Methods comparison for detecting trends in herbicide monitoring time-series in streams

An inadvertent consequence of pesticide use is aquatic pesticide pollution, which has prompted the implementation of mitigation measures in many countries. Water quality monitoring programs are an important tool to evaluate the efficacy of these mitigation measures. However, large interannual variability of pesticide losses makes it challenging to detect significant improvements in water quality and to attribute these improvements to the application of specific mitigation measures. Thus, there is a gap in the literature that informs researchers and authorities regarding the number of years of aquatic pesticide monitoring or the effect size (e.g., loss reduction) that is required to detect significant trends in water quality. Our research addresses this issue by combining two exceptional empirical data sets with modelling to explore the relationships between the achieved pesticide reduction levels due to mitigation measures and the length of the observation period for establishing statistically significant trends. Our study includes both a large (Rhine at Basel, ∼36,300 km2) and small catchment (Eschibach, 1.2 km2), which represent spatial scales at either end of the spectrum that would be realistic for monitoring programs designed to assess water quality. Our results highlight several requirements in a monitoring program to allow for trend detection. Firstly, sufficient baseline monitoring is required before implementing mitigation measures. Secondly, the availability of pesticide use data helps account for the interannual variability and temporal trends, but such data are usually lacking. Finally, the timing and magnitude of hydrological events relative to pesticide application can obscure the observable effects of mitigation measures (especially in small catchments). Our results indicate that a strong reduction (i.e., 70–90 %) is needed to detect a change within 10 years of monitoring data. The trade-off in applying a more sensitive method for change detection is that it may be more prone to false-positives. Our results suggest that it is important to consider the trade-off between the sensitivity of trend detection and the risk of false positives when selecting an appropriate method and that applying more than one method can provide more confidence in trend detection

Risk mitigation measures for pesticide runoff: How effective are they?

BACKGROUND: One of the most important sources of pesticide pollution of surface waters is runoff and erosion from agricultural fields after rainfall. This study analyses the efficacy of different risk mitigation measures to reduce pesticide runoff and erosion inputs into surface waters from arable land excluding rice fields. RESULTS: Three groups of risk mitigation measures were quantitatively analyzed: vegetative filter strips, micro-dams in row crops and soil conservation measures. Their effectiveness was evaluated based on a meta-analysis of available experimental data using statistical methods such as classification and regression trees, and exploratory data analysis. Results confirmed the effectiveness of vegetative filter strips and micro-dams. Contrary to common assumption, the width of vegetative filter strips alone is not sufficient to predict their effectiveness. The effectiveness of soil conservation measures (especially mulch-tillage) varied widely. This was in part due to the heterogeneity of the available experimental data, probably resulting from the inconsistent implementation and the inadequate definitions of these measures. CONCLUSION: Both vegetative filter strips and micro-dams are effective and suitable, and can therefore be recommended for quantitative assessment of environmental pesticide exposure in surface waters.However, the processes of infiltration and sedimentation in vegetative filter strips should be simulated with amechanistic model like Vegetative Filter Strip Modeling System, VFSMOD. The reduction effect ofmicro-dams can be modelled by reducing the runoff curve number, e.g., in the pesticide root zone model, PRZM. Soil conservationmeasures are in principle promising, but further well-documented data are needed to determine under which conditions they are effective

Aldose Reductase Inhibition Prevents Metaplasia of Airway Epithelial Cells

BACKGROUND: Goblet cell metaplasia that causes mucus hypersecretion and obstruction in the airway lumen could be life threatening in asthma and chronic obstructive pulmonary disease patients. Inflammatory cytokines such as IL-13 mediate the transformation of airway ciliary epithelial cells to mucin-secreting goblet cells in acute as well as chronic airway inflammatory diseases. However, no effective and specific pharmacologic treatment is currently available. Here, we investigated the mechanisms by which aldose reductase (AR) regulates the mucus cell metaplasia in vitro and in vivo. METHODOLOGY/FINDINGS: Metaplasia in primary human small airway epithelial cells (SAEC) was induced by a Th2 cytokine, IL-13, without or with AR inhibitor, fidarestat. After 48 h of incubation with IL-13 a large number of SAEC were transformed into goblet cells as determined by periodic acid-schiff (PAS)-staining and immunohistochemistry using antibodies against Mucin5AC. Further, IL-13 significantly increased the expression of Mucin5AC at mRNA and protein levels. These changes were significantly prevented by treatment of the SAEC with AR inhibitor. AR inhibition also decreased IL-13-induced expression of Muc5AC, Muc5B, and SPDEF, and phosphorylation of JAK-1, ERK1/2 and STAT-6. In a mouse model of ragweed pollen extract (RWE)-induced allergic asthma treatment with fidarestat prevented the expression of IL-13, phosphorylation of STAT-6 and transformation of epithelial cells to goblet cells in the lung. Additionally, while the AR-null mice were resistant, wild-type mice showed goblet cell metaplasia after challenge with RWE. CONCLUSIONS: The results show that exposure of SAEC to IL-13 caused goblet cell metaplasia, which was significantly prevented by AR inhibition. Administration of fidarestat to mice prevented RWE-induced goblet cell metaplasia and AR null mice were largely resistant to allergen induced changes in the lung. Thus our results indicate that AR inhibitors such as fidarestat could be developed as therapeutic agents to prevent goblet cell metaplasia in asthma and related pathologies

