Ayurveda, the traditional medicine of India, is based on the concept of three major constitutional types  defined as “Prakriti”. To the best of our knowledge, no study has convincingly correlated genomic variations with the classification of Prakriti. In the present study, we performed genome-wide SNP (single nucleotide polymorphism) analysis  of 262 well-classified male individuals (after screening 3416 subjects) belonging to three Prakritis. We found 52 SNPs (p ≤ 1 × 10−5) were significantly different between Prakritis, without any confounding effect of stratification, after 106 permutations. Principal component analysis (PCA) of these SNPs classified 262 individuals into their respective groups  irrespective of their ancestry, which represent its power in categorization. We further validated our finding with 297 Indian population samples with known ancestry. Subsequently, we found that PGM1 correlates with phenotype of Pitta as described in the ancient text of Caraka Samhita, suggesting that the phenotypic classification of India’s traditional medicine has a genetic basis; and its Prakriti-based practice in vogue for many centuries resonates with personalized medicine.The practice of  Ayurveda of India has a documented history dating back to 1500 BCE1Ayurveda is extensively used side by side and remains highly popular, especially in South Asia. The basic concepts of Ayurveda are; 1. five elements – panchabhuta – which constitute the physical universe including the human body and; 2. three doshas  or constitutional types of every human. These doshas refer broadly to the functions of motion, digestion and cumulation. Though all three doshas exist in every human being one is dominant based on which an individual’s Prakriti is determined. Prakritis are discreet phenotypes and they are determined on the basis of physical, psychological, physiological and behavioural traits, and independent of social, ethnic and geographical variables13Prakritis underlie an individual’s predisposition to disease as well as response to treatment, it is imperative in Ayurvedic practice to identify the Prakriti of a patient before treatmentAmong the traditional systems of medicine practiced all over the world, Prakriti in Ayurveda and its relationship with genomics was hypothesized over a decade agoPrakriti classification with genetic information and association of single nucleotide polymorphisms (SNPs) in HLA-DRB1CYP2C19EGLN111Prakriti, the association of genomic variations with Prakriti classification was lacking. This is the first attempt to classify the Prakritis using genome-wide SNP markers and to provide a scientific basis for Prakriti classification.Concept of Prakriti assessment, we have excluded females from this study . However, several studies have included of both male and female subjects for Ayurveda-based studies7815Ayurvedic methods (details given in the Methods). The composition of Prakriti was determined by senior Ayurvedic physicians and confirmed independently by ‘AyuSoft’ (http://ayusoft.cdac.in), a software developed based on information from classical Ayurvedic literature. The subjects, whose Prakriti was in concordance between the assessment by Ayurvedic physicians and by AyuSoft were only selected for this study. Of the total 3,416 individuals evaluated, 971 had 60%–93% dominance of one Prakriti  and genotypes were fetched using Birdsuite softwarePrakritis are given in A total of 3,416 normal healthy male subjects between 20–30 years of age were recruited by the Institute of Ayurveda and Integrative Medicine (IAIM), Bangalore, Karnataka (‘B’ in tables); Sinhgad College of Engineering (SCE) Pune, Maharashtra (‘P’ in tables); and Shri Dharmasthala Manjunatheshwara College of Ayurveda (SDMCA), Udupi, Karnataka (‘U’ in tables). Since the hormonal fluctuations during premenstrual and menstrual phases result in numerous physical and psychological disturbances, which may have confounding effect at the time of Prakriti , of whici.e. Indian population , different HapMap populations , different South-Asian populations of 1000 genome project  and Indian along with HapMap populations (IH). As expected, imputed genotypes were more accurate with Indian samples (IN)  compared to HM , IH  and SA  samples (19http://www.ccmb.res.in/bic/database_pagelink.php?page=snpdata). To achieve this, we followed two steps (i) imputation of 15 trios of Indo-European and 15 trios of Dravidian, and (ii) imputation of 229 unrelated individuals imputed with the reference genotype obtained from step-I. Further, we used this reference for imputing the Prakriti individuals. In the first step, we found 10.5% and 17.8% Mendelian inconsistency in two trios, (Kashmiri Pandit)  with the call rate 0.966 ± 0.0162 . In orde samples . In all  Pandit) , which wPrakriti samples were collected randomly and there was no major ancestral bias while collecting samples, we performed the principal component analysis (PCA)Prakriti samples . In order to check the ancestry of Prakriti individuals, we used our published data set of 297 Indian population samples with known ancestry19Prakriti and Indian ancestral samples. In order to remove the differentiation on spurious axes2 > 0.75), and performed PCA with 4,13,171 SNPs. Our analysis showed that most of the Prakriti samples clustered with Dravidian and Indo-European , and only 3 samples seemed to be Tibeto-Burman and admixed recently (2223242 > 0.75) for 245 Prakriti samples, of which 40 were outliers and have been removed in 10 iterations with σ ≥ 6 on eigenvector 1 to 10   ; and 205analysis .Prakriti as case and the remaining two Prakritis as controls, and performed association analyses in three combinations: Vata vs. Kapha and Pitta (V vs. PK); Pitta vs. Kapha and Vata (P vs. VK); Kapha vs. Pitta and Vata (K vs. VP). Prior to association analysis, 3,890; 4,153 and 4,124, respectively, markers were removed from 791186 markers, which were not in Hardy-Weinberg equilibrium (HWE) i.e. p-value < 0.001 in controls of V vs. PK, P vs. VK and K vs. VP; respectively. The three combination association results were further used to identify the SNPs that were significant. Considering the fact that none of the samples represents 100% single Prakriti, we did not expect very low p-value in the association analysis. In this scenario, truly associated loci may co-exist with false positive markers and can be identified by permutation analysis. As expected, we observed that SNPs having approximately same p-value in the extreme tail of theoretical distribution failed to achieve 106 permutations ; while on eigenvector 2  Kapha was significantly different compared to Vata and Pitta .It is well known that some markers differ in allele frequency more across ancestral population, compared to other set of markers. Moreover, natural selection might be the reason for this phenomenon because it acts locus-specific manner 52 SNPs . We furtntiation . In prinPrakriti . On eigePrakriti, we generated a statistical model (see methods). Initially, we applied it on 205 samples and found 23.9% (49 out of 205) were explained by the proposed model  pathway and Reactome event and found PGM1 gene associated with the Pitta phenotype. In Ayurveda, characteristics of Pitta include digestion, metabolism and energy production. Interestingly, we found PGM1 gene is in the center of many metabolic pathways i.e. glycolysis or gluconeogenesis (hsa00010); pentose phosphate pathway (hsa00030); galactose metabolism (hsa00052); purine metabolism (hsa00230) and; starch and sucrose metabolism (hsa00500)  were associated with two different Prakriti . In ordeakriti 29. To corrsa00500) . Our finPGM1 gene markers in Affymetrix data set and found 4 markers  were associated with Pitta Prakriti and all are in strong Linkage Disequilibrium (LD) . We found 23 variations in the gene, of which 8 were novel . The frequency of the mutant allele “C” was 5.8% in Pitta and 20% in Kapha Prakriti  using Sanger sequencing method. Initially, we analyzed the distribution of the genotype among participating centres and found “U” samples (collected from Udupi centre) were not in HWE  , which is more homogenous and constant in Pitta than with Vata and Kapha, and a genotype correlation exists for the characteristics of Prakriti classification.In addition, we have checked the ium (LD) . Therefore novel . InteresPrakriti . This re - 0.04) . Hence, an Kapha . We furtan Kapha . InteresPrakriti classification, as a foundation for the practice of Ayurveda, has a genetic basis and does provide clues for further studies.In conclusion, our preliminary study suggests that the Prakriti (the human classification of Indian ancient medicine) were carried out at three centres; 1. Institute of Ayurveda and Intergrative Medicine (IAIM), Bangalore, Karnataka; 2. Sinhgad College of Engineering (SCE) Pune, Maharashtra; and 3. Shri Dharmasthala Manjunatheshwara College of Ayurveda (SDMCA) Udupi, Karnataka. This study was approved by Institutional Ethics Committees (IECs) of all the collaborative centres and the methods were carried out in accordance with the approved guidelines. We have screened normal and healthy male subjects, who were between 20–30 years. Although several Ayurveda-based studies have included both male and female subjects7815Prakriti of an individual is determined based on defined anatomical, physiological psychological and behavioural characteristics. During actual assessment of Prakriti, the Ayurvedic physician needs to factor in these characteristics. One such aspect is the cyclical hormonal changes that occur in women, particularly the menstrual cycle. The hormonal fluctuations result in numerous physical and psychological disturbances, which occur in the premenstrual and menstrual phases. Existing evidence suggest that about 97% of young nulliparous women experience varying degrees of such disturbancesPrakriti assessment process. For example, premenstrual irritability occurring in a woman of Kapha Prakriti is confounding, since Kapha Prakriti individuals normally possess low irritability. Although the Ayurvedic physicians routinely enquire about the menstrual