Modeling storm events to investigate the influence of the stream¿catchment interface zone on stream biogeochemistry

We formulate a new mixing model to explore hydrological and chemical conditions under which the interface between the stream and catchment interface (SCI) influences the release of reactive solutes into stream water during storms. Physically, the SCI corresponds to the hyporheic/riparian sediments. In the new model this interface is coupled through a bidirectional water exchange to the conventional two components mixing model. Simulations show that the influence of the SCI on stream solute dynamics during storms is detectable when the runoff event is dominated by the infiltrated groundwater component that flows through the SCI before entering the stream and when the flux of solutes released from SCI sediments is similar to, or higher than, the solute flux carried by the groundwater. Dissolved organic carbon (DOC) and nitrate data from two small Mediterranean streams obtained during storms are compared to results from simulations using the new model to discern the circumstances under which the SCI is likely to control the dynamics of reactive solutes in streams. The simulations and the comparisons with empirical data suggest that the new mixing model may be especially appropriate for streams in which the periodic, or persistent, abrupt changes in the level of riparian groundwater exert hydrologic control on flux of biologically reactive fluxes between the riparian/hyporheic compartment and the stream water.

Development of mental disorders one year after exposure to psychosocial stressors; a cohort study in primary care patients with a physical complaint.

BACKGROUND: Mental disorders, common in primary care, are often associated with physical complaints. While exposure to psychosocial stressors and development or presence of principal mental disorders (i.e. depression, anxiety and somatoform disorders defined as multisomatoforme disorders) is commonly correlated, temporal association remains unproven. The study explores the onset of such disorders after exposure to psychosocial stressors in a cohort of primary care patients with at least one physical symptom. METHOD: The cohort study SODA (SOmatization, Depression and Anxiety) was conducted by 21 private-practice GPs and three fellow physicians in a Swiss academic primary care centre. GPs included patients via randomized daily identifiers. Depression, anxiety or somatoform disorders were identified by the full Patient Health Questionnaire (PHQ), a validated procedure to identify mental disorders based on DSM-IV criteria. The PHQ was also used to investigate exposure to psychosocial stressors (before the index consultation and during follow up) and the onset of principal mental disorders after one year of follow up. RESULTS: From November 2004 to July 2005, 1020 patients were screened for inclusion. 627 were eligible and 482 completed the PHQ one year later and were included in the analysis (77%). At one year, prevalence of principal mental disorders was 30/153 (19.6% CI95% 13.6; 26.8) for those initially exposed to a major psychosocial stressor and 26/329 (7.9% CI95% 5.2; 11.4) for those not. Stronger association exists between psychosocial stressors and depression (RR = 2.4) or anxiety (RR = 3.5) than multisomatoforme disorders (RR = 1.8). Patients who are "bothered a lot" (subjective distress) by a stressor are therefore 2.5 times (CI95% 1.5; 4.0) more likely to experience a mental disorder at one year. A history of psychiatric comorbidities or psychological treatment was not a confounding factor for developing a principal mental disorder after exposure to psychosocial stressors. CONCLUSION: This primary care study shows that patients with physical complaints exposed to psychosocial stressors had a higher risk for developing mental disorders one year later. This temporal association opens the field for further research in preventive care for mental diseases in primary care patients.

Safety of artemether-lumefantrine exposure in first trimester of pregnancy: an observational cohort.

BACKGROUND: There is limited data available regarding safety profile of artemisinins in early pregnancy. They are, therefore, not recommended by WHO as a first-line treatment for malaria in first trimester due to associated embryo-foetal toxicity in animal studies. The study assessed birth outcome among pregnant women inadvertently exposed to artemether-lumefantrine (AL) during first trimester in comparison to those of women exposed to other anti-malarial drugs or no drug at all during the same period of pregnancy. METHODS: Pregnant women with gestational age <20 weeks were recruited from Maternal Health clinics or from monthly house visits (demographic surveillance), and followed prospectively until delivery. RESULTS: 2167 pregnant women were recruited and 1783 (82.3%) completed the study until delivery. 319 (17.9%) used anti-malarials in first trimester, of whom 172 (53.9%) used (AL), 78 (24.4%) quinine, 66 (20.7%) sulphadoxine-pyrimethamine (SP) and 11 (3.4%) amodiaquine. Quinine exposure in first trimester was associated with an increased risk of miscarriage/stillbirth (OR 2.5; 1.3-5.1) and premature birth (OR 2.6; 1.3-5.3) as opposed to AL with (OR 1.4; 0.8-2.5) for miscarriage/stillbirth and (OR 0.9; 0.5-1.8) for preterm birth. Congenital anomalies were identified in 4 exposure groups namely AL only (1/164[0.6%]), quinine only (1/70[1.4%]), SP (2/66[3.0%]), and non-anti-malarial exposure group (19/1464[1.3%]). CONCLUSION: Exposure to AL in first trimester was more common than to any other anti-malarial drugs. Quinine exposure was associated with adverse pregnancy outcomes which was not the case following other anti-malarial intake. Since AL and quinine were used according to their availability rather than to disease severity, it is likely that the effect observed was related to the drug and not to the disease itself. Even with this caveat, a change of policy from quinine to AL for the treatment of uncomplicated malaria during the whole pregnancy period could be already envisaged.

Nation-wide survey on radiation doses in diagnostic and interventional radiology in Switzerland in 1998.

A nation-wide survey on radiation doses in diagnostic and interventional radiology was conducted in Switzerland in 1998 aiming at establishing their collective radiological impact on the Swiss population. The study consisted on the one hand of surveying the frequency of more than 250 types of examinations, covering conventional radiology, mammography, fluoroscopy, angiography, interventional radiology, CT, bone densitometry, conventional tomography and dental radiology. On the other hand, for each type of examination the associated patient dose was established by modeling. The results of this study show that about 9.5 million diagnostic and interventional examinations are performed annually in Switzerland (1.34 per caput) and that the associated annual collective dose is of the order of 7100 person.Sv (1.0 mSv per caput). Switzerland is similar to other European countries in terms of the frequency of examinations and the collective dose.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Exposure to bioaerosols, respiratory health and lung-specific proteins: a prospective study in garbage and wastewater workers.

Objectives To prospectively assess respiratory health in wastewater workers and garbage collectors over 5 years. Methods Exposure, respiratory symptoms and conditions, spirometry and lung-specific proteins were assessed yearly in a cohort of 304 controls, 247 wastewater workers and 52 garbage collectors. Results were analysed with random coefficient models and linear regression taking into account several potential confounders. Results Symptoms, spirometry and lung-specific proteins were not affected by occupational exposure. Conclusions In this population no effects of occupational exposure to bioaerosols were found, probably because of good working conditions.

Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.