NP7 protects from cell death induced by oxidative stress in neuronal and glial midbrain cultures from parkin null mice

Parkin mutations produce Parkinson’s disease (PD) in humans and nigrostriatal dopamine lesions related to increased free radicals in mice. We examined the effects of NP7, a synthetic, marine derived, free radical scavenger which enters the brain, on H₂O₂ toxicity in cultured neurons and glia from wild-type (WT) and parkin null mice (PK-KO).NP7, 5-10 μM, prevented the H₂O₂ induced apoptosis and necrosis of midbrain neuronal and glial cultures from WT and PK-KO mice. NP7 suppressed microglial activation and the H₂O₂ induced drop-out of dopamine neurons_. Furthermore, NP7 prevented the increased phosphorylation of ERK and AKT induced by H₂O₂. NP7 may be a promising neuroprotector against oxidative stress in PD.     

Hydrophobia, lectin binding, and molecular order in the stratum corneum of adult and neonatal rats and in human breast cells in culture.

A search for differences due to ANS staining (hydrophobia), Con A and PNA binding capacity, and birefringence was carried out on stratified epithelia of rat skin and human breast cells (HBC) in culture. Microfluorimetric measurements confirm that the ANS fluorescence of the stratum corneum from adults is higher than that of newborns. HBC exhibited an unexpected deep ANS-fluorescence. Differences in the binding capacity of the epithelial layers to Con A and PNA were detected with advancing age. Retardation measurements revealed that the form birefringence of the stratum corneum is higher in adult animals specially as revealed by the fact that its form birefringence curve branch from n = 1.414 to n = 1.479 is steeper, i.e. depict higher values. The strong birefringence of the cytoplasmic tonofilaments presented by cultured human breast cells was considered an unexpected finding and attributed to changes that the cells underwent following the in vitro conditions.     

Plasma Biomarkers Discriminate Clinical Forms of Multiple Sclerosis

Multiple sclerosis, the most common cause of neurological disability in young population after trauma, represents a significant public health burden. Current challenges associated with management of multiple sclerosis (MS) patients stem from the lack of biomarkers that might enable stratification of the different clinical forms of MS and thus prompt treatment for those patients with progressive MS, for whom there is currently no therapy available. In the present work we analyzed a set of thirty different plasma cytokines, chemokines and growth factors present in circulation of 129 MS patients with different clinical forms (relapsing remitting, secondary progressive and primary progressive MS) and 53 healthy controls, across two independent cohorts. The set of plasma analytes was quantified with Luminex xMAP technology and their predictive power regarding clinical outcome was evaluated both individually using ROC curves and in combination using logistic regression analysis. Our results from two independent cohorts of MS patients demonstrate that the divergent clinical and histology-based MS forms are associated with distinct profiles of circulating plasma protein biomarkers, with distinct signatures being composed of chemokines and growth/angiogenic factors. With this work, we propose that an evaluation of a set of 4 circulating biomarkers (HGF, Eotaxin/CCL11, EGF and MIP-1β/CCL4) in MS patients might serve as an effective tool in the diagnosis and more personalized therapeutic targeting of MS patients.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.