METAL-DIELECTRIC TRANSITION AND MAGNETIC ABSORPTION OF LIGHT IN FILMS OF MAGNETIC OXIDES

The optical and electrical properties of films of manganites and vanadium oxides with MI transition has been studied. The distinct optical response to the MI in films promising for applications has been obtained.Работа выполнена в рамках гос.задания №AAAA-A18-118020290104-2 по теме «Спин»

Comparative study of in situ N2 rotational Raman spectroscopy methods for probing energy thermalisation processes during spin-exchange optical pumping

Spin-exchange optical pumping (SEOP) has been widely used to produce enhancements in nuclear spin polarisation for hyperpolarised noble gases. However, some key fundamental physical processes underlying SEOP remain poorly understood, particularly in regards to how pump laser energy absorbed during SEOP is thermalised, distributed and dissipated. This study uses in situ ultra-low frequency Raman spectroscopy to probe rotational temperatures of nitrogen buffer gas during optical pumping under conditions of high resonant laser flux and binary Xe/N2 gas mixtures. We compare two methods of collecting the Raman scattering signal from the SEOP cell: a conventional orthogonal arrangement combining intrinsic spatial filtering with the utilisation of the internal baffles of the Raman spectrometer, eliminating probe laser light and Rayleigh scattering, versus a new in-line modular design that uses ultra-narrowband notch filters to remove such unwanted contributions. We report a ~23-fold improvement in detection sensitivity using the in-line module, which leads to faster data acquisition and more accurate real-time monitoring of energy transport processes during optical pumping. The utility of this approach is demonstrated via measurements of the local internal gas temperature (which can greatly exceed the externally measured temperature) as a function of incident laser power and position within the cell

Evaluation of midkine and anterior gradient 2 in a multimarker panel for the detection of ovarian cancer

The aims of this study were: to characterise and compare plasma concentrations of midkine (MDK) in normal healthy women with concentrations observed in women with ovarian cancer; and to establish and compare the performance of MDK with that of anterior gradient 2 protein (AGR2) and CA125 in the development of multi-analyte classification algorithms for ovarian cancer. Median plasma concentrations of immunoreactive MDK, AGR2 and CA125 were significantly greater in the case cohort (909 pg/ml, 765 pg/ml and 502 U/ml, respectively n = 46) than in the control cohort (383 pg/ml, 188 pg/ml and 13 U/ml, respectively n = 61) (p < 0.001). The area under the receiver operator characteristic curve (AUC) for MDK and AGR2 was not significantly different (0.734 ± 0.046 and 0.784 ± 0.049, respectively, mean ± SE) but were both significantly less than the AUC for CA125 (0.934 ± 0.030, p < 0.003). When subjected to stochastic gradient boosted logistic regression modelling, the AUC of the multi-analyte panel (MDK, AGR2 and CA125, 0.988 ± 0.010) was significantly greater than that of CA125 alone (0.934 ± 0.030, p = 0.035). The sensitivity and specificity of the multi-analyte algorithm were 95.2 and 97.7%, respectively. Within the study cohort, CA125 displayed a sensitivity and specificity of 87.0 and 94.6%, respectively. The data obtained in this study confirm that both MDK and AGR2 individually display utility as biomarkers for ovarian cancer and that in a multi-analyte panel significantly improve the diagnostic utility of CA125 in symptomatic women

Do serum biomarkers really measure breast cancer?

Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.United States. Dept. of Defense. Breast Cancer Research Program (Grant No. W81XWH-05-1-0292)National Institutes of Health (U.S.) (R01 CA-112437-01)National Institutes of Health (U.S.) (NIH CA 84955

Serum S100A6 Concentration Predicts Peritoneal Tumor Burden in Mice with Epithelial Ovarian Cancer and Is Associated with Advanced Stage in Patients

BACKGROUND:Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival. METHODOLOGY AND PRINCIPAL FINDINGS:Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease. CONCLUSIONS:S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers

Identification of Metabolites in the Normal Ovary and Their Transformation in Primary and Metastatic Ovarian Cancer

In this study, we characterized the metabolome of the human ovary and identified metabolic alternations that coincide with primary epithelial ovarian cancer (EOC) and metastatic tumors resulting from primary ovarian cancer (MOC) using three analytical platforms: gas chromatography mass spectrometry (GC/MS) and liquid chromatography tandem mass spectrometry (LC/MS/MS) using buffer systems and instrument settings to catalog positive or negative ions. The human ovarian metabolome was found to contain 364 biochemicals and upon transformation of the ovary caused changes in energy utilization, altering metabolites associated with glycolysis and β-oxidation of fatty acids—such as carnitine (1.79 fold in EOC, p<0.001; 1.88 fold in MOC, p<0.001), acetylcarnitine (1.75 fold in EOC, p<0.001; 2.39 fold in MOC, p<0.001), and butyrylcarnitine (3.62 fold, p<0.0094 in EOC; 7.88 fold, p<0.001 in MOC). There were also significant changes in phenylalanine catabolism marked by increases in phenylpyruvate (4.21 fold; p = 0.0098) and phenyllactate (195.45 fold; p<0.0023) in EOC. Ovarian cancer also displayed an enhanced oxidative stress response as indicated by increases in 2-aminobutyrate in EOC (1.46 fold, p = 0.0316) and in MOC (2.25 fold, p<0.001) and several isoforms of tocopherols. We have also identified novel metabolites in the ovary, specifically N-acetylasparate and N-acetyl-aspartyl-glutamate, whose role in ovarian physiology has yet to be determined. These data enhance our understanding of the diverse biochemistry of the human ovary and demonstrate metabolic alterations upon transformation. Furthermore, metabolites with significant changes between groups provide insight into biochemical consequences of transformation and are candidate biomarkers of ovarian oncogenesis. Validation studies are warranted to determine whether these compounds have clinical utility in the diagnosis or clinical management of ovarian cancer patients

The set of software tools for prompt assessment of possible pollution of the environment in case of an accident at a radioactively dangerous object

In case of an accident at a radioactively dangerous object it is necessary to assess promptly possible consequences. Such prompt assessments are almost impossible without appropriate software modelling tools. Nuclear Safety Institute of Russian Academy of Sciences has developed the set of computer programs that enables experts to assess promptly impact of possible accidents on population and environment. The set of software tools includes: models of radionuclides transport in the atmosphere and water objects (rivers, lakes and ponds). It also includes models of assessment of doses of radioactive exposure of humans. The adequate modelling is not possible without accurate electronic maps of the neighborhood of a Nuclear Power Plant (NPP). Thus the electronic maps of neighborhood of all Russian NPP's are supplemented to the set of tools. The necessary data on local population and water objects surrounding NPP's is also included and stored in GIS-based (Geographical Information System) database. That enables numerical modelling to be done within tens of minutes at most. The GIS enables use of spatial data on fallout of radioactive substances from the model of atmospheric transport to be used as input data for modelling of transport in water objects