Circulating microRNAs in sera correlate with soluble biomarkers of immune activation but do not predict mortality in ART treated individuals with HIV-1 infection: A case control study

Introduction: The use of anti-retroviral therapy (ART) has dramatically reduced HIV-1 associated morbidity and mortality. However, HIV-1 infected individuals have increased rates of morbidity and mortality compared to the non-HIV-1 infected population and this appears to be related to end-organ diseases collectively referred to as Serious Non-AIDS Events (SNAEs). Circulating miRNAs are reported as promising biomarkers for a number of human disease conditions including those that constitute SNAEs. Our study sought to investigate the potential of selected miRNAs in predicting mortality in HIV-1 infected ART treated individuals. Materials and Methods: A set of miRNAs was chosen based on published associations with human disease conditions that constitute SNAEs. This case: control study compared 126 cases (individuals who died whilst on therapy), and 247 matched controls (individuals who remained alive). Cases and controls were ART treated participants of two pivotal HIV-1 trials. The relative abundance of each miRNA in serum was measured, by RTqPCR. Associations with mortality (all-cause, cardiovascular and malignancy) were assessed by logistic regression analysis. Correlations between miRNAs and CD4+ T cell count, hs-CRP, IL-6 and D-dimer were also assessed. Results: None of the selected miRNAs was associated with all-cause, cardiovascular or malignancy mortality. The levels of three miRNAs (miRs -21, -122 and -200a) correlated with IL-6 while miR-21 also correlated with D-dimer. Additionally, the abundance of miRs -31, -150 and -223, correlated with baseline CD4+ T cell count while the same three miRNAs plus miR- 145 correlated with nadir CD4+ T cell count. Discussion: No associations with mortality were found with any circulating miRNA studied. These results cast doubt onto the effectiveness of circulating miRNA as early predictors of mortality or the major underlying diseases that contribute to mortality in participants treated for HIV-1 infection

On the Dark Side of Therapies with Immunoglobulin Concentrates: The Adverse Events

Therapy by human immunoglobulin G (IgG) concentrates is a success story ongoing for decades with an ever increasing demand for this plasma product. The success of IgG concentrates on a clinical level is documented by the slowly increasing number of registered indication and the more rapid increase of the off-label uses, a topic dealt with in another contribution to this special issue of Frontiers in Immunology. A part of the success is the adverse event (AE) profile of IgG concentrates which is, even at life-long need for therapy, excellent. Transmission of pathogens in the last decade could be entirely controlled through the antecedent introduction by authorities of a regulatory network and installing quality standards by the plasma fractionation industry. The cornerstone of the regulatory network is current good manufacturing practice. Non-infectious AEs occur rarely and mainly are mild to moderate. However, in recent times, the increase in frequency of hemolytic and thrombotic AEs raised worrying questions on the possible background for these AEs. Below, we review elements of non-infectious AEs, and particularly focus on hemolysis and thrombosis. We discuss how the introduction of plasma fractionation by ion-exchange chromatography and polishing by immunoaffinity chromatographic steps might alter repertoire of specificities and influence AE profiles and efficacy of IgG concentrates

Development and Validation of a Risk Score for Chronic Kidney Disease in HIV Infection Using Prospective Cohort Data from the D:A:D Study

