Carbenoxolone does not cross the blood brain barrier: an HPLC study

BACKGROUND: Carbenoxolone (CBX) is a widely used gap junctional blocker. Considering several reports indicating that transient gap junctional blockade could be a favourable intervention following injuries to central nervous tissue, and some current enthusiasm in studies using systemic injections of CBX, it is imperative to consider the penetration of CBX into central nervous tissue after systemic administrations. So far, only very indirect evidence suggests that CBX penetrates into the central nervous system after systemic administrations. We thus determined the amounts of CBX present in the blood and the cerebrospinal fluid of rats after intraperitoneal administration, using high performance liquid chromatography RESULTS: CBX was found in the blood of the animals, up to 90 minutes post-injection. However, the cerebrospinal fluid concentration of CBX was negligible. CONCLUSION: Thus, we conclude that, most likely, CBX does not penetrate the blood brain barrier and therefore recommend careful consideration in the manner of administration, when a central effect is desired

Carbenoxolone does not cross the blood brain barrier: an HPLC study

Abstract Background Carbenoxolone (CBX) is a widely used gap junctional blocker. Considering several reports indicating that transient gap junctional blockade could be a favourable intervention following injuries to central nervous tissue, and some current enthusiasm in studies using systemic injections of CBX, it is imperative to consider the penetration of CBX into central nervous tissue after systemic administrations. So far, only very indirect evidence suggests that CBX penetrates into the central nervous system after systemic administrations. We thus determined the amounts of CBX present in the blood and the cerebrospinal fluid of rats after intraperitoneal administration, using high performance liquid chromatography Results CBX was found in the blood of the animals, up to 90 minutes post-injection. However, the cerebrospinal fluid concentration of CBX was negligible. Conclusion Thus, we conclude that, most likely, CBX does not penetrate the blood brain barrier and therefore recommend careful consideration in the manner of administration, when a central effect is desired

Classification of osteoarthritis phenotypes by metabolomics analysis

Objectives To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. Design A case-only study design was utilised. Participants Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years. Outcome measures Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis. Results Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. The grouping was not associated with any known confounders including age, sex, BMI and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid and collagen metabolism, respectively. Conclusions The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females.

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females

Differential metabolomics networks analysis of menopausal status.

Menopause is an endocrine-related transition that induces a number of physiological and potentially pathological changes in middle-aged and elderly women. The intention of this research was to investigate the influence of menopause on the intricate relationships between major biochemical metabolites. The study involved metabolic profiling of 186 metabolic markers measured in blood plasma collected from 120 healthy female participants. We developed a method of network analysis using differential correlation that enabled us to detect and characterize differences in metabolites and changes in inter-relationships in pre- and post-menopausal women. A topological analysis was performed on the differential network that uncovered metabolite differences in pre-and post-menopausal women. In this analysis, our method identified two key metabolites, sphingomyelins and phosphatidylcholines, which may be useful in directing further studies into menopause-specific differences in the metabolome, and how these differences may underlie the body's response to stress and disease following the transition from pre- to post-menopausal status for women

The concentrations of MG-H1, MG, CEL and CML in SF of OA and OA+DM patients.

<p>(DM: diabetes). P values were adjusted for sex, age and BMI.</p

The concentrations of PC ae C34:3 and PC ae C36:3 in SF (A and B) and plasma (C and D) of OA and OA+DM patients.

<p>(DM: diabetes). P values were adjusted for sex, age and BMI.</p

Descriptive statistics of the study population^*.

<p>Descriptive statistics of the study population^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184105#t001fn001" target="_blank">*</a>}.</p