GSTT1, GSTP1, and GSTM1, play a major role in detoxification and metabolism of xenobiotics. We investigated the association between GST genotypes and whole blood arsenic concentrations (BASC) in Jamaican children with and without autism spectrum disorder (ASD). We used data from 100 ASD cases and their 1:1 age- and sex-matched typically developing (TD) controls (age 2–8 years) from Jamaica. Using log-transformed BASC as the dependent variable in a General Linear Model, we observed a significant interaction between GSTP1 and ASD case status while controlling for several confounding variables. However, for GSTT1 and GSTM1 we did not observe any significant associations with BASC. Our findings indicate that TD children who had the Ile/Ile or Ile/Val genotype for GSTP1 had a significantly higher geometric mean BASC than those with genotype Val/Val . Although, among the ASD cases, this difference was not statistically significant, the direction of the observed difference was consistent with that of the TD control children. These findings suggest a possible role of GSTP1 in the detoxification of arsenic.Arsenic is a toxic metalloid with known adverse effects on human health. Glutathione-S-transferase (GST) genes, including  Arsenic is a widespread environmental toxicant with harmful effects on human health. In some developing countries, arsenic exposure through drinking water and food is a serious threat to human health ,2. InorgGSTO) [GSTO1 and GSTO2 are involved in the reduction activities of arsenate, monomethylarsonic acid (MMeAsIII), and dimenthylarisinic acid (DMeAsIII) [Genetic regulatory mechanisms are involved in detoxification of heavy metals including arsenic . HoweverGSTO) . It has MeAsIII) .GSTP1) and glutathione S-transferase mu (GSTM1) also play important roles in detoxification of xenobiotics [GSTM1, may be associated with increased susceptibility to mercury toxicity [It is well-established that glutathione S-transferase pi  in relation to blood arsenic concentrations in Jamaican children with and without ASD.Based on data from 65 pairs of sex- and age-matched ASD cases and TD controls from our Jamaican Autism Study, we have previously reported a lack of association between blood arsenic concentrations and ASD in a multivariable model that adjusted for potential confounding factors such as source of drinking water, age of mother at child’s birth, parental education levels, socioeconomic status, and consumption of some fruits, vegetables, and seafood . In addiGSTT1, GSTM1, and GSTP1) and their interactions with these heavy metals in relation to ASD. Detailed information regarding the recruitment and assessment of ASD cases and TD controls was reported earlier [The Jamaican Autism Study is an age- and sex-matched case-control study of Jamaican children between 2–8 years of age that were enrolled during December 2009–May 2012. The overall goal of the Jamaican Autism Study is to investigate whether environmental exposures to arsenic, mercury, lead, cadmium, and manganese play a role in ASD and to assess the role of three glutathione S-transferase genes  information, such as ownership of a car by the family, parental education levels, and potential exposure to heavy metals through drinking water sources and food, especially the types and frequency of fruits, vegetables, and seafood consumed by the children. The types of seafood considered included: (1) saltwater fish; (2) freshwater fish ; (3) sardine or mackerel (canned fish); (4) tuna (canned fish); (5) salted fish (pickled mackerel); (6) shellfish ; and (7) shrimp. The types of fruits and vegetables were classified into different categories, based on their characteristics and species. Details regarding the categories of fruits and vegetables were reported earlier .About 5 mL of venous whole blood was drawn from each child. Samples for trace metals analysis (2 mL) were stored at −20 °C degrees while remaining samples for genetic analysis (3 mL) were stored at −80 °C degrees at UWI in Kingston, Jamaica. In addition, 2 mL of saliva was also collected from each child. Whole blood samples for trace metal analysis were shipped to the Michigan Department of Community Health (MDCH), and the whole blood and saliva samples for genetic analysis were shipped to the University of Texas School of Public Health at Houston. All parents/guardians provided written informed consent. This study was approved by the Institutional Review Boards of the University of Texas Health Science Center at Houston  and UWI, Mona campus, in Kingston, Jamaica. The data presented herein represent analysis of 100 1:1 matched case-control pairs for whom we had complete data.Arsenic exposure can be assessed using various biological specimens including blood, hair, urine, and fingernails depending on duration of exposure. Arsenic levels in the hair or fingernails reflect exposure from the past several months ,61. For Whole blood was collected in EDTA lavender top vacutainers for each participant at UWI. Blood components were centrifuged and separated into plasma, buffy coat, and red blood cell aliquots, then stored at −80 °C for future use. Frozen specimens were sent to the UTHealth School of Public Health, Human Genetics Center using the CryoPort high volume liquid nitrogen dry vapor shipper . Saliva was collected with Oragene Discover DNA Collection Kits for Research . If the child had difficulty in spitting 2 mL of saliva, then the Oragene Discover OGR-575 for Assisted Collection with sponges was used (DNA Genotek). Saliva samples were shipped from UWI to UTHealth at ambient temperature.GSTT1 and GSTM1 genotypes were detected using a multiplex polymerase chain reaction (PCR) reaction and methods established by Li et al. (2000) [GSTT1. Similarly, the absence of a 230 bp product indicates homozygous deficiency for the GSTM1 gene. As a quality control, a beta-globin amplicon of 280 bp was included in the assay. The GSTP1 polymorphism Ile105Val (rs1695) was genotyped using the TaqMan Drug Metabolism SNP Genotyping Assay C_3237198_20 using the thermal cycling parameters recommended by Life Technologies . The ABI 7700 Sequence Detection System (Life Technologies) was used for ascertainment of the genotypes.Genomic DNA was isolated from buffy coat using the Gentra PUREGENE Blood Kit  in accordance with the manufacturer’s protocol. If a buffy coat was not available, then DNA was isolated from the saliva sample with the Gentra PUREGENE DNA Purification Kit (Qiagen protocol 400244 Rev A) according to the methods established by DNA Genotek . The GST. (2000) . In thisi.e., µg/L) by applying the natural exponential function, herein called geometric means.Descriptive analyses were conducted to compare demographic and SES characteristics of ASD cases and TD controls. As the distribution of blood arsenic concentrations is skewed, the data were transformed using the natural logarithm (ln) to produce an approximately normal distribution. The means of the log transformed blood arsenic concentrations were transformed back to their original scale  and a heterozygote (I/D), we considered only a recessive model for both of them using a binary variable to represent their genotype: I* and DD. For the GSTP1 gene, all three genotypes, Ile/Ile, Ile/Val and Val/Val, were available. The effect of GSTP1 is still controversial, not only for the genetic models but also is for the direction of influence. In the literature, different genetic models  have been proposed for the GSTP1 polymorphism and the effect of a genotype  may be protective for some diseases and deleterious for others. For example, a recessive model of GSTP1 was used in a previous study [et al. (2010) and significant association of Ile/Val or Val/Val genotype with hepatocellular carcinoma was observed [GSTP1 gene using different genetic models, including the recessive  and the full models. For the GSTP1 polymorphism, we tested the Hardy-Weinberg equilibrium using the Chi-square test in the TD control group.For the us study , where aobserved . A studyobserved . In thisGSTP1, GSTM1, and GSTT1. Subsequently, we used General Linear Models (GLMs) with the log-transformed blood arsenic concentrations as the dependent variable to investigate the role of the three aforementioned GST genes as well as other exposure variables in blood arsenic concentrations. In order to minimize any potential effects of multicollinearity due to a high correlation between maternal and paternal education levels, we created a binary variable indicating whether both parents had education up to high school or at least one of the parents obtained education beyond high school. In all GLMs, we also controlled for the clustering effect of matching by including an appropriate number of dummy variables that represented the matched pairs . In multivariable GLMs, we assessed interactions between each of the three GST genes and ASD case status while controlling for potential confounding variables that included SES (car ownership), maternal age at child’s birth, parental education levels, source of drinking water, consumption of “yam, sweet potato, or dasheen”, “carrot or pumpkin”, “callaloo, broccoli, or pak choi”, “cabbage”, “avocado”, and frequency of seafood consumption. Since determination of potential confounding variables in models that involve interactive effects does not follow the classical definition of confounders in additive models, we adjusted for potential confounders that were determined a priori from our earlier work [GSTP1 genotypes in relation to blood arsenic concentrations, we used the CONTRAST statement in PROC GLM in SAS [GSTP1, separately for ASD cases and TD controls. We calculated unadjusted and adjusted geometric mean blood arsenic concentrations for groups of children with different GST genotypes . Though not the main objective of this paper, we used univariable and multivariable GLMs to compare geometric mean blood arsenic concentrations by ASD status based on the 100 matched pairs to our previously reported findings based on 65 matched pairs [In this study, we used conditional logistic regression (CLR) models to assess the association between ASD status and various exposure variables including genotypes for ier work . Since wM in SAS  to test ed pairs . For detGSTM1 and GSTT1 null genotype, respectively, for TD children. There was no significant deviation from Hardy-Weinberg equilibrium for the GSTP1 polymorphism in the TD controls (p = 0.69). In addition, there were no significant differences between ASD cases and TD controls with respect to the genotype frequencies of GSTM1, GSTP1, and GSTT1, . Demographic and other characteristics of children and their parents by ASD case status are presented in The mean age of children with ASD was 68.4 months and the mean age for TD children in the control group was 69.3 months. About 85.0% of the ASD cases and TD controls were male. Nearly all of the ASD cases (93.0%) and TD controls (99.0%) were Afro-Caribbean. Similarly, 93.0% of mothers and 99.0% of fathers were Afro-Caribbean. Our data indicate a prevalence of 26.0% and 22.0% for the p < 0.01) and cabbage , p < 0.01). In addition, a significantly lower proportion of ASD cases reported fruit and vegetable consumption. Comparisons of other variables related to dietary consumption between children with and without ASD are displayed in A comparison of dietary consumption between ASD cases and TD controls revealed that a significantly lower proportion of ASD cases reported eating sardine or mackerel (Matched Odds Ratio (MOR) = 0.26, 95% CI , GSTM1 (p = 0.95), GSTT1 (P = 0.28), and GSTP1 genotypes ). Furthermore, children who ate salt water fish had a significantly higher geometric mean blood arsenic concentration than those who did not eat this type of fish . In addition, a significant difference was found between geometric mean blood arsenic concentrations of children whose family consumed seafood more than 6 meals per week and those who did not . Associations of other exposure variables with blood arsenic concentrations are reported in A comparison of geometric mean blood arsenic concentrations between children who had different levels and types of exposures revealed the following findings. In the univariable analysis, there was