Ristola M. on työryhmien DAD Study Grp ; Royal Free Hosp Clin Cohort ; INSIGHT Study Grp ; SMART Study Grp ; ESPRIT Study Grp jäsen.Background Chronic kidney disease (CKD) is a major health issue for HIV-positive individuals, associated with increased morbidity and mortality. Development and implementation of a risk score model for CKD would allow comparison of the risks and benefits of adding potentially nephrotoxic antiretrovirals to a treatment regimen and would identify those at greatest risk of CKD. The aims of this study were to develop a simple, externally validated, and widely applicable long-term risk score model for CKD in HIV-positive individuals that can guide decision making in clinical practice. Methods and Findings A total of 17,954 HIV-positive individuals from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study with >= 3 estimated glomerular filtration rate (eGFR) values after 1 January 2004 were included. Baseline was defined as the first eGFR > 60 ml/min/1.73 m2 after 1 January 2004; individuals with exposure to tenofovir, atazanavir, atazanavir/ritonavir, lopinavir/ritonavir, other boosted protease inhibitors before baseline were excluded. CKD was defined as confirmed (>3 mo apart) eGFR In the D:A:D study, 641 individuals developed CKD during 103,185 person-years of follow-up (PYFU; incidence 6.2/1,000 PYFU, 95% CI 5.7-6.7; median follow-up 6.1 y, range 0.3-9.1 y). Older age, intravenous drug use, hepatitis C coinfection, lower baseline eGFR, female gender, lower CD4 count nadir, hypertension, diabetes, and cardiovascular disease (CVD) predicted CKD. The adjusted incidence rate ratios of these nine categorical variables were scaled and summed to create the risk score. The median risk score at baseline was -2 (interquartile range -4 to 2). There was a 1: 393 chance of developing CKD in the next 5 y in the low risk group (risk score = 5, 505 events), respectively. Number needed to harm (NNTH) at 5 y when starting unboosted atazanavir or lopinavir/ritonavir among those with a low risk score was 1,702 (95% CI 1,166-3,367); NNTH was 202 (95% CI 159-278) and 21 (95% CI 19-23), respectively, for those with a medium and high risk score. NNTH was 739 (95% CI 506-1462), 88 (95% CI 69-121), and 9 (95% CI 8-10) for those with a low, medium, and high risk score, respectively, starting tenofovir, atazanavir/ritonavir, or another boosted protease inhibitor. The Royal Free Hospital Clinic Cohort included 2,548 individuals, of whom 94 individuals developed CKD (3.7%) during 18,376 PYFU (median follow-up 7.4 y, range 0.3-12.7 y). Of 2,013 individuals included from the SMART/ESPRIT control arms, 32 individuals developed CKD (1.6%) during 8,452 PYFU (median follow-up 4.1 y, range 0.6-8.1 y). External validation showed that the risk score predicted well in these cohorts. Limitations of this study included limited data on race and no information on proteinuria. Conclusions Both traditional and HIV-related risk factors were predictive of CKD. These factors were used to develop a risk score for CKD in HIV infection, externally validated, that has direct clinical relevance for patients and clinicians to weigh the benefits of certain antiretrovirals against the risk of CKD and to identify those at greatest risk of CKD.Peer reviewe

