Trehalose Increases Freeze-Thaw Damage in Liposomes Containing Chloroplast Glycolipids

Chloroplast thylakoids contain three classes of glycolipids, monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and sulfoquinovosyldiacylglycerol (SQDG). We have investigated the stability of large unilamellar vesicles made from egg phosphatidylcholine (EPC) and different chloroplast glycolipids during freezing to -18 degreesC, as a function of the presence of three sugars: glucose, sucrose, or trehalose. Contrary to the situation in thylakoids, where cryoprotection increases from glucose < sucrose < trehalose, liposomes containing 50% DGDG showed the opposite behavior. In fact, carboxyfluorescein leakage increased over the control values (freezing in the absence of sugar) in the presence of trehalose. This effect was not seen in vesicles made from pure EPC, or a mixture of EPC and MGDG, or EPC and SQDG. Liposomes made from mixtures of all three glycolipids, however, showed even more leakage in the presence of trehalose than liposomes containing only DGDG and EPC. Copyright 1998 Academic Press.     

Lack of geographic variation in anonymous nuclear polymorphisms in the American oyster, Crassostrea virginica

Comparing geographic variation of noncoding nuclear DNA polymorphisms, which presumably are neutral to natural selection, with geographic variation of allozymes is potentially a good way to detect the effects of selection on allozyme polymorphisms. A previous study of four anonymous nuclear markers in the American oyster, Crassostrea virginica, found dramatic differences in allele frequency between the Gulf of Mexico and the Atlantic Ocean. In contrast, 14 allozyme polymorphisms were fairly uniform in frequency between the two areas. This led to the conclusion that all of the allozyme polymorphisms were kept uniform in frequency by balancing selection. To test the robustness of this pattern, six additional anonymous nuclear DNA polymorphisms were surveyed in oysters from Panacea, Fla, and Charleston, S.C. on the Gulf and Atlantic coasts, respectively. Unlike the previously studied DNA markers, the six DNA polymorphisms examined here show geographic variation that is not significantly greater than that of allozymes. The reason for the discrepancy between the two sets of DNA polymorphisms is unclear.      

Monitoring diabetes in patients with and without rheumatoid arthritis: a Medicare study

Introduction

Diabetes mellitus is a key predictor of mortality in rheumatoid arthritis (RA) patients. Both RA and diabetes increase the risk of cardiovascular disease (CVD), yet understanding of how comorbid RA impacts the receipt of guideline-based diabetes care is limited. The purpose of this study was to examine how the presence of RA affected hemoglobin A1C (A1c) and lipid measurement in older adults with diabetes.

Methods

Using a retrospective cohort approach, we identified beneficiaries ≥65 years old with diabetes from a 5% random national sample of 2004 to 2005 Medicare patients (N = 256,331), then examined whether these patients had comorbid RA and whether they received guideline recommended A1c and lipid testing in 2006. Multivariate logistic regression was used to examine the effect of RA on receiving guideline recommended testing, adjusting for baseline sociodemographics, comorbidities and health care utilization.

Results

Two percent of diabetes patients had comorbid RA (N = 5,572). Diabetes patients with comorbid RA were more likely than those without RA to have baseline cardiovascular disease (such as 17% more congestive heart failure), diabetes-related complications including kidney disease (19% higher), lower extremity ulcers (77% higher) and peripheral vascular disease (32% higher). In adjusted models, diabetes patients with RA were less likely to receive recommended A1c testing (odds ratio (OR) 0.84, CI 0.80 to 0.89) than those without RA, but were slightly more likely to receive lipid testing (OR 1.08, CI 1.01 to 1.16).

Conclusions

In older adults with diabetes, the presence of comorbid RA predicted lower rates of A1c testing but slightly improved lipid testing. Future research should examine strategies to improve A1c testing in patients with diabetes and RA, in light of increased CVD and microvascular risks in patients with both conditions.     

Explaining Adherence Success in Sub-Saharan Africa: An Ethnographic Study

Background

Individuals living with HIV/AIDS in sub-Saharan Africa generally take more than 90% of prescribed doses of antiretroviral therapy (ART). This number exceeds the levels of adherence observed in North America and dispels early scale-up concerns that adherence would be inadequate in settings of extreme poverty. This paper offers an explanation and theoretical model of ART adherence success based on the results of an ethnographic study in three sub-Saharan African countries.

Methods and Findings

Determinants of ART adherence for HIV-infected persons in sub-Saharan Africa were examined with ethnographic research methods. 414 in-person interviews were carried out with 252 persons taking ART, their treatment partners, and health care professionals at HIV treatment sites in Jos, Nigeria; Dar es Salaam, Tanzania; and Mbarara, Uganda. 136 field observations of clinic activities were also conducted. Data were examined using category construction and interpretive approaches to analysis. Findings indicate that individuals taking ART routinely overcome economic obstacles to ART adherence through a number of deliberate strategies aimed at prioritizing adherence: borrowing and “begging” transport funds, making “impossible choices” to allocate resources in favor of treatment, and “doing without.” Prioritization of adherence is accomplished through resources and help made available by treatment partners, other family members and friends, and health care providers. Helpers expect adherence and make their expectations known, creating a responsibility on the part of patients to adhere. Patients adhere to promote good will on the part of helpers, thereby ensuring help will be available when future needs arise.

Conclusion

Adherence success in sub-Saharan Africa can be explained as a means of fulfilling social responsibilities and thus preserving social capital in essential relationships.     

Creatine kinase isoenzyme activity during and after an ultra-distance (200 km) run

It is commonly assumed that creatine kinase (CK) activity in plasma is related to the state of an inflammatory response at 24-48 h, and also it has shown biphasic patterns after a marathon run. No information is available on CK isoenzymes after an ultra-marathon run. The purpose of the present study is to examine the CK isoenzymes after a 200 km ultra-marathon run and during the subsequent recovery. Blood samples were obtained during registration 1 2 h before the 200-km race and during the race at 100 km, 150 km and at the end of 200 km, as well as after a 24 h period of recovery. Thirty-two male ultra-distance runners participated in the study. Serum CPK showed a marked increase throughout the race and 24 h recovery period (p < 0.001). Serum CK during the race occurs mostly in the CK-MM isoform and only minutely in the CK-MB isoform and is unchanged in the CK-BB isoform. High-sensitivity C-reactive protein (hs-CRP), oestradiol, AST and ALT increased significantly from the pre-race value at 100 km and a further increase took place by the end of the 200 km run. The results of our study demonstrate a different release pattern of creatine kinase after an ultra-distance (200 km) run compared to the studies of marathon running and intense eccentric exercise, and changes in several biomarkers, indicative of muscle damage during the race, were much more pronounced during the latter half (100–200 km) of the race. However, the increases in plasma concentration of muscle enzymes may reflect not only structural damage, but also their rate of clearance.     

Motif kernel generated by genetic programming improves remote homology and fold detection

Background  

Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences.