Delivery of daunorubicin to cancer cells with decreased toxicity by association with a lipidic nanoemulsion that binds to LDL receptors

A lipidic nanoemulsion termed LDE concentrates in neoplastic cells after injection into the bloodstream and thus can be used as a drug carrier to tumour sites. The chemotherapeutic agent daunorubicin associates poorly with LDE; the aim of this study was to clarify whether the derivatization of daunorubicin by the attachment of an oleyl group increases the association with LDE, and to test the cytotoxicity and animal toxicity of the new preparation. The association of oleyl-daunorubicin (oDNR) to LDE showed high yield (93 +/- 2% and 84 +/- 4% at 1:10 and 1:5 drug:lipid mass, respectively) and was stable for at least 20 days. Association with oDNR increased the LDE particle diameter from 42 +/- 4 nm to 75 +/- 6 nm. Cytotoxicity of LDE-oDNR was reduced two-fold in HL-60 and K-562 cell lines, fourteen-fold in B16 cells and nine-fold in L1210 cells when compared with commercial daunorubicin. When tested in mice, LDE-oDNR showed remarkable reduced toxicity (maximum tolerated dose > 253 mu mol kg(-1), compared with <3 mu mol kg(-1) for commercial daunorubicin). At high doses, the cardiac tissue of LDE-oDNR-treated animals had much smaller structural lesions than with commercial daunorubicin. LDE-oDNR is therefore a promising new preparation that may offer superior tolerability compared with commercial daunorubicin.

Dating archeological ceramics from the Valley of Vitor, Arequipa by the TL method

The age of some ancient pottery from the Valley of Vitor in the region of Arequipa, Peru, is determined by the thermoluminescence (TL) method. For dating, a 325 degrees C TL peak was used and irradiation with -dose from 5 to 50Gy was carried out for the additive method, and from 0.4 to 5Gy for the regeneration method. For these dose values, the TL intensity is observed to grow linearly, obtaining an accumulated dose of 1.62 +/- 0.09Gy and 1.36 +/- 0.03Gy for the additive and regeneration methods, respectively. The age (A) of the sample was calculated by the two methods, being A=867 +/- 195 years after Christ (AC) for the additive method and A=1050 +/- 157 years AC for the regeneration method. Both results are within 800-1200 years AC, which is the period of the Wari culture.

Metodologia da determinação de prioridades no planeamento regional de saúde : uma proposta

RESUMO - O estabelecimento de prioridades determina a sustentabilidade de qualquer sistema de saúde, pelo que urge identificar os procedimentos, metodologias e critérios de priorização. Não existem critérios nem métodos universais de os combinar, sendo que a sua seleção depende do contexto de aplicação. O presente projeto de estudo exploratório-descritivo tem por finalidade a criação de uma proposta de metodologia a adotar na determinação de prioridades do Plano Regional de Saúde de Lisboa e Vale do Tejo 2011-2016, contextualizada à região, tempo e circunstâncias. O estudo está organizado em duas etapas metodológicas: uma revisão bibliográfica, dirigida à identificação do método e dos critérios de determinação de prioridades, e a realização de um painel de Delphi, para validação do método de determinação de prioridades proposto, definição dos critérios e suas ponderações. Tendo sido encontrada evidência na literatura sobre as vantagens da utilização da Análise Multicritério da Tomada de Decisão, através da utilização do Método Aditivo Linear, na determinação de prioridades em saúde, foi selecionada esta metodologia, que obteve a concordância de 85% dos participantes para a sua utilização no contexto em estudo, na primeira ronda do painel de Delphi. Os resultados preliminares do estudo, obtidos na primeira ronda, mostram que um dos onze critérios propostos foi excluído, tendo sido sugeridos sete novos critérios pelos participantes, que serão sujeitos a análise nas rondas subsequentes. Os resultados obtidos poderão servir de base a estudos mais aprofundados nesta área e contribuir para o debate sobre os critérios subjacentes ao processo de determinação de prioridades em saúde.

Chemical process to separate iron oxides particles in pottery sample for EPR dating