Klassifizierung Organischer Verbindungen in Toxische Wirktypen

Traditionally, quantitative structure-activity relationships (QSAR) for toxic effects are derived from a series of compounds belonging to the same chemical class. However, structural similarity does not necessarily lead to similar toxic effects. As a consequence, considerable effort has been made to move toxicity QSAR from a class-based perspective to one that is consistent with modes of toxic action (MOA). The prerequisite for screening large databases of compounds with MOA-based QSARs is the successful classification of compounds into the different MOAs. The goal of this work is therefore to improve MOA classification methods. Toxicity QSAR is based on three main disciplines: data analysis, chemistry and toxicology. Therefore this work is structured around these three disciplines with each chapter consisting of a study on MOA classificationx but with a main focus on one of these disciplines. Data analysis methods: Data analysis methods contribute in several ways to improved MOA classification. First of all, they provide classification methods. Secondly, data analysis methods are necessary to estimate the predictive power of classification models. The predictive power of different classification models was compared using data set of 104 compounds with seven different MOAs. The predictive power varied surprisingly little between the four compared methods and ranged from 52% to 59% overall correct classification when appropriate parameters and descriptor selection methods were chosen. The fact that no approach could further improve the predictive power was attributed to the combination of high diversity, small size of the data set and the large number of classes. Concerning the validation methods, descriptor selection appeared to be one of the crucial processes of too optimistic estimates of predictive power. However, a methodology to correct this optimistic bias in cross-validation methods was developed. Thus, the main conclusion from this study is that it is not possible to say a priori which classification methods works best for a given data set, therefore it is advisable to use both a linear and a nonlinear method for each data set. Chemistry: Progress in computational chemistry allows the calculation of an increasing number of molecular and atomic descriptors. In this second study, a data set of 220 phenols with exhibiting four MOAs was used. The structures were described by a representation previously developed in drug design. The structure representation used consists of vectors based on empirical atomic Pi-charge and sigma-electronegativity encoded by topological autocorrelation and of vectors based on hydrogen bonding potential encoded by surface autocorrelation. The performance of autocorrelation descriptors proved to be equal to the results obtained in the evaluation of previously published quantum-chemical descriptors with overall correct classification percentages over 90%. Thus, the greatest advantage of the proposed structure representation is the ease and speed of calculation. This advantage was utilized to screen all 3142 monocyclic phenols contained in the open NCI database in an exploratory study. Toxicology: If mechanistic knowledge of a toxic effect is available, this knowledge can simplify both descriptor selection and data analysis. As a third study a mechanistic QSAR model of uncouplers of oxidative and photo-phosphorylation was developed based on insights gained in kinetic experimental studies. The model was based on a data set of 21 phenols with measured biological activity, and additionally with experimental membrane-water distribution coefficients. This allowed the calculation of effect concentrations at the site of action, which is an advantage compared with the traditional approach of estimating the uptake with octanol-water coefficients. A linear regression model of good quality could be established using four mechanistic descriptors. The proposed model has a potential to establish MOA based QSARs containing data from different chemical classes which is one of the central goals of MOA based QSAR. In a last study, a pragmatic approach for a classification model for uncouplers was presented. It is based on a data set of 31 uncouplers and 18 compounds showing no activity in this MOA and could classify 46 of the 49 compounds correctly. The experiences made in these three studies can help to guide future studies. The size of data set and the knowledge of toxic mechanisms are taken as the two criteria which should guide through the QSAR model building process. Concerning the state of MOA classification in general one must say that this field is still in an early stage. However, given that tens of thousands of compounds will be tested over the next years, even models with intermediate or modest predictive power used for the prioritization of testing needs are of remarkable practical value.Die meisten quantitativen Struktur-Wirkungsbeziehungen (quantitative structure-activity relationships: QSAR) für toxische Effekte basieren auf Strukturen der gleichen chemischen Klasse. Strukturelle Ähnlichkeit führt jedoch nicht notwendigerweise zu ähnlichen toxischen Wirktypen. Aus diesem Grund wird in den letzten Jahren vermehrt versucht, QSARs von der Basis der chemischen Klasse weg zu bringen und auf der Basis toxischer Wirktypen (mode of toxic action: MOA) zu arbeiten. Die Voraussetzung, um grosse Datenbanken mit MOA-basierten QSARs nach problematischen Chemikalien zu durchsuchen, ist eine erfolgreiche Klassifikation der Chemikalien in ihre MOAs. Ziel dieser Arbeit ist deshalb, die Untersuchung und Verbesserung