Trace Elements in Scalp Hair Samples from Patients with Relapsing-Remitting Multiple Sclerosis

Background

Epidemiological studies have suggested a possible role of trace elements (TE) in the etiology of several neurological diseases including Multiple Sclerosis (MS). Hair analysis provides an easy tool to quantify TE in human subjects, including patients with neurodegenerative diseases.

Objective

To compare TE levels in scalp hair from patients with MS and healthy controls from the same geographic area (Sicily).

Methods

ICP-MS was used to determine the concentrations of 21 elements (Ag, Al, As, Ba, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sr, U, V and Zn) in scalp hair of 48 patients with relapsing–remitting Multiple Sclerosis compared with 51 healthy controls.

Results

MS patients showed a significantly lower hair concentration of aluminum and rubidium (median values: Al = 3.76 μg/g vs. 4.49 μg/g and Rb = 0.007 μg/g vs. 0.01 μg/g;) and higher hair concentration of U (median values U: 0.014 μg/g vs. 0.007 μg/g) compared to healthy controls. The percentages of MS patients showing hair elemental concentrations greater than the 95th percentile of controls were 20% for Ni, 19% for Ba and U, and 15% for Ag, Mo and Se. Conversely, the percentages of MS patients showing hair elemental concentrations lower than the 5th percentile of healthy controls were 27% for Al, 25% for Rb, 22% for Ag, 19% for Fe, and 16% for Pb. No significant association was found between levels of each TE and age, disease duration or Expanded Disability Status Scale (EDSS) score. After stratification by gender, healthy subjects did not show any significant difference in trace element levels, while MS patients showed significant differences (p<0.01) for the concentrations of Ag, Cr, Fe, Ni and Sr. No significant differences were also found, at p<0.01, in relation to the use of cigarettes, consume of water, vegetables and place of living.

Conclusion

The different distributions of TE in hair of MS patients compared to controls provides an additional indirect evidence of metabolic imbalance of chemical elements in the pathogenesis of this disease. The increase in U and decrease in Al and Rb levels in MS compared to controls require further assessments as well as the observed different distributions of other elements.     

Magnetic Resonance Imaging Allows the Evaluation of Tissue Damage and Regeneration in a Mouse Model of Critical Limb Ischemia

Magnetic resonance imaging (MRI) provides non-invasive, repetitive measures in the same individual, allowing the study of a physio-pathological event over time. In this study, we tested the performance of 7 Tesla multi-parametric MRI to monitor the dynamic changes of mouse skeletal muscle injury and regeneration upon acute ischemia induced by femoral artery dissection. T2-mapping (T2 relaxation time), diffusion-tensor imaging (Fractional Anisotropy) and perfusion by Dynamic Contrast-Enhanced MRI (K-trans) were measured and imaging results were correlated with histological morphometric analysis in both Gastrocnemius and Tibialis anterior muscles. We found that tissue damage positively correlated with T2-relaxation time, while myofiber regeneration and capillary density positively correlated with Fractional Anisotropy. Interestingly, K-trans positively correlated with capillary density. Accordingly, repeated MRI measurements between day 1 and day 28 after surgery in ischemic muscles showed that: 1) T2-relaxation time rapidly increased upon ischemia and then gradually declined, returning almost to basal level in the last phases of the regeneration process; 2) Fractional Anisotropy dropped upon ischemic damage induction and then recovered along with muscle regeneration and neoangiogenesis; 3) K-trans reached a minimum upon ischemia, then progressively recovered. Overall, Gastrocnemius and Tibialis anterior muscles displayed similar patterns of MRI parameters dynamic, with more marked responses and less variability in Tibialis anterior. We conclude that MRI provides quantitative information about both tissue damage after ischemia and the subsequent vascular and muscle regeneration, accounting for the differences between subjects and, within the same individual, between different muscles.     

Metabolomics Suggests That Soil Inoculation with Arbuscular Mycorrhizal Fungi Decreased Free Amino Acid Content in Roots of Durum Wheat Grown under N-Limited,P-Rich Field Conditions

Arbuscular mycorrhizal fungi (AMF) have a major impact on plant nutrition, defence against pathogens, a plant’s reaction to stressful environments, soil fertility, and a plant’s relationship with other microorganisms. Such effects imply a broad reprogramming of the plant’s metabolic activity. However, little information is available regarding the role of AMF and their relation to other soil plant growth—promoting microorganisms in the plant metabolome, especially under realistic field conditions. In the present experiment, we evaluated the effects of inoculation with AMF, either alone or in combination with plant growth–promoting rhizobacteria (PGPR), on the metabolome and changes in metabolic pathways in the roots of durum wheat (Triticum durum Desf.) grown under N-limited agronomic conditions in a P-rich environment. These two treatments were compared to infection by the natural AMF population (NAT). Soil inoculation with AMF almost doubled wheat root colonization by AMF and decreased the root concentrations of most compounds in all metabolic pathways, especially amino acids (AA) and saturated fatty acids, whereas inoculation with AMF+PGPR increased the concentrations of such compounds compared to inoculation with AMF alone. Enrichment metabolomics analyses showed that AA metabolic pathways were mostly changed by the treatments, with reduced amination activity in roots most likely due to a shift from the biosynthesis of common AA to γ-amino butyric acid. The root metabolome differed between AMF and NAT but not AMF+PGPR and AMF or NAT. Because the PGPR used were potent mineralisers, and AMF can retain most nitrogen (N) taken as organic compounds for their own growth, it is likely that this result was due to an increased concentration of mineral N in soil inoculated with AMF+PGPR compared to AMF alone.     

