of whole blood samples collected from 279 Italian MCS patients , and 238 Italian healthy volunteers . The enrollment of MCS patients was mostly carried out at Department of Medical Pathophysiology, University of Rome “La Sapienza”, Polyclinic Umberto I, and at IDI IRCCS .All subjects, presenting with symptoms compatible with MCS and QEESI score > 20 ,18,19,43Healthy subjects were recruited either among staff members of Polyclinic Umberto I, University “La Sapienza” and IDI IRCCS , or among staff members and volunteer blood donors at Polyclinic Hospital University of Messina , according to the following established inclusion criteria: a) absence of any clinically diagnosed disease; b) absence of allergic or immunologic disturbances; c) absence of supplementation with drugs or nutraceutical compounds in the last six weeks, at the time of blood sampling; d) absence of oxidative stress, i.e., whole blood total production of ROS/RNS below 500 cps/μL, as previously reported on the basis of a luminol-dependent chemiluminescent response to phorbol 12-myristate 13-acetate ; e) non-Genomic DNA (gDNA) was isolated from peripheral white blood cells using the Gentra Systems PureGene-DNA purification kit , according to manufacturer’s instructions. The DNA was quantified by spectrophotometric measurement at 260 nm using a Biophotometer . DNA quality was considered acceptable for samples having a 260/280 ratio ≥ 1.6. DNA integrity and the presence of contaminant RNA were checked by electrophoresis on 0.8% agarose gel, and subsequent UV detection of DNA bands using a gel photodocumentation system .The screening for the presence of ADORA2A rs2298383 (C > T) SNP was performed by Real-time PCR-based allelic discrimination using a pre-designed TaqMan-based Genotyping Assay , as previously described . PCR rean = 70; 18M, 52F) and healthy subjects , having different ADORA2A rs2298383 genotypes . PBMC were isolated from 300 µL of whole blood samples, after red blood cells lysis by RBC buffer available from Gentra Systems PureGene-DNA purification kit . The isolated RNA was quantified by spectrophotometric measurement at 260 nm using a Biophotometer , then, 2 μg RNA were reverse-transcribed to cDNA using the High-Capacity cDNA Archive kit  according to manufacturer’s instructions.Total RNA was extracted and purified from PBMC of randomly selected MCS patients (BACT) as endogenous control. PCR reactions were set up in a 96-well plate on a 7900HT Fast Real-Time PCR System , and were carried out in a final volume of 20 μL, containing Master mix (2×) Power up SYBR Green PCR , 0.2 µM for each primer, and 40 ng of cDNA. The used primer sequences were 5′-GGCTGCCCCTACACATCATC-3′ (fwd) and 5′-GCCAGGTACATGAGCCAGAGA-3′(rev) for ADORA2A, 5′-GCAGCCAACCTAAGCAAGAT-3′ (fwd) and 5′-TCACCTGACACATTCAAGTTC TG-3′ (rev) for IFNG, 5′-CCTCTGTTCTTCCTGCTA-3′ (fwd) and 5′-AGATGTCTGTTACGGTC-3′ (rev) for IL4, and 5′-TTGTTACAGGAAGTCCCTTGCC-3′ (fwd) and 5′-ATGCTATCACCTCCCCTG TGTG-3′ (rev) for BACT. A final dissociation curve analysis was carried out to evaluate the amplification specificity.mRNA transcript levels were quantified by SYBR green-based Real-time PCR, using β-actin , due to unavailability of serum samples. In order to minimize gender variations only female patients were included in these analyses. The measurements of cytokine serum levels were performed using a commercially available assay  as described by De Luca and co-workers .ADORA2A rs2298383 genotype distributions to HWE, based on a Web program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl).Continuous data are expressed as means ± standard deviation (S.D.), and the categorical variables as number and percentage. The Fisher’s exact test was applied to test the difference between cases and controls in terms of categorical variables, and the compliance of ADORA2A mRNA levels as the primary outcome variable. Taking into account a power of 80% and an alpha of 0.05, and a number of groups equal to three (as per three different genotypes), it was determined that a minimum number of 69 MCS patients would be needed. Thus, we chose to analyze gene expression levels in 70 MCS subjects.A sample size analysis was performed to calculate the minimum sample size required for gene expression studies. The sample size was calculatedconsidering ADORA2A, IFNG and IL4 between MCS group and control group were analysed by Mann-Whitney test, while differences between subgroups having different ADORA2A genotypes within either the MCS cohort or healthy cohort were analyzed by Kruskall-Wallis test. A Spearman correlation analysis was performed to test the relationships between the examined continuous variables.The distribution of values obtained by gene expression studies was tested by Kolmogorov-Smirnov test, and was not found normal. As a consequence, non-parametric tests were used for subsequent statistical analyses. Differences in the mean mRNA total levels of p-value ≤ 0.05 was considered statistically significant for all the analyses.Statistical analyses were performed using the software GraphPad Prism 5 . A


Overall, the results identified purchase/transport time and purchase order as the emerging and unchanged risk factors, respectively. Consumers’ preferences into channels for purchase  and transport (using cars or delivery) implied the convenience as the noticeable trend. Whereas, unexpected increases in purchase/transport time highlighted the underestimated risks in long-term exposure of foods under inadequate temperature. Food should not be exposed to danger zones > 1–2 h, but consumers might be unaware of the risk especially for preferred channels . In the case of unchanged risky behavior, more than half of consumers in both surveys did not follow proper purchasing orders. Our findings highlight the necessity for novel countermeasures and the improvement of current consumer guidelines against emerging and unchanged risky behaviors, respectively.Although consumers’ food purchase/transport have been reported as causes of food safety risks, there is a lack of empirical data that are feasible to identify persistent and emerging risky behaviors of consumers. This longitudinal trend study consists of individual consumer surveys in 2010 ( Consumers’ food and meal preparation behaviors from shopping to consumption have been associated with various human health issues, including foodborne diseases ,2,3. FooBecause consumers’ improper behaviors from the first step of the food preparation affect the risk level at following stages  , food puThe present study aims to conduct consumer surveys on risky behaviors during food purchase/transport and their changes over time. Comprehensive analysis on time-temperature control is needed in order to estimate the actual level of risks derived from consumers’ food purchase/transport behaviors, as follows: (1) consumers’ preferences for food purchase and transport methods, including where and how temperature abuse can occur; 2) food purchase and transport time, including whether the exposure of food to ambient environments exceeds the recommended limit; and, (3) risk perceptions of food purchasing, namely, whether consumers aware of the proper purchasing order and which factors affect the purchased foods. However, there has been no survey research on food purchase and/or transport to analyze not only consumers’ preferences, but also the time that is required for each step and consumers’ risk perceptions regarding time-temperature control and microbiological risk factors ,19,20. M food purn = 605) and 2010 (n = 609) with the same questionnaires regarding the following determinant factors for risky behaviors during food purchase/transport: consumer preferences for purchase/transport methods, time that is required for purchasing/transporting food products, and risk perceptions. A comparative analysis of the surveys was conducted to identify the major changes in each factor over time. Results from longitudinal analysis are expected to reveal the changes in the consumers’ behaviors during a decade and the insights whether those changes are positive or negative with the view to the proper consumer guidelines. The identification of emerging behaviors that can raise the risk level will imply the direction for establishing feasible countermeasures taken into consideration to drive the alteration of the behaviors of contemporary food consumers.In this study, quantitative surveys of primary food handlers were conducted in 2019  who were the main people involved in food preparation at home (hereafter defined as primary home food handlers) were recruited as the interviewees. Previous studies regarding home food safety have reported that hygienic perceptions and behaviors vary according to sociodemographic characteristics  ; these cA questionnaire was used as the research instrument for the survey and a same questionnaire was used as the research instrument for the longitudinal trend surveys that were conducted in 2010 and 2019. A consulting committee organized by a government institution  with experts in consumer surveys , consumer organization, and food safety and hygiene laboratory  developed the questionnaire. All members of the consulting committee participated in composing a draft questionnaire and then revising the final version to verify its applicability. In addition, Gallup Korea reviewed the survey instrument for clarity and validity.The questionnaires were developed for the identification of consumers’ behaviors with perceptions linked to the risks of food purchase/transport. Those behaviors and the questionnaire contents were mainly organized based on the internationally recognized food safety guidelines provided by major institutions, including Food and Drug Administration (FDA), Food Safety and Inspection Service in U.S. Department of Agriculture (USDA FSIS) ,15,17. Gn = 15; randomly selected) and expert researchers . The pretesters were asked to evaluate the questionnaire with the perspectives to the terminologies that needed to be revised to improve clarity, unclear, and/or difficult expressions, and contents that might induce the respondents to feel displeasure or resistance during the survey [To revise the draft questionnaire for clarity and validity, a pilot test was conducted by the pretesters who were consumers , food purchase/transport time (Q3–Q4), and risk perceptions (Q5–Q7). The specific questions were, as follows: Q1. Where do you buy each food ? Q2. How do you transport purchased food from each place to your home? Q3. How long does it take you to buy food at each place? Q4. How long does it take you to transport the food? Q5. What do you buy first between food and nonfood items? Q6. What do you buy first between refrigerated/frozen foods or foods that can be stored at room temperature? Q7. Which factors do you consider when purchasing food?n = 609) and 2019 . The, same questionnaires were and method were used for sociodemographic group from each survey to examine how behaviors with risk perceptions from socio-demographic groups of primary food handlers have changed over time [The present study is constructed with two individual surveys conducted in 2010  was utilized to analyze the obtained data.Consumers’ preferences regarding transport methods also showed distinct changes toward more convenient methods in survey 2 (2019) when compared with survey 1 (2010): (1) an increase in the use of cars or delivery rather than walking to traditional markets or supermarkets/small markets near home, and (2) an increase in the use of delivery from large discount stores and department stores . GrowingConsumers should buy nonfood stuffs and foods that can be stored at room temperature earlier, followed by refrigerated and frozen foods. However, as shown in Respondents from both surveys indicated the ‘shelf life’ as the information of highest interest, as shown in Problems in time-temperature control during consumers’ food purchase and transport have also been linked to the potential risks in food quality and safety, as the occurrence of the deterioration of food products at the post-harvest or post-processing levels are mostly attributed to inadequate infrastructure for storage and/or transport . This trConsumers’ preferences into purchase and transport channels implied the convenience as the noticeable trend ,47. The Increases in total food purchase and transport time suggest the importance of time-temperature control. Although food safety guidelines suggest following the “2-h rule and 1-h rule”  ,15,17,52Perceptions of microbiological risks that are derived from purchasing orders are representative unchanged risky behavior over time. These results indicate that many consumers overlook the importance of time-temperature control of food at the purchasing step. Although previous studies regarding consumers’ risky behaviors of food preparation have reported improved risk perceptions and knowledge  ,55, imprThis study newly identified risk factors of food purchase/transport, highlighting the impact of consumers’ behavior studies which have been mainly focused on hygienic practices during the food preparation steps after the food purchase/transport ,60,61,62Our longitudinal trend study implied the necessity of the improvement on conventional consumer guidelines to cover contemporary trends in food purchase/transport with the perspectives to the time-temperature control during food purchase/transport: (1) chilling of perishable foods during transportation, especially in car trunks; (2) handling of delivered groceries from receipt to storage; (3) purchasing of food in the proper order; and, (4) compliance with the “2-h rule and 1-h rule” by considering the temperature of the environment to which foods are exposed and the total time required for food purchase/transport. Since unchanged and emerging risky behaviors observed in this study also highlighted the limitation of current risk management represented by the guideline, the development of novel consumer education materials and programs  should be considered. To sum up, the implications from this research are expected to suggest food purchase/transport as underestimated topic in the research area of the link between food preparation behaviors and consumers’ health. Because this study focused on the identification of risky behaviors rather than the establishments of the causes and/or factors for those behaviors, further researches should be followed in order to identify the major determinants of identified risks , especially for the multiple factors that can increase risk levels.


We propose here a method to analyze whether financial and macroeconomic shocks influence the entropy of financial networks. We derive a measure of entropy using the correlation matrix of the stock market components of the DOW Jones Industrial Average (DJIA) index. Using VAR models in different specifications, we show that shocks in production or the DJIA index lead to an increase in the entropy of the financial markets. The use of networks to analyze economic and financial phenomena is developing fast. There have been numerous recent significant contributions that have further developed our understanding about the potential of using networks or network-based perspectives in studying financial phenomena see ,6,7,8,9),7,8,96,7Among the approaches used, the entropy-based methods have provided valuable results. We can include here the contribution of  who apprAnother strand of research using entropy has focused on its capacity to reveal the state of the financial markets. For example, Ref.  has showA related research looked at the long run relationship between stock and commodities (see ). The maIn a recent paper, Ref.  has showIn this paper, we aim at building upon recent contributions on the use of entropy in modelling financial markets. Specifically, we aim at looking at whether and how macroeconomic and financial shocks contributed to the variation in entropy of financial markets. To achieve this, we built on previous studies that also used time-varying measures of entropy derived from correlation matrices (see  or 15]))15]), anThere are several contributions that we make in this paper. First, we propose one of the first studies that approaches the changes of the structure of networks following macroeconomic and financial shocks. Second, we characterize the changes in the networks through the use of entropy derived on the basis of matrix correlations of stocks composing the network.This section details the methods and tools used in the paper. The techniques are quite standard; however, the presentation might be of interest to those not familiar with it.Our approach to construct correlation-based matrices is standard. We look at the correlation between the stock components of the DOW Jones Industrial Average index  properties of the correlation matrices, as seen from the next sections.The singular value decomposition , when applied to a matrix allows us to obtain the following decomposition:A, with m rows and n columns. The matrix U has m rows and k columns, while V has n rows and k columns. The matrix S can then be written by:k is determined through the expression S has two important properties, namely positive elements as well as a decreasing order for the values of Here, we applied the decomposition to a given matrix It must be underscored (we thank a reviewer for pointing this out) that the initial matrix is squared and we can just define the E, throughout the text. The construction of the E measure of entropy is based on previous contributions by ..dot-com In this section, we take a first step towards answering the main research question of the paper: how do macroeconomic and financial shocks affect the entropy of the financial markets? We test here for Granger causality between interest rate (as well as shadow interest rate), the inflation, industrial production index as well as DJIA, on one hand, and the entropy (computed either using a 2-year or a 3-year window), on the other hand.The role of this step is to establish, in a bivariate setting, whether the selected macroeconomic and financial variables have predictive power with respect to the entropy of financial markets. While this test is limited through the simple use of a bivariate setting and the lack of a structural model, it is still an important step before using a VAR model as we do in the next section.The results are shown below in For the case of the industrial production, the evidence is the strongest, with evidence of Granger causality at almost all the lags. In contrast, there is no evidence for Granger causality from inflation to entropy. Finally, for the case of the DJIA index, the evidence that supports the Granger causality is limited to just one lag.The evidence presented