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A E Aliev

Publications and source records attributed to A E Aliev.

4 recordsLinked to original sources
PubMed2005

Evidence of a distinct lipid fraction in historical parchments: a potential role in degradation?

Parchment, a biologically based material obtained from the processed hides of animals such as cattle and sheep, has been used for millennia as a writing medium. Although numerous studies have concentrated on the structure and degradation of collagen within parchment, little attention has been paid to noncollagenous components, such as lipids. In this study, we present the results of biochemical and structural analyses of historical and newly manufactured parchment to examine the potential role that lipid plays in parchment stability. The lipid fraction extracted from the parchments displayed different fatty acid compositions between historical and reference materials. Gas chromatography, small-angle X-ray scattering, and solid-state NMR were used to identify and investigate the lipid fraction from parchment samples and to study its contribution to collagen structure and degradation. We hypothesize that the origin of this lipid fraction is either intrinsic, attributable to incomplete fat removal in the manufacturing process, or extrinsic, attributable to microbiological attack on the proteinaceous component of parchments. Furthermore, we consider that the possible formation of protein-lipid complexes in parchment over the course of oxidative degradation may be mediated by reactive oxygen species formed by lipid peroxidation.

Animals↗
PubMed2003

Another approach to protons with constricted mobility in white matter: pilot studies using wideline and high-resolution NMR spectroscopy.

We report a new approach for the identification of an independent method of studying the semi-solid pool of protons, i.e., protons with constrained motion as a result of being bound to lipid and protein matrices. These protons cannot be observed using conventional imaging techniques since their transverse relaxation times are much shorter than the minimum echo times that are currently available on clinical scanners. In this pilot study, in vitro multicomponent transverse relaxation experiments were made on human white matter slices, fixed in formalin (7 normal and 5 with multiple sclerosis). The transverse relaxation decay curves were multiexponential and were decomposed to yield three primary components. The shortest T(2) component that we obtained (a component too short to be seen by in vivo methods) was of the order of microseconds. We hypothesize that this might correspond to the macromolecular pool of lipid protons trapped within the myelin sheaths. To our knowledge, this is the first attempt at extracting this ultra short T(2) component from human white matter. Subsequently, an attempt was made to directly detect the lipid protons in a proton NMR spectrum and, if possible, measure their concentration in some of the tissues, using the technique of magic angle spinning.

Brain↗
PubMed1998

Precision in estimating the frequency separation between spectral lines.

It is common to estimate the frequency separation between peaks in a digitized frequency-domain spectrum by fitting an appropriate function to the experimental spectrum using least-squares procedures. In this paper, we assess from first principles the precision associated with such measurements of frequency separation. In addition to the frequency separation between the peaks, other parameters involved in fitting the spectrum are the peak widths, the lineshape functions (Gaussian, Lorentzian, etc.) for the peaks, and the peak amplitudes. The precision also depends on the signal-to-noise ratio and the spacing between adjacent data points in the digitized spectrum. It is assumed that the residuals considered in the least-squares fitting procedure are the differences between the intensities of corresponding digitized data points in the experimental and fitted spectra. Under these conditions, analytical expressions for the precision in peak separation are derived for the following cases: (i) when the amplitudes of two peaks are known and the two peaks have known equal widths; (ii) when the ratio of the amplitudes of two peaks is known, and the widths of the two peaks are known to be equal, but the actual value of the peak width is not known. In each case, the situation with two Gaussian peaks and the situation with two Lorentzian peaks are considered. In all cases, the absolute precision P(eta) in the estimated frequency separation eta between the two peaks is approximated by an equation of the type P(eta) approximately F(eta/Delta, alpha)SK, where Delta is the peak width, alpha is the ratio A2/A1 of amplitudes of the two peaks, S is the signal-to-noise ratio, and K is the density of data points in the frequency-domain spectrum. The form of the function F(eta/Delta, alpha) depends on the type of lineshape (Gaussian or Lorentzian), and depends on which of the parameters A1, A2, and Delta are known independently of the fitting procedure. Attempts to extend our first-principles approach to assess the precision in least-squares estimates of frequency separation between peaks in more complex situations than those discussed above generally lead to analytical expressions that are formidably complicated. In such cases, numerical approaches based on the theoretical framework developed here may be employed to assess the precision in estimating the frequency separation.

Magnetic Resonance Spectroscopy↗