no significant association between blood arsenic concentrations with the three GST genes . As shown in vs. 2.72 µg/L, p < 0.01). Similarly, in the TD controls, children with genotype Ile/Ile had significantly higher adjusted geometric mean blood arsenic concentration than those with genotype Val/Val . Additionally, in the recessive model, TD children with Ile/Ile or Ile/Val genotypes had significantly higher adjusted geometric mean blood arsenic concentrations compared to those with the Val/Val genotype .In multivariable analyses, we detected a significant interaction between ASD status and GSTP1 and blood arsenic concentrations, though the observed differences in blood arsenic concentrations had similar trends. For example, in the recessive model, ASD cases with Ile/Ile or Ile/Val genotypes had adjusted geometric mean blood arsenic concentration of 3.71 µg/L compared to 3.29 µg/L for ASD cases with the Val/Val genotype (p = 0.29). Similar analyses for GSTT1 and GSTM1 did not result in any significant associations with the blood arsenic concentrations in children with or without ASD . Details regarding the GSTP1 results are shown in However, in the ASD case group, there were no significant associations between genotypes of GSTT1, GSTM1, and GSTP1) in blood arsenic concentrations of Jamaican children with and without ASD. Using univariable and multivariable GLMs, we investigated the additive and interactive effects of genotypes of the GST genes and ASD case status in blood arsenic concentrations. We did not find any statistically significant associations between the blood arsenic concentrations and the genotypes of GSTT1 or GSTM1 in Jamaican children with and without ASD. However, our findings revealed a significant interaction between ASD status and GSTP1 genotype in relation to blood arsenic concentrations (p = 0.04 for the interaction terms based on the full genetic model). Specifically, based on the recessive genetic models, in TD Jamaican children we found that those with genotype Ile/Ile or Ile/Val for GSTP1 had significantly higher geometric mean blood arsenic concentration than those with genotype Val/Val, . Although ASD cases with Ile/Ile or Ile/Val genotypes had adjusted geometric mean blood arsenic concentration of 3.71 µg/L compared to 3.29 µg/L for ASD cases with the Val/Val genotype, the effect of the GSTP1 gene on adjusted geometric mean blood arsenic concentrations was not statistically significant for the ASD cases, (p = 0.29). However, the direction of the observed difference was consistent with that of the TD control group. The observed mean blood arsenic concentrations for TD Jamaican children were more than three times higher than 1 µg/L, which is considered to be the baseline for unexposed individuals in the US by the Agency for Toxic Substances and Disease Registry (ATSDR) [GSTP1 in the ASD cases.In this study, we have investigated the role of three GST genes ( (ATSDR) . This maGSTP1 Val/Val genotype have a significantly lower geometric mean blood arsenic concentration than those with other genotypes , perhaps due to a higher detoxification of arsenic from blood. To the best of our knowledge, we are the first to report such evidence supporting the possible role of GSTP1 in arsenic regulation: A single amino acid substitution  in GSTP1 may produce a variant enzyme and seems to have a significant impact on blood arsenic concentration. Moreover, since differences in gene expression have been observed between children with ASD and TD children [GSTP1 locus or elsewhere in the genome that were not assessed in our study may have also had an impact on blood arsenic concentrations. Further epidemiological studies and experimental studies using animal models may uncover the underlying mechanisms of this phenomenon.Detoxification of arsenic is a complicated process that is currently not well understood . It is bchildren , and arschildren ,73, it ichildren  at the GGSTO genes may be responsible for catalyzing the conjunction of cellular glutathione and MMAIII [et al. (2007) reported that genes that are similar to the GST genes in mammals play an important role in defense against reactive oxygen species and overall cellular detoxification metabolism related to arsenic in the yeast S. cerevisiae [et al. (2007), a significant interaction was found between the secondary methylation ratio (DMA/MMA) and the GSTT1 gene [GSTT1 genotypes showed that (1) for individuals with the GSTT1 wild type genotype, a 10-fold increase in the primary methylation (MMA/(AsIII+AsV)) ratio was associated with a 1.67 increased risk of skin lesions (95% CI: 1.06–2.64); and (2) for individuals with the GSTT1 wild type, a 10-fold increase in secondary methylation ratio was associated with decreased odds of skin lesions compared to GSTT1 null . It was anticipated that perhaps GSTT1 would have the same effect as GSTO1, or that GSTT1 was in linkage disequilibrium with another polymorphism that is responsible for this observed effect. However, because we do not have data for urinary concentration and the detailed components of arsenic , our results cannot be directly compared with those from this study.It has been reported that the d MMAIII . A studyrevisiae . In the TT1 gene . In addiet al. (2010), it was shown that individuals with the heterozygous genotype of GSTP1 Ile105Val have a higher capacity to metabolize inorganic arsenic (AsIII + AsV) to the methylated form (MMAV), but a lower metabolic capacity to metabolize AsV to AsIII [In a study by Agusa to AsIII . AssuminGSTP1 genotypes in the regression model. In this study we had 100 pairs of ASD cases and their age- and sex matched TD controls. In order to account for potential correlation between ASD cases and TD controls due to matching, we included 99 dummy variables that represented the 100 matched pairs in all GLMs. Since determination of potential confounding variables in models that involve interactive effects does not follow the classical definition of confounders in additive models, we adjusted for potential confounders that were determined a priori from our earlier report [GSTP1 have already taken into account for potential effects of various food consumed by the groups compared.Statistical power could be improved when identifying factors associated with blood arsenic concentrations by pooling data from ASD cases and TD controls and including an interaction term for ASD case status and r report . PotentiGSTP1) may be at a higher risk of exposure to arsenic, hence at higher risk of aforementioned diseases or disorders that are associated with arsenic exposure. Since genetic risk factors are not modifiable, we recommend implementation of interventions focused on dietary and environmental factors that could help to reduce exposure to arsenic in Jamaican children as we have recommended in our previous report [Previously, we have reported that blood arsenic concentrations in Jamaican children are about four times higher than those of children in the US or Canada . We haves report .GSTP1 in relation to blood arsenic concentrations in Jamaican children. This finding may suggest a more complex relationship between arsenic exposure and ASD in children. As shown in a twin study by Hallmayer et al. (2011), environmental factors may have a greater influence than genetic factors on the susceptibility to ASD. Characterization of environmental exposures, including arsenic, is therefore of importance to our understanding of the etiology of ASD [GSTP1 genotypes for the ASD group requires further investigation.Previously, based on 65 matched pairs, we have also reported that blood arsenic concentrations in Jamaican children were not associated with ASD status . Though y of ASD . The lacet al. [GSTP1 genotypes in the ASD group. Finally, we acknowledge that it is possible that the observed association between the genotypes of GSTP1 and the blood arsenic concentration may not necessarily represent an effect of GSTP1, but simply imply that GSTP1 is on linkage disequilibrium with the true causal polymorphism that was not measured in this study.We acknowledge several limitations in this study. First, since the control children for this study were selected to match the ASD cases by sex and age from the Kingston area, they may not represent a random sample from the population of all children in Jamaica. Additionally, our controls belonged to a lower SES group than our ASD cases. Therefore, the findings reported in this study may not be generalizable to populations other than that in which the samples were selected. Due to limited resources, in this study we assessed only total blood arsenic concentrations. In addition, we acknowledge that we did not use fasting blood to measure the arsenic concentrations, which would probably provide a more consistent measurement of arsenic exposure. Moreover, we do not have the exact time of the day in which blood is drawn from children in this study. This limits our ability to control for the timing of blood draw as a potential confounding factor. However, we acknowledge that assessment of inorganic and organic urine arsenic concentrations would allow a better assessment for risk of exposure to inorganic arsenic in Jamaican children and a more detailed analysis of the role of GST genes in arsenic metabolism, as reported in McCarty et al. . AlthougGSTP1 genotype Ile/Ile or Ile/Val had a significantly higher blood arsenic concentration than the TD children with genotype Val/Val. These findings are consistent with a possible role for GSTP1 in the detoxification of arsenic. Although we observed a similar pattern for the ASD cases, this association was not statistically significant. Since higher levels of blood arsenic is associated with several diseases and disorders, our results also suggest that the effect of GSTP1 genotype on the ability to metabolize arsenic may influence the risk of these conditions in TD children in Jamaica. Since genetic risk factors are not modifiable, we recommend implementation of interventions focused on dietary and environmental factors that could help to reduce exposure to arsenic in Jamaican children; as was recommended in our previous report.In this article, we have shown that the TD children in Jamaica with


The aim of this study was to assess the burden of respiratory disease, considering the influenza A pandemic season (H1N1pdm09), within the Brazilian Network for Surveillance of Severe Maternal Morbidity, and factors associated with worse maternal outcome.A multicenter cross-sectional study, involving 27 referral maternity hospitals in five Brazilian regions. Cases were identified in a prospective surveillance by using the WHO standardized criteria for potentially life-threatening conditions (PLTC) and maternal near miss (MNM). Women with severe complications from respiratory disease identified as suspected or confirmed cases of H1N1 influenza or respiratory failure were compared to those with other causes of severe morbidity. A review of suspected H1N1 influenza cases classified women as non-tested, tested positive and tested negative, comparing their outcomes. Factors associated with severe maternal outcome (SMO = MNM + MD) were assessed in both groups, in comparison to PLTC, using PR and 95 % CI adjusted for design effect of cluster sampling.Among 9555 cases of severe maternal morbidity, 485 (5 %) had respiratory disease. Respiratory disease occurred in one-quarter of MNM cases and two-thirds of MD. H1N1 virus was suspected in 206 cases with respiratory illness. Around 60 % of these women were tested, yielding 49 confirmed cases. Confirmed H1N1 influenza cases had worse adverse outcomes (MNM:MD ratio < 1 (0.9:1), compared to 12:1 in cases due to other causes), and a mortality index > 50 %, in comparison to 7.4 % in other causes of severe maternal morbidity. Delay in medical care was associated with SMO in all cases considered, with a two-fold increased risk among respiratory disease patients. Perinatal outcome was worse in cases complicated by respiratory disease, with increased prematurity, stillbirth, low birth weight and Apgar score < 7.Respiratory disease, especially considering the influenza season, is a very severe cause of maternal near miss and death. Increased awareness