Abstract

Background: Under inflammatory conditions, the activation of corticotropin-releasing factor (CRF) receptor has been shown to inhibit pain through opioid peptide release from immune cells or neurons. CRF's effects on human and animal pain modulation depend, however, on the distribution of its receptor subtypes 1 and 2 (CRF-R1 and CRF-R2) along the neuraxis of pain transmission. The objective of this study is to investigate the respective role of each CRF receptor subtype on centrally administered CRF-induced antinociception during inflammatory pain.

Methods: The present study investigated the role of intracerebroventricular (i.c.v.) CRF receptor agonists on nociception and the contribution of cerebral CRF-R1 and/or CRF-R2 subtypes in an animal model of Freund's complete adjuvant (FCA)-induced hind paw inflammation. Methods used included behavioral experiments, immunofluorescence confocal analysis, and reverse transcriptase-polymerase chain reaction.

Results: Intracerebroventricular, but systemically inactive, doses of CRF elicited potent, dose-dependent antinociceptive effects in inflammatory pain which were significantly antagonized by i.c.v. CRF-R1-selective antagonist NBI 27914 (by approximately 60%) but less by CRF-R2-selective antagonist K41498 (by only 20%). In line with these findings, i.c.v. administration of CRF-R1 agonist stressin I produced superior control of inflammatory pain over CRF-R2 agonist urocortin-2. Intriguingly, i.c.v. opioid antagonist naloxone significantly reversed the CRF as well as CRF-R1 agonist-elicited pain inhibition. Consistent with existing evidence of high CRF concentrations in brain areas such as the thalamus, hypothalamus, locus coeruleus, and periaqueductal gray following its i.c.v. administration, double-immunofluorescence confocal microscopy demonstrated primarily CRF-R1-positive neurons that expressed opioid peptides in these pain-relevant brain areas. Finally, PCR analysis confirmed the predominant expression of the CRF-R1 over CRF-R2 in representative brain areas such as the hypothalamus.

Conclusion: Taken together, these findings suggest that CRF-R1 in opioid-peptide-containing brain areas plays an important role in the modulation of inflammatory pain and may be a useful therapeutic target for inflammatory pain control.

The aim of the present study was to evaluate the variations in the phytochemical compositions and the antimicrobial activities of Syzygium grande and Oenanthe javanica leaf extracts against Bacillus sp. The antibacterial activity of the methanol, 70% methanol, ethyl acetate, and hexane extracts of both plant species were examined in vitro, against various Bacillus species. In addition, the 1H Nuclear Magnetic Resonance (1H NMR) spectroscopy-based metabolomics was employed to gain insights into the correlations between the chemical constituents of the bioactive extract to the biological activity. The hexane extract of S. grande leaves and methanolic extract of O. javanica leaves showed more potent anti Bacillus activity, among the test extracts. Principal component analysis (PCA) successfully differentiated the extracts on the basis of their metabolite profiles. The results of the Partial Least Square (PLS) analysis showed that the non-polar compounds of the hexane extract of S. grande (alanine, betulin, β-sitosterol, β-caryophyllene, acetic acid and 3-hydroxybutyric acid) were strongly correlated to its anti-Bacillus activity. On the other hand, choline, ellagic acid, and gallic acid, the metabolites present in the methanolic extract of O. javanica, were strongly correlated to its anti-Bacillus activity. On this basis, it was therefore concluded that these conmpounds could be the potential bioactive constituents, contributing to the anti-Bacillus activity of the individual plant species. Further in-depth investigations into the potential utilization of the two plants for useful applications in managing and control of Bacillus sp., will help pave the way towards their valorization.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.