Integrated analysis of cellulose structure and properties using solid-state low-field H-NMR and photoacoustic spectroscopy.

In this study, we explore the structural intricacies of cellulose, a polymer composed of glucose monomers arranged in a linear chain, primarily investigated through solid-state NMR techniques. Specifically, we employ low-field proton nuclear magnetic resonance (H-NMR) to delve into the diverse hydrogen atom types within the cellulose molecule. The low-field H-NMR technique allows us to discern these hydrogen atoms based on their distinct chemical shifts, providing valuable insights into the various functional groups present in cellulose.

Exploring the Molecular Structure and Treatment Dynamics of Cellulose Fibres with Photoacoustic and Reversed Double-Beam Spectroscopy.

In this study, we explored the structural and chemical modifications of cellulose fibres subjected to chemical and mechanical treatments through an innovative analytical approach. We employed photoacoustic spectroscopy (PAS) and reversed double-beam photoacoustic spectroscopy (RDB-PAS) to examine the morphological changes and the chemical integrity of the treated fibres. The methodology provided enhanced sensitivity and specificity in detecting subtle alterations in the treated cellulose structure.

Determination of Carbon Chain Lengths of Fatty Acid Mixtures by Time Domain NMR.

Average carbon chain length is a key parameter that defines the quality of liquid biofuels. In this paper, a method for the determination of carbon chain lengths of fatty acid mixtures is presented. The approach is based on proton relaxation rates measured by time domain nuclear magnetic resonance.

Estimation of the onset time of cerebral ischemia using T1rho and T2 MRI in rats.

Background And Purpose: Time of ischemia onset is the most critical factor for patient selection for available drug treatment strategies. The purpose of this study was to evaluate the abilities of the absolute longitudinal rotating frame (T(1ρ)) and transverse (T(2)) MR relaxation times to estimate the onset time of ischemia in rats.

Methods: Permanent middle cerebral artery occlusion in rats was used to induce focal cerebral ischemia and animals were imaged with multiparametric MRI at several time points up to 7 hours postischemia.

Correlating tissue outcome with quantitative multiparametric MRI of acute cerebral ischemia in rats.

Predicting tissue outcome remains a challenge for stroke magnetic resonance imaging (MRI). In this study, we have acquired multiparametric MRI data sets (including absolute T(1), T(2), diffusion, T(1rho) using continuous wave and adiabatic pulse approaches, cerebral blood flow (CBF), and amide proton transfer ratio (APTR) images) during and after 65 mins of middle cerebral artery occlusion (MCAo) in rats. The MRI scans were repeated 24 h after MCAo, when the animals were killed for quantitative histology.

Estimation of VLDL, IDL, LDL, HDL2, apoA-I, and apoB from the Friedewald inputs--apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes.

Background: There is an unmet need for a straightforward and cost-effective assessment of multiple lipoprotein risk factors for vascular diseases.

Aims: 1) To study the relation of various lipoprotein lipid and apolipoprotein (apo) measures on the Friedewald inputs, i.e.

Assessment of human brain temperature by 1H MRS during visual stimulation and hypercapnia.

Brain temperature is determined by the interplay between the cerebral metabolic rate of oxygen (CMRO2) and cerebral blood flow (CBF). In this study, single-voxel 1H nuclear MRS, with an accuracy of +/-0.2 degrees C for temperature determination, was used at 3 T to measure human brain temperature during visual stimulation (which increases both CBF and CMRO2) and hypercapnia (which increases CBF only).

Determination of regional brain temperature using proton magnetic resonance spectroscopy to assess brain-body temperature differences in healthy human subjects.

Proton magnetic resonance spectroscopy ((1)H MRS) was used to determine brain temperature in healthy volunteers. Partially water-suppressed (1)H MRS data sets were acquired at 3T from four different gray matter (GM)/white matter (WM) volumes. Brain temperatures were determined from the chemical-shift difference between the CH(3) of N-acetyl aspartate (NAA) at 2.

Independent component analysis to proton spectroscopic imaging data of human brain tumours.

In proton magnetic resonance spectroscopic imaging (1H MRSI), the recorded spectra are often linear combinations of spectra from different cell and tissue types within the voxel. This produces problems for data analysis and interpretation. A sophisticated approach is proposed here to handle the complexity of tissue heterogeneity in MRSI data.

Self-organizing maps can determine outcome and match recipients and donors at orthotopic liver transplantation.

Background: There is a relative lack of donor organs for liver transplantation. Ideally, to maximize the utility of those livers that are offered, donor and recipient characteristics should be matched to ensure the best possible posttransplant survival of the recipient.

Methods: With prospectively collected data on 827 patients receiving a primary liver graft for chronic liver disease, we used a self-organizing map (SOM) (one form of a neural network) to predict outcome after transplantation using both donor and recipient factors.

Decreased brain creatine levels in elderly apolipoprotein E epsilon 4 carriers.