about this condition, preventive vaccination during pregnancy, early diagnosis and treatment are required to improve maternal health. Improvement in maternal health aiming a reduction in maternal mortality is a priority worldwide, in an attempt to accomplish the established millennium development goals set for 2015 . HoweverIn 2009, the World Health Organization (WHO) standardized the definitions of potentially life-threatening conditions (PLTC) and maternal near miss (MNM) . PLTC isRecently, the concept of “obstetric transition” was incorporated into the study of maternal morbidity and mortality. The concept illustrates a global trend in which a pattern of high maternal mortality rates with predominantly direct obstetric causes  has been replaced by lower maternal mortality rates with an increasing proportion of indirect causes , institutionalization and medicalization of childbirth and increased rate of obstetric interventions . ObstetrAmong the indirect causes of maternal morbidity and mortality, respiratory disease plays a significant role, either due to the presence of severe infection or complications of the underlying conditions, such as asthma and heart disease. Physiological and anatomical changes that occur during pregnancy to provide accommodation for the growing uterus, can affect the known clinical presentation of respiratory signs and symptoms. Adequate diagnosis and treatment of respiratory disease may be delayed , 9. In aIt became clear throughout the 2009 H1N1 influenza pandemic [termed A(H1N1)pdm09] worldwide –17 that We proposed a novel approach to analyzing the burden of 2009 H1N1 influenza virus infection and other respiratory disease among patients with severe maternal morbidity. Cases complicated by severe respiratory disease were compared to cases with morbid conditions due to other causes (such as hemorrhage and hypertension). In addition, factors possibly associated with a higher risk of SMO were evaluated by using the WHO standardized definitions of morbidity in 27 referral maternity hospitals.This study is a secondary analysis of the Brazilian Network for Surveillance of Severe Maternal Morbidity including 27 referral maternity hospitals in Brazil. The study evaluated severe maternal morbidity cases, from a prospective surveillance, according to the 2009 WHO newly publicized criteria for these conditions .The methodological details of the original study have already been published elsewhere , 20. BriThis was not a population based study, however, there was a concern to reduce the impact of nonrandom sampling and an effort to consider representativeness of the national territory  and of facilities from public and private sectors, university and non-university hospitals. All selected hospitals had to provide information concerning their characteristics, including location, complexity of level of care, population covered, number of maternity beds and availability of resources for severe cases.Quality control was carried out during various phases of the study. Initially, training was provided to the entire team participating in the study, using a detailed operations manual, with the definition of each variable. Meetings were held between the local research team and the coordinating team of the study to standardize data. Case review was conducted by the local investigator. Subsequently, the coordinating team of the study performed random reviews of manual and electronic forms for data consistency in visits to monitor the centers’ performance. Periodically, review of the electronic system was carried out to check for data inconsistency, along with systematic case review. Some reported conditions were delay or substandard care, which had been previously reported . ReasonsFor the present analysis, we considered severe respiratory disease as a suspected or confirmed case of influenza or acute respiratory failure, defined as incapacity of the respiratory system to promote adequate gas exchange, with arterial blood gas parameters: PaO2 < 60 mmHg or peripheral saturation < 90 %, associated or not with PaCO2 > 50 mmHg. Clinical parameters such as tachypnea (respiratory rate-RR > 20) or bradypnea (RR <6), use of accessory respiratory muscles, nasal flaring, associated with torpor or agitation were also considered. For suspected or confirmed cases of A(H1N1)pdm09, the definition of cases considered only those with severe morbidity, including acute respiratory insufficiency, sepsis, intensive care admission, intubation and others. Cases of H1N1 without severe complications were not included. A review of all H1N1 Influenza cases was necessary to confirm whether laboratory tests had been performed and to obtain the results of these tests, since data in the original study had not been collected in detail. Case review was requested from each local center and new data were distributed into three groups: non-tested, positive and negative cases for H1N1 influenza virus.Initially, the prevalence of PLTC, MNM and MD was calculated per group, as well as the respective health indicators related to maternal morbidity and mortality: maternal near miss ratio, severe maternal outcome ratio, mortality index and maternal mortality ratio, according to the WHO definition . To evalSubsequently, we performed an analysis considering the total number of cases with severe respiratory disease versus cases with other causes of severe maternal morbidity. In each group, PLTC (less severe cases) and Severe Maternal Outcome (SMO: MNM + MD) cases were compared to evaluate the factors potentially associated with more severe disease, including delay in obstetric care, also using the Prevalence Ratios plus their respective 95 % CI adjusted for the design effect of cluster sampling. The prevalence of sociodemographic, obstetric and perinatal factors were evaluated between the two groups using Chi-square tests. Values statistically significant were considered those with a p-value under 0.05. The statistical procedures for analysis were performed with SPSS and Stata.During the 12-month study period, 82,388 women were screened. Of these, 9555 had criteria for severe maternal morbidity. Among these 9555 women, only 485 (5 %) had severe respiratory disease. However, in this group with respiratory illness, symptom severity progressed more rapidly, if compared to other causes of severe morbidity  than those without suspected H1N1 influenza A virus  . The number of women who did not undergo pregnancy resolution and remained pregnant during the severe morbid event was much higher in the respiratory disease group. Around 35 % were “still pregnant” compared to 5 % in the group with severe maternal morbidity due to other causes.Concerning characteristics of pregnancy and perinatal results Table , the groOur study presents the burden of severe respiratory diseases among cases of severe maternal morbidity and results of the 2009 H1N1 influenza pandemic, considering 27 referral maternity hospitals in Brazil. Overall, the prevalence of respiratory disease was rare (5 %). Nevertheless, respiratory disease accounted for one-quarter of MNM cases and two-thirds of MD. Worse adverse outcomes occurred among cases of confirmed A(H1N1)pdm09, with an impressive MNM:MD ratio below one, meaning that there were more deaths than near miss cases in this group.The mortality index (MI) was over 50 % in the H1N1 group, compared to 7.4 % for other causes of severe maternal morbidity. The MI is known to correlate to quality of care and when the index is above 20 %, it represents substandard care . NumbersConsidering the impact of the A(H1N1)pdm09 on maternal health , a greatClinical evaluation should determine treatment, in order to ensure timely and effective interventions. In our study, around 60 % of suspected cases of H1N1 influenza A virus were tested. In accordance with previous reports, symptoms were more severe in positive cases . The majOver half of the reported cases of severe respiratory disease were not due to suspected influenza infection. Acute respiratory failure was the cause, including a broad number of conditions, as follows: pulmonary edema, cardiac disease community-acquired pneumonia, aspiration, pulmonary embolism, asthma exacerbation or venous embolism. Unfortunately we do not have detailed information on each of the mentioned causes.Nevertheless, these complications include mostly indirect causes of maternal morbidity and mortality, which represent novel or preexisting health problems unrelated to pregnancy, such as cardiac disease and asthma. Asthma is the most common medical condition that may worsen during pregnancy and it is often underdiagnosed and under-treated . Direct It is very important to understand all differential diagnosis, since timely and adequate interventions can potentially improve maternal outcome. Future studies focusing on the specific differences in diagnosis and management of causes of acute respiratory failure should consider the main aspects on diagnosis and management of these conditions. Pneumonia in pregnancy and postpartum, for example, is the leading cause of fatal none obstetric infection and can be caused by bacteria, virus , fungus and mycobacteria and the clinical features include fever, cough, dyspnea and hypoxia .Another important cause of severe complications is pulmonary edema, which can be consequence of left ventricular systolic or diastolic dysfunction, or due to the use of tocolytic agent, fluid overload, severe hypertension or severe renal disease. The clinical presentation of pulmonary edema is normally dyspnea, tachypnea, tachycardia, chest pain and diffuse crackles. There can be evidence of cardiac dysfunction, specific alterations in the electrocardiogram and radiographic abnormalities .During labor or immediate postpartum, a rare and feared complication is the aspiration of gastric contents, if needed intubation for general anesthesia, due to increased intraabdominal pressure and predisposing physiological changes of pregnancy such as relaxation of the lower esophageal sphincter and delayed gastric emptying. However, in the last decades, the incidence of aspiration significantly declined, even with food intake during labor .The definitions of severe respiratory complications that are usually reported can be rather confusing and sometimes difficult to incorporate . Recent In the current study, we couldn’t accurately establish any of the above conditions, since we did not collect data on clinical symptoms  neither obtained the results of those specific laboratory findings or imaging. The diagnosis of H1N1 influenza was also not standardized through all hospitals included. We understand that timing of sample collection, quality of sample and laboratory procedures are key for the accurate diagnosis and unfortunately we do not have data on details regarding these procedures.Another limitation was the lack of data on the use of antiviral therapy or vaccination. For cases of SMO, complicated by documented organ dysfunction, ARDS would probably be the diagnosis of respiratory disease. Nevertheless, confirmation was lacking for all cases. In addition, we do not have a control group, with no underlying complication, what would be key to access risk factors. We only have data on severe maternal morbidity cases, comparing less severe (PLTC) to more severe cases (SMO).Factors associated with SMO, included non-white color, history of diabetes, low weight and substance abuse, along with delay in care, were reported for the majority of conditions under study. Substance abuse associated with increased risk of severity in cases of respiratory disease, is in agreement with previous reports , 34. DruPregnancy characteristics and perinatal outcomes according to the main cause of severe morbidity showed that pregnancies complicated by respiratory disease present an increased rate of preterm delivery and worse perinatal outcomes. This finding had already been demonstrated , 36. StuSevere respiratory disease, especially considering the influenza season, is one of the most serious causes of maternal near miss and death. Increased awareness of this condition, preventive vaccination during pregnancy, early diagnosis and treatment are required to improve maternal health.


