Selective reduction in body fat mass and plasma leptin induced by angiotensin-converting enzyme inhibition in rats

Objective: There is emerging evidence that angiotensin stimulates adipocyte differentiation and lipogenesis. This study tested the hypothesis that inhibition of angiotensin II by treatment with an angiotensin-converting enzyme inhibitor, perindopril, would reduce fat mass in rats. Design: After a 12-day baseline, rats were divided into two groups: one was untreated and the other received perindopril (1.2 mg kg⁻¹ per day) in drinking water for 26 days.Subjects: In total, 16 male Sprague–Dawley rats aged 10 weeks at the start of the study. Measurements: Plasma leptin was measured in samples collected at baseline, half-way through and at the end of treatment. Body weight, food and water intake were measured daily throughout the experiment. Body fat mass, bone and lean mass were determined by dual energy X-ray absorptiometry (DEXA) at the end of the treatment period. Results: Daily food intake was the same in both groups throughout the study. By the end of treatment, animals receiving perindopril showed a modest reduction in weight gain relative to the untreated animals (62.4±5.0 g vs 73.0±4.0 g; P<0.05). DEXA analysis showed that body composition was greatly altered and the perindopril-treated group had 26% less body fat mass than the untreated group (61.0±5.2 g vs 44.4±4.2 g; P<0.01). The reduction in body fat mass was correlated with reductions in the weight of both the epididymal and retroperitoneal fat pads (P<0.001). Similarly, plasma leptin was reduced by perindopril treatment (4.64±0.56 ng ml⁻¹) compared to the untreated group (8.27±1.03 ng ml⁻¹; P<0.001). In contrast, there were no differences in lean or bone mass between the two groups.Conclusion: Oral treatment with perindopril selectively reduced body fat mass without influencing daily food intake. In contrast, there were no differences in lean or bone mass between the two groups

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.

Mice lacking angiotensin-converting enzyme have increased energy expenditure, with reduced fat mass and improved glucose clearance

In addition to its role in the storage of fat, adipose tissue acts as an endocrine organ, and it contains a functional renin-angiotensin system (RAS). Angiotensin-converting enzyme (ACE) plays a key role in the RAS by converting angiotensin I to the bioactive peptide angiotensin II (Ang II). In the present study, the effect of targeting the RAS in body energy homeostasis and glucose tolerance was determined in homozygous mice in which the gene for ACE had been deleted (ACE^-/-) and compared with wild-type littermates. Compared with wild-type littermates, ACE^-/- mice had lower body weight and a lower proportion of body fat, especially in the abdomen. ACE^-/- mice had greater fed-state total energy expenditure (TEE) and resting energy expenditure (REE) than wild-type littermates. There were pronounced increases in gene expression of enzymes related to lipolysis and fatty acid oxidation (lipoprotein lipase, carnitine palmitoyl transferase, long-chain acetyl CoA dehydrogenase) in the liver of ACE^-/- mice and also lower plasma leptin. In contrast, no differences were detected in daily food intake, activity, fed-state plasma lipids, or proportion of fat excrete in fecal matter. In conclusion, the reduction in ACE activity is associated with a decreased accumulation of body fat, especially in abdominal fat depots. The decreased body fat in ACE^-/- mice is independent of food intake and appears to be due to a high energy expenditure related to increased metabolism of fatty acids in the liver, with the additional effect of increased glucose tolerance.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

Diabetes risk reduction behaviours of rural postpartum women with a recent history of gestational diabetes

