Effect of Selected Cooking Ingredients for Nixtamalization on the Reduction of Fusarium Mycotoxins in Maize and Sorghum.

Although previous studies have reported the use of nixtamalization for mycotoxins reduction in maize, the efficacy of calcium hydroxide and other nixtamalization cooking ingredients for mycotoxin reduction/decontamination in sorghum and other cereals still need to be determined. The current study investigated the effect of five nixtamalization cooking ingredients (wood ashes, calcium hydroxide, sodium hydroxide, potassium hydroxide, and calcium chloride) on the reduction of Fusarium mycotoxins in artificially contaminated maize and sorghum using liquid chromatography-tandem mass spectrometry. All tested cooking ingredients effectively reduced levels of mycotoxins in the contaminated samples with reduction initiated immediately after the washing step. Except for the calcium chloride nixtamal, levels of fumonisin B1, B2, and B3 in the processed sorghum nixtamal samples were below the limit of detection. Meanwhile, the lowest pH values were obtained from the maize (4.84; 4.99), as well as sorghum (4.83; 4.81) nejayote and nixtamal samples obtained via calcium chloride treatment. Overall, the results revealed that the tested cooking ingredients were effective in reducing the target mycotoxins. In addition, it pointed out the potential of calcium chloride, though with reduced effectiveness, as a possible greener alternative cooking ingredient (ecological nixtamalization) when there are environmental concerns caused by alkaline nejayote.

Comprehensive study on Escherichia coli genomic expression: Does position really matter?

As a biorefinery platform host, Escherichia coli has been used extensively to produce metabolites of commercial interest. Integration of foreign DNA onto the bacterial genome allows for stable expression overcoming the need for plasmid expression and its associated instability. Despite the development of numerous tools and genome editing technologies, the question of where to incorporate a synthetic pathway remains unanswered. To address this issue, we studied the genomic expression in E. coli and linked it not only to 26 rationally selected genomic locations, but also to the gene direction in relation to the DNA replication fork, to the carbon and nitrogen source, to DNA folding and supercoiling, and to metabolic burden. To enable these experiments, we have designed a fluorescent expression cassette to eliminate specific local effects on gene expression. Overall it can be concluded that although the expression range obtained by changing the genomic location of a pathway is small compared to the range typically seen in promoter-RBS libraries, the effect of culture medium, environmental stress and metabolic burden can be substantial. The characterization of multiple effects on genomic expression, and the associated libraries of well-characterized strains, will only stimulate and improve the creation of stable production hosts fit for industrial settings.

Doubly robust conditional logistic regression.

Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model. In this paper, we propose a doubly robust estimator of the exposure-outcome odds ratio in conditional logistic regression models. This estimator protects against bias in the odds ratio estimator due to misspecification of the part of the model that contains the cluster-varying covariates. The doubly robust estimator uses two conditional logistic regression models for the odds ratio, one prospective and one retrospective, and is consistent for the exposure-outcome odds ratio if at least one of these models is correctly specified, not necessarily both. We demonstrate the properties of the proposed method by simulations and by re-analyzing a publicly available dataset from a matched case-control study on induced abortion and infertility.

Development of a prediction model for long-term quality of life in critically ill patients.

PURPOSE: We developed a prediction model for quality of life (QOL) 1 year after intensive care unit (ICU) discharge based upon data available at the first ICU day to improve decision-making. METHODS: The database of a 1-year prospective study concerning long-term outcome and QOL (assessed by EuroQol-5D) in critically ill adult patients consecutively admitted to the ICU of a university hospital was used. Cases with missing data were excluded. Utility indices at baseline (UIb) and at 1 year (UI1y) were surrogates for QOL. For 1-year non-survivors UI1y was set at zero. The grouped lasso technique selected the most important variables in the prediction model. R2 and adjusted R2 were calculated. RESULTS: 1831 of 1953 cases (93.8%) were complete. UI1y depended significantly on: UIb (P<0.001); solid tumor (P<0.001); age (P<0.001); activity of daily living (P<0.001); imaging (P<0.001); APACHE II-score (P=0.001); ≥80 years (P=0.001); mechanical ventilation (P=0.006); hematological patient (P=0.007); SOFA-score (P=0.008); tracheotomy (P=0.018); admission diagnosis surgical P<0.001 (versus medical); and comorbidity (P=0.049). Only baseline health status and surgical patients were positively associated with UI1y. R2 was 0.3875 and adjusted R2 0.3807. CONCLUSION: Although only 40% of variability in long-term QOL could be explained, this prediction model can be helpful in decision-making.

Data-Adaptive Bias-Reduced Doubly Robust Estimation.

Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite- or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.

Impact of de-escalation of beta-lactam antibiotics on the emergence of antibiotic resistance in ICU patients: a retrospective observational study.

PURPOSE: Antibiotic de-escalation is promoted to limit prolonged exposure to broad-spectrum antibiotics, but proof that it prevents the emergence of resistance is lacking. We evaluated determinants of antibiotic de-escalation in an attempt to assess whether the latter is associated with a lower emergence of antimicrobial resistance. METHODS: Antibiotic treatments, starting with empirical beta-lactam prescriptions, were prospectively documented during 2013 and 2014 in a tertiary intensive care unit (ICU) and categorized as continuation, de-escalation or escalation of the empirical antimicrobial treatment. Determinants of the de-escalation or escalation treatments were identified by multivariate logistic regression; the continuation category was used as the reference group. Using systematically collected diagnostic and surveillance cultures, we estimated the cumulative incidence of antimicrobial resistance following de-escalation or continuation of therapy, with adjustment for ICU discharge and death as competing risks. RESULTS: Of 478 anti-pseudomonal antibiotic prescriptions, 42 (9 %) were classified as escalation of the antimicrobial treatment and 121 (25 %) were classified as de-escalation, mainly through replacement of the originally prescribed antibiotics with those having a narrower spectrum. In multivariate analysis, de-escalation was associated with the identification of etiologic pathogens (p < 0.001). The duration of the antibiotic course in the ICU in de-escalated versus continued prescriptions was 8 (range 6-10) versus 5 (range 4-7) days, respectively (p < 0.001). Mortality did not differ between patients in the de-escalation and continuation categories. The cumulative incidence estimates of the emergence of resistance to the initial beta-lactam antibiotic on day 14 were 30.6 and 23.5 % for de-escalation and continuation, respectively (p = 0.22). For the selection of multi-drug resistant pathogens, these values were 23.5 (de-escalation) and 18.6 % (continuation) respectively (p = 0.35). CONCLUSION: The emergence of antibiotic-resistant bacteria after exposure to anti-pseudomonal beta-lactam antibiotics was not lower following de-escalation.

Increasing the power of the Mann-Whitney test in randomized experiments through flexible covariate adjustment.

The Mann-Whitney U test is frequently used to evaluate treatment effects in randomized experiments with skewed outcome distributions or small sample sizes. It may lack power, however, because it ignores the auxiliary baseline covariate information that is routinely collected. Wald and score tests in so-called probabilistic index models generalize the Mann-Whitney U test to enable adjustment for covariates, but these may lack robustness by demanding correct model specification and do not lend themselves to small sample inference. Using semiparametric efficiency theory, we here propose an alternative extension of the Mann-Whitney U test, which increases its power by exploiting covariate information in an objective way and which lends itself to permutation inference. Simulation studies and an application to an HIV clinical trial show that the proposed permutation test attains the nominal Type I error rate and can be drastically more powerful than the classical Mann-Whitney U test.