Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners.

OBJECTIVE: The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists. METHODS: We enrolled 434 subjects with the following diagnoses: chronic inflammatory demyelinating polyneuropathy (99), Charcot-Marie-Tooth disease type 1A (124), hereditary neuropathy with liability to pressure palsy (46), diabetic polyneuropathy (67), and controls (98). In each diagnostic class, 90% of subjects were used as training set for SLAs to establish the best performing SLA by tenfold cross validation procedure and 10% of subjects were employed as test set. Performance indicators were accuracy, precision, sensitivity, and specificity. RESULTS: SVM showed the highest overall diagnostic accuracy both in training and test sets (90.5 and 93.2%) and ranked first in a multidimensional comparison analysis. Overall accuracy of neurophysiologists ranged from 54.5 to 81.8%. CONCLUSIONS: This proof of principle study shows that SVM provides a high electrodiagnostic accuracy in polyneuropathies. We suggest that the use of SLAs in electrodiagnosis should be exploited to possibly provide a diagnostic support system especially helpful for the less experienced practitioners.

Alterations of gene expression indicating effects on estrogen signaling and lipid homeostasis in seabream hepatocytes exposed to extracts of seawater sampled from a coastal area of the central Adriatic Sea (Italy).

Recent evidences suggest that the toxicological effects of endocrine disrupting chemicals (EDCs) involve multiple nuclear receptor-mediated pathways, including estrogen receptor (ER) and peroxisome proliferator-activated receptor (PPAR) signaling systems. Thus, our objective in this study was to detect the summated endocrine effects of EDCs with metabolic activity in coastal waters of the central Adriatic Sea by means of a toxicogenomic approach using seabream hepatocytes. Gene expression patterns were also correlated with seawater levels of polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs). We found that seawater extracts taken at certain areas induced gene expression profiles of ERα/vitellogenin, PPARα/Stearoyl-CoA desaturase 1A, cytochrome P4501A (CYP1A) and metallothionein. These increased levels of biomarkers responses correlated with spatial distribution of PAHs/PCBs concentrations observed by chemical analysis in the different study areas. Collectively, our data give a snapshot of the presence of complex EDC mixtures that are able to perturb metabolic signaling in coastal marine waters.

Tri-m-cresyl phosphate and PPAR/LXR interactions in seabream hepatocytes: revealed by computational modeling (docking) and transcriptional regulation of signaling pathways.

The interactions between tri-m-cresyl phosphate (TMCP; an organophosphate flame retardant) and peroxisome proliferator activated receptors (PPARs) or liver X receptor α (LXRα) were investigated in seabream hepatocytes. The study was designed to characterize the binding of TMCP to PPARα, PPARγ and LXRα by computational modeling (docking) and transcriptional regulation of signaling pathways. TMCP mainly established a non-polar interaction with each receptor. These findings reflect the hydrophobic nature of this binding site, with fish LXRα showing the highest binding efficiency. Further, we have investigated the ability of TMCP to activate PPAR and LXR controlled transcriptional processes involved in lipid/cholesterol metabolism. TMCP induced the expression of all the target genes measured. All target genes were up-regulated at all exposure doses, except for fatty acid binding protein 7 (FABP7) and carnitine palmitoyltransferase 1B. Collectively, our data indicate that TMCP can affect fatty acid synthesis/uptake and cholesterol metabolism through LXRα and PPARs, together with interactions between these transcription factors in seabream liver.

Effects of Diisodecyl Phthalate on PPAR:RXR-Dependent Gene Expression Pathways in Sea Bream Hepatocytes.

Evidence that endocrine-disrupting chemicals (EDCs) may target metabolic disturbances, beyond interference with the functions of the endocrine systems has recently accumulated. Among EDCs, phthalate plasticizers like the diisodecyl phthalate (DiDP) are commonly found contaminants of aquatic environments and have been suggested to function as obesogens by activating peroxisome proliferator activated receptors (PPARs), a subset of nuclear receptors (NRs) that act as metabolic sensors, playing pivotal roles in lipid homeostasis. However, little is known about the modulation of PPAR signaling pathways by DiDP in fish. In this study, we have first investigated the ligand binding efficiency of DiDP to the ligand binding domains of PPARs and retinoid-X-receptor-α (RXRα) proteins in fish using a molecular docking approach. Furthermore, in silico predictions were integrated by in vitro experiments to show possible dose-relationship effects of DiDP on PPAR:RXR-dependent gene expression pathways using sea bream hepatocytes. We observed that DiDP shows high binding efficiency with piscine PPARs demonstrating a greater preference for RXRα. Our studies also demonstrated the coordinate increased expression of PPARs and RXRα, as well as their downstream target genes in vitro. Principal component analysis (PCA) showed the strength of relationship between transcription of most genes involved in fatty acid metabolism and PPAR mRNA levels. In particular, fatty acid binding protein (FABP) was highly correlated to all PPARs. The results of this study suggest that DiDP can be considered an environmental stressor that activates PPAR:RXR signaling to promote long-term changes in lipid homeostasis leading to potential deleterious physiological consequences in teleost fish.