Experimental and theoretical studies of CO2 spectra for planetary atmosphere modelling: region 600-9650 cm(-1) and pressures up to 60 atm.

Extensive experimental studies of room-temperature carbon dioxide absorption coefficients are reported for a wide range of wavenumbers and pressures requested in atmospheric spectra modelling. The quality of measurements is optimised by the use of two complementary setups with long- and short-path optical cells for low and high gas densities, respectively. The recorded spectra provide a representative picture of band-shape evolutions from gaseous to nearly liquid phases of CO2 and enable a theoretical analysis of line-mixing effects. Various kinds of vibrational bands (Σ←Σ, Πâ†Σ as well as Πâ†Π transitions) are modelled using a specific, non-Markovian in the general case, approach of Energy-Corrected Sudden type which is based on the symmetric relaxation matrix and, in contrast to the standard ECS model used for infrared absorption calculations, ensures automatically the fundamental relations of detailed balance and double-sided sum rules. Moreover, this method properly accounts for the vibrational angular momenta of the initial and final molecular states and allows including Coriolis resonances via the usual Herman-Wallis factors in the dipole transition moments. With a set of ECS parameters previously obtained for isotropic and anisotropic Raman spectra modelling, completely neglected imaginary part of the relaxation operator and a simple change in the tensorial rank to get the dipole absorption case in the working formulae, the computed spectra reproduce quite correctly the vibrotational band shapes up to 20 amagat without any additional parameter. An empirical correction factor tentatively introduced to account globally for the Coriolis effects on the relaxation matrix leads to better matches with high-density band shapes but its role merits further studies with an accurately modelled imaginary part of the relaxation matrix.

High-sensitivity blood-based detection of breast cancer by multi photon detection diagnostic proteomics.

We have developed several new methods for blood-based cancer detection by diagnostic proteomics. Ultrasensitive methods of immunoassay using multiphoton-detection (IA/MPD) increase sensitivity by 200- to 1,000-fold (1 femtogram/mL). This has allowed the measurement of cancer biomarkers with very low concentrations in blood that could not be measured for full patient cohorts with conventional immunoassays. Sensitivity and specificity in cancer detection have been found to be potentiated by use of immunoassay panels which include tissue-specific cancer biomarkers as well as cytokines and angiogenic factors. The ultrasensitive immunoassays revealed that patient to patient variations in the concentrations of individual biomarkers in blood can extend over many orders of magnitude (up to six) and that the distributions of biomarker concentrations over patient cohorts are non-Gaussian. New methods of data analysis which correlate abundances of multiple, different biomarkers have been developed to deal with such data sets. Sensitivity and specificity of about 95% have been achieved for blood-based detection of breast cancer in pilot studies on 250 patients and 95 controls. Pilot studies indicate that this methodology may also allow differentiation of malignant breast cancer from benign lesions and can provide similar sensitivity and specificity for other epithelial cancers such as prostate cancer, ovarian cancer and melanoma. The methods developed for selection, application, and evaluation of very high sensitivity biomarker panels are expected to have general relevance for diagnostic proteomics.

Looking for Thom's biomarkers with proteomics.

In recent years, large numbers of putative disease biomarkers have been identified. Combinations of protein biomarkers have been proposed to overcome the lack of single, magic-bullet identifiers of disease conditions. The number of biomarkers in a panel must be kept small to avoid the combinatorial explosion that requires very large, uneconomical sample cohorts for validation. Recent results on high sensitivity blood-based diagnostic proteomics (Godovac-Zimmermann, J et al., J. Proteome Res. 2006) suggest that the keys to identifying useful panels include judicious application of physiological knowledge to choose appropriate combinations of local, tissue/disease markers and global, systemic markers and to use very high sensitivity protein detection. Biomarkers that show non-Gaussian landscapes reminiscent of Rene Thom's multiple, stable-state landscapes seem to have the greatest predictive value for breast cancer (Godovac-Zimmermann, J. et al., J. Proteome Res. 2006).