habits of patients while assessing the Prakriti, it would have been difficult for us to make similar enquiries to young, healthy women who volunteered to join this study. The health status of an individual was assessed based on the Ayurvedic criteria, that include; normal desire for food, easy digestion of ingested food, excretion of feces, excretion of urine, excretion of flatus, functioning of sensory organs, comfortable sleep, easy awakening, and attainment of strength, bright complexion and longevity. Subjects with smoking habit, diabetes, hypertension and other chronic diseases were excluded from the study. Blood pressure (BP) was measured for each subject and BP > 130/90 mm of Hg were excluded from the study. Chronic systemic diseases such as rheumatoid arthritis, cancer, etc., and subjects having recent history of acute ailments such as fever due to infections were also excluded.Selection of subjects and evaluation of the Prakriti assessment of each subjects. In the first stage, senior Ayurvedic physicians assessed the Prakriti of the subjects, applying classical Ayurveda parameters of Prakriti determination. In second stage, the same subjects were assessed using Ayusoft, a Prakriti software (www.ayusoft.cdac.in), which contains a comprehensive questionnaire, which had been developed based on the information from original Ayurvedic literature. In the third stage, another team of Ayurvedic physicians, who were not aware of the outcomes of assessment by senior physicians and Ayusoft, compared the Prakritis analysis. Subjects with ≥60% of single Prakriti dominance and having concordance in all the three stages were selected for the genome-wide analysis. Quantitative analysis of Prakriti was performed using Ayusoft along with traditional ayurvedic measures for the Prakriti assessment. The reason for considering ≥60% of a particular Prakritias a dominant was mainly due to feasibility and concordance. Single dosha Prakriti with high percentage of one dosha rarely exist, hence most of the individuals possess dual-dosha PrakritiPrakriti. Subjects ≥60% of one dominant Prakriti were selected and blood was drawn after obtaining their informed written consent. A total of 3,416 healthy individuals were screened for their Prakriti, as per the details given above. From the total, 971 subjects who showed a predominant Prakriti of ≥60% were included in the analysis , following manufacturer’s protocols. About 250 ng of genomic DNA was digested with Nsp I and Sty I restriction enzymes, followed by ligation of Nsp/Styadaptors, using T4 DNA ligase. PCR was performed using the primers that are specific to these adopters. After checking the amplicons on 2% agarose gel, they were purified with deep-well plate using magnetic beads and the fragments were eluted using EB buffer, followed by quantification and fragmentation. The fragmented PCR products (<180 bp) were end-labeled using labeling kit. Labeled fragments were hybridized onto the Affymetrix (6.0) SNP arrays using hybridization cocktail. Hybridization was performed in hybridization oven for about 18 hrs at 50 °C. After hybridization, arrays were washed, stained, scanned and analyzed using Affymetrix Genotyping Console 2.0 and GeneChip® Operating Software (GCOS). The samples which passed the quality controls i.e. call rate >95% and CQC > 0.4 were considered. Affymetrix power tool (apt-geno-qc) was used for calculation of dm (dynamic model) value. The samples having dm.all_qc<0.83 were removed from further analysis and genotypes were fetched with Birdsuite software from Broad InstituteDNA was isolated from the blood samples using standard protocolIn order to validate the Affymetrix data set, we randomly selected 48 markers  from AffPGM1 gene  using AmpliSeqTM primer pools and Ion AmpliSeqTM HiFi master mix (Ion AmpliSeq kit version 2.0 Beta). The amplified products were pooled and treated with 2 μl of FuPa reagent. The amplicons were then ligated with adapters from the Ion XpressTM barcoded adapters 17–64 kit according to the manufacturer’s instructions (Ion Torrent). After ligation, the amplicons were purified by Agencourt® AMPure® XP Reagent and additional amplification was performed to complete linkage between adapters and amplicons. In order to determine the library concentration, an Agilent 2100 Bioanalyzer high-sensitivity DNA kit  was used to visualize the size range of the libraries. Equimolar concentrations of all the libraries were pooled and diluted. Using Ion One TouchTM 200 Template Kit v@ DL , emulsion PCR was carried out according to the manufacturer’s instructions. Ion Spheres (ISPs) were recovered according to the Ion Sphere Particles 200 recovery protocol. Sequencing was done following the Ion PGMTM 200 Sequencing Kit Protocol . The 318 sequencing chip was loaded and run on an Ion Torrent PGM (Ion Torrent). Base calling and alignment were performed using the Torrent Suite 3.0 software (Ion Torrent). In order to find the significant variation in the PGM1 whole exome data, we performed association analysis using plink softwareWe sequenced the whole-exons and UTRs of GM1 gene  for randPrakriti samples  along with randomly selected 1108 Indian samples. Pair of primers (Forward primer: 5′- GCACGTTTCTTACAGCAGCT-3′ and Reverse primer: 5′-ACCTTACCTTGTACCCCAGC-3′) were designed, synthesized and PCR was performed on the GeneAmp 9700 Thermal Cycler  using the following cycling conditions: 95 °C for 5 min, 35 cycles at 95 °C for 30 s, 58 °C for 30 s, 72 °C for 2 min and a final extension at 72 °C for 7 min. Amplicons were purified using with USB ExoSAP-IT (Affymetrix) according to the manufacturers instructions. The purified products were directly sequenced using the Big Dye Terminator cycle sequencing kit  and analyzed using 3730 DNA Analyzer , Sanger sequencing was carried out for 496  CA, USA). The geni.e. 150 Dravidians, 80 Indo-European, 35 Austro-Asiatic, 27 Tibeto-Burman and 5 Great Andamanese. In addition, 15 trios of Dravidian  and 15 trios of Indo-European  were used for imputation19ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2009-01_phaseIII/plink_format/) and 1000 genome project data (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/) for imputation and comparative analysis.For comparative analysis, we used Affymetrix (6.0) array data of 297 well-classified Indian samples with known lingustic and ethnic affiliations i.e. Indian triose (15 trios of Dravidian and 15 trios of Indo-European), HapMap samples , Indian + HapMap samples and samples of 1000 genome project with South-Asian ancestry . Accuracy of imputation was calculated by comparing imputed and true genotype in 110 simulations for 2%, 5% and 10% masked data. To perform the above analysis, we used a perl script.Imputation was performed for the missing genotypes using Beagle-v3.3.2 softwarePrakriti samples, we followed two steps; In the first step, imputation and phasing was done for 15 trios of Dravidian  and 15 trios of Indo-European  together without reference population. SNPs which do not follow Mendelian rule in trios were checked and masked with Beagle utility program18. Number of SNPs per family, which do not follow Mendelian consistency are given in Prakriti samples with reference of imputed trios and unrelated Indian population samples and selected only those markers which were having R2 > 0.95 for further analysis.To impute missing genotype in Prakriti samples, we used 297 known ancestry of Indian population dataset (previously published) and performed the PCA. Stratification was checked and 40 outlier samples were excluded with cutoff sigma value ≥0.6  on 1–10 eigenvectors in 10 iterations  and calculated p-value from theoretical distribution. In order to exclude the markers, which could be in association by chance, we also performed adaptive permutation approach  for maximum 106 iteration withplink and considered only those markers who achieved maximum 106 permutations and have p-value ≤ 1 × 10−5 in theoretical distribution  can be calculated with equation (1)In order to prove the power of these markers in samples of unknown al model . First, Prakriti Wvs for a sample s with n number of associated markers for p Prakriti can be calculated using equation (2)Un-standardized total weight of the vs, Wps and Wks corresponding to Vata, Pitta and Kapha using equation Prakriti. If total number of sample is N then standardized weight can be calculated using equation (3)Hence, for a single sample there will be 3 weights WPrakriti is relative proposition (tridosha), so we calculated the differences of standardized weight for all 6 permutations; ΔVP, ΔVK, ΔPV, ΔPK, ΔKP and ΔKP for each samples and calculated representative statistics Rp. For example, representative statistics for Kapha Rk can be calculated using equation (4)p value could be positive for 2 negative Δ values. Hence, we considered only those Rp values, which have both Δ value positive. Moreover, we consider only those samples which have Rp ≥ 3 to find dominant Prakriti. We applied this model to Indian population and selected 37 samples on the basis of Rp and Δ values.Since multiplication of 2 negative values is positive, the Rhttp://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/ and; windowbed(Bedtools: https://code.google.com/p/bedtools/) was used to find the SNPs within 10 kb flanking region of the genes. Only genic markers were used for genotype-phenotype correlation. Genic SNPs were selected and considered for further analysis. To correlate the function of associated markers with characteristics of the individual Prakriti, we checked in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Reactome event using NCBI2R R package.We used SIFT algorithm (http://sift.jcvi.org) for predicting the effects of non-synonomous variant (rs11208257) on protein function.We considered markers within 10 kb flanking region of gene as genic and other as non-genic. Physical location of the genes (knownGene.txt.gz) and SNPs (snp135.txt.gz) were fetched from How to cite this article: Govindaraj, P. et al. Genome-wide analysis correlates Ayurveda Prakriti. Sci. Rep.5, 15786; doi: 10.1038/srep15786 (2015).


Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models  were employed to predict the role of climatic factors  on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (−) and one month ago (−)), and average relative humidity (current and 9 months ago (−)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future.This study aimed to investigate and quantify the relationship between climate variation and incidence of  Vibrio parahaemolyticus, and seafood is the major food type causing food poisoning  was obtained from the Food and Drug Administration, Department of Health, Taiwan due to data access limitations. The climate data was obtained from the Central Weather Bureau, Taiwan, and were aggregated from all recording stations. They were maximum, average and minimum temperature , maximum, average, and minimum relative humidity , as well as average and maximum daily rainfall . Data for oceanic temperature and salinity  were also obtained from Ministry of Science and Technology, Taiwan. A total of 144 monthly data values were obtained. The study area range is at latitude 20°00’North to 29°58’North and longitude 115°00’East to 124°99’East. Regional differences were not considered in this study because Taiwan is a small island [Monthly data for 2000–2011 of l island .V. parahaemolyticus outbreak cases, we employed a Box & Jenkins [Salmonella [E. coli [Leptospira interrogans [In order to accommodate the autocorrelation and seasonality in the time course of  Jenkins  seasonallmonella ,26, path[E. coli , and Leperrogans .S. The model could be written more formally as:ty was the outbreak variable, tε was a white noise process, B was lag (or back) operator; non-seasonal components AR(p): ϕ(B) = 1 − ϕ1(B) − ϕ2(B)2 −…−ϕp(B)p, MA(q): θ(B) = 1 + θ1(B) + θ2(B)2 +…+ θq(B)q; seasonal components AR(P): φ(SB) = 1 − φ1(SB) − φ2(2SB)2 −…−φp(PSB), MA(Q): Θ(SB) = 1 + Θ1(SB) + Θ2(2SB)2 +…+ ΘQ(QSB), and d was the degree of non‑seasonal differentiation, D was the degree of seasonal differentiation, and S was the length of the seasonal cycle. On the left side of Equation (1) the seasonal and non-seasonal AR components multiplied each other, and on the right side of Equation (1) the seasonal and non-seasonal MA components multiplied each other. The seasonal AR components captured the seasonal pattern of the dependent variable, and non-seasonal AR components captured the lag-time structure of the dependent variable.The seasonal ARIMA model incorporates both non-seasonal and seasonal factors in a multiplicative model. One shorthand notation for the model was ARIMA ×S model were then determined by examining the autocorrelation function (acf) and the partial autocorrelation function (pacf). A detailed explanation of ARIMA procedures was provided in Box and Jenkins [V. parahaemolyticus outbreaks. All ARIMA modelling and the corresponding statistical tests were performed in the SAS/ETS 9.3 statistical software.The outbreak variable was first tested for seasonal variation and stationarity by the Augmented Dickey-Fuller (ADF) unit root test. The orders of the ARIMA ×  Jenkins . The clir > 0.99) with each other, and variable avgtemp had the highest correlation with outbreaks. We therefore selected avgtemp as the temperature variable. For the same reason, maxrfd was selected as the rainfall variable and avgrh that for relative humidity. Vibrio incidence was also significantly and positively correlated with average temperature (avgtemp) (r = 0.2675), maximum daily rainfall (maxrfd) (r = 0.1402), and ocean temperature (octemp) (r = 0.1938). Average relative humidity (avgrh) and ocean salinity (ocsant) were not clearly correlated with Vibrio outbreaks.To avoid the collinearity problem of the independent variables, we calculated the Pearson correlation coefficients between all pairs of climate variables . The temV. parahaemolyticus outbreaks occurred between 2000 and 2011 in Taiwan. On average, 26.9 cases each month occurred shown in the last column of We summarized the descriptive statistics of outbreaks and climate variables in Vibrio parahaemolyticus outbreaks in Taiwan from January 2000 to December 2011 compared to climatic variables for the same period is shown in Vibrio parahaemolyticus incidence showed three major outbreaks in Taiwan . The bivariate analysis between crude climatic variables and V. parahaemolyticus incidence shows that the three major outbreaks were correlated to an increase of average temperature  unit root test was performed. The ADF test determined whether the autoregressive term had a unit root. The model, with the time trend specification, was:a0 was a constant, a2, r the coefficients on a time trend (t) and lag 1 period of ty, P the lag order of the autoregressive (AR) process, and iβ the coefficient of the lag order p of AR terms. The hypotheses of ADF test was Ho: γ = 0, H: r < 0, and MacKinnon [p-values was performed.As a first step in ARIMA modelling, we made the response series stationary, acKinnon  one-sidep-values were small enough (p < 0.05) to cause to reject the null hypothesis that the series had a unit root, so that the time series of V. parahaemolyticus outbreaks was stationary.The testing results were shown in We further used the auto-correlation (acf) and partial auto-correlation function (pacf) plots to identify the order of the ARIMA model for the stationary series. The acf and pacf pattern of the original time-series determined the initial autoregressive (AR) and moving average (MA) order. 12, ARIMA12, ARIMA12 and ARIMA × 12  had showed better predictive Root Mean Square Error (RMSE) and lowest Akaike Information Criterion (AIC). The estimated models were tested for residuals autocorrelation. The presence of autocorrelation was investigated using the acf, the pacf and the correlogram reported in the 12 had the best predictive RMSE and lowest AIC. We, therefore, used the model ARIMA 12, with AR at lag 12 and MA at lag 12, as the baseline univariate model for further comparisons. The selected model could be written as follows:ty represented the V. parahaemolyticus outbreaks. In order to incorporate the climate variables as input variables to the baseline univariate model, we first used ccf to examine the correlations between V. parahaemolyticus outbreaks and the environmental series. 12) with one or more climatic variables included were estimated. Several models were computed and only models with statistically significant coefficient were selected, ensuring the non-autocorrelation of residuals at 5% significant level. The estimated coefficients of ARIMA12 for V. parahaemolyticus outbreak series with climatic variables were shown in 12 with average temperature, current and one month previous maximum daily rainfall, current and 9 months previous relative humidity, ocean temperature, and ocean salinity of 6 months previous as covariates had the best prediction RMSE and lowest AIC. The selected model could be written as follows:ty represented the V. parahaemolyticus outbreaks, t−y12 12 months previous outbreak, avgtempt current average temperature, maxrfdt current maximum daily rain fall, maxrfdt-1 one month previous maximum daily rain fall, avgrht current average relative humidity, avgrht-9 9 months previous average relative humidity, octempt current ocean temperature, and ocsantt-6 ocean salinity of 6 months previous. Comparing the model with the baseline univariate model 12), we found that including the environmental input series improved the AIC by 1.57% and the predictive RMSE by 2.54% compared to the baseline model.To account for annual seasonality, we fitted several further univariate ARIMA models with different orders and subsequently excluded any models in which the residuals exhibited autocorrelation. Of which four univariate models, ARIMAV. parahaemolyticus outbreaks. Thus outbreaks might increase when increasing in current temperature or ocean temperature or, when increasing in ocean salinity of 6 months previous. However, there were significant negative relationships of outbreaks with maximum daily rainfall of a month previous or relative humidity of 9 months previous. This relationship meant that increased heavy rain or increased relative humidity 9 months previously may reduce current outbreaks.The best fit model indicated that average temperature, ocean salinity of 6 months previous, and ocean temperature were all significantly and positively related to V. parahemolyticus outbreaks. Rainfall was negatively related to V. parahemolyticus outbreaks.The results from our models were consistent with expectations and with results in the literature. The variables associated with food‑borne diseases were ambient temperature, ocean temperature, ocean salinity, and rainfall. Ambient temperature, ocean temperature, and ocean salinity were positively related to V. parahaemolyticus. V. parahaemolyticus can grow well at below 10 °C, moreover, its generation time can be as fast as 10 min at ~37 °C [V. parahaemolyticus in shellfish [et al. [Campylobacter enteritis using time-series analysis to study short-term associations between temperature and number of Campylobacter reports in England and Wales. They also adjusted for long-term trend and seasonal patterns. They found a linear relationship between mean weekly temperature and reported C. enteritis.Theoretically, temperature was likely to influence the growth of t ~37 °C . Our emphellfish ,34. The  [et al.  also invV. vulnificus, V. parahaemolyticus could tolerate higher salinity levels [V. parahamolyticus could survive at salinity ranging from 10 to 34 ppt, with 23 ppt as its optimum salinity [et al. cconducted meta-analysis to exam impacts of salinity on V. parahaemolyticus in oysters at harvest and in harvest waters [In addition, our results also confirmed the importance of salinity as a climate predictor of seafood poisoning outbreaks. Compared to y levels ,36. V. psalinity . Althougsalinity ,37, veryt waters . They foV. parahemolyticus outbreaks as in covariate #3 and covariate #4. Deepanjali, Kumar and Karunasagar [V. parahaemolyticus abundance was high during the dry season and low after rainy seasons. This might result from pathogen concentrations in estuaries being diluted by heavy rain thus reducing contamination of oysters and other filter feeding shell fish. In both Brisbane and Townsville rainfall was also a significant factor in regression models of climate effects on cases of Salmonella infection [Rainfall was negatively related to unasagar  also founfection .V. parahaemolyticus outbreaks not only of ambient temperature, but also of ocean factors such as ocean temperature and ocean salinity. Marques et al. [Vibrio spp. because it may alter the deposition of material in bivalve shells or the composition of plankton exoskeletons. This will in turn directly change the habitat of Vibrio spp., so forcing the organism to adapt genetically and produce strains with great virulence. We likewise demonstrated that knowing the effects of multiple climate factors on food‑borne diseases was necessary for an intensive examination of accuracy in predicting disease. Moreover, effort should also be devoted to individualize more precisely the consequences of these interactions on V. parahaemolyticus infections in order to better identify control and mitigation measures [Furthermore, our findings also indicated that ambient temperature, ocean temperature, ocean