Hydrogen peroxide formation and lipid peroxidation in rat uterus—effect of hormones and vitamin E

The effect of estradiol-17β and progesterone given separately as well as in combination on the rate of hydrogen peroxide formation and lipid peroxidation in the uteri of ovariectomized rats was studied. Estradiol in 3μg dose per day per animal elicited maximum stimulatory response and progesterone (100μg), on the other hand, was without any such effect. However, progesterone given along with estradiol completely prevented the effect due to the latter. In the same way, vitamin E, a well known antioxidant was found to be extremelv effective in protecting the uterus from the highly peroxidative action of estradiol-17β.     

Pathogenesis of SIVmac251 after atraumatic inoculation of the rectal mucosa in rhesus monkeys

Intrarectal inoculation of rhesus monkeys with low doses of SIV_mac led to a prolonged clinical and virological latency that was not observed for high intrarectal doses or for intravenous inoculation. Animals infected intrarectally with low virus doses remained negative for serum antibody responses to SIV for at least one year even though they readily transferred SIV to naive recipients via transfusion of whole blood.     

DENS: data center energy-efficient network-aware scheduling

In modern data centers, energy consumption accounts for a considerably large slice of operational expenses. The existing work in data center energy optimization is focusing only on job distribution between computing servers based on workload or thermal profiles. This paper underlines the role of communication fabric in data center energy consumption and presents a scheduling approach that combines energy efficiency and network awareness, named DENS. The DENS methodology balances the energy consumption of a data center, individual job performance, and traffic demands. The proposed approach optimizes the tradeoff between job consolidation (to minimize the amount of computing servers) and distribution of traffic patterns (to avoid hotspots in the data center network).     

Revisiting the notion of deleterious sweeps

It has previously been shown that, conditional on its fixation, the time to fixation of a semi-dominant deleterious autosomal mutation in a randomly mating population is the same as that of an advantageous mutation. This result implies that deleterious mutations could generate selective sweep-like effects. Although their fixation probabilities greatly differ, the much larger input of deleterious relative to beneficial mutations suggests that this phenomenon could be important. We here examine how the fixation of mildly deleterious mutations affects levels and patterns of polymorphism at linked sites—both in the presence and absence of interference amongst deleterious mutations—and how this class of sites may contribute to divergence between-populations and species. We find that, while deleterious fixations are unlikely to represent a significant proportion of outliers in polymorphism-based genomic scans within populations, minor shifts in the frequencies of deleterious mutations can influence the proportions of private variants and the value of F_ST after a recent population split. As sites subject to deleterious mutations are necessarily found in functional genomic regions, interpretations in terms of recurrent positive selection may require reconsideration.     

Discrimination models using variance-stabilizing transformation of metabolomic NMR data

After the extensive work that is being done in the areas of genomics, proteomics, and metabolomics, the study of metabolites has come of interest in its own right. Metabolites in biological systems give an understanding of the state of the system and provide a powerful tool for the study of disease and other maladies. Several analytical techniques such as mass spectrometry and high-resolution NMR spectroscopy have been used to study metabolites. The data, however, from these techniques remains quite complex. Traditionally, multivariate analyses have been used for such data. These methods however have an underlying assumption that the data is multivariate normal with a constant variance. This is not necessarily the case. It has been shown that a generalized log transformation renders the variance of the data constant effectively making the data more suitable for multivariate analysis. We demonstrate the effectiveness of these transformations on NMR data taken on a set of 18 abalone that were categorized as either being healthy, stunted, or diseased. We show how the transformation makes multivariate classification of the abalone into the healthy, stunted and diseased categories much more effective and gives a tool for identifying potential metabolic biomarkers for disease.     

Discriminant models for high-throughput proteomics mass spectrometer data

We use several different multivariate analysis methods to discriminate between diseased and healthy patients using protein mass spectrometer data provided by Duke University. Two problems were presented by the university; one in which the responses (diseased or healthy) of the patients were not known and second, when the responses were known. In the latter case, the data can be used as a 'training' set. We attempted both problems. In particular, we use principle component analysis along with clustering methods to discriminate for the first problem set and partial least squares coupled with logistic and discriminant methods when the responses were known. In addition, we were able to detect regions of interest in the spectrum where there were differences in the protein patterns between healthy and diseased patients. There was considerable effort involved in the preprocessing of the data. We used a binning approach to reduce the number of variables rather than peak heights or peak areas. We performed a square root transformation on the data to help stabilize the variance; this in turn made a significant improvement in clustering results.     

Food Choices and Consequences for the Nutritional Status: Insights into Nutrition Transition in an Hospital Community

Introduction

Although economic development is generally accompanied by improvements in the overall nutritional status of the country’s population the ‘nutritional transition’ often involves a shift to high energy diets and less exercise with negative consequences. This pilot study was done to examine if education of parents operates at the household level to influence dietary choices and the nutritional status of children in a small community of hospital workers.

Material and Methods

3 groups of persons with varying skill and education levels participated. Weighed food logs were used in all households to calculate ‘adult equivalent’ per-capita-consumption. Nutrients were calculated using nutrients calculator software. BMI was used to classify children as underweight, normal weight and overweight.

Results

128 individuals participated from 30 families included 47 children. 10 children (21%) were underweight, 29 (62%) were normal and 8 (17%) were overweight. Energy consumption was highest in families with overweight children 2692 +/-502 compared to 2259 +/-359 in families with normal weight and 2031+/-354 in the family of underweight children. These differences were statistically significant. 42% underweight children belonged to Class 1 at the lowest skill level and there were no overweight children in this group. Most of the overweight children belonged to Class 2. In Class 3 there were no underweight children and the majority was normal weight children.

Conclusion

Underweight children came from the poorer households. Per capita intake of the family as a whole correlated well with BMI in the children. There was increased obesity in middle income families belonging to Class 2—probably in families who move up the scale from deprivation. Nutritional status in children correlated mostly with maternal education status.     

Use of polyamidoamine dendrimers to engineer BDNF-producing human mesenchymal stem cells

We report the use of polyamidoamine (PAMAM-NH₂) dendrimers along with other non-viral vehicles for the in vitro transfection of human bone marrow mesenchymal stem cells (hMSCs) and for engineering MSCs to secrete brain-derived neurotrophic factor (BDNF). Different generations of cationic polyamidoamine dendrimers (generations 3–6) were tested on HEK 293T cells. hMSCs were then transfected with PAMAM-NH₂ G4 dendrimers and Lipofectamine 2000, which elicited the expression of GFP reporter in around 6 and 20% of the cells, respectively. Both vehicles were then shown to elicit the expression of BDNF in MSCs from a bicistronic cassette. Non-virally induced neurotrophin expression may be a safe and easy method for adapting autologous stem cells for therapeutic treatment of diseases and neural system injuries.     

Immune response to leishmania: paradox rather than paradigm

The leishmaniases are a group of diseases caused by protozoan parasites of the genus Leishmania. Various Leishmania species can cause human infection, producing a spectrum of clinical manifestations. It is estimated that 350 million people are at risk, with a global yearly incidence of 1-1.5 million for cutaneous and 500,000 for visceral leishmaniasis (VL). VL is a major cause of morbidity and mortality in East Africa and the Indian subcontinent. Coinfection with HIV enhances the risk of the disease. The only control measure currently available in India is case detection and treatment with antimonial drugs, which are expensive, not always available and cannot be self-administered. Newer drugs like oral miltefosine have not become widely available. Vector and reservoir control is difficult due to the elusive nature of the vector and the diversity of the animal reservoir. A detailed knowledge of immune response to the parasite would help in designing prophylactic and therapeutic strategies against this infection.