here, although not enough per se, justifies considering a more extensive econometric framework in the next section.In this section, we aim at answering the main research question in this paper: if and how financial and macroeconomic shocks affect the entropy of financial markets. To answer it, we build a VAR model consisting of monthly frequency series. We use the following set of variables: c is a vector of constants Formally, the VAR model is written as follows:The variables, except the interest rate (as it is a custom in the literature), are log-differenced, since they have a unit root see . We testWe consider a baseline estimation, with the federal funds rate as the measure for the interest rate, and, additionally, the shadow policy rate, for an alternative estimation. Furthermore, for robustness, we also consider an alternative measure of entropy based on a three-year moving window.To identify the structural shocks, including the monetary policy shocks, we use a Choleski ordering of the variables, with the following ordering: industrial production, consumer price index, interest rate, entropy, DOW 30. This is a rather typical ordering, with the variables moving the fastest, i.e., the stock market variables (including the entropy) ordered as last. The setting implies that the industrial production and the consumer price index do not contemporaneously react to interest rate changes, while the interest change does not respond to contemporaneous changes in the entropy measure or in the stock market.This approach allows us to uncover whether the macroeconomic and financial variables considered here have any statistically significant impact on the main variable of interest, i.e., the entropy of the financial markets. The approach relies on a certain setting for identifying the structural shocks, see above, and thus it is better fit to answer the main question of the paper as compared to the simple Granger test for causality.x-axis indicates the horizon of the response in months. log_entropy2y is the log-difference for entropy using a two-year window, while log_entropy3y is computed using a 3-year window, log_ip is the log-difference for industrial production, intrate is the interest rate used in the model and log_dow is the log-difference of the DJIA index.The results for the estimated baseline VAR models using the federal funds rate as the interest rate are shown below. We present the impact of the different structural shocks on entropy. The We also performed a robustness exercise, using a similar setting and the same variables, except the interest rate, for which the shadow rate is used. The alternative estimates are shown in This section provides a tentative answer to the main question of the paper: if and how do the macroeconomic and financial variables influence the entropy measure of financial markets. We found that there is a statistically significant role for industrial production and the DJIA index. While we would have expected a statistically significant impact of the stock market dynamics, the finding related to the industrial production index raises interesting issues that are worth studying further.This finding could imply that the connections between the nodes of the financial market also reflect a strong connection with the real economy. On one hand, we know that stocks tend to organize themselves in financial networks in clusters following the specific of their activity . On theThe main purpose of this study was to extend the previous literature regarding the modelling of the entropy within a macro-finance framework. We started by considering the derivation of entropy based on a moving window consisting of the DOW 30 index components for which we computed the correlations. Using a singular value decomposition, and a sliding window of 2 or 3 years of observations, we were able to derive a time-varying measure of entropy.The principal contribution of the paper was to use the derived measure of entropy within a VAR framework including the industrial production, the inflation, the interest rate, the entropy as well as the DOW 30 returns. In this modelling framework, we were able to determine the impact of financial and macroeconomic shocks on entropy.In our VAR framework, entropy responds positively to shocks in the monetary policy, although the impact is hardly statistically significant. However, there are positive responses that are statistically significant following industrial production shocks (after 2–3 months), as well as a positive response after a shock in the stock market (DJIA).Furthermore, the results are robust to the use of different interest rate measures , as well as to the different ways to construct the entropy (by varying the size of the moving window).Compared to the results when using the simpler bivariate Granger causality tests, we see that the industrial production leads again to significant responses in entropy, while the inflation rate does not significantly impact it. There is a positive impact by the interest rate, although barely statistically significant. However, the DJIA index has a significant impact, although the results for the Granger causality tests were quite weak. The two frameworks are, however, not fully comparable since the VAR framework is multivariate and uses a certain procedure for identifying the structural shocks, while the Granger causality test is a simpler bivariate framework.This is one of the first contributions not only to employ the entropy of a financial network in a typical macro-financial model, but also to obtain evidence regarding the movement of entropy following macroeconomic and financial shocks. The results are significant, since they show that the networks are not static at all, but they do react to changing in the macroeconomic and financial conditions.The contributions here can be further developed and tested in many ways, and they show that there is a significant potential for entropy measures of networks to be used within standard macroeconomic and financial models.


Evidence suggests that microRNAs (miRNAs) regulate the expression of genes involved in bone metabolism. This study aimed to investigate the role of miR-505 in the osteogenic differentiation of MC3T3-E1 cells.We performed miRNA sequencing to identify differentially expressed miRNAs between MC3T3-E1 cells treated with osteogenic induction medium (OIM) and control cells. Bioinformatics analysis was performed by using the TargetScan and miRDB databases. The expression of miR-505 in MC3T3-E1 cells was detected during osteogenic differentiation. After transfection with miR-505 mimic or miR-505 inhibitor, MC3T3-E1 cells were induced to differentiate into osteoblasts, and the expression of osteogenic differentiation markers (Runt-related transcription factor 2 (RUNX2), alkaline phosphatase (ALP), osteopontin (OPN), osteocalcin (OCN), and osterix (OSX)) was detected.miR-505 was the most downregulated miRNA among the differentially expressed miRNAs. The RUNX2 gene was identified as a potential target of miR-505 using the target prediction program. miR-505 expression was downregulated during osteogenic differentiation of MC3T3-E1 cells. The expression of osteogenic marker genes was inhibited in MC3T3-E1 cells after transfection with miR-505. However, the expression of osteogenic marker genes was upregulated after transfection with miR-505 inhibitor.This study is the first to report miR-505 could bind to the RUNX2 gene and thus regulate partly the dysfunction of osteoblasts differentiation, which is expected to be targets for the treatment of osteoporosis. Osteoporosis is a chronic systemic bone disease manifesting as lower bone mass, which eventually contributes to increased risk of fractures , 2. SevemiRNAs are a class of multifunctional noncoding small RNAs that regulate cell functions and signaling pathways by inhibiting the expression of target mRNAs , 7. miRNTo clarify involvement of miRNAs in osteogenic differentiation of MC3T3-E1 cells, we performed microRNA array and found that miR-505 was the significantly differentially expressed miRNA. In experiment study, we found that miR-505 was downregulated in the process of osteogenic differentiation of MC3T3-E1 cells. Moreover, we also determined that miR-505 could directly bind to RUNX2 and thus inhibit osteogenic differentiation.The following reagents were used in this study: α-MEM medium ; fetal bovine serum (FBS) and Opti-MEM medium ; 0.25% trypsin, dexamethasone, ascorbic acid, and β-glycerol phosphate ; Lipofectamine RNAiMAX reagent, TRIzol reagent, and protein marker ; PrimeScript RT reagent Kit with gDNA Eraser kit and SYBR Green PCR Master Mix ; miRNA quantitation and U6 calibration qRT-PCR kit, miR-505 mimics, inhibitor, and negative control (NC) ; Alizarin red staining reagent ; RUNX2, OSX, OPN, ALP, GAPDH, miR-505, and U6 primers ; BCA protein assay kit , RIPA lysis buffer, and SDS-PAGE Gel configuration Kit ; PVDF membrane and chemiluminescence ECL solution ; anti-OPN, anti-OCN, and anti-GAPDH antibodies ; anti-RUNX2 antibody ; anti-ALP antibody ; and other reagents generated by our group.2 and 37 °C, and the cells were subcultured once a day. A single cell suspension was prepared from MC3T3-E1 cells growing under steady-state conditions and inoculated in a 6-well culture plate. The density of MC3T3-E1 cells was more than 80% after plating in a 6-well culture