Neuropathie optique lors d'un traitement par disulfirame [Optic neuropathy while taking disulfiram]

INTRODUCTION: Disulfiram has been used since the late 1940s to treat chronic alcoholism. This drug interferes with alcohol metabolism resulting in an acetaldehyde increase. This causes painful symptoms, encouraging abstinence. Side effects include rare cases of bilateral optic neuropathies. Visual recovery occurs frequently upon cessation of therapy. METHOD AND OBSERVATION: We retrospectively studied patients referred for visual loss while treated with disulfiram between 1987 and 2005. Fourteen patients (three females, 11 males; aged 35-62 years) complained of visual loss, but a toxic, disulfiram-related, optic neuropathy was diagnosed in only five patients. Following cessation of disulfiram therapy, visual acuity and field improved in all five patients. DISCUSSION: and conclusion: When disulfiram toxicity is suspected with optic neuropathy, cessation of treatment is mandatory. Visual prognosis is good in the majority of cases, as illustrated by our series. Disulfiram toxicity can be diagnosed only after excluding all other possible causes of visual loss.

New insight on bio-sensing by nano-fabricated memristors

We report here on a new insight for bio- sensing based on the memristive effect of functional- ized Schottky-barrier memristive silicon nanowire in dry environment. The device concept is discussed. Elec- trical measurements confirm the bio-detection by the narrowing of the memristive Ids − Vds hysteresis upon interaction of antigen with antibody-functionalized nanowire.

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.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Assessment of early-stage optic nerve invasion in retinoblastoma using high-resolution 1.5 Tesla MRI with surface coils: a multicentre, prospective accuracy study with histopathological correlation.

OBJECTIVES: To assess the accuracy of high-resolution (HR) magnetic resonance imaging (MRI) in diagnosing early-stage optic nerve (ON) invasion in a retinoblastoma cohort. METHODS: This IRB-approved, prospective multicenter study included 95 patients (55 boys, 40 girls; mean age, 29 months). 1.5-T MRI was performed using surface coils before enucleation, including spin-echo unenhanced and contrast-enhanced (CE) T1-weighted sequences (slice thickness, 2 mm; pixel size <0.3 × 0.3 mm(2)). Images were read by five neuroradiologists blinded to histopathologic findings. ROC curves were constructed with AUC assessment using a bootstrap method. RESULTS: Histopathology identified 41 eyes without ON invasion and 25 with prelaminar, 18 with intralaminar and 12 with postlaminar invasion. All but one were postoperatively classified as stage I by the International Retinoblastoma Staging System. The accuracy of CE-T1 sequences in identifying ON invasion was limited (AUC = 0.64; 95 % CI, 0.55 - 0.72) and not confirmed for postlaminar invasion diagnosis (AUC = 0.64; 95 % CI, 0.47 - 0.82); high specificities (range, 0.64 - 1) and negative predictive values (range, 0.81 - 0.97) were confirmed. CONCLUSION: HR-MRI with surface coils is recommended to appropriately select retinoblastoma patients eligible for primary enucleation without the risk of IRSS stage II but cannot substitute for pathology in differentiating the first degrees of ON invasion. KEY POINTS: • HR-MRI excludes advanced optic nerve invasion with high negative predictive value. • HR-MRI accurately selects patients eligible for primary enucleation. • Diagnosis of early stages of optic nerve invasion still relies on pathology. • Several physiological MR patterns may mimic optic nerve invasion.

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.

Unilateral optic disc edema following trabeculectomy.

Two cases of a benign form of optic disc edema after successful trabeculectomy are reported. In both patients, optic disc edema was noted 2 to 4 weeks after trabeculectomy. The edema occurred without loss of visual acuity or field. The absolute intraocular pressure and intracranial pressure were normal--that is, the edema was not a syndrome of hypotony or pseudotumor cerebri. However, both patients had intracranial pressure in the high-normal range. The decrease in intraocular pressure into the low normal range after trabeculectomy may have altered the intracranial pressure:intraocular pressure ratio at the lamina cribrosa enough to produce optic disc edema.

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.