salinity had higher impacts than rainfall on microbial seafood safety. This echoed reports in the literature showing that temperature change plays an important role in food‑borne diseases ,23,26,27s et al.  contend measures .V. parahaemolyticus in Taiwan due to climate changes could be explained as below. V. parahaemolyticus is part of the natural flora of estuarine and coastal marine environments. Climate change will likely influence the vulnerability of estuaries to eutrophication in several ways, including changes in temperature, sea level, and exchange with the coastal ocean and salinity [Vibrios, which are a genus of thermodependent bacteria which thrive in naturally warm sea water. Furthermore recent studies regarding climate impacts on marine systems in Taiwan has provided some evidence. For example, Chang et al. [et al. [et al. [et al. [V. parahaemolyticus in Taiwan could be caused by climate changes.Overall, increases of seafood contamination by salinity ,40,41. Tsalinity . This ing et al.  and Lee  [et al.  observed [et al.  also obs [et al.  also proet al. [V. parahaemolyticus abundance. Third, our analysis found that key predictors were related to the number of cases of V. parahaemolyticus infection. We suggested therefore that future studies should investigate the individual effects of these predictors on fishery production, fishery manufacturing, and the distribution system [This research made an important contribution to the academic literature and provided the potential for positively influencing risk management practice; there were nonetheless limitations that provide opportunities for further research. First, our results were only for Taiwan. To generalise our findings we encouraged the implementation of similar studies in other countries. Second, we evaluated only temperature, humidity, and rainfall as predictors. We support, therefore, the inclusion in future studies of other oceanic factors, such as ocean turbidity, dissolved oxygen, and pH. For example, Parveen et al.  indicaten system ,46.V. parahaemolyticus outbreaks and climatic change factors. Our results showed that average temperature, ocean salinity of 6 months previous, and ocean temperature were all significantly and positively related to V. parahaemolyticus outbreaks. However, there were significant negative relationships of outbreaks with maximum daily rainfall of a month previous or relative humidity of 9 months previous. Our findings also indicated that ambient temperature, ocean temperature, ocean salinity had higher impacts than rainfall on microbial seafood safety. Overall, our findings predicted that, in Taiwan, food poisoning caused by V. parahaemolyticus was related with climate factors. It is hoped that the findings of this research will help to guide public health and community interventions to protect Taiwanese consumers. Thus, future research into the health impacts of extreme weather events, and early warning and response tools is required.This research provided empirical evidence for the relationship between


By inadvertently collecting animals from different source populations, however, such efforts risk reintroducing individuals that have not been in genetic contact for significant periods of time. Using genetic analyses and 44 years of data from Camp Leakey, an orang-utan rehabilitation site on Borneo, we determined the minimum extent to which orang-utans representing non-native, geographically and reproductively isolated taxa were reintroduced into the surrounding wild population. We found two reintroduced females were from a non-native subspecies, and have since produced at least 22 hybridized and introgressed descendants to date, of which at least 15 are living. Given that Bornean orang-utan subspecies are thought to have diverged from a common ancestor around 176,000 years ago, with marked differentiation over the last 80,000 years, we highlight the need for further evaluation of the effects of hybridizing orang-utans of different taxa — particularly in light of the ~1500 displaced orang-utans awaiting urgent reintroduction. As endangered mammals are increasing in number in sanctuaries worldwide, we stress the need for re-examination of historical reintroductions, to assess the extent and effects of  Mustela nigripesLeontopithecus rosaliaCeratotherium simum cottoniChelonoidis hoodensis5Gorilla beringei beringei) in groups habituated to human observation, versus an annual 0.7%±0.06% decline in the number of unhabituated conspecificsLycaon pictus) under study in the Serengeti Ecosystem has been attributed to a rabies vaccination programme, which may have inadvertently compromised dogs’ immune responses through their immobilization, vaccination and radio collaringPopulations of large and endangered mammals are increasingly impacted by humans, and may live – even in wild or semi-wild settings – under intensive human management  to a remnant population of 50 Florida panthers (P. c. coryi) approximately doubled the estimated genetic diversity of the population, and was associated with a marked reduction in the frequency of deleterious traits associated with the breeding of related individuals, such as kinked tails, cryptorchidism and poor sperm quality1819Canis lupus), the arrival of a single wolf to a bottlenecked Scandinavian population originating from only two individuals was sufficient to dramatically increase genetic diversity (heterozygosity) and exponentially increase population growth2324The hybridization of genetically distant individuals may, under some circumstances, increase population fitness1516Despite this, the hybridization and introgression of representatives of genetically divergent lineages may have negative effects on a population’s overall fitness, as exemplified by the lack of fertility common in offspring resulting from the inter-breeding of different species262728303235c. 963 chimpanzees (Pan troglodytes), c. 72 bonobos (P. paniscus), c. 106 gorillas (Gorilla gorilla)  and c. 1516 orang-utans (Pongo spp.)Pongo pygmaeus) and Sumatra (Pongo abelii), with three geographically and reproductively isolated subspecies on Borneo: P. p. wurmbii in Central and southern West Kalimantan, P. p. morio in North and East Kalimantan and Sabah, and P. p. pygmaeus in northern West Kalimantan and Sarawak. Each Bornean subspecies is thought to have shared a common ancestor around 176,000 years ago, before radiating to different refugia following climatic fluctuations in the Pleistocene384038More than 2,650 great apes are confined in sanctuaries across the ranges of all four species: P. pygmaeus. Over 14 years, Galdikas and Brindamour rehabilitated and released more than 90 ex-captive or displaced orang-utans into the wild at Camp Leakey, on the southernmost promontory of Central Kalimantan44c. 470 kilometres from Tanjung Puting, and others from captivity in Java. In spite of this, YeagerIn 1971, however – when Galdikas and Brindamour established their pioneering research and reintroduction site, Camp Leakey, in Tanjung Puting National Park – all orang-utans were classified as a single species, Camp Leakey’s contemporary population comprises approximately 60 individuals, including 8 reintroduced female orang-utans. Two further reintroduced females have recently died. All 10 females reproduced; in one case, offspring are now into their fourth generation. As some paternities are known2, 1.38 μl 10 mM dNTP mix, 0.83 μl 10 mg/mL BSA, 0.134 μl each primer, 0.28 μl Taq polymerase (Life Technologies), 15.7 μl ddH2O and 2.75 μl DNA. Thermal cycling conditions were as follows: initial denaturation at 94 °C for 12 minutes; 40 cycles of 94 °C for 40 seconds, 61 °C for 30 seconds and 72 °C for 1 minute; and a final extension at 72 °C for 10 minutes. To avoid amplification of nuclear mitochondrial insertions (‘numts’), which have proved problematic in prior studies of hominoid mtDNA taxaFaecal samples were collected from orang-utans in and around the Camp Leakey study area, and genomic DNA extracted, as previously describedet al., 2001et al., 2010Sequences were manually corrected in MEGA 6.06Mitochondrial DNA control region sequences were generated from 10 presumed unrelated adult orang-utans  that were reintroduced to the wild at Camp Leakey in the 1970s and 1980s. An additional sequence was obtained from the first-generation offspring (Siswi) of a now-deceased reintroduced female (Siswoyo), and assumed to represent this female given that mtDNA is near-clonally maternally inherited. Of the 11 founders represented, five haplotypes – labelled A to E – were observed in the alignment. Of these, two (A and B) were identical to previously published sequences , while three  were novel (GenBank accession codes KU523975-KU523977). Haplotype C featured an insertion that was confirmed by sequencing an additional PCR product amplified from the same DNA extract. Haplotypes D and E each featured a transition, at different bases, when compared with published sequences. These transitions were consistent in all sequences generated from both PCR protocols.P. p. wurmbii. We found that nine orang-utan mtDNAs, comprising four haplotypes  did indeed fall in this clade. In addition, two females, Rani and Siswoyo, were found to share a haplotype (A) that clustered in the tree with mtDNAs derived from the subspecies P. p. pygmaeus from Northern West Kalimantan or Sarawak  observed a high number of shared private alleles between chimpanzees from the Gulf of Guinea (Pan troglodytes ellioti) and from Southern Cameroon (P. t. verus), suggesting recent introgression within a purported hybrid zoneet al. (2012) identified natural hybridization of anubis (Papio anubis) and yellow  baboons in the Amboseli basin in southern Kenya. This hybridization was notably asymmetric: more anubis baboons dispersed into, and were reproductively successful, within yellow baboon populations than vice-versa(Macaca mulatta) into long-tailed (M. fascicularis) macaques(Pan troglodytes verus) into central (P. t. troglodytes) and eastern (P. t. schweinfurthii) chimpanzeesPan troglodytes troglodytes) and eastern (P. t. schweinfurthii) chimpanzees, pointing out that the variation in these chimpanzees can be better described as following a pattern of increasing genetic differentiation with increasing distance38In spite of this, there is evidence in other primate taxa that hybridization occurs naturally along the borders of different species’ and subspecies’ ranges. Gonder Guidelines for the Reintroduction of Animals Born or Held in Captivity, the Association of Zoos and Aquariums suggested that the genotype of reintroduction candidates “should be the closest possible match” to that of the destination population, “so that subspecific distinctions can be maintained”71In the decades following the reintroductions at Camp Leakey, extensive guidelines have emerged to better facilitate wildlife reintroductions. Over time, these guidelines have placed more explicit focus on genetic considerations. In their et al.et al.In practice, however, these guidelines and legislation are challenging to follow or enforce. In many cases, it is simply too expensive to perform genetic testing: rescuing and accommodating displaced animals takes priority in the face of extremely limited funds. The origin of displaced animals might therefore be inferred as the region from which they were rescued .


Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla of soil bacterial compositions. Overall, soil bacterial compositions generally changed from Actinobacteria (Acidobacteria)-dominant to Proteobacteria-dominant following afforestation. Soil N fractions, especially for dissolved organic nitrogen (DON), were significantly correlated with most bacterial groups and bacterial diversity, while potential competitive interactions between Proteobacteria  and Cyanobacteria were suggested. In contrast, nitrate nitrogen (NO3−-N) influenced soil bacterial compositions less than other N fractions. Therefore, the present study demonstrated that bacterial diversity and specific species respond to farmland-to-forest conversion and hence have the potential to affect N dynamic processes in the Loess Plateau.In the present paper, we investigated the effects of afforestation on nitrogen fractions and microbial communities. A total of 24 soil samples were collected from farmland (FL) and three afforested lands, namely Robinia pseudoacacia L (RP), Caragana korshinskii Kom (CK), and abandoned land (AL), which have been arable for the past 40 years. Quantitative PCR and Illumina sequencing of 16S rRNA genes were used to analyze soil bacterial abundance, diversity, and composition. Additionally, soil nitrogen (N) stocks and fractions were estimated. The results showed that soil N stock, N fractions, and bacterial abundance and diversity increased following afforestation.  With rising concern regarding climatic consequences, afforestation has often been proposed as an effective and cost-efficient mitigation response to climate change13491154+ and NO3−, for soil bacteria to live on13Soil N respond to the quantity and quality of soil organic matter (SOM) input with changes in the microbial process133616The Loess Hilly Region (LHR) of China, with severe soil erosion and low vegetation coverage82526Here, we explored the effect of afforestation on the soil bacterial communities and the N fractions in the Loess Hilly Region (LHR) of China. We hypothesized that afforestation would result in changes in the soil bacteria, including bacterial abundance and diversity, these observed increase would be driven by above-ground inputs and below-ground soil parametersThe total nitrogen (TN) content and stocks at a depth of 0–10 cm in RP40, CK40 and AL40 were higher by 65.08–188.61% and 68.67–201.65%, than those in FL , respect2 = 0.286, P = 0.001), the soil bacterial communities between replicates in RP40 were more similar to that in CK40 than AL40 and FL, and both aggregation degrees of the sample points in AL40 were far away from that in FL (unweighted -Unifracted PCoA)  and displayed an increasing trend from FL to RP40 after afforestation . The abued PCoA) .Proteobacteria, Actinobacteria and Acidobacteria were the most abundant and changed intensely in response to afforestation , Cyanobacteria  and Nitrospirae  were also affected by afforestation. Furthermore, at the class level, compared with farmland, increases in the abundance of most class groups  in response to conversion were observed except for Acidimicrobia and Phycisphaerae . Furthermore, Acidimicrobia and Nitrospirae were significantly changed (p < 0.05).Changes in the relative abundance of specific taxa of soil bacteria at the phylum and class level were also observed . At the estation , with avsphaerae . Practicnt class , and theSignificant correlations existed among the soil N fractions, bacterial abundance and alpha diversity  (P < 0.01), except for the correlation between alpha diversity and NN content (p = 0.053). Moreover, the changes in beta diversity were mainly due to changes in the relative abundances of specific bacteria taxa after afforestation, which were related to the soil N fractions . After aet al.3132126359The change in land use from agriculture to forestry imply that the annual cycle of cultivating and harvesting crops was replaced by a considerably longer forest cycle69Soil N fractions can be presented as N-containing compounds , which vary with litter deposition, roots, and plant compositions121629Proteobacteria, Acidobacteria, and Actinobacteria, were frequently discussed in previous studies43444528Proteobacteria have been described as fast growing copiotrophs that thrive in environments with high carbon availability, whereas Acidobacteria and Actinobacteria were oligotrophic groups that preferred nutrient-poor environments47Actinobacteria (Acidobacteria)-dominant in farmland to Proteobacteria-dominant in afforested soil  and corresponded to increased N fractions  in the present study, which was inconsistent with previous studies16Cyanobacteria contribute to N accumulation in terrestrial ecosystems. Competition for nutrients with large Rhizobiales in afforested soil  may explain the decreased abundance of Cyanobacteria we observed following afforestation. However, NO3−-N did not determine the soil bacterial compositions. This result is inconsistent with a previous study where NO3−-N played an important role in driving soil bacterial compositions during the secondary succession of abandoned land in the Loess Plateau3−-N could impact soil bacterial compositions in abandoned land. It is possible that forests compete with soil microbes for more NO3−-N3−-N and soil bacterial compositions. Both NO3−-N and the relative abundance of Nitrospira were higher in abandoned land than other land use types, also supporting the changes of NO3−-N following afforestation. Consequently, our study demonstrated a relationship between soil N fractions and bacterial compositions, suggesting that soil N fractions changed with different soil bacterial compositions following afforestation. Although our study provides a scientific and reasonable explanation for the nitrogen fractions and the composition of soil bacteria following afforestation, there are also some uncertain factors that drove the changes in the underlying relative abundance of species, including temporal variability, with a snapshot and over time, perturbed environments, and more species colonized or that came out of dormancy. These factors are essential to discuss in a future study.Additionally, soil microbes change the N cycling and availability by transforming N to more mobile forms122951a phylum , which aractions , Fig. 1.Proteobacteria, Actinobacteria, and Acidobacteria could reflect the soil nutrients status after afforestation. Furthermore, N fractions were also correlated with soil bacterial phylum, especially for DON. Increases of other fractions also depended on changes of soil bacterial species. Thus, apart from the above inputs, the changes in the soil bacterial communities should be taken into account when assessing the effect of afforestation on the soil N stock and fractions on the Loess Hilly Region (LHR) of China and vice versa. In the future, more attention should be paid to plant-soil-microbe interactions from aboveground and belowground during N cycling.Our study documented that afforestation of former farmland, apart from influencing the above-ground plant compositions, resulted in below-ground that were characterized by changes of soil properties , N stocks, N fractions  and soil microbial community. Shifts in inputs with changes of plant compositions can increase the N stocks and fractions following afforestation. Similarity, soil bacterial abundance and diversity were also related with increased litter and root biomass. Changes of three dominant phyla such as This study was conducted in the Wuliwan catchment, located in the middle area of the Loess Plateau  . The stuRobinia pseudoacacia L. (RP) or Caragana Korshinskii Kom (CK) and from abandoned land. Details of four land use types are provided in −2), fine root N (mean 13.19 g/kg), pH (mean 8.39) and SWC (mean 15.28%); all the data in our study showed an increasing trend except pH.The study catchment, the Wuliwan catchment, has been an experimental site of the Institute of Soil and Water Conservation, Chinese Academy of Science (CAS) since 1973. Widespread vegetation restoration has been implemented to remedy the soil degradation problem during the past decade. As shown in Robinia pseudoacacia L. (RP40), Caragana Korshinskii Kom (CK40), abandoned land (AL40), and one farmland (FL) planted with millet  were selected. Moreover, both Robinia pseudoacacia L. (RP) and Caragana korshinskii Kom. (CK) have been widely restored on the Loess Plateau. The abandoned land (AL40) that was afforested from farmland was not tilled for 40 years. For each land use type, we sampled three replicates for a total of 12 forested sites (three 25 m × 50 m), and in each site (25 m × 50 m), we designed two plots with sizes of 25 m × 25 m, which were also located on the same physiographic units; prior to afforestation, all the plots were essentially farmlands.Sampling was carried out in April 2014. Based on the vegetation restoration project, three 40-year-old land types, A total of 24 samples (4 land use types × 3 sites × 2 plots) were collected from the top 0–10 cm using a soil auger (diameter 5 cm) from the four land use types. Briefly, after removing the litter layer, we collected 10 samples with an “S” shape and incorporated spatial heterogeneity at the plot scale, for a total of six replicate soil samples per land use type. The samples were immediately sieved through 2-mm mesh to eliminate large rocks and roots. A part of the soil samples to be used for DNA extraction were transported from field to laboratory in ice bags and then stored in the laboratory at −80 °C. A second portion to be used to determine the total nitrogen were air-dried and stored at room temperature; another part of the soil samples to be used to determine the other N fractions were stored at 4 °C. Additionally, three soil profiles were dug randomly in each plot and were sampled in each sampling interval to determine the soil bulk density.−1) was determined using the Kjeldahl method, and the contents of ammonium nitrogen (AN) (mg.kg−1) and nitrate nitrogen (NN) (mg.kg−1) were determined using KCl digestion, whereas the dissolved organic nitrogen (DON) was determined using potassium peroxy disulfate oxidation before the treated liquids were assessed by an Automated Chemistry Analyzer−1) was measured using the chloroform fumigation extraction methodThe total nitrogen (TN)  storage at each sampling depth. The N stocks of different sampling depths were calculated as follows:−1) of TN, CSTN is the content (g·kg−1) of STN, ρ is the bulk density (g·cm−3), H is the soil horizon thickness (cm), and δ is the fraction (%) of gravels >2 mm in size in the soil. Because the soil gravel size of loess in China is mostly below 2 mm, this fraction was assumed to be 0where N stocks is the density . The concentration and quality of the DNA were measured using a spectrophotometer . The extracted soil DNA was stored at −80 °C for PCR amplification and analysis.2 > 0.992).Bacterial 16S rRNA copy numbers were estimated by primer set 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′)PCR amplification of the bacterial 16S rRNA targeting the V4 regionhttp://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16S rRNA gene sequence was analyzed by RDP Classifier (http://rdp.cme.msu.edu/) against the Silva (SSU115) 16S rRNA database using a confidence threshold of 70%. Finally, the complete dataset was sent to the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI) under the accession number of SRP056716.The reads were demultiplexed, quality-filtered, and processed using QIIME and based on three criteria. First, the 300-bp reads were truncated at any site with an average quality score < 20 over a 50-bp sliding window. The truncated reads that were shorter than 50 bp were then discarded. Second, exact barcode matching, two-nucleotide mismatches in primer matching, and reads containing ambiguous characters were removed. Third, only sequences that overlapped longer than 10 bp were assembled according to their overlap sequence. Reads that could not be assembled were discarded. Operational Taxonomic Units (OTUs) were clustered with a 97% similarity cutoff using UPARSE , were used to show the community diversity. Unweighted-Unifracted PcoA was performed to assess the cluster of soil bacterial communities according to land use typesPearson correlations among litter, litter-N, fine root biomass, fine root-N, soil N fractions, bacterial abundance and alpha diversity  were determined using the SPSS 22.0 software package. The linear regression analysis between the PcoA 1 and the SWC and pH were calculated using the SPSS 22.0 software package. The changes in the soil N stocks and fractions and the soil bacterial abundance and diversity among the land use types were tested with One-Way ANOVA using the R v.3.1.3 program. All p-values were considered significant if at or below the 0.05 threshold. Additionally, we used the “best” model building procedure in PRIMER v.7 to identify all possible combinations of factors that contributed to the highest proportion of soil bacterial phylum and other species; factor addition was evaluated stepwise and was based on sufficient improvement in the model’s RHow to cite this article: Ren, C. et al. Responsiveness of soil nitrogen fractions and bacterial communities to afforestation in the Loess Hilly Region (LHR) of China. Sci. Rep.6, 28469; doi: 10.1038/srep28469 (2016).