plate, and the osteogenic induction medium , 50 μg/mL ascorbic acid , and 10 mmol/L β-glycerophosphate sodium ) was replaced every 2 days.Mouse progenitor osteoblasts MC3T3-E1 were purchased from the Cell Bank of the Chinese Academy of Sciences. The cell culture medium was 10% FBS in MEM containing 0.35 g/L glutamine. The cells were cultured in a constant-temperature cell culture incubator at 5% CO5 cells per well) were initially seeded in a six-well plate, at 70% confluence; the cells were treated with growth medium (control) or OIM for 10 days, respectively.The RiboArray miDETECT mouse array  was used to detect the microRNA expression levels in 3 independent replicates for MC3T3-E1 without osteoblast induction and 3 independent replicates for MC3T3-E1 which underwent for 10 days in OIM. Briefly, MC3T3-E1 cells  according to the manufacturer’s instructions. NanoDrop ND-1000 (Thermo) was used to measure the concentration of purified RNA and detect the quality of RNA. RNA labeling, microarray chip hybridization, and scanning were performed according to the method and instructions provided by the Affymetrix Multispecies miRNA-2 Array.http://www.targetscan.org/vert_71/) and miRDB (http://mirdb.org) databases. Then, a Venn diagram of the results from these two databases was generated using Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/). Overlapping genes were then submitted to DAVID 6.8 (https://david.ncifcrf.gov/tools.jsp) to identify Gene Ontology (GO) categories , molecular function (MF), and cellular component (CC)) and perform KEGG pathway analysis.First, we predicted miR-505 targets using the TargetScan  of each specimen and the internal reference gene U6 were obtained. The relative content of miR-505 was calculated relative to housekeeping U6 with the 2equation . The relequation . The expMC3T3-E1 cells were induced to undergo osteogenesis for 21 days, after which the culture medium was discarded, and the cells were washed with PBS 2 times, fixed with 4% paraformaldehyde for 30 min, washed with PBS 2 times, stained with alizarin red 10 min, washed completely with PBS, and observed by inverted microscopy.5/mL. MC3T3-E1 cells were randomly divided into 4 groups: miR-505 mimics, miR-505 mimics-NC, miR-505 inhibitor, and miR-505 inhibitor-NC. Lipofectamine RNAiMAX liposomes were used for transfection. Then, miRNA duplexes at a concentration of 30 nmol/L were mixed with 150 μL Opti-MEM medium. Concurrently, Lipofectamine RNAiMAX reagent was mixed with 150 μL Opti-MEM medium at 7.5 μL per well. After incubation for 5 min, the miRNA and Lipofectamine RNAiMAX solutions were mixed and incubated at room temperature for 20 min. The medium in the 6-well plate was discarded, and 700 μL Opti-MEN culture medium was added to each well. Then, the incubated miRNA/Lipofectamine RNAiMAX mixture was added to each well and gently mixed. After the cells were incubated in a constant-temperature cell culture incubator at 37 °C for 6 h, α-MEM with 10% FBS was added, and the cells continued to be cultured. Then, the osteogenic induction medium was added for 3 days.MC3T3-E1 cells were digested by trypsin and resuspended in α-MEM containing 10% FBS. The cells were inoculated in 6-well culture plates at a density of 2.5 × 10To determine the success of miR-505 transfection, the expression of miR-505 was detected by real-time quantitative RT-PCR. After transfection with 15, 30, or 60 nmol/L concentration of miR-505 mimics, miR-505 mimics-NC, miR-505 inhibitor, or miR-505 inhibitor-NC for 24 h, total RNA was extracted to detect the mRNA expression and miR-505 expression levels. Optimal concentration was identified as used for further experiments. All of the transfections were performed by using Lipofectamine 3000  based on the provided directions [The cells were transfected with miR-505 mimics, miR-505 mimics-NC, miR-505 inhibitor, or miR-505 inhibitor-NC for 24 h and cultured for 3 additional days after replacement of the medium with osteogenic induction medium. The mRNA expression of RUNX2, OSX, ALP, and OPN in cells was detected by collecting and extracting total RNA.MC3T3-E1 cells were transformed with miR-505 mimics, miR-505 mimics-NC, miR-505 inhibitor, or miR-505 inhibitor-NC and induced to undergo osteogenesis for 3 days. Total protein was collected and lysed with RIPA buffer. The protein concentration was determined by BCA protein quantitative test. The total protein was transferred to a PVDF membrane by SDS-PAGE. After blocking with skimmed milk powder, 1:1000-diluted anti-RUNX2, anti-OPN, anti-OCN, anti-ALP, and anti-GAPDH antibodies were added overnight at 4 °C. After rinsing, the secondary antibody was incubated at 1:2000, at room temperature for 2 h.5 cells per well. 3′ UTR of the RUNX2 gene containing putative miR-505 targeting site was amplified by chemical synthesis and was inserted into the psiCHECK2 vector . When the confluence was up to 70%, MC3T3-E1 were transfected with related mixtures including 50 ng RUNX2 wild-type or RUNX2 mutant-type 3′ UTR reporter plasmids, miR-505 mimics or miR-505 mimics NC in a final concentration of 20 nM, and Lipofectamine 2000 for 48 h. Luciferase activity was detected using the dual-luciferase reporter gene kit .MC3T3-E1 cells were firstly inoculated into 12-well plates at a density of 1 × 10t test. The results were considered to be statistically significant when P < 0.05. All statistical analyses were performed using SPSS 19.0 .The experimental data are expressed as the mean ± standard deviation. Differences between two groups were analyzed by two-sided Student’s P value ≤ 0.05). Among the differentially expressed miRNAs, miR-550 was the most downregulated miRNA. A heatmap of differentially expressed miRNAs between Con and OIM groups is shown in Fig. We used volcano plot to show the inter-relationships between differentially expressed mRNAs in MC3T3-E1 cells in the control (Con) and OIM groups . Compared with that in miR-505 inhibitor-NC-treated cells, the expression of miR-505 in miR-505 inhibitor-treated cells was significantly downregulated in a concentration-dependent manner , and the expression of miR-505 was also downregulated in a concentration-dependent manner .The expression of miR-505 transfected at concentrations of 15, 30, or 60 nmol/L in MC3T3-E1 cells was detected by RT-PCR. The results showed that the expression of miR-505 in MC3T3-E1 cells of the miR-505 mimic group was significantly upregulated in a concentration-dependent manner compared with that in the corresponding NC group. The higher the concentration of miR-505 mimics, the higher the expression of miR-505, and the difference was statistically significant (Fig. To verify the effect of miR-505 on the osteogenic differentiation of MC3T3-E1 cells, miR-505 mimics, miR-505 mimics-NC, miR-505 inhibitor, and miR-505 inhibitor-NC were transfected into cells at a final concentration of 30 nmol/L, and then, the cells were treated with osteogenic induction medium for 3 days. The expression of osteogenic-related genes was detected by RT-PCR and Western blot. The results showed that compared with that in the miR-505 mimics-NC group, the mRNA Fig. a–c and pThe predictive complementary sequences are shown in Fig. To further determine whether miR-505 can regulate osteogenic differentiation by targeting the RUNX2 gene, we detected the expression of the Runx2 gene by RT-PCR after transfection with miR-505 mimics or miR-505 inhibitor. The results showed that compared with that in the miR-505 mimic-NC group, the mRNA expression level of the RUNX2 gene in cells overexpressing miR-505 was significantly downregulated, while the expression level of the RUNX2 gene in the miR-505 inhibitor group was significantly upregulated Fig. c. In addIn this study, we found that miR-505 was downregulated during osteogenic differentiation of MC3T3-E1 cells. Moreover, miR-505 negatively regulated osteogenic differentiation of MC3T3-E1 cells by targeting RUNX2.Osteoporosis is a relatively prevalent disease that mainly leads to loss of bone mass and microstructural deterioration of bone tissues and eventually increases the susceptibility of patients to bone fractures , 17. It In this study, we found that miR-505 was downregulated during osteogenic differentiation of MC3T3-E1 cells. Moreover, we found that miR-505 directly binds to the RUNX2 3′ UTR, thus inhibiting the osteogenic differentiation of MC3T3-E1 cells. Kapora et al.  demonstrA large number of studies have confirmed that miRNAs are involved in the regulation of osteoblast differentiation. miR-29a modulates the expression of RUNX2 in osteoblasts by regulating the Wnt signaling pathway, ERK1/2 pathway, and Akt phosphorylation . Sun et Software prediction analysis showed that miR-505 could target the RUNX2 gene sequence. After overexpression of miR-505 mimics in MC3T3-E1 cells, the expression of RUNX2 was downregulated, suggesting that RUNX2 could be a target gene of miR-505. The RUNX2 gene is a landmark gene of osteogenic differentiation and bone formation . RUNX2 cIn summary, the results obtained in the present study indicated that miR-505 was downregulated during osteogenic differentiation in vitro and that its overexpression could inhibit osteogenic differentiation and suppress the growth of osteoblast cells by targeting RUNX2. Therefore, miR-505 may be a promising therapeutic target for the promotion of new bone regeneration.