von MOA-Klassifikationsmodellen. QSARs zur Toxizität basieren hauptsächlich auf drei Disziplinen: Datenanalyse, Chemie und Toxikologie. Diese Arbeit wurde deshalb auch anhand dieser drei Disziplinen gegliedert, wobei in jedem Kapitel ein MOA-Datensatz mit Hauptbezug auf eine dieser Disziplinen untersucht wurde. Datenanalyse: Methoden der Datenanalyse tragen in verschiedenen Bereichen zu Verbesserungen der MOA-Klassifikation bei, wobei die Entwicklung von Klassifikationsmethoden und von Methoden zur Abschätzung der Vorhersagekraft die wichtigsten sind. Anhand eines MOA-Datensatzes mit 104 Verbindungen und sieben MOAs wurde die Vorhersagekraft vier verschiedener Klassifikationsmodelle unterschiedlicher Komplexizität getestet. Die Unterschiede in der Vorhersagekraft der Modelle waren relativ gering und reichten mit optimierter Parameterwahl und mit Deskriptorselektionsmethoden von 52% zu 59% insgesamt korrekt vorhergesagten Verbindungen. Die Tatsache, dass sich die Vorhersagekraft mit keinem Ansatz weiter erhöhen liess, wurde auf die sehr hohe molekulare Diversität des Datensatzes und auf dessen geringe Grösse des im Verhältnis zur Anzahl Klassen, zurückgeführt. In weiteren Untersuchungen konnte die Deskriptorselektion als eine der Hauptursachen für die Überschätzung der Vorhersagekraft identifiziert werden. Deshalb wurde eine Methodologie, um solche Überschätzungen in Kreuzvalidierungsmethoden zu korrigieren, erarbeitet. Hauptfolgerung dieser Studie war, dass es sich nicht a priori sagen lässt, welche Klassifikationsmethoden für einen bestimmten Datensatz am besten funktionieren wird und deshalb jeweils lineare und nichtlineare Methoden ergänzend eingesetzt werden sollten. Chemie: Fortschritte in der theoretischen Chemie erlauben die zunehmend genaue Berechnung einer Vielzahl von molekularen und chemischen Deskriptoren. Die zweite Studie beruht auf einem Datensatz von 220 Phenolen mit vier MOAs. Eine für die Wirkstoffforschung entwickelte Strukturrepräsentation wurde dabei verwendet. Die Strukturrepräsentation basiert auf empirisch berechneten atomaren Pi-Ladungen und Sigma-Elektronegativitäten, welche mit Autokorrelationsvektoren codiert wurden. Zusätzlich wurden Wasserstoffbrückenbindungspotentiale mit Oberflächenautokorrelation codiert. Die Vorhersagekraft der Klassifikationsmodelle wurde auf über 90% korrekt vorhergesagter Verbindungen abgeschätzt und war damit vergleichbar mit denjenigen, die in publizierten Arbeiten mit quanten-chemischen Deskriptoren erreicht wurde, wobei die hier vorgeschlagenen Deskriptoren erheblich einfacher und schneller zu berechnen sind. Dieser Vorteil wurde genutzt für eine explorative Analyse der 3142 monocyclischen Phenole in der open NCI Database. Toxikologie: Ist mechanistisches Wissen über toxische Effekte verfügbar, kann es die Wahl der Deskriptoren und die Datenanalyse vereinfachen. In einer dritten Studie wurde ein mechanistisches QSAR-Modell für Entkoppler der oxidativen und Photo-Phosphorylierung entwickelt, das auf Ergebnissen experimentell hergeleiteter kinetischer Modelle basiert. Ein Datensatz von 21 Phenolen mit gemessener Aktivität und zusätzlich mit gemessenen Membran-Wasser-Verteilungskoeffizienten wurde an dieser Stelle verwendet. Daraus liess sich die Konzentration der Verbindungen am Wirkort berechnen. Basierend auf vier mechanistischen Deskriptoren liess sich ein lineares Regressionsmodell guter Qualität bilden. Das vorgeschlagene Modell hat ein Potential, auch Verbindungen anderer chemischer Klassen in das Modell zu integrieren, welches ein zentrales Ziel des MOA-basierten Ansatzes darstellt. In einer zusätzlichen Studie wurde ein pragmatischer Ansatz zur Klassifikation von Entkopplern entwickelt. Er basiert auf einem Datensatz von 31 aktiven Entkopplern und 18 nicht aktiven Verbindungen und erlaubte, 46 der 49 Verbindungen korrekt zu klassifizieren. Was den Stand der MOA-Klassifikationsmethoden im Allgemeinen betrifft, muss gesagt werden, dass sich dieses Gebiet noch in einem frühen Stadium befindet. Die Tatsache, dass in den nächsten Jahren, zehntausende von Chemikalien getestet werden sollen, bedeutet aber auch, dass schon Modelle mit relativ bescheidener Vorhersagekraft von grossem praktischem Nutzen sein können

A quantitative structure-activity relationship model for the intrinsic activity of uncouplers of oxidative phosphorylation

A quantitative structure−activity relationship (QSAR) has been derived for the prediction of the activity of phenols in uncoupling oxidative and photophosphorylation. Twenty-one compounds with experimental data for uncoupling activity as well as for the acid dissociation constant, pKa, and for partitioning constants of the neutral and the charged species into model membranes were analyzed. From these measured data, the effective concentration in the membrane was derived, which allowed the study of the intrinsic activity of uncouplers within the membrane. A linear regression model for the intrinsic activity could be established using the following three descriptors: solvation free energies of the anions, an estimate for heterodimer formation describing transport processes, and pKa values describing the speciation of the phenols. In a next step, the aqueous effect concentrations were modeled by combining the model for the intrinsic uncoupling activity with descriptors accounting for the uptake into membranes. Results obtained with experimental membrane−water partitioning data were compared with the results obtained with experimental octanol−water partition coefficients, log Kow, and with calculated log Kow values. The properties of these different measures of lipophilicity were critically discusse