Although atypical antipsychotic drugs (APDs) have led to significant advances in the treatment of psychotic disorders, they still induce metabolic disturbances. We aimed at characterizing the metabolic consequences of a risperidone treatment and at establishing a link with noninvasive MR markers, in order to develop a tool for predicting symptoms of the metabolic syndrome. Fat deposition and liver morphometry were assessed by T1-weighted imaging. Fatty acid composition and fat accumulations in tissues were determined using MR spectroscopy with and without water suppression, respectively. Risperidone treatment induced a weight gain accompanied with metabolic disturbances such as hyperglycemic status, an increase in visceral adipose tissue (VAT), and liver fat depositions. Correlations using Methylene-Water Ratio (MWR) and Polyunsaturated Index (PUI) demonstrated a concomitant increase in the weight gain, VAT and liver fat depositions, and a decrease in the quantity of polyunsaturated fatty acids. These results were consistent with a hepatic steatosis state. We evaluated the ability of MR techniques to detect subtle metabolic disorders induced by APDs. Thus, our model and methodology offer the possibility to investigate APDs side effects in order to improve the health conditions of schizophrenic patients. Antipsychotic drugs (APDs) are widely used in current psychiatric practice and are commonly classified as typical  or atypical (second generation). Atypical APDs have been introduced in clinical practice after 1990, including clozapine, olanzapine, quetiapine, and risperidone. Atypical APDs cause less extrapyramidal symptoms (tremors) than typical APDs. However, both of them produce a weight gain , which i versus subcutaneous fat), insulin-resistance, or sedentary lifestyle [The excess of visceral adipose tissue (VAT) mass is particularly correlated to the prevalence of metabolic syndrome and insulin resistance . Indeed,ifestyle –11. The  in vivo assessment of body fat, such as anthropometry [ in vivo hepatic triglyceride (TG) quantification in order to diagnose hepatic steatosis in clinical practice. Due to its good correlation with histological data from liver biopsies [Several techniques are widely used for theopometry , hydrodeopometry , 15. Theopometry –21. CT aopometry , 22–26. opometry . T1-weigopometry , 26, 28.opometry , 29. MRSbiopsies , MRS is biopsies , 28.Previous work showed the relevance of a mouse model for assessing weight gain induced by risperidone treatment . In the The Ethic community approved all protocols for animal experiments. Twelve seven-week- old female mice of C57BL/6N strain were purchased from Charles River laboratories .−1 of long-acting risperidone  (risperidone group). The other group received IP injections of the vehicle of long-acting risperidone (control group). Mice were housed in a cage and maintained under a 12 h light-12 h dark cycle with free access to food and water. MR examinations were performed on the twenty-four mice. Pelleted food was weighted weekly to assess total grams of food consumed per cage and per day, and then divided by six to estimate the average weekly food consumption per mouse. Following MR examinations, the twenty-four mice were sacrificed by an overdose of pentobarbital. Biological samples were collected for histological examination.Twenty-four female mice were examined in this study. Two groups of twelve animals were randomized by weight. One group was treated weekly over 24 weeks with intraperitoneal (IP) injection of 12.5 mg kgAll MR examinations were done after the 24 weeks of risperidone treatment on a 7 T Bruker Biospec  imaging system equipped with a 40 cm horizontal bore magnet. Anesthesia was induced by 2% isoflurane and maintained at 1%–1.5% along acquisition, depending on respiration frequency. A pneumatic pillow  was used to perform and monitor triggered respiratory gating. Each mouse was placed in a cylindrical coil (39 mm inner diameter). Multislice gradient echo sequence was triggered with respiration and performed to assess mouse position inside the magnet, with the following sequence parameters: TR/TE (Repetition Time/Echo Time) = 200/3 ms, FA (Flip Angle) = 30°.Liver and adipose tissue images were acquired by T1-weighted imaging, using axial and coronal Rapid Acquisition with Relaxation Enhancement (RARE) sequence, which was gated with respiration. Multislice coronal  and axial  RARE images were collected with a TR of 400 ms, a TE of 9 ms, a 256 × 256 data matrix, and a number of repetitions (NEX) of 2. Reconstructions were done to evaluate the volumes of liver, Inguinal Adipose Tissue , and VAT  with the following parameters: voxel volume 3 × 3 × 3 mm; TR = 10,000 ms; TE = 20 ms. To avoid obvious blood vessel and subcutaneous fat contributions, the voxel was carefully placed on previous anatomic axial and coronal T1-weighted images . The mag In vivo, hepatic fatty acid proportion is determined as the percentage of the bulk methylene resonance to water [The MWR was assessed from localized proton spectroscopy performed without water suppression.to water . So, MWR 1 = terminal methyl (CH3) at 0.9 ppm; 2 = methylene groups of saturated fatty acids and the “saturated” components of mono- and poly fatty acids (CH2)n at 1.3 ppm; 3 = allylic as the methylene adjacent to methene groups (CH2) at 2.1 ppm; 4 = diallylic as the methylene inserted between sequential methene groups of fatty acid (CH–CH2–CH) at 2.8 ppm; 5 = methene as the olefinic groups (CH=CH) at 5.3 ppm.The localized proton spectroscopy was acquired with manual water suppression using VAPOR (VAriable Power and Optimized Relaxation delays) sequence module and a signal accumulation amounted to 64. The different ratios were calculated from peak intensities of the lipid spectra. Previous works reported that the T1 relaxation values of liver methylene and methene groups were respectively between 0.39 to 1.20 s and 0.60 to 1.16 s , 35. In Imethene, Iallylic, Imethylene, and Imethyl are, respectively, the signal amplitude of the methene, allylic methylene, bulk methylene, and terminal methyl peaks.Calculation of hepatic lipid indexes was based on the proportion of constitutive saturated mono- and poly unsaturated fatty acids according to Johnson and colleagues' study . Using MIdiallylic is the signal amplitude of the diallylic methylene peak. SI is calculated as the complemented index of UIs and can be easily assessed as follows:UIs formula is based on the principle that methene functional group is always next to allylic methylene group in monounsaturated fatty acids and with both allylic and diallylic methylene groups in Poly Unsaturated Fatty Acids (PUFAs). Thus UIs formula is as follows:lleagues  and the Spontaneous motor activity was measured using an actimeter . This apparatus allowed horizontal motor activity , rearing behavior (number of rears), and mean velocity (in centimeters per second). These parameters gave clues about ability of mice to explore a new environment. Motor activity was recorded for ten minutes.μL of tail tip blood was collected to assess fasting glucose levels. Thereafter, mice received 2 g/kg of 15% dextrose by ip injection. Sample tail tip blood glucose levels were measured using a glucometer  at 10, 20, 30, 60, 90, and 120 min.After fasting in fresh cages for 6 hours, approximately 25 μm-thick frozen-liver sections, using Harris haematoxylin for nucleus coloration. The surface of liver lipid droplets was quantified using image J software . Lipid droplet volumes were calculated from the surface previously measured by the software. Results were presented as a volume variation relative to the control group.IAT and livers were collected and weighted. Livers were then fixed in 4% paraformaldehyde for Oil Red O (ORO) staining. Analysis of lipid deposition was made by ORO staining on 7 Dosage of cholesterol and TG was performed immediately after MR. Plasma levels of total cholesterol (TC), TGs, and high density lipoprotein cholesterol (HDLc) were determined using commercially available kits . Non-HDL cholesterol (Non-HDLc) was calculated by subtraction of HDLc from TC.Frozen liver tissue (50 mg) was homogenized in SET buffer , followed by two freeze-thaw cycles and three times passages through a 27-gauge syringe needle, and a final freeze-thaw cycle. Protein content was determined using the BCA method , and TG and cholesterol were measured as described above.P < 0.05.All statistical analyses were performed using StatWiev software . Data are reported as mean ± standard deviation (SD).The criterion for statistical significance was t-test was used. If one or more group was not normally distributed, the nonparametric Mann-Whitney was used.When distributions were normal in each group, a Student's To assess differences on autopsy, MR volumetry, and MRS experimental datasets, we performed ANCOVA tests using mice weight as a covariate in order to adjust statistical analyses and suppress the influence of the weight gain.For correlations among data, a Spearman test was used.A two-way ANOVA was performed to test weight gain and food intake.Statistical analyses were performed as follows. versus 24.58 ± 1.21 g  at the end of the experiment ; P < 0.01, n = 20;  versus 18.82 ± 7.61 mg/dL; P < 0.01, n = 24). But no significant difference was found concerning fasting TG  and HDLc concentrations , on SI , and on PUI  as illustrated in P < 0.001, n = 20; Given the limit of 50 Hz for spectral resolution, 4 acquisitions had not been performed, 2 in each group. Therefore, 20 animals were considered in lipid indexes calculations described previously in the methods section and in Two aspects are addressed in this study: first, our experimental model, which gradually developed a metabolic disturbance pattern associated with a long-term risperidone treatment, and second, our methodology, based on magnetic resonance, which evidenced that these noninvasive techniques were suitable for the detection of body fat alterations and the resulting hepatic steatosis.In our experimental mouse model, the risperidone treatment induced a weight gain after 24 weeks. We concomitantly described here components of the metabolic syndrome  in mice The second aspect of this study is the use of noninvasive MR techniques. The volumetric analysis was performed through the use of T1-weighted imaging and was allowed the quantification of fat deposition in subcutaneous and visceral areas. Our findings showed a closed correlation between VAT and hyperglycemic status, in good accordance with clinical evidences suggesting a link between the prevalence of metabolic syndrome and an abnormal visceral adiposity . This isn-3) species in particular, are most susceptible to free radical attacks leading to a reallocation of unsaturations [The low concentration of liver fat makes the hepatic steatosis difficult to diagnose by MR techniques. Clinical and preclinical studies showed modifications in nature and proportion of fatty acids in a suffering liver , 43. Theurations , 45. Usiurations , 46. In urations . DespiteNevertheless, these techniques have their own limitations, especially when the target organ for instance liver is subjected to respiratory motion. Although respiratory gating improves the quality of acquisitions, residual breathing artifacts may yield to uncertainty in amplitude and phase variations in localized MR spectra. These artifacts may induce bias on the spectral resolution which is dependent on the