Ancient potteries usually are made of the local clay material, which contains relatively high concentration of iron. The powdered samples are usually quite black, due to magnetite, and, although they can be used for thermoluminescene (TL) dating, it is easiest to obtain better TL reading when clearest natural or pre-treated sample is used. For electron paramagnetic resonance (EPR) measurements, the huge signal due to iron spin-spin interaction, promotes an intense interference overlapping any other signal in this range. Sample dating is obtained by dividing the radiation dose, determined by the concentration of paramagnetic species generated by irradiation, by the natural dose so as a consequence, EPR dating cannot be used, since iron signal do not depend on radiation dose. In some cases, the density separation method using hydrated solution of sodium polytungstate [Na(G)(H(2)W(12)O(40))center dot H(2)O] becomes useful. However, the sodium polytungstate is very expensive in Brazil: hence an alternative method for eliminating this interference is proposed. A chemical process to eliminate about 90% of magnetite was developed. A sample of powdered ancient pottery was treated in a mixture (3:1:1) of HCI, HNO(3) and H(2)O(2) for 4 h. After that, it was washed several times in distilled water to remove all acid matrixes. The original black sample becomes somewhat clearer. The resulting material was analyzed by plasma mass spectrometry (ICP-MS), with the result that the iron content is reduced by a factor of about 9. In EPR measurements a non-treated natural ceramic sample shows a broad spin-spin interaction signal, the chemically treated sample presents a narrow signal in g= 2.00 region, possibly due to a radical of (SiO(3))(3-), mixed with signal of remaining iron [M. lkeya, New Applications of Electron Spin Resonance, World Scientific, Singapore, 1993, p. 285]. This signal increases in intensity under -gamma-irradiation. However, still due to iron influence, the additive method yielded too old age-value. Since annealing at 300 degrees C, Toyoda and Ikeya IS. Toyoda, M. Ikeya, Geochem. J. 25 (1991) 427-445] states that E `(1)-signal with maximum intensity is obtained, while annealing at 400 degrees C E`(1)-signal is completely eliminated, the subtraction of the second one from 300 degrees C heat-treated sample isolate E`(1)-like signal. Since this is radiation dose-dependent, we show that now EPR dating becomes possible. (C) 2008 Elsevier B.V. All rights reserved.

Chemical process to separate iron oxides particles in pottery sample for EPR dating

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Impacto da saúde bucal na qualidade de vida de pacientes maiores de 50 anos de duas instituições públicas do município de Araraquara-SP

Introduction: Oral health can affect quality of life, and the OHIP-14 index (Oral Health Impact Profile) is useful for evaluating this impact. Objective: to investigate the impact of oral health conditions on the quality of life of patients over  50  years, assessing, initially, the consistency of the short form of the Oral Health Impact Profile index (OHIP-14). Material and method: A cross-sectional study was performed among 149 patients of two public institutions for routine dental (UNESP) and medical practice (Municipal). They were interviewed using the OHIP-14 formulary, assessing its internal consistency (Cronbach´s alpha coefficient) and the OHIP-mean (additive method). The patients were distributed according to sex, age, and education level. The comparisons of interest were made using Student´s t test at a 5% level of significance. Result: A total of consecutive patients (n = 149) participated of this study (87% response rate). Cronbach´s alpha coefficient was 0.78, denoting a good consistency of the OHIP index. The OHIP mean was 4.98. The most prevalently affected OHIP domains were dimensions of physical pain: painful aching (11.40%) and uncomfortable eating foods (21.50%). There was non-significant difference (p > 0.05) between the mean OHIP value in relation to each of gender, age, and education level. Conclusion: The OHIP-14 is a reliable instrument of assessing oral health-related quality of life, and among patients under routine practice, it was found a low impact of oral conditions on their quality of life in the studied institutions (UNESP and Municipal).

Tracer applications of noble gas radionuclides in the geosciences

Noble gas radionuclides, including 81Kr (t1/2 = 229,000 years), 85Kr (t1/2 = 10.8 years), and 39Ar (t1/2 = 269 years), possess nearly ideal chemical and physical properties for studies of earth and environmental processes. Recent advances in Atom Trap Trace Analysis (ATTA), a laser-based atom counting method, have enabled routine measurements of the radiokrypton isotopes, as well as the demonstration of the ability to measure 39Ar in environmental samples. Here we provide an overview of the ATTA technique, and a survey of recent progress made in several laboratories worldwide.We review the application of noble gas radionuclides in the geosciences and discuss how ATTA can help advance these fields, specifically: determination of groundwater residence times using 81Kr, 85Kr, and 39Ar; dating old glacial ice using 81Kr; and an 39Ar survey of the main water masses of the oceans, to study circulation pathways and estimate mean residence times. Other scientific questions involving a deeper circulation of fluids in the Earth's crust and mantle are also within the scope of future applications. We conclude that the geoscience community would greatly benefit from an ATTA facility dedicated to this field, with instrumentation for routine measurements, as well as for research on further development of ATTA methods.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

EpiJen:a server for multistep T cell epitope prediction

Background - The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online. Results - To identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study. Conclusion - EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Transporter associated with antigen processing preselection of peptides binding to the MHC:a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

Tagged atom method of studying the biosynthesis of chlorophyll (Metod mechenych atomov v izuchenii biosinteza chlorofilla)

Translated from a publication of the Academy of Sciences, of Belorussiya, S.S.R., Minsk, 1956.