It is well known that etomidate may cause adrenal insufficiency. However, the clinical relevance of adrenal suppression after a single dose of etomidate remains vague. The aim of this study was to investigate the association between the administration of a single dose of etomidate or an alternative induction regime ahead of major cardiac surgery and clinical outcome parameters associated with adrenal suppression and onset of sepsis.The anesthesia and intensive care unit (ICU) records from patients undergoing cardiac surgery over five consecutive years (2008 to 2012) were retrospectively analyzed. The focus of the analysis was on clinical parameters like mortality, ventilation hours, renal failure, and sepsis-linked serum parameters. Multivariate analysis and Cox regression were applied to derive the results.P = 0.6) on hospital mortality in the multivariate Cox analysis.In total, 3,054 patient records were analyzed. A group of 1,775 (58%) patients received a single dose of etomidate; 1,279 (42%) patients did not receive etomidate at any time. There was no difference in distribution of age, American Society of Anesthesiologists physical score, duration of surgery, and Acute Physiology and Chronic Health Evaluation II score. Postoperative data showed no significant differences between the two groups in regard to mortality (6.8% versus 6.4%), mean of mechanical ventilation hours (21.2 versus 19.7), days in the ICU (2.6 versus 2.5), hospital days (18.7 versus 17.4), sepsis-associated parameters, Sequential Organ Failure Assessment score, and incidence of renal failure. Administration of etomidate showed no significant influence (This study found no evidence for differences in key clinical outcome parameters based on anesthesia induction with or without administration of a single dose of etomidate. In consequence, etomidate might remain an acceptable option for single-dose anesthesia induction. Etomidate is considered an almost ideal induction agent because of the rapid onset and the predictable short duration of action in combination with minimal implications for the cardiovascular and pulmonary system . These eEtomidate is frequently used for anesthesia induction ahead of cardiac surgery. In this context, smaller studies showed that etomidate is associated with increased vasopressor requirements . Other aAhead of this study, the local ethics committee (Ethics Committee of Friedrich-Alexander University Erlangen-Nürnberg) confirmed that no further patients’ consent and institutional ethical approval were required. This decision was based on the retrospective, descriptive, and anonymous study design and the fully closed patient files.All anesthesia and ICU records from adult patients undergoing general anesthesia for major on-pump cardiac surgery at the university hospital in five consecutive years (January 2008 to December 2012) were analyzed retrospectively. The filed anesthesia records were retrieved from an electronic patient data management system (PDMS)  including an unambiguous and unique case identifier number (UCIN) . The SOP for preoperative evaluation includes the documentation of the four-scale Mallampati test result . DocumenSOPs for anesthesia induction and maintenance demand a consistent approach, including arterial catheterization with local anesthesia, intravenous application of an opioid, a hypnotic drug, and a neuromuscular blocking agent. However, every anesthesiologist was free to vary the anesthetic drug regime for induction and maintenance. Etomidate (0.25 to 0.35 mg/kg) was the most commonly used hypnotic drug in this analysis. Alternatively, a combination of 1.5 to 2.0 mg/kg esketamine and 0.5 to 2 mg/kg propofol was administered for anesthesia induction. Cis-atracurium (0.15 to 0.2 mg/kg) was administered as standard neuromuscular blocking agent. In the case of a rapid sequence induction (RSI), 0.6 to 0.9 mg/kg rocuronium was given. The direct laryngeal view was graded in accordance with the four-scale CML classification . AnestheP <0.05). Gaussian distribution of continuous variables was tested by using the Kolmogorov-Smirnov test. If continuous variables show Gaussian distribution, they are expressed as mean value and standard error and were compared by using the two-tailed Student t test. Categorical variables are given as absolute number and percentage of occurrence. A univariate analysis of categorical variables was performed by using the chi-squared test. Multivariate analysis for categorical variables and Cox analysis for survival variables was conducted where meaningful. An ad posteriori sample size calculation with estimated mortality rates of 3% and 5%, a statistical power of 0.8, and a confidence interval of 95% revealed a required number of at least 1,506 cases to enclose.Statistical processing was performed by using SPSS® 20.0 . All other preoperative and demographic items show no significant difference.The definitive analysis includes a total of 3,054 out of 3,212 available cases, indicating a dropout rate of 5%. A group of 1,775 (58%) patients received etomidate ahead of cardiac surgery to facilitate anesthesia induction and endotracheal intubation. A group of 1,279 patients (42%) did not receive etomidate. The underlying demographic and preoperative data are shown in Table P <0.001). The rate of emergency cardiac surgery is significantly higher in the etomidate group. All other intraoperative items show no significant difference. A multivariate analysis revealed male gender, the absence of etomidate, and high Mallampati score as independent risk factors for poor laryngoscopic view given as CML 3 or 4 (Table Intraoperative data are shown in Table P = 0.013). An isolated view on second postoperative day reveals significantly higher discharge rate of the etomidate group: 297 (17%) versus 158 (13%) (P <0.001). Summation of postoperative days 1 and 2 shows that there is no difference between the two study groups: 852 (68%) versus 1,180 (68%) (P = 0.937). On subsequent postoperative days, there was no difference between the groups until the end of observation on day 5.The main results addressing mortality and ICU course are presented in Tables P <0.001), ASA score of 4 or 5 , ICU re-admission , requirement of renal replacement therapy , and an ICU stay of longer than 3 days  were identified as mortality-predicting variables in a multivariate Cox regression analysis. For the administration of etomidate, body mass index (BMI) of more than 40 kg/m2, age of more than 75 years, and gender Cox analysis showed no significant influence on mortality (Table APACHE score of more than 25 (hazard ratio (HR) 1.1; Key clinical outcome variables in patients with and without etomidate-facilitated anesthesia induction ahead of major cardiac surgery were comparable and showed no statistically significant differences. The demographic and preoperative data of our cohort indicate a good comparability of the two study groups as there are no statistical differences in age, BMI, APACHE II score, ASA status distribution, and preoperative blood test results. The higher frequency of female patients in the etomidate group is statistically significant but clinically of limited relevance. Little is known about gender differences in postoperative outcome after major cardiac surgery. The literature describes female gender as a risk factor for poor outcome after cardiac surgery because women are usually much older than male patients when undergoing major cardiac surgery ,29. The Multivariate analysis identified high Mallampati score and male gender as risk factors for poor direct laryngoscopy. Patients who received etomidate for anesthesia induction showed significantly better intubation conditions than patients with an alternative induction regime, which is also reported by a former study . VasopreThe hypothesis of this study was that anesthesia induction using etomidate might lead to poorer clinical outcome due to impaired immune response. This comes along with higher vulnerability for infections and consecutively increased inflammatory serum parameters like CRP, PCT, and leukocyte count. Statistical processing is focused on relative differences between the study groups instead of deviation from normal values in order to account for the inherent inflammation associated with on-pump cardiac surgery. A recently published study showed that PCT is reliable in predicting severity of multi-organ failure and outcome of systemic inflammatory response . In our SOFA score is a reliable and approved sepsis-associated score to reflect multi-organ failure, severity, and outcome in patients with sepsis ,32,33. IIn patients who already have sepsis, the discussion of whether single-dose etomidate increases mortality risk is controversial. Some retrospective analyses suggested an increased risk of mortality in septic patients who received etomidate for intubation ,6,9,32. However, our study has some limitations which are based mainly on the retrospective design of the analysis. Owing to missing data in the pre- and postoperative serum test results, these items could not be taken into account for the Cox analysis. The SOFA and APACHE II scores show missing data and were not taken into account for the Cox analysis. The SOFA score was documented only for patients with more than 24 hours in the ICU. Cases which were discharged in the morning after surgery lack of SOFA score. APACHE II score was obtained when preoperative data were available. Where patients were transferred from external wards, these datasets were not available and APACHE II score could not be obtained.Owing to its retrospective character, the study lacks a randomization protocol. Although the selection of induction regime with or without etomidate was based on the personal preferences of the attending anesthesiologist, a high number of anesthesiologists (135) performing the anesthesia procedures of the study might preclude a selection bias.This study found no evidence for differences in key clinical outcome parameters based on anesthesia induction with or without administration of a single dose of etomidate. This holds particularly true for mortality, length of hospital stay, length of ICU stay, re-admission to the ICU, duration of mechanical ventilation, postoperative systemic inflammatory response or sepsis discriminated by inflammatory parameters, renal failure, or SOFA score. Moreover, multivariate analysis revealed better intubation conditions in the etomidate group. Despite the known and undisputable risk of adrenal suppression and taking the beneficial effects for hemodynamic unstable and cardiac impaired patients into account, etomidate should remain an acceptable option in clinical routine for anesthesia induction.Single-dose etomidate ahead of major on-pump cardiac surgery● is not associated with higher postoperative mortality.● is not associated with an increased rate of postoperative infection discriminated by serum tests and morbidity scores.● is not associated with a higher SOFA score or an increased rate of renal replacement therapy.● does not change length of ICU or total hospital stay.APACHE II: Acute Physiology and Chronic Health Evaluation II; ASA: American Society of Anesthesiologists; BMI: body mass index; CK: creatinkinasis; CML: Cormack and Lehane; CRP: C-reactive protein; HR: hazard ratio; ICU: intensive care unit; PCT: procalcitonine; PDMS: patient data management system; SIRS: systemic inflammatory response syndrome; SOFA: Sequential Organ Failure Assessment; SOP: standard operating procedure; UCIN: unique case identifier number.The authors declare that they have no competing interests.SH helped to design the study, to program the study database, to perform the statistical processing of the data, and to interpret the results and wrote the manuscript. JS helped to design the study, to interpret the results, and to supervise manuscript writing. IC helped to program the study database, to transcribe data from PDMSs to the study database, to interpret the results, and to supervise manuscript writing. AA helped to transcribe data from PDMSs to the study database. FH and AM helped to perform the statistical processing of the data and to interpret the results. All authors read and approved the final manuscript.


