SEM and Neurohistological Observations of Nerve Endings in the Middle Region of the Tongue of the Collared Peccary (Tayassu tajacu): A Silver Impregnation Method

The presence of lingual papillae and the nerve endings in the middle region of the tongue mucosa of collared peccary (Tayassu tajacu) were studied using scanning electron microscopy and light microscopy, based upon the silver impregnation method. The middle region of tongue mucosa revealed numerous filiform and fungiform papillae. The thick epithelial layer showed epithelial cells and a dense connective tissue layer containing nerve fibre bundles and capillaries. The sensory nerve endings, intensely stained by silver impregnation, were usually non-encapsulated and extended into the connective tissue of the filiform and fungiform papillae very close to the epithelial cells. In some regions, the sensory nerves fibres formed a dense and complex network of fine fibrils. The presence of these nerve fibrils may characterize the mechanisms of transmission of sensitive impulses to the tongue mucosa.

Early Phase of Allergic Airway Inflammation in Diabetic Rats: Role of Insulin on the Signaling Pathways and Mediators

Background: Allergic lung inflammation is impaired in diabetic rats and is restored by insulin treatment. In the present study we investigated the effect of insulin on the signaling pathways triggered by allergic inflammation in the lung and the release of selected mediators. Methods: Diabetic male Wistar rats (alloxan, 42 mg/kg, i.v., 10 days) and matching controls were sensitized by s.c. injections of ovalbumin (OA) in aluminium hydroxide, 14 days before OA (1 mg/0.4 ml) or saline intratracheal challenge. A group of diabetic rats were treated with neutral protamine Hagedorn insulin (NPH, 4 IU, s.c.), 2 h before the OA challenge. Six hours after the challenge, bronchoalveolar lavage (BAL) was performed for mediator release and lung tissue was homogenized for Western blotting analysis of signaling pathways. Results: Relative to non-diabetic rats, the diabetic rats exhibited a significant reduction in OA-induced phosphorylation of the extracellular signal-regulated kinase (ERK, 59%), p38 (53%), protein kinase B (Akt, 46%), protein kinase C (PKC)-alpha (63%) and PKC-delta (38%) in lung homogenates following the antigen challenge. Activation of the NF-kappa B p65 subunit and phosphorylation of I kappa B alpha were almost suppressed in diabetic rats. Reduced expression of inducible nitric oxide synthase (iNOS, 32%) and cyclooxygenase-2 (COX-2, 46%) in the lung homogenates was also observed. The BAL concentration of prostaglandin (PG)-E(2), nitric oxide (NO) and interleukin (IL)-6 was reduced in diabetic rats (74%, 44% and 65%, respectively), whereas the cytokine-induced neutrophil chemoattractant (CINC)-2 concentration was not different from the control animals. Treatment of diabetic rats with insulin completely or partially restored all of these parameters. This protocol of insulin treatment only partially reduced the blood glucose levels. Conclusion: The data presented show that insulin regulates MAPK, PI3K, PKC and NF-kappa B pathways, the expression of the inducible enzymes iNOS and COX-2, and the levels of NO, PGE(2) and IL-6 in the early phase of allergic lung inflammation in diabetic rats. It is suggested that insulin is required for optimal transduction of the intracellular signals that follow allergic stimulation. Copyright (C) 2010 S. Karger AG, Basel

Consistency in approximate entropy given by a volumetric estimate

Non-linear methods for estimating variability in time-series are currently of widespread use. Among such methods are approximate entropy (ApEn) and sample approximate entropy (SampEn). The applicability of ApEn and SampEn in analyzing data is evident and their use is increasing. However, consistency is a point of concern in these tools, i.e., the classification of the temporal organization of a data set might indicate a relative less ordered series in relation to another when the opposite is true. As highlighted by their proponents themselves, ApEn and SampEn might present incorrect results due to this lack of consistency. In this study, we present a method which gains consistency by using ApEn repeatedly in a wide range of combinations of window lengths and matching error tolerance. The tool is called volumetric approximate entropy, vApEn. We analyze nine artificially generated prototypical time-series with different degrees of temporal order (combinations of sine waves, logistic maps with different control parameter values, random noises). While ApEn/SampEn clearly fail to consistently identify the temporal order of the sequences, vApEn correctly do. In order to validate the tool we performed shuffled and surrogate data analysis. Statistical analysis confirmed the consistency of the method. (C) 2008 Elsevier Ltd. All rights reserved.