In addition, we validate DFTB simulated far-IR spectra for several phosphine- and thiolate-ligated gold clusters against experimental and DFT spectra. The transferability of the parameter set is evaluated using DFT and DFTB potential energy surfaces resulting from the chemisorption of a PH3 molecule on the gold (111) surface. To demonstrate the potential of the DFTB method for quantum chemical simulations of metalloid gold clusters that are challenging for traditional DFT calculations, we report the predicted molecular geometry, electronic structure, ligand binding energy, and IR spectrum of Au108S24(PPh3)16.We report a parameterization of the second-order density-functional tight-binding (DFTB2) method for the quantum chemical simulation of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces. Our parameterization extends the previously released DFTB2 “auorg” parameter set by connecting it to the electronic parameter of phosphorus in the “mio” parameter set. Although this connection could technically simply be accomplished by creating only the required additional Au–P repulsive potential, we found that the Au 6p and P 3d virtual atomic orbital energy levels exert a strong influence on the overall performance of the combined parameter set. Our optimized parameters are validated against density functional theory (DFT) geometries, ligand binding and cluster isomerization energies, ligand dissociation potential energy curves, and molecular orbital energies for relevant phosphine-ligated Au We report a parameterization of the density-functional tight-binding (DFTB) method for the accurate prediction of molecular, electronic and vibrational structure of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces. Density functional theory (DFT) methods are most often employed in theoretical investigations, as they are capable to accurately describe electronic, geometrical, and vibrational structure of gold clusters and nanoparticles.16–29 In particular, the DFT-based simulation of IR, Raman, and UV-vis spectra has been achieved for a range of gold clusters, thus providing useful theoretical fingerprints to distinguish between bonding arrangements and orientations between gold atoms and ligands, and ligand–ligand interactions within clusters.23,30–34Atomically precise ligated gold clusters of nanometer dimension receive continued attention due to their unique catalytic properties35 and routine theoretical investigations are limited to moderate system sizes. One way of reducing the computational cost is to entirely neglect the ligands and only perform DFT calculations on the gold cores. An alternative way is to employ simplified ligand models where e.g. triphenylphosphine (PPh3) is replaced by phosphine (PH3) or trimethylphosphine (PMe3).17,18,22,25,26,28,29,36 This, as with the complete ligand removal approach, has the additional benefit that conformational searching is simplified, as the torsions of the three phenyl groups per ligand give rise to a large number of local minima with similar energies. Nevertheless, it is well known that the electronic effects of the larger ligands are different from those of the smaller ones, for instance inductive effects,36–39 and a computational truncation of the ligands will influence the chemistry and therefore description of the catalytic properties in calculations. Integrated schemes such as ONIOM40 may be used to capture such electronic effects in calculations on the untruncated “real” systems; however, the choice for high and low levels of theory and the definition of the interface between them is not straightforward. A rigorous ONIOM study requires benchmarks of the selected methods against a high-level calculation for the “real” system,41 which is often computationally unfeasible. In practice therefore, integrated methods are often difficult to employ in the context of ligated nanoscale metal clusters. Besides the computational effort related to the proper modeling of the ligands, the size of the metal cluster itself can become problematic for conventional DFT studies, severely impacting the size range of gold clusters to be investigated for property control and fine-tuning. Exacerbating this problem is the recent emergence of larger, so called metalloid, clusters such as Au108S24(PPh3)16.25 To make matters even worse, interactions of deposited gold clusters with substrate surfaces such as SiO2 and TiO2 may play an important role in the catalytic reaction,10–14,42 which further increases the computational expense of DFT studies. A recent review on connections between theory and experiment for gold nanoclusters has thus posed the question as to how theoretical calculations can be expanded to treat larger sizes and length scales.15Unfortunately, DFT calculations can become prohibitively expensive with system size,ab initio or first principles methods.43–48 Among them, density-functional tight-binding (DFTB),45,49–53 an approximation to DFT formulated in the framework of non-orthogonal tight binding, has emerged as one of the most accurate, and potentially versatile choices. DFTB is capable of simulating systems containing many thousands of atoms with an accuracy comparable to traditional DFT methods.46,54,55 As the DFTB method takes advantage of the two-center approximation, tabulated Hamiltonian and overlap integrals within the Slater–Koster scheme,51,56 it is two to three orders of magnitudes faster than DFT. In order to apply DFTB into the theoretical study of ligated gold clusters, it is necessary to provide accurate parameters for all binary chemical element interactions, most notably Au–S and Au–P. The Au–S interaction in combination with the “mio” parameter set51,52,57 was included in the “auorg” parameter set for gold–thiolates clusters,58,59 but the parameters for the Au–P interaction have not been developed.Semi-empirical electronic structure methods offer the capability to simulate large systems with explicit inclusion of electronic structure by introducing empirical parameters and methodological approximations to rigorous i.e. ligand binding energies, relative isomer energies, and ligand dissociation energy profiles, (3) electronic structure, (4) vibrational normal modes of Au–P containing clusters, and (5) the adsorption of a PH3 molecule on the gold (111) surface. Finally, we present DFTB-based predictions for structural, energetic, and vibrational properties of the recent experimentally reported metalloid gold cluster Au108S24(PPh3)16.25In this work, we report a parameterization of the Au–P interactions for the second-order DFTB (DFTB2) method for the sake of compatibility with the previously developed parameters. The accuracy of these new DFTB parameters is probed by comparing the corresponding properties against DFT-calculated values and available experimental results in terms of: (1) root mean square deviations of optimized geometries, (2) energetic properties, 22.160 and will not be repeated here. In this work, we only focus on generating parameters for the DFTB2 method, which is also referred to as self-consistent-charge (SCC)-DFTB.51 The DFTB2 total energy can be viewed as a 2nd order Taylor expansion of the Kohn–Sham energy with respect to a reference initial electron density ρ0 and electron density fluctuations Δρ is the initial Hamiltonian constructed from the superposition of neutral atomic densities in a two-center approximation,49 and the |Ψi〉 are occupied valence molecular orbitals (MOs), expanded as a linear combination of optimized pseudo-atomic orbitals |ϕμ〉. The optimization of these pseudo-atomic valence orbitals and orbital densities for a given chemical element constitutes the determination of the electronic parameters. ΔqA is a point charge61 on atom A, and γABΔqAΔqB represents the Coulomb interaction energy between the two point charges;51 when A = B, γAA is the chemical hardness or second derivative of the total energy with respect to the charge on atom A. γAB is defined asS is an exponentially decaying short-range function that depends on the distance between the two atoms rAB = |rA − rB| and their chemical hardness, given in form of the so-called Hubbard parameter U. The latter is calculated prior to molecular DFTB calculations for each chemical element using the DFT method, typically employing the Perdew–Burke–Ernzerhof (PBE) functional.62A comprehensive review of DFTB methods can be found elsewhere and the overlap integrals 〈ϕμ|ϕν〉 are pre-tabulated for each chemical element pair. As in all tight binding approaches, the DFTB2 total energy consists of an electronic energy, which is the sum of the first two terms in ErepAB. The latter are formulated as a two-center term that depends only on the chemical element type of atoms A and B and their interatomic distance rAB.60 In principle, these pairwise repulsive potentials can be pre-calculated analytically from DFT for diatomic molecules. However, it was found that the performance of such DFT-based repulsive potentials is usually not sufficiently accurate for general purposes.59 Therefore, in practice, one computes DFT- or wave function theory (WFT)-based reference relative energies for model systems that contain various bond lengths of the chemical element pair in question, and fits the total repulsive energy such as to minimize the difference between reference and resulting DFTB relative energies. For this purpose, the repulsive potentials are approximated by a combination of exponential and spline functionsr0nAB,” is a spline knot at the nth interval, and the “anAB,” are the polynomial spline coefficients. These variables are considered the free empirical parameters and optimized either by hand or automatically63–65 to minimize the difference between reference and DFTB relative energy for a series of training systems. In addition to relative energies, repulsive potentials can also be optimized to fit molecular geometries by minimizing the difference between the reference DFT or