Neuroprotective effects of melanocortins in experimental spinal cord injury. An experimental study in the rat using topical application of compounds with varying affinity to melanocortin receptors.

The possibility that local administration of low molecular weight non-peptide compounds with varying affinities at melanocortin receptors in the spinal cord will influence pathophysiological outcome of spinal cord injury (SCI) was examined in a rat model. Five new Melacure compounds ME10092, ME10354, ME10393, ME10431 and ME10501 were used in this investigation. Each compound was dissolved in saline and tested at 3 different doses, i.e. 1 microg, 5 microg and 10 microg total dose in 10 microl applied topically 5 min after SCI. The animals were allowed to survive 5 h and trauma induced edema formation, breakdown of the blood-spinal cord barrier (BSCB) and cell injuries were examined and compared with untreated injured rats. A focal SCI inflicted by an incision into the right dorsal horn of the T10-11 segments resulted in marked edema formation, breakdown of the BSCB to Evans blue albumin and caused profound nerve cell injury in the T9 and the T12 segments. Topical application of ME10501 (a compound with high affinity at melanocortin, MC-4 receptors) in high doses (10 microg) resulted in most marked neuroprotection in the perifocal spinal cord (T9 and T12) segments. On the other hand, only a mild or no effect on spinal cord pathology was observed in the traumatized animals that received ME10092, ME10354, ME10393 and ME10431 at 3 different doses. These observations suggest that non-peptide compounds with varying affinity to melanocortin receptors are able to influence the pathophysiology of SCI. Furthermore, compounds acting at melanocortin, MCR4 receptors are capable to induce neuroprotection in spinal cord following trauma.

Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose.

Mycotoxin contamination of cereal grains can be detected and quantified using complex extraction procedures and analytical techniques. Normally, the grain odour, i.e. the presence of non-grain volatile metabolites, is used for quality classification of grain. We have investigated the possibility of using fungal volatile metabolites as indicators of mycotoxins in grain. Ten barley samples with normal odour, and 30 with some kind of off-odour were selected from Swedish granaries. The samples were evaluated with regard to moisture content, fungal contamination, ergosterol content, and levels of ochratoxin A (OA) and deoxynivalenol (DON). Volatile compounds were also analysed using both an electronic nose and gas chromatography combined with mass spectrometry (GC-MS). Samples with normal odour had no detectable ochratoxin A and average DON contents of 16 microg kg(-1) (range 0-80), while samples with off-odour had average OA contents of 76 microg kg(-1) (range 0-934) and DON contents of 69 microg kg(-1) (range 0-857). Data were evaluated by multivariate data analysis using projection methods such as principal component analysis (PCA) and partial least squares (PLS). The results show that it was possible to classify the OA level as below or above the maximum limit of 5 microg kg(-1) cereal grain established by the Swedish National Food Administration, and that the DON level could be estimated using PLS. Samples with OA levels below 5 microg kg(-1) had higher concentration of aldehydes (nonanal, 2-hexenal) and alcohols (1-penten-3-ol, 1-octanol). Samples with OA levels above 5 microg kg(-1) had higher concentrations of ketones (2-hexanone, 3-octanone). The GC-MS system predicted OA concentrations with a higher accuracy than the electronic nose, since the GC-MS misclassified only 3 of 37 samples and the electronic nose 7 of 37 samples. No correlation was found between odour and OA level, as samples with pronounced or strong off-odours had OA levels both below and above 5 microg kg(-1). We were able to predict DON levels in the naturally contaminated barley samples using the volatile compounds detected and quantified by either GC-MS or the electronic nose. Pentane, methylpyrazine, 3-pentanone, 3-octene-2-ol and isooctylacetate showed a positive correlation with DON, while ethylhexanol, pentadecane, toluene, 1-octanol, 1-nonanol, and 1-heptanol showed a negative correlation with DON. The root mean square error of estimation values for prediction of DON based on GC-MS and electronic nose data were 16 and 25 microg kg(-1), respectively.

PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand-receptor interactions.

A novel method has been developed for the analysis of ligand-receptor interactions. The method utilizes binding data generated from the analysis of chimeric proteins with chimeric peptides. To each chimeric part of the peptide and receptor are assigned descriptors, thus creating a matrix of X descriptors. These descriptors are then correlated with the experimentally determined interaction binding affinities for each chimeric receptor/peptide pair by use of partial least-squares projection to latent structures (PLS). The method was applied to analyze the interactions of chimeric MSH-peptides with wild-type MC1 and MC3 receptors, and MC1/MC3 receptor chimeras (in total 40 peptide-receptor combinations). Two types of PLS models could be created, one that revealed the relationships between receptor and peptide structure and peptide binding pK(i) values (i.e., affinity) (R2 and Q2 being 0.71 and 0.62, respectively), and another that revealed the relationships between peptide and receptor structure and peptide-receptor selectivity (R2 and Q2 being 0.64 and 0.57, respectively). After addition of cross-terms these models improved significantly; the R2 and Q2 being 0.93 and 0.75 for affinity, and 0.92 and 0.72 for selectivity, respectively. The analysis shows that the high affinity of the MSH-peptides is primarily achieved by interactions of the peptides' C-terminal amino acids with TM2 and TM3 of the receptor, and, to a lesser extent, by the interaction of the N-terminus with TM1, TM2 and TM3 of the receptor. However, in contrast, the MC1 receptor selectivity is primarily determined by an interaction of the peptides' N-termini with TM2/3 of the receptor. Moreover, the cross-terms of the PLS model revealed the existence of a strong interaction between TM6/7 and TM2/3 of the receptors.

Volatiles for mycological quality grading of barley grains: determinations using gas chromatography-mass spectrometry and electronic nose.

The possibility of using an electronic nose or gas chromatography combined with mass spectrometry (GC-MS) to quantify ergosterol and colony forming units (CFU) of naturally contaminated barley samples was investigated. Each sample was split into three parts for (i) ergosterol and CFU analysis, (ii) measurements with the electronic nose and (iii) identification of volatiles collected on an adsorbent with a GC-MS system. Forty samples were selected after sensory analysis to obtain 10 samples with normal odour and 30 with some degree of off-odour. The data set of volatile compounds and the data collected from the electronic nose were evaluated by multivariate analyse techniques. SIMCA classification (soft independent modelling of class analogy) was used for objective evaluation of the usefulness of the data from the GC-MS or electronic nose measurements for classification of grain samples as normal or with off-odour. The main volatile compounds of grain with normal odour were 2-hexenal, benzaldehyde and nonanal, while 3-octanone, methylheptanone and trimethylbenzene were the main volatile compounds of grain with off-odours. Using data from the electronic nose three samples of 40 were misclassified, while data analysis of the volatile compounds detected with the GC-MS, led to six misclassified samples. Regression models (partial least-squares, PLS) were built to predict ergosterol- and CFU-levels with data from the GC-MS or electronic nose measurements. PLS models based on both GC-MS and electronic nose data could be used to predict the ergosterol levels with high accuracy and with low root mean square error of prediction (RMSEP). CFU values from naturally infected grain could not be predicted with the same degree of confidence.