homogenization of the magnetic field (shim), and consequently modify the calculation of saturation/unsaturation indices. To minimize breathing artifacts, the ideal condition should define a TR value as close as possible to the steady state duration of breathing. Nevertheless, the use of low TR values leads to a T1-weighting at the expense of the signal/noise ratio. Compromisingly, we selected a high TR value to promote the signal/noise ratio. The magnetic field strength represents also a parameter which conditions a good signal/noise ratio, but depends only on the properties of the machine. The arrival of 3 T magnets for patients in clinical practice and 7 T magnets for clinical research may promote the use of these noninvasive techniques for diagnosis assistance through optimized signal/noise ratio and acquisition time.Although our experimental model is far from obesity models, the subtle metabolic disturbances observed here demonstrated that MR approaches leave the opportunity to evaluate consequences of the risperidone treatment. We highlight three noninvasive markers to predict metabolic disturbances and hepatic steatosis severity when critical limits are reached and exceeded: mice weight up to 26 g, MWR up to 2.5%, and PUI down to 0.17. As a perspective to improve health conditions of schizophrenic patients, this experimental approach (model and methodology) could represent a relevant tool to (i) define the best effective dose of ADPs to maintain CNS effects and minimize metabolic dark-side effects; (ii) evaluate the benefit of a functional food such as dietaries supplemented with omega-3  or curcu in vivo by MR methods. Three parameters have been selected: MWR, PUI, and mice weight as reliable factors to define the severity of the disturbances. Thus, our model and methodology offer the possibility to investigate APDs side effects more precisely, in order to improve health conditions of schizophrenic patients.In this study, we proposed a pathophysiologic model of slight metabolic disturbances induced by a long-term risperidone treatment, which have been evaluated


Smoldering multiple myeloma (SMM) is an asymptomatic precursor stage of multiple myeloma (MM) characterized by clonal bone marrow plasma cells (BMPC) ≥ 10% and/or M protein level ≥ 30 g/L in the absence of end organ damage. It represents an intermediate stage between monoclonal gammopathy of undetermined significance (MGUS) and symptomatic MM. The risk of progression to symptomatic MM is not uniform, and several parameters have been reported to predict the risk of progression. These include the level of M protein and the percentage of BMPC, the proportion of immunophenotypically aberrant plasma cells, and the presence of immunoparesis, free light-chain (FLC) ratio, peripheral blood plasma cells (PBPC), pattern of serum M protein evolution, abnormal magnetic resonance imaging (MRI), cytogenetic abnormalities, IgA isotype, and Bence Jones proteinuria. So far treatment is still not recommended for SMM, because several trials suggested that patients with SMM do not benefit from early treatment. However, the Mateos et al. trial showed a survival benefit after early treatment with lenalidomide plus dexamethasone in patients with high-risk SMM. This trial has prompted a reevaluation of early treatment in an asymptomatic patient population. Smoldering multiple myeloma (SMM) was first described in 1980 in the New England Journal of Medicine (NEJM) . SMM accIn 1980, Kyle and Greipp first introduce the concept of smoldering (asymptomatic) multiple myeloma (SMM) in defining six myeloma patients in whom the percentage of plasma cells and level of M protein were higher than those seen in MGUS and fulfilled the criteria for diagnosis of MM but have no anemia, hypercalcemia, and lytic bone lesions. These patients remained stable without specific therapy for five or more years . In 1988Most of the patients diagnosed with SMM will progress to symptomatic MM. However, SMM is not a uniform disease and patients with SMM do not progress to symptomatic MM at the same rate. Hence, it is important to define the risk of progression. A number of parameters have been described to predict risk of progression to symptomatic MM .P < 0.001) [The level of serum M protein and the percentage of BMPC were associated with the risk of progression from SMM to active disease. Kyle et al. divided patients with SMM into three prognostic groups by the percentage of BMPC and level of serum M protein . The median time to progression (TTP) in groups 1, 2, and 3 were 2, 8, and 19 years, respectively . Kastrit< 0.001) . It was < 0.001) –18.P = 0.01). This was also observed in the nonevolving SMM arm, in which the 3-year progression rate was 15% for patients with aPCs/BMPC ≥ 95%, whereas no progression was observed in the aPCs/BMPC < 95% arm (P = 0.01) [The proportion of immunophenotypically aberrant plasma cells  within the bone marrow plasma cells (aPCs/BMPC) ≥ 95% and immunoparesis  were risk factors for progression. Pèrez-Persona et al. reported that a risk of progression to active MM at 5 years of 4%, 46%, and 72%, respectively, for patients with none, 1, or 2 risk factors (aPCs/BMPC ≥ 95% and immunoparesis) [ = 0.01) .P < 0.0001) [P < 0.005). The median TTP for patients with a FLC ratio ≥ 100 was 18 versus 73 months for patients with FLC ratio < 100 [The serum immunoglobulin FLC ratio was an independent predictor of progression to active MM. Larsen et al. concluded that a high serum-free light-chain (FLC) ratio ≥ 100 was a biomarker of early progression to active MM. The median TTP in the FLC ratio ≥ 100 arm was 15 months versus 55 months in the FLC < 100 arm ( 0.0001) . Kastritio < 100 . Dispenzio < 100 .P < 0.01) [6/L and/or >5% cytoplasmic immunoglobulin (Ig) positive plasma cells per 100 peripheral blood mononuclear cells) could predict the evolution of SMM to active MM. The progression rate at two years for patients with high PBPC was 71% versus 25% without high PBPC (P = 0.001). Corresponding progression rate at 3 years were 86% versus 35%, respectively (P < 0.001) [Detection of circulating peripheral blood plasma cells (PBPC) helped to predict disease course in patients with SMM. Witzig et al. detected the number of PBPC in 57 patients with SMM and found that the median TTP for patients with abnormal PBPC (defined as an increase in number or proliferative rate of PBPC) was 0.75 years versus 2.5 years for those patients without abnormal PBPC ( < 0.01) . Bianchi< 0.001) .P = 0.007) [Based on the pattern of changes in serum M protein, SMM was classified into evolving SMM and nonevolving SMM. Evolving SMM was characterized by a progressive increase in serum M protein, a previously recognized MGUS, and a significantly higher proportion of IgA type. Nonevolving SMM was characterized by stable M protein until disease progression. The median TTP for patients with evolving SMM was 1.3 years versus 3.9 years for those with nonevolving SMM (= 0.007) .P < 0.01) [P < 0.001). The 2-year progression rate for patients with 0 or 1 focal lesion was 20% versus 70% for those patients with >1 focal lesion [P = 0.001) [Moulopoulos et al. assessed the prognostic significance of magnetic resonance imaging (MRI) in patients with asymptomatic myeloma and reported that the median TTP for patients with abnormal MRI was 16 months versus 43 months for those with normal MRI ( < 0.01) . Hillengl lesion . KastritP = 0.001), and with t was 2.91 years , P = 0.003). The progression rate at three years for patients with +1q21 was 43% , and 35% for those with hyperdiploidy  [P = 0.001. There was a trend to shorter TTP with deletion 17p  [Neben et al. analyzed the impact of chromosomal aberrations on progression in patients with SMM and found that the presence of del (17p13), t, +1q21 and hyperdiploidy predicted shorter TTP. The median TTP for patients with del (17p13) was 2.04 years (versus 5.62 years without del (17p13), = 0.016) . RajkumaP = 0.21) [Whether IgA isotype was a harmful prognostic factor for progression of SMM to active MM was unclear. Weber et al. assessed the clinical features in 101 patients with SMM and found that IgA isotype was present in 23 of 101 patients. There was a trend that the median TTP for patients with IgA isotype was shorter than those with IgG isotype  .P = 0.02) [The predictive role for Bence Jones proteinuria was controversial and of uncertain importance. Dimopoulos et al. reported that Bence Jones proteinuria represented an independent prognostic factor. Bence Jones protein > 50 mg/d was present in 28 of 95 patients. The median TTP for patients with Bence Jones protein > 50 mg/d was 19 months versus 29 months for those with Bence Jones protein ≤ 50 mg/d ( = 0.02) .SMM has no obvious symptoms and may require different treatment strategies. For patients with SMM, it is not clear whether it is better to immediately treat after diagnosis or to delay treatment until symptomatic MM develops. Many investigators performed clinical trials to determine whether early treatment using conventional and novel agents resulted in improved clinical outcomes for patients with SMM, when compared with deferred treatment .Three small randomized studies –7 comparSeveral trials compared single agent bisphosphonate with observation in patients with SMM. Martin et al. performed a single-arm pilot study to assess the effects of pamidronate on 12 patients with SMM or IMM and suggested that pamidronate treatment reduced bone turnover but had no significant antitumour effect . Two ranP = 0.0004). The median TTP was significantly longer in the thalidomide plus zoledronic acid arm than in the zoledronic acid alone arm . The one-year progression-free survival (PFS) in the thalidomide plus zoledronic acid arm was 86% versus 55% in the zoledronic acid alone arm (P = 0.0048). There was no difference in OS between the arms [Three nonrandomized, phase II trials evaluating thalidomide-based treatment suggested that long-term thalidomide therapy was poorly tolerated and most of the patients discontinued treatment due to peripheral neuropathy –10. A rathe arms .P < 0.001). 94% of the patients in the treatment arm versus 80% in the observation arm were alive at three years (P = 0.03) [Early treatment for patients with high-risk SMM delayed disease progression and increased OS. Mateos et al. evaluated treatment in patients with high-risk SMM. High-risk MM was defined as BMPC ≥ 10% and a monoclonal component  or only one of the two criteria described above, plus aPCs/BMPC ≥ 95%, with decrease of one or two uninvolved immunoglobulins ≥25%, without CRAB. They randomly assigned 119 patients with high-risk SMM to lenalidomide-dexamethasone treatment or observation. Lenalidomide-dexamethasone treatment significantly delayed disease progression and improved OS. The three-year PFS for patients in the treatment arm was 77% versus 30% for those in the observation arm ( = 0.03) .Some phase II trials are ongoing to determine whether the use of agents such as siltuximab (anti-IL-6 mAb), elotuzumab, MLN9708 (ixazomib), or BHQ880  is active in high-risk SMM . The resCurrently, treatment is not recommended for SMM based on data derived from several small randomized controlled trials. However, the recent Mateos et al. trial showed improved OS with lenalidomide and dexamethasone treatment among patients with high-risk SMM. This suggested that high-risk SMM should be targeted for early intervention. However, the Mateos et al. trial had a small sample size. Much larger trials that select patients with high-risk SMM are needed to provide more evidence. The Mateos et al. trial used a combination regimen; therefore, whether the benefit was due to lenalidomide is unclear. More data were needed to isolate the effect of lenalidomide, and more trials were needed to evaluate the effect of other novel drugs which are not as expensive as lenalidomide on high-risk SMM. In addition, the criteria for high-risk SMM should be established. Mateos et al. used two separate criteria to identify high-risk SMM, one defined by Kyle et al. and another by Pèrez-Persona et al. This method was validated to identify patients who progress to active MM within almost 2 years after diagnosis. However, such criteria limit the general applicability of this approach because flow cytometry was required. Currently, a number of parameters have been described to predict risk of progression to symptomatic MM, prompting the following questions: (1) what probability of 2-year progression to symptomatic MM will be defined as high risk? (2) Which predictors should be used alone or in combination to identify high-risk SMM? Currently, there is no consensus, but it is generally accepted that patients with BMPC ≥ 60%, FLC ratio ≥ 100, or abnormal MRI  are at high risk and need to be treated immediately. After reviewing all of the data, patients with BMPC ≥ 60%, FLC ratio ≥ 100, or abnormal MRI  have ≥70% probability of progression to active MM . If inde


