"Therefore, the composition of a given proteome can be altered depending on the biological state of the organism . Thus, protein identification as well as relative or absolute quantification of a given protein in different biological conditions, i.e. healthy and diseased states , and the identification of post-translational changes are the basis for understanding the fundamental processes that occur in living organisms . The development of new analytical–chemical strategies for protein analysis is important for progress in proteomics . "
[Show abstract][Hide abstract] ABSTRACT: Labeling of peptides and proteins using chelators binding metal ions has become a novel approach for quantitative proteomics in recent years. The aim of this work was the optimization of a new method for peptide derivatization with lanthanide labels (holmium, lutetium, and thulium) followed by nano high performance liquid chromatography (nano-HPLC) separation with UV detection. Matrix-assisted laser desorption ionization–mass spectrometry (MALDI MS) was used to confirm the derivatization and to identify the derivatized peptides. Peptides were labeled with the three different lanthanide metals using a bifunctional DOTA-based (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) reagent. The results demonstrate that the derivatization reaction using the chelating reagent DOTA-NHS-ester was effective for single peptides and peptide mixtures. Furthermore, an efficient pre-cleaning method was applied by nano-HPLC using a C-18 trap column for elimination of the excess of labeling reagent. The application of the optimized method to label peptides in a Cytochrome C digest delivered comparable results to those obtained with model peptides.
"ideal biomarker should have the following aspects in order to be able to predict or diagnose a specific dis - ease or condition ( Pepe et al . , 2001 ) : 1 ) simple and safe assays ; 2 ) provide guidance to ease decision - making ; 3 ) establish correlation between an assessment result and a clinical condition ( Fletcher et al . , 2012 ) in a sen - sitive ( a positive outcome when the disease is present ) and a specific ( a negative outcome when the disease is absent ) way . As with "
[Show abstract][Hide abstract] ABSTRACT: Glycans are chains of carbohydrates attached to proteins (glycoproteins and proteoglycans) or lipids (glycolipids). Glycosylation is a post-translational modification and glycans have a wide range of functions in the human body including involvement in oncological diseases. Change in a glycan structure can not only indicate the presence of a pathological process but, more importantly, in some cases also its stage. Thus, a glycan analysis has the potential to be an effective and reliable tool in cancer diagnostics. Lectins are proteins responsible for natural biorecognition of glycans; even carbohydrate moieties still attached to proteins or whole cells can be recognised by lectins, which makes them an ideal candidate for designing label-free biosensors for glycan analysis. This review seeks to summarise evidence that the glycoprofiling of biomarkers by lectin-based biosensors can be of significant help in detecting prostate cancer.
Chemical Papers 01/2015; 69(1):90-111. DOI:10.1515/chempap-2015-0052 · 0.88 Impact Factor
"The aim of a cancer monitoring program
is to detect tumors at early stage in order to have a
successful treatment. A screening tool should not
be expensive and invasive in order to permit its
widespread application (30). Toxicity is very important
in any experimental therapeutic agent, and
oncolytic viruses are not different in killing cancer
[Show abstract][Hide abstract] ABSTRACT: In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated.
In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN.
The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R(2)) between the actual and predicted values was determined as 0.897118 for all data.
It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).
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