Apolipoprotein E (ApoE) genotype has been shown to influence results in neuroimaging studies using a number of various imaging modalities. No in vivo data exists on whether or not there are ApoE-related changes observable by proton magnetic resonance spectroscopy (MRS). In this study we measured absolute peak areas of proton MR spectra obtained from the occipital cortex in 22 non-demented elderly with (n = 8) or without (n = 14) the ApoE epsilon4 allele.

Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures.

Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques.

Quantification of human brain metabolites from in vivo 1H NMR magnitude spectra using automated artificial neural network analysis.

Long echo time (TE=270 ms) in vivo proton NMR spectra resembling human brain metabolite patterns were simulated for lineshape fitting (LF) and quantitative artificial neural network (ANN) analyses. A set of experimental in vivo 1H NMR spectra were first analyzed by the LF method to match the signal-to-noise ratios and linewidths of simulated spectra to those in the experimental data. The performance of constructed ANNs was compared for the peak area determinations of choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) signals using both manually phase-corrected and magnitude spectra as inputs.

Application of self-organizing maps in conformational analysis of lipids.

The characteristics of lipid assemblies are important for the functions of biological membranes. This has led to an increasing utilization of molecular dynamics simulations for the elucidation of the structural features of biomembranes. We have applied the self-organizing map (SOM) to the analysis of the complex conformational data from a 1-ns molecular dynamics simulation of PLPC phospholipids in a membrane assembly.

Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks.

Purpose: Experiments were carried out to assess the potential of artificial neural network (ANN) analysis in the differential diagnosis of brain tumours (low- and high-grade gliomas) from non-neoplastic focal brain lesions (tuberculomas and abscesses), using proton magnetic resonance spectroscopy (1H MRS) as input data.

Methods: Single-voxel stimulated echo acquisition mode (STEAM) (echo time of 20 ms) spectra were acquired from 138 subjects including 15 with low-grade gliomas, 47 with high-grade gliomas, 18 with tuberculomas, 18 with abscesses and 40 healthy controls. Two neural networks were constructed using the spectral points from 0.

Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors.

The diagnosis of cirrhosis in patients with hepatitis C virus (HCV) infection is currently made using a liver biopsy. In this study we have trained and validated artificial neural networks (ANN) with routine clinical host and viral parameters to predict the presence or absence of cirrhosis in patients with chronic HCV infection and assessed and interpreted the role of the different inputs on the ANN classification. Fifteen routine clinical and virological factors were collated from 112 patients who were HCV RNA positive by reverse transcriptase-polymerase chain reaction (RT-PCR).

Automated quantification of human brain metabolites by artificial neural network analysis from in vivo single-voxel 1H NMR spectra.

A real-time automated way of quantifying metabolites from in vivo NMR spectra using an artificial neural network (ANN) analysis is presented. The spectral training and test sets for ANN containing peaks at the chemical shift ranges resembling long echo time proton NMR spectra from human brain were simulated. The performance of the ANN constructed was compared with an established lineshape fitting (LF) analysis using both simulated and experimental spectral data as inputs.

Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of 1H NMR spectroscopy data.

Efficient and relevant classification of clinical findings, i.e. diagnostic decision making, poses a major challenge in medicine.

Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31P NMR pig liver study.

Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation.

Automated classification of human brain tumours by neural network analysis using in vivo 1H magnetic resonance spectroscopic metabolite phenotypes.

We present a novel method to integrate in vivo nuclear magnetic resonance spectroscopy (MRS) information into the clinical diagnosis of brain tumours. Water-suppressed 1H MRS data were collected from 33 patients with brain tumours and 28 healthy controls in vivo. The data were treated in the time domain for removal of residual water and a region from the frequency domain (from 3.

Artificial neural network analysis of 1H nuclear magnetic resonance spectroscopic data from human plasma.

Nuclear magnetic resonance (NMR) spectroscopy is finding increasing use in studies of plasma and lipoproteins in health and disease, including cancer. Analysis of the NMR data is not straightforward due to complex systems and also partly unknown underlying biochemistry. Here we demonstrate how artificial neural networks can be utilised in biomedical NMR.

Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from 1H NMR data of human plasma.

Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient "black-box' tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed.

A comparative study of 1H NMR lineshape fitting analyses and biochemical lipid analyses of the lipoprotein fractions VLDL, LDL and HDL, and total human blood plasma.

The purpose of this work was two-fold. In the first instance, 1H NMR spectra of the ultracentrifuged lipoprotein fractions (VLDL, LDL and HDL) from six volunteers with different clinical conditions were measured. The methylene regions of the experimental spectra were modelled in the frequency domain using non-linear lineshape fitting analyses.

Proton nuclear magnetic resonance lineshape studies on human blood plasma lipids from newborn infants, healthy adults, and adults with tumors.

The usefulness of proton NMR spectroscopy of human blood plasma for cancer research has been extensively studied in recent years. Two main starting points have been offered by Fossel et al. (N.