There is evidence that early screening for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) improves outcomes. We compared the predictive accuracy of two recently published screening algorithms  2012) for SSc-associated PAH (SSc-PAH) with the commonly used European Society of Cardiology/European Respiratory Society (ESC/ERS 2009) guidelines.We included 73 consecutive SSc patients with suspected PAH undergoing right heart catheterization (RHC). The three screening models were applied to each patient. For each model, contingency table analysis was used to determine sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values for PAH. These properties were also evaluated in an ‘alternate scenario analysis’ in which the prevalence of PAH was set at 10%.RHC revealed PAH in 27 (36.9%) patients. DETECT and ASIG algorithms performed equally in predicting PAH with sensitivity and NPV of 100%. The ESC/ERS guidelines had sensitivity of 96.3% and NPV of only 91%, missing one case of PAH; these guidelines could not be applied to three patients who had absent tricuspid regurgitant (TR) jet. The ASIG algorithm had the highest specificity (54.5%). With PAH prevalence set at 10%, the NPV of the models was unchanged, but the PPV dropped to less than 20%.In this cohort, the DETECT and ASIG algorithms out-perform the ESC/ERS guidelines, detecting all patients with PAH. The ESC/ERS guidelines have limitations in the absence of a TR jet. Ultimately, the choice of SSc-PAH screening algorithm will also depend on cost and ease of application.The online version of this article (doi:10.1186/s13075-015-0517-5) contains supplementary material, which is available to authorized users. Systemic sclerosis (SSc) is a multisystem connective tissue disease characterized by vasculopathy and fibrosis. Pulmonary arterial hypertension (PAH) is one of the most severe organ complications and a leading cause of death in SSc. Despite advanced PAH therapies, the 3-year survival of SSc-associated PAH (SSc-PAH) is around 50% . Recent The most commonly used pulmonary hypertension screening guidelines from the European Society of Cardiology/European Respiratory Society (ESC/ERS) are based on symptoms and transthoracic echocardiography (TTE) . But theThese limitations of current screening algorithms emphasize the need for alternative approaches to improve the selection of patients for referral for right heart catheterization (RHC), the ‘gold standard’ test for the diagnosis of PAH. Emerging screening algorithms incorporate pulmonary function tests (PFTs) and biomarkers such as N-terminal pro-B type natriuretic peptide (NT-proBNP) -12. In 2The DETECT  study investigators recently developed a new detection algorithm for PAH in patients with SSc . This stPatients included in this analysis were from the Australian Scleroderma Cohort Study (ASCS). The ASCS is a multi-center study of risk and prognostic factors for cardiopulmonary outcomes in SSc. All patients fulfil either American College of Rheumatology or Leroy and Medsger criteria for SSc ,16. The TTE) of at least 40 mm Hg, and/or DLCO less than 50% predicted with FVC of more than 85% predicted, without adequate explanation on high-resolution computed tomography (HRCT) lung or ventilation-perfusion (V/Q) scanning or both—undergoes RHC.We included consecutive adult (≥18 years) SSc patients from the ASCS between December 2007 and December 2012, who were considered to be at high risk for PAH according to the ASCS screening guidelines and had undergone RHC. The existing Australian screening guidelines require that all patients undergo an annual clinical assessment, TTE and PFTs. Any patient identified as having possible PAH—that is, systolic pulmonary arterial pressure  classification, in this study, patients were classified as non-PH or WHO group 1 PH (PAH), WHO group 2 PH (left heart disease-associated PH), or WHO group 3 PH (lung disease/hypoxia-associated PH). PH was defined as a mean pulmonary artery pressure (mPAP) of at least 25 mm Hg on RHC at rest; therefore, non-PH was defined as mPAP of less than 25 mm Hg. PAH was defined as mPAP of at least 25 mm Hg at rest and pulmonary capillary wedge pressure (PCWP) of not more than 15 mm Hg with no more than mild ILD on HRCT and an FVC of more than 60% predicted. WHO group 2 PH was defined as mPAP of at least 25 mm Hg at rest and PCWP of more than 15 mm Hg. WHO group 3 PH was defined as mPAP of at least 25 mm Hg at rest and PCWP of not more than 15 mm Hg with FVC of less than 70% predicted plus moderate or severe ILD on HRCT . PatientAs described above, the DETECT screening algorithm Figure  includesThe ASIG screening algorithm Figure  is compo2) [A positive screen for RHC referral in the ESC/ERS guidelines Figure  is TRV o2) .In summary, TTE is a component of both the ESR/ERS guidelines and the DETECT algorithm. In the former, it is the sole investigation component; in the later, TTE forms ‘step 2’ of the algorithm and is required if a patient’s points from step 1 are greater than 300. TTE is not mandated in the ASIG algorithm, although it is recommended in patients who screen positive, in order to obtain more information about cardiac valves and myocardial function. Step 1 of the DETECT algorithm requires six variables: three measured in blood tests (including NT-proBNP), one determined by ECG (right axis deviation), one determined by pulmonary function tests (FVC/DLCO ratio), and another determined by physical examination (telangiectasia). In contrast, the ASIG algorithm is composed of two tests—a single blood test for NT-proBNP and pulmonary function tests—to determine DLCO percentage predicted and the FVC/DLCO ratio.Data such as demographic and clinical variables and cardiac and pulmonary assessments were obtained from the ASCS database. All physical examination and investigation data were collected within 1 month of the first RHC, before starting advanced PAH therapy. TTE was performed according to standardized procedures only at tertiary centers. Pulmonary involvement was assessed by PFTs or HRCT or both. All DLCO values were reported as percentage predicted values and corrected for hemoglobin . All patt test with unequal variances, whereas continuous non-parametric variables were compared by using Kruskall-Wallis and Mann-Whitney U tests. The differences in frequency were determined by using chi-square and Fisher’s exact tests. The predictive accuracy of the algorithms is presented as sensitivity, specificity, PPV and NPV, with 95% confidence intervals (CIs). An ‘alternate case scenario’ analysis .Data are presented as mean ± standard deviation for continuous variables and as number (percentage or proportion) for categorical variables. Normally distributed continuous variables were compared by using the Student Of 79 consecutive SSc patients with suspected PAH undergoing RHC who fulfilled the inclusion criteria, RHC revealed PH in 45 (57.0%) patients and PAH (WHO group 1) in 29 (36.7%) patients. Among them, six patients were excluded for the following reasons: three because of FVC of less than 40% predicted and three because they had more than one missing variable in the DETECT algorithm. Of the remaining 73 patients, in the DETECT algorithm analysis, four patients were missing RA area only, two patients were missing urate level only, and 12 patients were missing ECG data. There were no missing NT-proBNP data.Among the 73 patients, PH was confirmed in 39 (53.4%) patients: 27 (36.9%) with PAH, 4 (5.5%) in the WHO group 2 PH, and 8 (11.0%) in the WHO group 3 PH. Finally, 61 patients (27 PAH patients versus 34 non-PH patients) were included in the analyses of the performance of the algorithms for PAH. In three patients, TRV was undetectable, and in these cases the ESC/ERS guidelines could not be applied. Twenty-four patients had been included in the derivation study  for the P = 0.027). Similar to previous reports, PAH patients were more likely to have the limited subtype of SSc, to be ACA-positive, and to have telangiectasia than non-PH patients. As expected, patients with PAH had a shorter 6-minute walk distance (6MWD), lower DLCO level, higher NT-proBNP level, larger RA area, and higher TRV than the non-PH group. The FVC percentage predicted values were similar between the two groups, but FVC/DLCO ratio was significantly higher in the PAH group than in the non-PH group. We did not find any difference in the frequency of ECG right axis deviation between the two groups, but significantly higher serum urate levels were found in the PAH group  score of all 61 patients was 330.4 (315.1 to 340.5), and 53 patients would be referred for TTE and proceed to ‘step 2’, so the referral rate for TTE was 87%. In ‘step 2’, the median (interquartile range) score of 53 included patients was 48.9 (37.2 to 55.3). Finally, 49 patients attained a score that would result in referral for RHC, and the final referral rate was 80%. No PAH patient was missed by the algorithm, but 22 patients who would have been referred for RHC were found to have non-PH on RHC. When the DETECT algorithm was used, 45% of RHCs did not confirm a diagnosis of PAH.Compared with the DETECT algorithm, the ASIG algorithm performed equally well in sensitivity (100%) and NPV (100%), a little better in PPV (60%), and moderately better in specificity (54.5%). The referral rate for RHC was 68%, and the proportion of RHCs that did not confirm a diagnosis of PAH was 40%.2, this patient was missed by the ESC/ERS guidelines. This patient had telangiectasia, negative ACA antibody, normal NT-proBNP level of 73.7 pg/mL, no right axis deviation on ECG, well-preserved WHO functional class (WHO FC) of 2, and 6MWD of 467 m, but PFT showed a very low DLCO predicted value of 44% and high FVC/DLCO ratio of 2.27, with a normal FVC percentage predicted value of 100%. The performance characteristics of the three models for PAH are presented in Table Compared with the other two algorithms, the ESC/ERS guidelines had the lowest sensitivity, specificity, and NPV, which were 96.3%, 32.3%, and 90.9%, respectively, and similar PPV of 55.3%. One PAH patient was missed and 47 patients would have been referred for RHC with a referral rate of 81%. The proportion of RHCs that did not confirm a diagnosis of PAH was 45%. The ‘missed’ PAH patient had a mildly increased pulmonary pressure with mPAP of 26 mm Hg on RHC. Because of very mild TR jet with TRV of 2.2 m/s and normal RA area of 14 cmThe prevalence of PAH in this cohort was 37%, which was higher than the expected prevalence of 10% according to the commonly reported data in the literature . This waP <0.0001 for azathioprine; 6/49 versus 7/12, P <0.0001 for corticosteroids), suggesting that these drugs may be associated with one or more of the parameters in the DETECT algorithm such as raised serum urate, although the small number of patients on these medications prevents us from drawing definitive conclusions in this regard.Use of immunosuppressive therapy (defined as any exposure irrespective of dose or duration) in the 12 months immediately preceding the date of screening by patients who screened positive and patients who screened negative according to each algorithm was compared. There were no statistically significant differences in the use of cyclophosphamide, methotrexate, or mycophenolate for each algorithm, nor for