WFT energy gradient and the DFTB energy gradient for a set of equilibrium and non-equilibrium geometries in the training set. The possible constraints on the repulsive potentials include a cutoff radius, a limit on the number of allowable extrema, and a continuity requirement up to a given derivative order.In the framework of the two-center approximation, the Hamiltonian integrals 2.2rwf used in the definition of atomic confinement potentials for generating pseudo-atomic orbitals |ϕμ〉 as well as the confinement radii rdens for the atomic density; additional electronic parameters are the atomic orbital energies and the atomic Hubbard parameters U for each chemical elements.51 While the AO energies and the Hubbard parameters U are normally taken from DFT calculations of the free atom, the other electronic parameters and pairwise repulsive potentials are subject to optimization with the goal to reproduce certain desired properties; for instance, electronic band structure, atomization energies, reaction energies, and geometries (energy gradients or atomic forces). In order to parameterize the Au and P interactions for DFTB2, we adopted the Au electronic parameters from the “auorg” set published by Fihey et al. (referred to as auorgα),58 and a modified version of “auorg” by Oliveira et al. (referred to as auorgχ).59 The difference between these two parameter sets lies in the Au 6p-orbital energy; in the auorgα set it was taken as the true PBE orbital energy, while in the auorgχ set it was empirically shifted upward by ≈+0.0279 hartree. The main purpose for this orbital energy shift was to obtain improved values for cohesive energies of pure gold nanoclusters with respect to PBE.59 Following the work of “auorg”, only 5d and 6 s valence electrons are considered, in total 11 valence electrons per Au atom. The parameters of the other elements in the auorgα and auorgχ sets were taken from the “mio” parameter set.51,52 Consequently, we adopted the electronic parameters for P taken from the “mio” parameter set as well. It is important to mention that the 3d orbital energy of the P atom had been shifted by +0.5 hartree from its PBE-calculated value of 0.02044 hartree, according to Gaus et al. to reduce the overbinding in phosphate compounds, as the shift had also been adopted in the “3ob” parameter set for the DFTB3 method.52,66 However, in the case of Au–P interactions, such a drastic shift of the P 3d virtual orbital energy introduces significant underbinding. We therefore decided to investigate the effect of the P 3d orbital energy level in detail. We systematically increased the value of the P 3d orbital energy by increments of 0.1 hartree from its PBE computed and the “mio” shifted value, and used our genetic algorithm (GA) optimization tool65 to automatically generate repulsive potentials for the Au–P interaction with these orbital energy shifts. In this way we tested the performance of the resulting Au–P parameterization with special consideration of geometries and binding energies for the adsorption of a PH3 on Au (111). An energy value of 0.12044 hartree was determined as the optimal compromise for the P 3d orbital. We refer to Table S1 in the ESIAs indicated above, there are two groups of DFTB2 parameters that need to be determined: (1) the electronic parameters, and (2) the repulsive potentials for pairs of chemical elements. The electronic parameters are comprised of the radii et al.59 we denote the original PBE Au 6p energy value by ‘α’ and its modified value by ‘χ’. The shifted “mio” P 3d energy will leave these notations unchanged, while the use of our new optimized P 3d orbital energy value of 0.12044 hartree will be denoted with a prime, ‘′’. Hence, auorgα coupled with the original “mio” P is unchanged, while the auorgα and auorgχ coupled with the new P 3d-orbital energy are referred to as auorgα′ and auorgχ′, respectively. Because auorgχ exhibits the largest underestimation of the Au–P electronic binding energy , we did not generate the Au–P repulsive potential for this orbital energy combination. We decided to introduce a nomenclature for denoting the different choices of Au 6p and P 3d orbital energies in our parameters. Following Oliveira 65 After several preliminary tests, we employed a cutoff radius of 4 Å and 5 spline knots  surface. Surprisingly, auorgα′ and auorgχ′ optimized Au–P repulsive potentials are almost identical, see Fig. S1 in the ESI.α can also be optimized in a similar way to maximize its performance, however, for the sake of simplicity and transferability we decided to use the same Au–P repulsive potential for auorgα′ and auorgα.The Au–P repulsive potentials were optimized on the basis of shell-resolved self-consistent charge electronic energies to fit ligand binding energies and forces for the training set listed in 2.358 the generalized gradient approximation (GGA) PBE density functional was selected to generate reference geometries and cluster binding energies. In this work, we opted for the TPSS density functional67 because it was noted by Kepp68 and Goel et al.69 that this meta-GGA functional reproduces experimental or high-level theory bond energies and ligand–gold distances better than the standard GGA PBE functional. For the training set of small clusters in 70 No dispersion correction was employed in the calculations generating the training set in order to avoid complications originating from a possible convolution of DFTB repulsive energy terms and the long-distance dispersion term.In the original parameterization of the auorg set,3, PMe3, PPh3 and small- to moderate-sized gold clusters. For the larger complexes, which are listed in 3), 1,1-bis(diphenylphosphino) propane (dppm), 1,3-bis(diphenylphosphino) propane (dppp), methyldiphenylphosphine (PMePh2), tris(2-(diphenylphosphino) ethyl) phosphine (PP3) and 1,8-bis(diphenylphosphino) octane (dppo). Additionally, there are thiol-containing ligands in some clusters comprised of reduced S2− and meta-methylbenzenethiol (m-MBT). For these test sets, the reference calculations were performed at the TPSS/def2-SVP70 level of theory. Here we included the empirical D3 dispersion contribution71,72 in both DFT and DFTB2 calculations.73 The D3 dispersion correction was used to improve description of ligand–ligand interactions and ligand effects which are deemed to be important factors on the structural, electronic, and vibrational properties of ligated gold clusters.23 In the DFT calculations we employed an effective core potential (ECP)74 for the Au atoms. In order to accelerate the DFT calculations, we employed the resolution-of-the-identity (RI) approximation with the corresponding auxiliary basis sets.75 All non-periodic DFT calculations were carried out using the implementation of the ORCA code.76 DFTB2 single point energy and geometry optimization calculations were performed with the DFTB+ code.77 All DFTB calculations were carried out with the shell-resolved SCC option “OrbitallyResolvedSCC = Yes”. The auorg parameter was designed such that this option mostly affects the charge distribution on the gold atoms themselves and their interactions with the other elements.In order to benchmark the accuracy of the new parameters, ligand binding energies and optimized geometries were compared to their TPSS counterparts for various complexes of PH6(dppp)4]2+, [Au8(PPh3)8]2+ and [Au9(PPh3)8]3+ clusters. The DFTB2 IR vibrational spectroscopy calculations were computed using the GAMESS-US code.87,88 The simulated IR spectra were obtained by convoluting the calculated stick spectra using a Lorentzian line shape function with 3 cm−1 full width at half maximum.To test the accuracy of the DFTB parameters for the prediction of vibrational spectroscopic data, we compared DFTB- and PBE-calculated far-IR spectra to the experimental spectra for +, Au22(PH3)12 and [Au20(PMe3)16]4+ complexes exhibit RMSD values larger than 0.7 Å. While the RMSD of auorgα′ and auorgχ′ for these three clusters, and auorgα for Au22(PH3)12 [Au20(PMe3)16]4+ remain in an acceptable range around ≈0.7 Å, the auorgα RMSD value of 1.18 Å appears problematic for [Au7(PH3)7]+.7 core shape that can only be stabilized when larger ligands such as PMe3 or PPh3 are used. It is worth mentioning that these model systems are experimentally not stable in general, because the gold core prefers a planar structure rather than a 3D geometry, unless ligands are present.92,93 The model geometries here were constructed by replacing the experimentally used ligands 7]+ cluster, lower RMSDs were observed for auorgα′ and auorgχ′ because these two parameters increase the contribution of the P 3d-orbital in stabilizing the complex due to its lower orbital energy.After closer inspection, we conclude that this large value results from a strongly distorted Auα′ and auorgχ′ parameters, with energy deviations as high as −18 kcal mol−1 for the smallest PH3-ligated clusters  in complexes with their experimentally used larger ligands including PPh3, PMePh2, PP3, dppp, dppm, dppo, and m-MBT, see α and auorgα′ parameters is presented and discussed in the main text. The performance of the auorgχ′ parameter is presented in the ESI.