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

Background

Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.

Methods

Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual.

Results

Factors identified in the ALM model were: use of an intra-aortic balloon pump; O₂ delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO₂. The r² value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r² = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r² of 0.535 (p <0.0001) and a cross validation prediction r² of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001).

Conclusions

ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.     

The Open AUC Project

Progress in analytical ultracentrifugation (AUC) has been hindered by obstructions to hardware innovation and by software incompatibility. In this paper, we announce and outline the Open AUC Project. The goals of the Open AUC Project are to stimulate AUC innovation by improving instrumentation, detectors, acquisition and analysis software, and collaborative tools. These improvements are needed for the next generation of AUC-based research. The Open AUC Project combines on-going work from several different groups. A new base instrument is described, one that is designed from the ground up to be an analytical ultracentrifuge. This machine offers an open architecture, hardware standards, and application programming interfaces for detector developers. All software will use the GNU Public License to assure that intellectual property is available in open source format. The Open AUC strategy facilitates collaborations, encourages sharing, and eliminates the chronic impediments that have plagued AUC innovation for the last 20 years. This ultracentrifuge will be equipped with multiple and interchangeable optical tracks so that state-of-the-art electronics and improved detectors will be available for a variety of optical systems. The instrument will be complemented by a new rotor, enhanced data acquisition and analysis software, as well as collaboration software. Described here are the instrument, the modular software components, and a standardized database that will encourage and ease integration of data analysis and interpretation software.     

Monitoring liver alterations during hepatic tumorigenesis by NMR profiling and pattern recognition

Human hepatocellular carcinoma (HCC) is the most recurrent malignancy of the liver and represents one of the main causes of cancer death worldwide. Furthermore, the liver is the most frequent site of metastatic colonization, and hepatic metastases are far more common than primary cancers in Western countries. A possible way of investigating liver diseases is to study the tissue metabolic profiles. High-resolution nuclear magnetic resonance (NMR) spectroscopy of hepatic tissue extracts was combined with pattern-recognition and visualization techniques to uncover metabolic differences among analyzed tissue types. Extracts were from primary HCC, chronic hepatitis-C-virus related cirrhotic tissues, hepatic metastases from colorectal carcinomas, and non-cirrhotic normal liver tissues adjacent to metastases as controls. We identified all metabolites present in the NMR spectra, and after statistical evaluation of all spectral classes, we were able to define the metabolic changes underlying the different liver conditions and diseases. In particular, the lactate and the glucose tissue signals were found to primarily discriminate the different histological samples. We followed the biochemical changes of human hepatic lesions through primary (HCC) and secondary (metastases from colorectal carcinoma) liver tumors, cirrhotic tissues, and non-cirrhotic histologically-confirmed normal liver tissues adjacent to metastases, achieving a metabolic differentiation of the various pathological states based upon the variation of the intracellular lactate/glucose ratio. It is suggested that such a signal pattern may act as a potential marker for assessing pathological hepatic lesions.     

Long term bed rest with and without vibration exercise countermeasures: effects on human muscle protein dysregulation

The present investigation, the first in the field, was aimed at analyzing differentially, on individual samples, the effects of 55 days of horizontal bed rest, a model for microgravity, on myosin heavy and myosin light chain isoforms distribution (by SDS) and on the proteome (by 2-D DIGE and MS) in the vastus lateralis (VL), a mixed type II/I (～50:50%) head of the quadriceps and in the calf soleus (SOL), a predominantly slow (～35:65%) twitch muscle. Two separate studies were performed on six subjects without (BR) and six with resistive vibration exercise (RVE) countermeasures, respectively. Both VL and SOL underwent in BR decrements of ～15% in cross-sectional area and of ～22% in maximal torque that were prevented by RVE. Myosin heavy chain distribution showed increased type I and decreased type IIA in BR both in VL and in SOL, the opposite with RVE. A substantial downregulation of proteins involved in aerobic metabolism characterized both in SOL and VL in BR. RVE reversed the pattern more in VL than in SOL, whereas proteins involved in anaerobic glycolysis were upregulated. Proteins from the Z-disk region and from costamers were differently dysregulated during bed rest (both BR and RVE), particularly in VL.     

Translationally optimal codons associate with aggregation-prone sites in proteins

We analyze the relationship between codon usage bias and residue aggregation propensity in the genomes of four model organisms, Escherichia coli, yeast, fly, and mouse, as well as the archaeon Halobacterium species NRC-1. Using the Mantel-Haenszel procedure, we find that translationally optimal codons associate with aggregation-prone residues. Our results are qualitatively and quantitatively similar to those of an earlier study where we found an association between translationally optimal codons and buried residues. We also combine the aggregation-propensity data with solvent-accessibility data. Although the resulting data set is small, and hence statistical power low, results indicate that the association between optimal codons and aggregation-prone residues exists both at buried and at exposed sites. By comparing codon usage at different combinations of sites (exposed, aggregation-prone sites versus buried, non-aggregation-prone sites; buried, aggregation-prone sites versus exposed, non-aggregation-prone sites), we find that aggregation propensity and solvent accessibility seem to have independent effects of (on average) comparable magnitude on codon usage. Finally, in fly, we assess whether optimal codons associate with sites at which amino acid substitutions lead to an increase in aggregation propensity, and find only a very weak effect. These results suggest that optimal codons may be required to reduce the frequency of translation errors at aggregation-prone sites that coincide with certain functional sites, such as protein-protein interfaces. Alternatively, optimal codons may be required for rapid translation of aggregation-prone regions.