azathioprine or corticosteroids for the ESC/ERS and ASIG algorithms. Interestingly, azathioprine and corticosteroids were used by more patients who screened positive than patients who screened negative using the DETECT algorithm  and for all types of PH patients  was also evaluated. We did this because it could be argued that these ‘screening’ algorithms need to detect patients at high risk of PH, regardless of etiology, which then can be further elucidated with ‘diagnostic’ testing. For precapillary PH, the ASIG algorithm performed best with respect to sensitivity (100%) and NPV (100%), without missing any true positive patients. The sensitivity and NPV of the DETECT algorithm decreased to 97.1% and 92.3%, with one ILD-associated PH (WHO group 3 PH) patient missed. ESC/ERS guidelines did not miss any extra patients except for the PAH patient as noted above. For screening for all types of PH patients, the three models performed equally in sensitivity and NPV. The DETECT algorithm missed one WHO group 3 patient, the ASIG algorithm missed one WHO group 2 patient, and the ESC/ERS guidelines missed one WHO group 1 patient. The specificities of these three models for screening for precapillary PH and PH were very similar to those seen in screening for PAH, and the PPVs increased slightly. The ASIG algorithm still performed the best with respect to specificity and PPV Table .Table 4The DETECT algorithm is a novel evidence-based screening model for PAH in patients with SSc and was developed from a worldwide multi-center cross-sectional study. This algorithm was verified in the derivation cohort to have high sensitivity and NPV , which aIt is important to note that the entry criteria for the DETECT study were a DLCO of less than 60% and SSc disease duration of more than 3 years. However, in clinical practice, these restrictions may miss some PAH patients who have either early SSc or a preserved DLCO. Therefore, to assess the performance of DETECT in a less selective group of patients, we did not apply these entry criteria in the present study. In fact, in our study, 6.5% of PAH patients (n = 4) had DLCO greater than 60% and 8.2% of PAH patients (n = 5) had disease duration of less than 3 years. If we had applied these entry criteria of the original DETECT study, we would have missed these patients. Hence, our study has shown that the DETECT algorithm may be reliably applied to patients with DLCO of more than 60% and disease duration of less than 3 years.The ASIG algorithm was published shortly before DETECT, and the higher specificity (54.5%), NPV (92.3%), and PPV (61.5%) compared with the ESC/ERS guidelines have been verified by a recent validation study . In the Compared with the other two algorithms, ESC/ERS guidelines had lower sensitivity and NPV. One PAH patient was missed by the algorithm because of very mild TRV and normal right heart size. However, this patient had some indicators of PAH , which may be the reasons why this patient was not missed by the DETECT and ASIG algorithms. It must also be noted that the ESC/ERS guidelines could not be applied to three patients in whom there was no TR jet on TTE.At present, in clinical practice, the ESC/ERS guidelines rely mainly on TTE but are still the main screening tool for PAH. Furthermore, doctors are familiar with TTE and often judge the likelihood of PAH through measurement of TRV only. However, TRV cannot be obtained in 20% to 39% of patients ,7. In ouIn clinical practice, in addition to WHO group 1 PAH, WHO groups 2 and 3 PH occur in patients with SSc ,26. AccoDespite the limitations of the three screening algorithms we evaluated, they each performed reasonably well in detecting SSc-PAH, echoing the sentiments of many experts that any reasonable screening algorithm applied for the early detection of SSc-PAH is better than usual care. That said, accuracy, accessibility, and economic considerations are likely to influence the choice of algorithm for SSc-PAH screening.This study has some limitations. Firstly, the patients in this study were a population enriched for PAH, although this was also the case for the DETECT and ASIG derivation studies. Therefore, the prevalence of PAH was significantly higher than the expected prevalence of 10% in an unselected SSc population, as reported in the literature . Our altSecondly, as this was a retrospective study, the ability to avoid missed data is limited, so some patients with more than one missing variable were excluded from the analyses. Another limitation is the small case numbers, especially for validating the performance of the ASIG algorithm, as patients involved in the original derivation study for the ASIG algorithm were excluded. Furthermore, although the patients to whom the three screening algorithms were applied in the current study were not included in the original ASIG algorithm derivation study, they were still recruited from the same source. This may have enhanced the performance of the ASIG algorithm in this setting. Accordingly, the findings should be confirmed in a larger external group of patients. The impact of concomitant immunosuppressive therapy on results of screening with each of the algorithms also requires evaluation in future studies. Finally, patients with severe ILD were excluded from this study. The performance of screening algorithms such as DETECT and ASIG in the setting of coexistent ILD merits further evaluation.In summary, the DETECT and ASIG algorithms out-perform the ESC/ERS guidelines, with a high sensitivity, which is the most important feature for a screening algorithm, reducing or eliminating missed diagnoses. The ESC/ERS guidelines have limitations in patients with undetected or mild TR jet. The specificity of all of the screening models is low, as may be expected, but the ASIG algorithm performed better with respect to specificity and reduced the number of referrals for RHC. Ultimately, it is likely that the choice of SSc-PAH screening algorithm will also depend on cost and ease of application.


No correlations were found between plant height and most root traits within the CWC germplasm or its subset. Region of origin had significant impact on rooting depth in the CWC germplasm. Wheat genotypes collected from Australia, Mediterranean, and west Asia had greater rooting depth than those from south Asia, Latin America, Mexico, and Canada. Soft wheat had greater rooting depth than hard wheat in the CWC germplasm. The genetic variability identified in this research for root traits can be exploited to improve drought tolerance and/or resource capture in wheat.Root traits influence the amount of water and nutrient absorption, and are important for maintaining crop yield under drought conditions. The objectives of this research were to characterize variability of root traits among spring wheat genotypes and determine whether root traits are related to shoot traits , regions of origin, and market classes. Plants were grown in 150-cm columns for 61 days in a greenhouse under optimal growth conditions. Rooting depth, root dry weight, root: shoot ratio, and shoot traits were determined for 297 genotypes of the germplasm, Cultivated Wheat Collection (CWC). The remaining root traits such as total root length and surface area were measured for a subset of 30 genotypes selected based on rooting depth. Significant genetic variability was observed for root traits among spring wheat genotypes in CWC germplasm or its subset. Genotypes Sonora and Currawa were ranked high, and genotype Vandal was ranked low for most root traits. A positive relationship (R Triticum spp.) is one of the most important food crops in the world in terms of the area harvested, production, and productivity Wheat (Oryza sativa L.) because thicker roots have large xylem vessels with increased axial conductance and are more efficient in penetrating deep soil layers to extract water Cicer arietinum L.) Festuca arundinacea Schreb.) The effectiveness of a deep root system in maintaining yield under drought conditions has been confirmed by simulation studies across several years and environments in the USA Despite the importance of root traits in drought tolerance, little work has been done to include drought-adaptive root traits in breeding for drought-tolerant wheat varieties. Most wheat improvement programs have concentrated on above-ground components, particularly for decreasing plant height and increasing harvest index. Crop breeding programs have largely ignored root traits, mainly because of the difficulties associated with root recovery and evaluating root traits in situ. In addition, large phenotypic plasticity of root traits in response to changes in soil conditions, and lack of high-throughput and cost-effective screening techniques make root studies highly challenging The region of origin of crop plants has implications in plant breeding as they act as potential centers to locate useful genes. Region of origin may provide useful sites for germplasm exploration to identify traits that improve productivity Based on kernel hardness and color, wheat genotypes can be classified into different market classes. Suitability of each market class to a location depends largely on rainfall, temperature, and soil conditions. Recent findings suggested that market classes of wheat differed for coleoptile length and effect of coleoptile length on seedling emergence The objectives of this research were to (i) characterize variability of root traits among spring wheat genotypes, (ii) determine whether root traits are related to plant height, shoot dry weight, tiller number per plant, and coleoptile length, and (iii) determine whether the regions of origin and market classes of genotypes have any influence on root traits.The germplasm used in this study was Cultivated Wheat Collection (CWC) −3. Turface is calcined, non-swelling illite and silica clay, and allows easy separation of roots. The rooting medium was fertilized with Osmocote , a controlled-release fertilizer with 19∶6∶12 N:P2O5:K2O, respectively, at 4 g per column before sowing. A systemic insecticide, Marathon 1% G (a.i.: Imidacloprid: 1–[(6–Chloro–3–pyridinyl)methyl]–N–nitro–2–imidazolidinimine; OHP, Inc., Mainland, PA) was applied at 1 g per column before sowing to control sucking pests. Twenty seeds of each genotype were weighed before sowing to estimate seed size . Three seeds of a single genotype were sown at 4 cm depth in each column on 28 December 2011 and 6 December 2012. After emergence, plants were thinned to one plant per column, which was maintained until harvest. Plants were irrigated daily (0.9±0.1 L per day) through an automated drip irrigation system until harvest to avoid water stress. Emissions from drip-tubes were examined weekly for proper water delivery. Irrigation was provided three times per day at 06∶00, 12∶00, and 18∶00 h. Plants were maintained under optimum temperature  conditions from sowing to harvest at a photoperiod of 16 h. The fungicide, Bumper 41.8 EC –4–propyl–1,3–dioxolan–2–yl]Methyl]–1H–1,2,4–triazole; 1.2 mL L−1; Makhteshim Agan of North America, Inc., Raleigh, NC) was also applied at 20 d after sowing to prevent powdery mildew. The insecticide and fungicide treatments helped to maintain the plants without any pest or pathogen problems until harvest. Plants were harvested at 61 