†The accuracy of the new DFTB2 parameters for geometries and ligand binding energies was further assessed for a series of gold clusters 4]2+ (BOTSOS), [Au6(PPh3)6]2+ (CATPAO10), and [Au8S2(dppm)4]2+ (LEVKIJ). In the previous section, [Au7(PH3)7]+, Au22(PH3)12 and [Au20(PMe3)16]4+ model clusters were found to be the most problematic cases. Here, with the experimentally used ligands, DFTB-optimized structures of [Au7(PPh3)7]+, [Au20(PP3)4]4+, and Au22(dppo)6 have lower RMSD values than their previously described truncated model clusters with PH3 and PMe3. auorgα′ slightly outperforms auorgα in most of the cases (by only up to 0.1 Å for the [Au22(dppo)6] neutral complex). It is important to keep in mind that the RMSDs over atom position convolutes deviations in angles and torsions with the bond lengths. If one only considers bond length comparison, Au–Au and Au–P deviations are within 0.06 Å from experimental values.The RMSDs between experimental and computed cluster geometries shown in χ′ geometries, 6(dppp)4]2+ (BOTSOS), [Au7(PPh3)7]+ (BIXZAK), [Au8(PPh3)8]2+ (OPAUPF), and [Au9(PPh3)8]3+ (MIVPOX-D2h). The analogous comparison for auorgα and auorgχ′ is shown in Fig. S3 in the ESI.94 The figure highlights the good performance of the theoretical methods in the description of the cluster core geometries relative to experiment. We note that ligand orientations can be strongly impacted by crystal field effects that are not present in our gas phase theoretical calculations, and thus we will not discuss differences in ligand geometries.In order to graphically illustrate the differences between experimental and computed DFT and auorgα and auorgα′, and Fig. S4 in the ESIχ′.The evaluation of predicted averaged and normalized ligand binding energies follows the schemes used above for the smaller-sized and moderate-sized clusters. The ligand binding energies are listed in Table S4 in the ESI,6(dppp)4]2+ up to [Au13(dppm)6]5+, the auorgα′ binding energy deviations of ≤5 kcal mol−1 are in very good agreement with the DFT reference. auorgα trends towards strong underbinding in all cases. In the case of clusters with more core Au atoms (n ≥ 20), auorgα and auorgα′ have both noticeable underbinding as high as 20 and 15 kcal mol−1, respectively. The results are consistent with the overall trend that was already seen in the small-sized and moderate-sized ligand complexes above. To further investigate the effect of geometry on the ligand binding energies, TPSS single point energy calculation using the DFTB optimized geometries were carried out, and the corresponding deviations in ligand binding energies are shown in −1. It follows that, if highly accurate ligand binding energies are required, DFTB geometry optimizations followed by TPSS single point energy calculations can provide a reasonable “shortcut” over straightforward DFT calculations. In addition to the binding energies, DFTB isomerization energies are compared to the DFT values as well for [Au9(PPh3)8]3+ and [Au11(PMePh2)10]3+ clusters, see Fig. S5 in the ESIAll predictions by DFTB tend towards underbinding, with only three minor exceptions. From the smallest 2+ cluster as a representative for the experimentally relevant complexes with larger ligands. Starting from the DFT optimized geometries of the cluster, the Au–P bond of the ligand in question was simply stretched up to a distance of 20 Å. The relative energy ΔE of this practically dissociated geometry was defined in all methods as 0 kcal mol−1. In reality, complex structural relaxation of the cluster and ligand would occur obscuring methodological differences, which is the reason for presenting rigid scans. Ligand removal of nanoscale gold clusters is a key step in making the clusters catalytically more active by increasing the gold core interaction with the substrate or reactants. Previous studies have reported the partial removal of ligands of atomically-precise gold clusters Aucf.−1  surface.Overall, the DFTB curves mimic the TPSS curves closely in both energy of the binding region and the shape of the energy curves. The rigid scan does not include a barrier and converges within about 12 Å to the dissociation limit. As for the DFTB curves, we observe underbinding in the region from 2.5 to 6 Å in all of the plots cf.. DFTB is3.4α′ parameter, but show the corresponding performance of the other parameters in the ESI.†α′ calculated energy levels of the frontier molecular orbitals for selected gold clusters. The plots showing frontier orbitals computed with auorgα′ and auorgχ′ are shown in Fig. S5 in the ESI.α′ tends to underestimate those for the singly charged [Au7(PPh3)7]+ (BIXZAK), the doubly charged [Au8(PPh3)8]2+ (OPAUPF), and the triply charged [Au11(PMePh2)10]3+ (ZUCMEP) clusters. In these particular three clusters, the DFTB HOMO energy shifts are −0.28, −0.32, and −0.40 eV for Au7, Au8 and Au11 respectively, while their LUMO shifts are −0.63, −0.89, −0.92 eV, obviously quantitatively larger. This imbalance results in their smaller HOMO–LUMO gap values. Nonetheless, the HLG values of DFTB/auorgα′ are quantitatively close to the TPSS calculated values with deviations no greater than 0.6 eV.To evaluate the ability of DFTB to accurately describe electronic structures of phosphine-stabilized gold clusters, we investigated the frontier orbital energies  and HOMO–LUMO gaps (HLGs) of a number of medium-sized gold clusters using DFT and DFTB methods. In this discussion, we concentrate on the auorg6(dppp)4]2+, [Au7(PPh3)7]+ (BIXZAK), [Au8(PPh3)8]2+ (OPAUPF) and [Au9(PPh3)8]3+ (MIVPOX-D2h) clusters for the TPSS and DFTB2/auorgα′ methods. Despite some inevitable quantitative differences in the orbital topology, the amplitudes of the HOMO and LUMO orbitals obtained with the DFTB2/auorgα′ method are very close to the ones obtained by TPSS/def2-SVP. Noticeable qualitative differences are the LUMOs of Au7 and Au8 where DFTB both overestimated the contribution from the gold core and less contributions from the phosphine ligands, possibly providing a rationale for the previously discussed large LUMO energy shifts of Au7 and Au8. The HOMO and LUMO plots of DFTB2/auorgα and DFTB2/auorgχ′ are shown in Fig. S8 and S9 in the ESI.α′ orbitals; with DFTB2/auorgα also overestimating contribution from the gold core while DFTB2/auorgχ′ underestimates the gold and overestimates the contribution from phosphine ligands in Au7. We note that these differences within the different DFTB parameter sets originate mostly from the different choices of the virtual orbital energies 4]2+ (BOTSOS), [Au8(PPh3)8]2+ (OPAUPF), and [Au9(PPh3)8]3+ (MIVPOX-D2h) clusters have been studied previously both experimentally and computationally using DFT19 and therefore represent good choices for the performance of our DFTB parameters for predicting vibrational spectra. The calculated DFTB IR spectra were evaluated by comparing them with experimental data and DFT-calculated IR, see −1 and Au–P modes above 400 cm−1. Both PBE/def2-SVP and DFTB2/auorgα′-calculated IR spectra match the previously reported experimental and M06/LANL2DZ predicted spectra, having the same mode description for each vibration. Also, the DFTB and PBE normalized spectral shapes are in good agreement, especially concerning the main peaks involving Au–P interactions. A noticeable difference between the experimental and calculated spectra of Au8 and Au9 is the presence of an experimentally distinct peak at 398 cm−1. While all DFT and DFTB methods are able to predict peaks that are comparable with the position of this feature, the calculated intensities in DFTB are considerably weaker. Nevertheless, both methods have shown that this peak can be attributed to phenyl group twisting vibrations. Another difference is the presence of a peak at 317 cm−1 in the Au8 and Au9 DFTB calculated spectra that is missing in the DFT spectra. The DFTB peak matches a seemingly broad experimental peak in the same region. In the previous study,19 this peak was not assigned to any vibrational mode, as DFT was not able to predict this feature. DFTB revealed that these peaks near 317 cm−1 of Au8 are related to vibrations with weaker intensity assigned to a phenyl rocking vibration that is attached to protruding Au1–P8. In the case of Au9, this peak has several contributing vibrations that are also assigned to a phenyl rocking of the complex's several phosphine ligands. The summary of contributing transitions for all clusters are given in Tables S7–S9 in the ESI.58,59The IR spectra of − clusters, and six Au18 clusters protected with various types of ligands to the corresponding DFT and experimental spectra in the case of Au18(S-c-C6H11)14.23,31,32 The comparison is presented in the ESI, Fig. S13 and S14.108S24(PPh3)16. We focus our attention on the far-IR region because it is of particular interest in understanding the core vibrations of Au, S, and P atoms. The calculated IR spectrum has several large peaks in the 500–600 cm−1 region which are attributed to PPh3 distortions. The multiple peaks in the 200–350 cm−1 region are caused by various normal modes of both Au–P and Au–S vibrations (see Tables S10–S12 in the ESI4S4 planar rings: (1) Au–S stretching at 241.1 cm−1, (2) S–Au–S–Au–S symmetric stretching at 277.2 cm−1 and (3) S–Au–S symmetric stretch at 517.05 cm−1. The inset of −1 which are mostly attributed to Au44 core distortion.The calculation of IR spectra of large-scale systems within the normal mode approximation, requiring the calculation of the Hessian matrix, is beyond the capability of most contemporary DFT codes on current computer systems within a reasonable amount of time. In this case, DFTB can be particularly useful as it has been shown in the benchmark sections that DFTB can reproduce the experimental IR spectra very well. In this case study, we carried out DFTB2/auorg4S4 planar ring motif of Au108S24(PPh3)16 has not yet been studied using IR spectroscopy, there are no experimental validations so far about its normal modes. The calculated Au–S stretches found in this planar ring motif are compared with that of the Au4(SCH3)4, [Au25(SCH3)4]− and [Au37(PPh3)10(SR)10Cl2]+ clusters.31–33 The S–Au–S–Au–S asymmetric and symmetric stretching values are found to be 274 cm−1 and 277 cm−1 (see Peak 2 of the inset in −1) of Au4(SCH3)4 cluster. Other IR studies of thiolate-containing Au nanoclusters such as [Au25(SCH3)18]− and [Au37(PPh3)10(SR)10Cl2]+ have reported their Au–S symmetric stretches at 293 cm−1 and 239 cm−1, respectively, which are relatively close to our DFTB-calculated Au–S stretch values; 241 cm−1 as shown as Peak 1 in the inset of −1 as presented in Tables S11 and S12,108S24(PPh3)16 cluster. Peak 3 in the inset of −1, which is another characteristic S–Au–S symmetric stretch. One can depict the similarity of the Au–S stretches in the 200–300 cm−1 regions with that of other sulfur-capped Au clusters.20,23,31–33,96,97 Based on the good agreement between our DFTB calculated IR spectra with DFT calculated as well as the experimental spectra for gold clusters in the benchmark, the predicted IR spectrum of Au108S24(PPh3)16 is reliable and provides a useful fingerprint to identify this cluster in future studies.Since the novel Au458,59 which were themselves based on the mio parameter set.51,52,57 The present effort thus expands the applicability of the DFTB2 method to organometallic gold complexes with ligands containing the full set of chemical elements contained in the mio parameter set: H, C, N, O, P, and S. In the construction of the repulsive potentials we considered three different combinations of the gold 6p and phosphorus 3d virtual atomic orbital energies. The performance of our parameters was evaluated using density functional theory (DFT) geometries, ligand binding and cluster isomerization energies, ligand dissociation potential energy curves, molecular orbital energies, and simulated far-IR spectra. We further compare predicted geometries with X-ray crystallographic structures and experimental far-IR spectra, and evaluate parameter transferability for phosphine chemisorption on a gold surface.Parameters for the density-functional tight-binding (DFTB) method were generated to describe gold–phosphorus interactions for simulations of phosphine-stabilized nanoscale gold clusters. We build on preceding works reporting second-order DFTB (DFTB2) parameters for hybrid gold–thiolates compounds,i.e. auorgα < auorgχ′ ≈ auorgα′. The total ligand binding energy increases both with cluster and ligand size. This means that for a given gold cluster, the variation in the ligand binding energy in DFTB needs to be almost completely described by the electronic energy, as the Au–P bond distances only marginally change with different ligands. However, the current DFTB electronic parameters are not flexible enough to accurately describe the variations in the electronic ligand binding energies. Similarly, the same trend holds for increasing gold cluster size. According to the benchmark results, we found that DFTB2/auorgα is the best option to study small ligands and small Au clusters. DFTB2/auorgα′ is the best option to study large ligands, large Au clusters, or Au surfaces. For the moderate-sized ligands or gold clusters, both DFTB2/auorgα and DFTB2/auorgα′ are good options to be used. The performance of auorgχ′ for Au–P interactions is similar to that of auorgα′ because the effects of shifting the Au 6p orbital energy from −0.02786 to −0.00001 hartree is less significant than shifting the P 3d orbital energy from 0.12044 to 0.52044 hartree. However, the effect of shifting the Au 6p orbital energy has not been investigated for Au– interactions, thus the use of auorgα′ is more preferable over auorgχ′. Besides consideration of cluster and ligand sizes, the geometrical environment of the Au–P bond also influences the performance of our parameters. Surface-like Au atoms present a challenge while more pyramidalized Au atoms are better described. Possible reasons are the minimum basis set or the missing multipolar charge contribution. Such situations are rare, however, and our auorgα′ parameter performs overall well in theoretical studies of relevant nanoscale gold clusters that experimentally feature large capping ligands. In particular, our DFTB parameterization enables the simulation of ligand dissociation, reliably predicting how post-treatment can be done in experiment without inducing concomitant side-effects such as agglomeration. The auorgα′ parameter set will be publicly distributed via the http://www.dftb.org website. To switch from DFTB2/auorgα′ to DFTB2/auorgα, one can simply modify the P 3d-orbital energy according to In general it is found that the absolute ligand binding energies increase with decreased virtual orbital energies, 108S24(PPh3)16 nanocluster25 and investigate its molecular and electronic structure. The optimized Cartesian coordinates of Au108S24(PPh3)16 are provided in the ESI.108S24(PPh3)16 nanocluster will serve as useful reference for future studies.We employed the new DFTB parameters to determine the geometric structure of an AuDFTB approaches with appropriately developed parameters for the description of molecular and electronic structure and energetics as well as vibrational spectra are promising to provide insight as to how to tune catalyst reactivity for both product selectivity and reaction specificity. Further development of DFTB parameters for the interaction of gold clusters with various substrate surfaces will advance the development of transformative catalytic systems. This work is vital to herald the promised potential of unprecedented reaction tunability using cluster-based catalysts.http://energy.gov/downloads/doe-public-access-plan).This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (There are no conflicts to declare.SC-011-D0SC04514D-s001SC-011-D0SC04514D-s002SC-011-D0SC04514D-s003SC-011-D0SC04514D-s004SC-011-D0SC04514D-s005


Urinary biomarkers for organ dysfunction could predict the outcomes of severe trauma patients. However, the use of neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker of trauma is not well studied.To evaluate the association between the short-term prognosis of trauma patients and NGAL levels.We conducted a single center study and compared predictive performances between NGAL levels and the trauma severity.A total of 104 patients were included in the study. Patients were divided into two groups based on ISS score of 16. There was no significant difference in patient characteristics based on trauma severity. However, the lactate level was significantly higher in the more severe group. There was a significant association between urinary NGAL levels and trauma severity indicators, such as intensive care unit stay (ICU) (p = 0.005) and emergency care unit (ECU) stay (p = 0.049). In addition, receiver operating curve analysis showed that as a predictor, NGAL could be used for detecting severity with moderate precision, especially for short-term outcomes (specificity 70.6 for ICU and 69.0 for ECU stay).In this study, we revealed that the level of NGAL could predict the degree of invasiveness in trauma patients with moderate precision and estimate the duration of treatment during the acute phase. It is necessary to examine the validity of the findings of this study using a prospective, cohort, and multi-center collaborative study design. To this date, trauma treatment is still difficult and challenging, and early severity prediction is important for improving prognosis in trauma patients . Fast evUrinary biomarkers for organ dysfunction could predict outcomes of severe trauma patients, such as those involved in battle fields . Among tThis is a single center study at the Yokosuka Kyosai Hospital, Japan. The study was carried out for a year, between October 1, 2017 and September 30, 2018. The facility has a critical care center with a 740-bed hospital in Yokosuka City which has a population of 395,903 . This stWe obtained patient characteristics and clinical information from the visiting hospital, such as age in years, sex, lactate level, creatinine at admission and at day 5, ISS score, previous history of chronic kidney disease, CKD, and heart disease. Further we chose large blood transfusion, surgery, and transcatheter arterial embolization (TAE) as trauma-specific treatment. The primary outcomes were intensive care unit (ICU) stay period, the emergency care unit (ECU) stay period, and length of hospital stay (LOS) because no mortality was observed during the study period. Additionally, urinary NGAL was measured at the time of visit of the patient as a routine blood test procedure and reexamined the day after admission. We measured NGAL with the CLIA (chemiluminescent immunoassay) technique, using the ARCHITECT uNGAL assay from the Abbott Japan, Tokyo, Japan. We did not utilize creatinine correction to urinary NGAL, because original values and corrected ones are highly correlated and a few previous studies have used the former values . Inclusip < 0.05 was considered statistically significant. For statistical analysis software, IBM-SPSS Statistics 23  and MedCalc for Windows, version 15.0  were used.Univariate analysis was performed on the items according to the severity of trauma . The Mann-Whitney U test was used for continuous variables and Fisher’s exact test for categorical variables. Furthermore, in order to examine the relationship between the outcome and NGAL levels, multiple regression analysis was performed using all measurement items. Collinearity was checked with the variation inflation factor (VIF). The VIF value larger than four was considered to violate a premise of collinearity and to cast multi-collinearity which determined if a variable should be excluded from the model . A two-sThere were 246 trauma patients who visited the institution and were hospitalized during the study period. After the exclusion criteria were applied, a total of 104 patients were included for further analysis. There was no significant difference in patient characteristics based on trauma severity, except that lactate level was significantly high in the more severe group with ISS ≥16 . Urinary NGAL could differentiate AKI and no AKI within the high resource utilization cohort, indicating that it could be a confounder if using NGAL to indicate injury severity –24. ThusIn this study, we revealed that the level of NGAL could moderately predict the degree of invasiveness in trauma patients and estimate the duration of treatment during the acute phase. It is necessary to examine the validity of the findings of this study by a prospective, cohort, and multi-center collaborative study design.S1 Data(XLSX)Click here for additional data file.