Introduction: For most women, gestational diabetes is temporary; however, an episode of gestational diabetes mellitus (GDM) confers an approximately seven-fold increased risk of developing type 2 diabetes mellitus. Objective: To examine readiness to adopt diabetes risk reduction behaviours and the prevalence of these behaviours among rural women with GDM during their last pregnancy.
Methods: The study design was a self-administered mailed questionnaire seeking information about demographics, stage of change, physical activity level and dietary fat intake. Setting: Regional outpatient context. Participants: Women with a single episode of GDM between 1 July 2001 and 31 December 2005 (n = 210). Main outcome measures: Stage of change for physical activity, weight loss and reducing dietary fat behaviour; meeting activity targets, body mass index (BMI) and dietary fat score.
Results: Eighty-four women returned completed questionnaires (40% response rate). Of the 77 women eligible (mean age 35 ± 3.8 years), 58% met recommended activity targets. Sixty-three percent of women were overweight or obese: mean BMI 29.6 kg/m2 (± 7.30). Women reported a high level of preparedness to engage in physical activity, weight loss and reduction of fat intake. Thirty-nine percent of women had not had any postpartum follow-up glucose screening. Women who remembered receiving diabetes prevention information were significantly more likely to meet physical activity targets (p<0.05).
Conclusions: Readiness to engage in behaviour change was high among this group of rural women for all three diabetes risk reduction behaviours measured. However, despite a high proportion of women meeting activity targets and reducing fat intake, the majority of women remained overweight or obese. Postpartum follow-up glucose testing needs to be improved and the impact of diabetes prevention information provided during pregnancy warrants further study.

Mechanism of a green graphene oxide reduction with reusable potassium carbonate

 A green method for the deoxygenation of graphene oxide (GO) was developed using K2CO3 as a reusable reduction agent. The size and thickness of the reduced GO are less than 1 μm and around 0.85 nm, respectively. Carbon dioxide is the only byproduct during this process. The reduction mechanism of the graphene oxide includes two reduction steps. On the one hand, ionic oxygen generated from the electrochemical reaction between hydroxyl ions and oxygen in the presence of K2CO3 reacts with carbonyl groups attached to the GO layers at 50°C. On the other hand, ionic oxygen attacks hydroxyl and epoxide groups, which become carbonyl groups and then are converted to carbon dioxide by K2CO3 at 90°C. These oxygenous groups are finally converted to CO2 from graphene layers, leading to the formation of graphene sheets. Headspace solid-phase microextraction and gas chromatography-mass spectrometry detected the existence of n-dodecanal and 4-ethylbenzoic acid cyclopentyl ester during the reduction, suggesting that oxygen functional groups on the GO layers are not only aligned, but randomly dispersed in some areas based on the proposed mechanism.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

Significant pre-mass extinction animal body-size changes : evidences from the Permian–Triassic boundary brachiopod faunas of South China

This paper has undertaken a quantitative and statistical analysis of brachiopod body-size changes through the marine Permian–Triassic boundary section at Zhongzhai, Guizhou Province, South China, and found that (1) pre-mass extinction dwarfing is evident for at least the rugosochonetid species chosen for this study; (2) Tethyochonetes species reduced their size earlier than that in the Neochonetes species; and (3) no significant size reduction occurred in the newly evolved species of these two genera. Inter-species competition for resources between Neochonetes species and Tethyochonetes species and the reduction of food supply in the upper part of the uppermost Permian is here proposed to explain these observed stratigraphic patterns of brachiopod body-size changes throughout the Zhongzhai section. In the case of the newly evolved species showing no significant body-size change, morphological innovations (adaptations) in the process of speciation are considered to have significantly enhanced these newly evolved species' flexibility and survival in coping with degrading environmental conditions.

Mass balance for a novel RO/FO hybrid system in seawater desalination

Seawater Reverse Osmosis (SWRO) desalination is being used by several countries to aid the current demand for fresh water, hence numerous large scale and small scale desalination plants have been built during last decade. Despite major advancements in SWRO technology, the desalination industry is still facing significant practical issues. Two of the major issues are (1) generation of higher volumes of pre-treatment sludge, and (2) overall water recovery. This paper proposes a novel hybrid reverse osmosis (RO) - forward osmosis (FO) system to overcome the above two drawbacks. Mass balance calculations based on laboratory experiments have been used to predict increased water recovery and reduced pre-treatment sludge volume arising from large scale (340,000 m3/day of intake) and small scale (15,000 m3/day of intake) hybrid SWRO desalination plants. The percentage reduction of pre-treatment sludge volume, increase in overall RO water recovery, FO membrane area required and dilution in RO reject have been estimated.