d after sowing when more than 50% of the population reached flowering stage.This research was conducted in controlled environment facilities (greenhouse) at the Department of Agronomy, Kansas State University, Manhattan, KS. Two independent experiments (2011 and 2012) were conducted to evaluate the variability of root traits among spring wheat genotypes. The greenhouse was equipped with an automated sulfur vaporizer  that vaporized sulfur for 1 h between 23∶00 and 24∶00 h. Sulfur vaporization was done from the start of the experiment as a preventive measure against powdery mildew. Plants were grown in PVC columns with inside diameter of 7.5 cm and height of 150 cm. The columns had plastic caps at the bottom with a central hole of 0.5 cm diameter for drainage. Rooting medium was Turface MVP , which had a bulk density of 576.66±32 kg mShoot traits measured in this study were plant height, number of tillers per plant, shoot dry weight, and coleoptile length (see the measurement details below). These traits were measured on all 297 genotypes in the CWC germplasm. Height, tiller number, and growth stage of all plants were recorded 1 d before harvest. Plant height was determined as the distance between Turface level and the last leaf ligule. At harvest, the PVC columns were gently inverted at about 140° to let the contents of columns (Turface and plants with the entire root system) slip down to the ground. Roots were carefully separated from the Turface without any breakage in the root system. The shoot of each plant was separated by cutting at the base of the stem. After removing shoots, roots were laid on a flat surface and stretched to measure their length (from the base of the stem to the tip of the root system) as an estimate of rooting depth. Rooting depth was measured using the above procedure for all 297 genotypes in the CWC germplasm. The root system was then washed, placed between moist paper towels, sealed in Ziploc bags , transported to the laboratory, and stored at 4°C. Fifteen genotypes that were ranked the highest and 15 genotypes that were ranked the lowest for rooting depth were selected for further complete root analyses. This subset of 30 genotypes represented cultivars from Australia, Turkey, USA, Mexico, and Canada. The subset included genotypes representing the four market classes, SWS, SRS, HWS, and HRS. Root system of each of these 30 genotypes was stretched and sliced into 30-cm long portions. Each portion was submerged in a water bath (20 cm×15 cm×2 cm) to maximize separation of roots and to minimize their overlap, and scanned using an Epson photo scanner (Epson Perfection V700 with 6400 dpi resolution) . Images of scanned roots of the 30 genotypes were analyzed using WinRHIZO Pro image analysis system  to estimate total root length , total root surface area, root volume, average root diameter, length, surface area and RLD of roots in 0–30 cm soil depth, fine root (roots with diameter <0.25 mm) length, fine root surface area, and fine root volume Coleoptile length was measured according to the procedure of The experimental design was a randomized complete block in 2011 (Experiment 1) and 2012 (Experiment 2) for the greenhouse studies. There were two blocks (replications) in both years. Analysis of variance was performed on genotypes using the GLM procedure in SAS  for root and shoot traits. The probability threshold level (α) was 0.05. Genotype was treated as a fixed effect, and replication nested within year was treated as a random effect. Genotype, replication, and year were used as class variables. Separation of means was done using the LSD test (P<0.05). The CORR procedure in SAS was used to find out the correlation between different root and shoot traits. Pearson correlation coefficient was used as a measure of degree of correlation between root and shoot traits. The REG procedure in SAS was used to regress root traits against shoot traits.2, total root surface area in the subset  was found between root dry weight and shoot dry weight within the CWC germplasm . When genotypes in the CWC germplasm were categorized into eight regional groups based on their country of origin, significant difference in the mean rooting depth was observed among the eight regions P<0.05; . The wheConsiderable genetic variability was observed for root traits in the CWC germplasm or its subset. The extent of genetic variability is indicated by the large range observed for root traits. Because roots followed a zigzag pattern of growth within the columns, in many cases rooting depth attained values that exceeded column height. The P>0.05 for genotype-by-year interaction  implies Genotypes Sonora and Currawa were ranked high and genotype Vandal was ranked low for most root traits in the CWC germplasm or its subset. The contrasting genotypes for root traits identified in this study , 3 offerIn the present research, total root surface area showed a positive correlation with tiller number and shoot dry weight . Previou2 = 0.41; The positive correlation between root dry weight and shoot dry weight (RPisum sativum L.) The absence of correlation between plant height and root traits ; Fig. 3B2 = 0.43; 2>0.70) The positive relationship between coleoptile length and total root length  y region . This imNone of the root traits evaluated in this research showed a correlation with seed size within the CWC germplasm or its subset , 5. SlopThis research found that the geographic regions from which wheat genotypes originated had significant impacts on rooting depth . The whe2≥0.35) was found between (1) root dry weight and shoot dry weight within the CWC germplasm and between total root surface area and tiller number; total root surface area and shoot dry weight; and total root length and coleoptile length within the subset. There was no correlation between plant height and most root traits within the CWC germplasm or its subset. Region of origin of wheat genotypes had significant impact on rooting depth in the CWC germplasm. The wheat genotypes collected from Australia, Mediterranean, and west Asia regions had greater rooting depth than those collected from south Asia, Latin America, Mexico, and Canada. Rooting depth differed among market classes of wheat genotypes in the CWC germplasm. Soft wheat had greater rooting depth than hard wheat in the CWC germplasm. The genetic variability identified in this research for root traits can be exploited to improve drought tolerance and/or resource capture in wheat. Our future research will evaluate drought tolerance of the contrasting genotypes identified in this study for root traits under controlled environment and field conditions.In summary, significant genetic variability was observed for root traits in the CWC germplasm or its subset. Genotypes Sonora and Currawa were ranked high and genotype Vandal was ranked low for most root traits. A strong positive relationship Click here for additional data file.


Rhizomucor variabilis. The generated ITS1 sequences matched 100% with the published sequences available in GenBank. In addition, these yogurt samples were also tested for the presence of five types of bacteria  causing foodborne disease in humans, and found negative for all of them.In our previous study, we described the development of an internal transcribed spacer (ITS)1 sequencing method, and used this protocol in species-identification of isolated fungi collected from the manufacturing areas of a compounding company known to have caused the multistate fungal meningitis outbreak in the United States. In this follow-up study, we have analyzed the unopened vials of Greek yogurt from the recalled batch to determine the possible cause of microbial contamination in the product. A total of 15 unopened vials of Greek yogurt belonging to the recalled batch were examined for the detection of fungi in these samples known to cause foodborne illness following conventional microbiological protocols. Fungi were isolated from all of the 15 Greek yogurt samples analyzed. The isolated fungi were genetically typed by DNA sequencing of PCR-amplified ITS1 region of rRNA gene. Analysis of data confirmed all of the isolated fungal isolates from the Greek yogurt to be  The primary mission of U.S. Food and Drug Administration is to enforce the Food, Drug and Cosmetic Act and regulate food, drug and cosmetics. This act prohibits the distribution of adulterated food which includes those products that are defective, unsafe, and filthy or produced under unsanitary conditions. To accomplish this mission, FDA uses the presence of human-pathogenic microorganisms that include bacteria, virus, parasites and fungi in these commodities as one of the regulatory action criteria .Mucor circinelloides was the potential microorganism that triggered the recall of some of its Greek yogurt, and Salmonella or E. coli were not associated with the foodborne illnesses.Approximately 300 individuals reported developing gastrointestinal symptoms after consuming Greek yogurt in several states of United States including Arizona, Delaware, New York, Maryland, Ohio, Pennsylvania, California, Indiana and Florida. The consumers also stated bloating and swelling of some of the products. The potentially affected products were manufactured and distributed nationwide from one of its facility, and the consumers received the products through retail and club stores. The company voluntarily recalled more than half-a-dozen yogurt varieties with expiration dates September 11 to October 07 of 2013, and stopped distribution of the product. Later, the yogurt maker made an announcement that Although the kingdom fungi are estimated to have approximately 1.5 million to 5 million species, its taxonomy is still constantly changing with only 5% of the species being formally classified. However, to this point approximately 100,000 fungal species have been described . NeverthFungi are recognized as one of the main contaminants of dairy products that include yogurt and sour milk. However, these microorganisms can also cause spoilage in a wide range of processed, preserved and refrigerated food products. Conversely, a number of fungi (some species of yeast) are considered as a significant group of microorganisms in food industry for their positive contributions in fermentation of bread, alcoholic beverages and cheese ,8. Thus In our previous study, we described the development of a molecular method based on ITS1 sequencing that can identify fungi at the species level. Using this DNA sequencing protocol, we successfully typed the isolated fungi from the manufacturing premises of a compounding company known to have caused the multistate fungal meningitis outbreak .Salmonella, Listeria, Staphylococcus, Bacillus and Escherichia coli) causing foodborne illness in humans. These samples were found negative for all five types of bacteria tested. However, all of the samples analyzed were found positive for the presence of fungi. The recovered fungal isolates were genetically characterized by our recently described method based on DNA sequencing of PCR-amplified ITS1 products [In this study, we have examined the unopened vials of Greek yogurt from the recalled batch to determine the microbial contamination in the product. The samples were tested for the detection of human-pathogenic fungi and five types