Older patients undergoing surgery tend to have a higher frequency of delirium. Delirium is strongly associated with poor surgical outcomes. This study evaluated the association between pre-operative medication use and post-operative delirium (POD) in surgical oncology patients receiving comprehensive geriatric assessment (CGA).2. Discrimination of the model was assessed by an analysis of the area under receiver operating curve (AUROC).A total of 475 patients who were scheduled for cancer surgery and received CGA from January 2014 to June 2015 were included. Pre-operative medication review through CGA was conducted on polypharmacy (≥5 medications), delirium-inducing medications (DIMs), fall-inducing medications (FIMs), and potentially inappropriate medications (PIMs). POD was confirmed by psychiatric consultation, and DSM-V criteria were used for diagnosing delirium. The model fit of the prediction model was assessed by computing the Hosmer-Lemeshow goodness-of-fit test. Effect size was measured using the Nagelkerke R2 = 3.842, p = 0.871 for model I and χ2 = 8.130, p = 0.421 for model II). The Nagelkerke R2 effect size and AUROC for model I was 0.215 and 0.833, respectively. Model II had the Nagelkerke R2effect size of 0.174 and AUROC of 0.819.Two models were constructed for multivariate analysis based on univariate analysis; model I included dementia and DIM in addition to age and sex, and model II included PIM instead of DIM of model I. Every one year increase of age increased the risk of POD by about 1.1-fold. DIM was a significant factor for POD after adjusting for confounders . PIM was also a significant factor for POD . The Hosmer-Lemeshow test results revealed good fits for both models  contains supplementary material, which is available to authorized users. In Korea, the proportion of the population aged 65 years or over in 2014 was 12.7 %. By 2030, that will increase to 24.3 % and is expected to rise to 32.3 % by 2040. The most common cause of death in those ≥ 65 years of age in 2013 was cancer [Older patients suffer from comorbid diseases and have a high risk of post-operative complications –4. In paPatients on chronic drugs unrelated to their surgical procedure are more likely to have post-operative complications . Older cPre-operative CGA can predict post-operative morbidity and mortality in older patients undergoing surgery as well as effects on the treatment plan , 12. AltAll patients ≥ 65 years of age scheduled for cancer surgery were eligible in this study. We retrospectively studied consecutive subjects ≥ 65 years of age who were scheduled for cancer surgery and presented for preoperative CGA at the geriatric center of Seoul National University Bundang Hospital between January 2014 and June 2015. We excluded patients who refused surgery or did not have cancer surgery.Baseline patient characteristics collected from electronic medical records included age, sex, height, weight, cancer type, and comorbidities. Serum creatinine, lean body weight, sex, and age were used to estimate renal function by Cockcroft-Gault equation. The risk of delirium was measured by the Nursing Delirium Screening Scale, with scores ranging from 0 to 5; a score of 2 or higher suggests an increased risk of POD .Pre-operative CGA was performed using an established tool by the geriatric team, which consisted of geriatricians, nurse specialists, dietitians, and pharmacists. Pharmacists were wholly responsible for the drug assessment. Cognitive function was evaluated using the Korean version of the Mini-Mental Status Examination (MMSE-KC), with scores ranging from 0 to 30 (dementia is a score < 17) .Medication review was performed as follows; 1) Patients were guided to carry records of their medications (prescriptions) or their actual medications in advance of CGA. 2) Pharmacists interviewed patients and caregivers to figure out all prescription and non-prescription medications. Information of PRN use was also obtained. In the process, pharmacists recorded number of medications, pre-operative discontinuation-requiring medications (PDRMs), delirium-inducing medications (DIMs), fall-inducing medications (FIMs), and PIMs.PDRMs were defined as medications that should be discontinued before surgery due to surgical risks, such as antithrombotic agents for post-operative hemorrhage, metformin for lactic acidosis, exogenous hormones for venous thromboembolism, and herbal medications for the uncertainty over their actual contents Table .Table 1P®. Deliriants including high dose narcotics, benzodiazepines, and anticholinergic medications were also included in DIMs  and adjusted odds ratio (AOR) were calculated from univariate and multivariate analyses, respectively. P-values less than 0.05 were considered statistically significant.The Mann–Whitney-U-test was used to compare continuous variables between patients with and without POD, and the chi-square test was used for categorical variables. Multivariable logistic regression analysis was used to identify independent risk factors for POD. Factors having 2. Discrimination of the model was further assessed by an analysis of the area under receiver operating curve (AUROC), which assesses the ability of the risk factor to predict POD. All statistical analyses were carried out using the Statistical Package for Social Sciences version 17.0 for Windows .The model fit of the prediction model was assessed by computing the Hosmer-Lemeshow goodness-of-fit test. Effect size was measured using the Nagelkerke Rn = 35), changed treatment plan (n = 13), and age < 65 years (n = 4). Accordingly, data from 475 cancer patients who underwent pre-operative CGA and cancer surgery were used for the analysis.A total of 527 patients were scheduled for cancer surgery and received CGA from January 2014 to June 2015. Fifty two patients were excluded due to refusal for surgery (n = 353), breast cancer (n = 93), gynecology cancer (n = 15), genitourinary cancer (n = 5), and other cancers (n = 9). Delirium score ≥ 2 was found in 3.2 % of patients. Around half of patients had polypharmacy (50.5 %), PDRM (57.3 %), and DIM (42.1 %). Patients on FIM and PIM comprised 76.8 % and 26.7 %, respectively.As shown in Table p = 0.018). Among comorbidities, dementia had a significant relationship with POD; patients with POD had more than five times higher proportion of dementia presence compared to those without POD (p = 0.026). Low BMI was associated with POD with marginal significance (p = 0.054).POD was diagnosed in 19 patients (4 %). Patients who experienced POD were older than those without POD (p < 0.001). Patients in POD group took more multiple medications and FIM rather than patients in no POD group, although statistical significance was not obtained.More than two-fold patients in the POD group took DIM and PIM before operation  and PIM  were significant factors for POD after adjusting for confounders.2 = 3.842, p = .871). The Nagelkerke R2 effect size was 0.215 and AUROC was 0.833 , and the Nagelkerke R2effect size was 0.174. AUROC was 0.819 , patients with DIM had 12.8 times (95 % CI 2.8-57.7) increased risk of POD after adjusting for confounders, and patients with PIM had 5.5-fold (95 % CI 2.0-15.1) increased risk of POD after adjusting for covariates.p < 0.001) similar to our results  [Consistent with our results, many studies showed that old age is significantly associated with POD –27. One  < 0.05) . Another < 0.05) .The association between male sex and POD is unclear. Studies on patients undergoing elective orthopedic surgery reported that male sex was a significant predictor for delirium, but only in patients without dementia , 31. A sDIM was the strongest factor for POD in this study. More than 12-times higher risk of POD was found in pre-operative DIM users. Many studies support our results. In a study using Japanese population receiving lung and esophageal cancer surgery, use of benzodiazepines was a significant factor for POD  . AnotherThe American Geriatrics Society Guideline recommends PIM should be avoided in older patients due to the risk of POD . Our stuThe model including DIM was also higher effect size (21.5 %) than that including PIM (17.4 %), although the sizes were small. However, the Hosmer-Lemeshow test results showed that the fits of both models were satisfactory. Moreover, our models had an AUROC of around 0.8, indicating that the ability of the two models to predict is much better than by chance alone (0.5). An AUROC > 0.7 for the models indicated that they had acceptable capacities for patients with POD as compared to those without POD .2, and AUROC.To the best of our knowledge, this is the first study to develop prediction models by pre-operative medication use for POD and validate them using various statistical tools, such as the Hosmer-Lemeshow goodness-of-fit test, Nagelkerke RHowever, this study has several limitations. First, the incidence of POD could be underestimated due to the definition of POD. Previous studies reported the frequency of POD in older patients as 9–29 % , 26. TheIn spite of the shortcomings, this study provides compelling evidence for the importance of assessing pre-operative medication. The results of this study could be utilized to develop and implement individually tailored geriatric interventions to prevent POD. Moreover, the predictive value of CGA could be greater because confirmed cases of delirium were only included in this study.The exposure to DIM and PIM in older patients before cancer surgery was associated with increased incidence of POD. This is an important finding, and should be considered in the preoperative assessment of older patients, preferably by interdisciplinary teamwork including a pharmacist.AUROC, area under receiver operating curve; CGA, comprehensive geriatric assessment; DIM, delirium-inducing medication; FIM, fall-inducing medication; PDRM, pre-operative discontinuation requiring medication; PIM, potentially inappropriate medication; POD, post-operative delirium.