of bacteria  known to cause foodborne illness following FDA’s BAM protocol [A total of fifteen unopened vial of Greek yogurt were analyzed initially by conventional microbiologic protocol for the detection of fungi (yeast and molds) and five human-pathogenic bacteria .Rhizomucor variabilis (GenBank Accession No. JF904893). No intra-specific genetic variation was noticed among the recovered fungi from the 15 closed containers of Greek yogurt . This tool has been most extensively used over two decades including for the completion of “Human Genome Project” to get the first human genome, and is considered as the gold standard for genetic analysis. Sequence characterization of human-pathogenic microorganisms has been widely used for their rapid detection from sporadic cases, surveillance and outbreak investigations. This molecular tool has not only provided species-specific identification but also helped in conducting epidemiologic surveys to determine the transmission dynamics of emerging infectious diseases of public health importance .Enterocytozoon spp. The ITS sequence characterization described 15 new genotypes of E. bieneusi in wildlife for the first time [E. bieneusi in HIV-infected patients in Lima, Peru by analyzing stool samples from 89 microsporidia-positive patients, and the analysis identified 11 distinct genotypes of E. bieneusi (Peru-1 to Peru-11). Of these, six were new genotypes, and the remaining five genotypes had ITS sequences identical to those previously reported in humans, cats, pigs, and wild mammals [E. bieneusi samples belonging to 338 fecal samples collected from cattle farms in Florida, Maryland, Michigan, New York, North Carolina, Pennsylvania, Virginia, and Portugal. Results of this study indicated that microsporidiosis was prevalent in cattle and there was an extensive genetic diversity of E. bieneusi isolates found in them [Of the various conserved or regulatory gene used as a genetic marker, the rRNA has been proven to be most successful in developing rapid diagnostic methods for the identification of human-pathogenic microorganisms as well as their vectors ,15,16. Arst time . The ITS mammals . The ITS in them .Rhizopus species causing meningitis in human [Rhizopus [Furthermore, the ITS region has also been recognized as a suitable genetic marker for species-specific identification in fungi ,23,24,25Rhizopus .Since the ITS region is now being considered as formal fungal barcodes, recommendation has been to create a separate public reference data set for ITS fungal sequences . AttemptMucor circinelloides to be the pathogen that triggered the recall of some of their products. However, when these samples from the same batch known to have caused the gastrointestinal disease in individuals who had consumed this product were sequence characterized at ITS1 region, DNA sequencing confirmed the isolated fungi in all 15 unopened vials of Greek yogurt analyzed to be Rhizomucor variabilis (this study). The data analysis also confirmed absence of genetic variations among the generated ITS1 sequences of the isolated fungi, and these sequences matched completely with the available sequence of Rhizomucor variabilis in the public domain.Fungi are commonly associated with the spoilage and bio-deterioration of a large variety of food. A number of DNA-based molecular methods have been developed and used for their detection in food. Reverse transcription (RT)-PCR and real-time RT-PCR assays were developed targeting actin locus for rapid detection and quantification of viable yeasts and molds contaminating yogurt and pasteurized food products . A PCR aSalmonella, Listeria, Staphylococcus, Bacillus and E. coli), known to cause the foodborne illness in humans by following FDA’s Bacteriological Analytical Manual (BAM) and American Association of Analytical Chemists (AOAC) protocols [A total of fifteen Greek yogurt samples were analyzed for the detection of five pathogenic bacteria  Screening Method [Salmonella using the ELFA based VIDAS system and Salmonella SLM assay kit , in the Greek yogurt sample analyzed. The analysis was terminated, if the results were negative.The g Method ,35. The Listeria monocytogenes, the test portion from 10 units was weighed into two composites, and each composite consisted of 50 g from each of the 5 units for a total of 250 g following the BAM and AOAC protocols [Listeria species using the ELFA based VIDAS system and Listeria LIS assay kit (bioMérieux). The analysis was terminated, if the results were negative.For the analysis of rotocols ,35. ThenStaphylococcus aureus analysis was performed using the most probable number method following the BAM protocol [−1 by removing 50 g and adding 450 mL of Butterfield’s phosphate buffer (BPO4) , and homogenized. The 10−1 dilution was further diluted to 10−2, 10−3, and 10−4 by serially diluting the 10 mL of previous dilution to 90 mL BPO4. Then 1 mL of each serial dilution  was transferred in triplicate into 10 mL Trypticase Soy (TSB) broth  containing 10% NaCl  and 1% Sodium Pyruvate (Fisher Scientific), and incubated at 35 °C for 48 h. If any turbidity was noticed, it was streaked on to a Baird-Parker (BP) agar plate , with Egg Yolk Tellurite , and incubated at 35 °C for 48 h. After incubation, the BP plates were observed for bacterial growth. If no typical colonies of S. aureus observed, the analysis was terminated.The protocol . IndividBacillus analysis was also performed by using the most probable number protocol [−1 dilution by weighing 50 g of product and adding 450 mL BPO4 buffer (Fisher Scientific), and homogenized. The 10−1 dilution was further diluted to 10−2 and 10−3 by serially diluting 10 mL of previous dilution to 90 mL BPO4. Then 1 mL of all serial dilutions  were transferred in triplicate, into 10 mL TSB containing Polymyxin B , and incubated at 30 °C for 48 h. Any turbid tubes were then sub-cultured to Mannitol-Egg Yolk-Polymyxin (MYP)  with polymyxin B (Sigma), Egg yolk emulsion (BD Difco™) agar plates, and incubated at 30 °C for 48 h. If B. cereus specific pink colonies were not noticed, the analysis was terminated.The protocol . The proEscherichia coli in the Greek yogurt sample, the AOAC Total Coliform and Escherichia coli in All Foods, Substrate Supporting Disc Method was used [−1 by homogenizing 50 g of product with 450 mL of BPO4 buffer (Fisher Scientific). The 10−1 dilution was further diluted to 10−2, 10−3, and 10−4 by serially diluting 10 mL of previous dilution to 90 mL BPO4. Then 1 mL of all of the above serial dilutions  were transferred in triplicate into 10 mL Lauryl Tryptose (LST) broth (BD Difco™). Afterwards, one ColiComplete Substrate Supporting Disc  containing 5-bromo-4-chloro-3-indolyl-b-d-galactopyranoside  and 4-methylumbelliferyl-b-d-glucuronide (MUG) was added to each LST tube. The LST tubes were then incubated at 35 °C for 30 h, and observed for blue color and fluorescence under long wave UV light (366 nm). In the absence of UV fluorescence and blue color the product was considered negative for the presence of coliforms and E. coli.For the presence of the Total Coliform and was used . IndividCandida albicans (ATCC #10231) and Aspergillus brasilensis (ATCC #16404) cultures were carried through the analysis to serve as positive culture controls. The positive and negative controls were analyzed concurrently.To recover fungi from the Greek yogurt samples, a loop full of each yogurt sample was inoculated into a sterile tube containing 100 mL of sterile Sabouraud Dextrose (BD Difco™) broth, and incubated at 25 °C for 5 days. After completion of 5 days of incubation, the broth was streaked in duplicate to Malt Extract (BD Difco™) agar plates, and incubated at 25 °C for an additional 7 days. In addition, all the samples were also directly streaked on these plates, and incubated at 25 °C for 7 days. These plates were read and the growth was recorded after 24 h interval every day for the next 7 days. Fungi observed on any of the agar plates were examined by staining and microscopy. The sterile media were tested as negative control. Furthermore, For DNA extraction, approximately 100 mg of fungal specimen was homogenized in a clean and autoclaved mortar and pestle, if desired liquid nitrogen was used. The homogenate was purified by RNeasy Plant Mini Kit DNA, following manufacturer’s protocol and recommendations for the purification of total genomic DNA from fungi . The concentration of purified DNA samples was measured at 260-nm absorbance using a NanoDrop-1000 spectrophotometer , and stored at −20 °C until used.2  and the template DNA (50 ng) diluted in PCR grade water. The QIAGEN HotStarTaq Master Mix is a premixed solution containing HotStarTaq DNA Polymerase, PCR Buffer, and dNTPs with a final concentration of 1.5 mM MgCl2 and 200 µM each dNTP. The PCR reactions were run for 35 cycles  in a GeneAmp PCR 9700 thermocycler , with an initial hot start (94 °C for 15 min) and a final extension (72 °C for 10 min). The PCR products were analyzed by agarose gel electrophoresis, and visualized after ethidium bromide staining.The fragments of ITS region of rRNA gene was amplified by a previously described forward (5'-TCC GTA GGT GAA CCT GCG G-3') and reverse (5'-GCT GCG TTC TTC ATC GAT GC-3') primer set  was used® DTR Gel Filtration Cartridges  following manufacturer’s protocol (Edge Bio), and sequenced using the AB 3500 XL automated DNA sequencers (Applied Biosystems).The PCR products were enzymatically cleaned before cycle sequencing by an addition of 3 µL of the ExoSAP‐IT  to 5 µL of each amplified PCR product. The mixture was incubated at 37 °C for 20 min followed by 80 °C for 15 min on a GeneAmp PCR 9700 thermocycler (Applied Biosystems). The purified PCR products were sequenced using AB Big-Dye 3.1 dye chemistry (Applied Biosystems). A total of 20 μL sequencing reactions contained 2 μL of cleaned PCR product, 1 μL of BigDye Terminator v3.1 Ready Reaction Mix, 2 μL of 5× Sequencing Buffer, 1.6 pmol of Forward or Reverse sequencing primer and PCR grade water. The sequencing reactions were performed for 25 cycles  and hold at 4 °C in a GeneAmp PCR 9700 thermocycler (Applied Biosystems). The sequencing reactions were then cleaned with the PerformaThe accuracy of nucleotide sequence was achieved by performing two-directional DNA sequencing, and by sequencing of a new PCR-amplified product if necessary. Multiple alignments of the nucleotide sequences were completed by BioEdit and Geneious programs with manual adjustments. Nucleotide sequences of the ITS region of rRNA gene of recovered fungal species produced in the study were deposited in GenBank database under accession number KJ162161 to KJ162175.Rhizomucor variabilis. Our findings suggest that the presence of this human-pathogenic microorganism may be the reason for the recent gastrointestinal disease reported in individuals who consumed the Greek yogurt of this particular batch. The results suggest that the ITS1 region is a reliable benchmark for rapid detection and differentiation of human-pathogenic fungi and molds. This molecular detection tool will help in achieving the mission of our agency by notably analyzing food contaminated with molds of public health importance.In conclusion, our results confirmed that the recovered fungal species from all of the 15 unopened Greek yogurt containers to be