SCE variability in lymphocytes and fibroblasts

Summary To determine whether the sister chromatid exchange (SCE) distributions obtained in lymphocytes and fibroblasts from different individuals are comparable, a controlled study was set up. Peripheral blood and skin biopsies were taken on the same day from five individuals living for years under the same environmental conditions. All samples were treated in the same fashion, and the SCEs were scored in 50 metaphases of peripheral blood lymphocytes and of skin fibroblasts in an early and in a late passage. A repeat blood sample was taken from the same five indivuduals 1 year later. Based on the results obtained in this first part of the study, five randomly chosen healthy blood donors were sampled at different times and studied in the same fashion. Each chromosome was identified, and the SCE scores were tabulated per chromosome over 50 metaphases. The statistical analysis consisted of fitting log linear models to these scores and examining the best fit by determining the exceedance probabilities (observed significance level). For lymphocytes, the results indicated that the SCE distributions depended only on the chromosome examined, and not on BrdU-exposure time, individuals, or time of sampling. Treatment with ethyl methane sulfonate (EMS) increased the number of SCEs proportionally on all chromosomes. Analysis of the SCE scores on lymphocytes and fibroblasts of the five individuals and on their low and high passage fibroblast cultures revealed the necessity of including higher order interactions in order to fit a suitable model to the data. Therefore comparison of the SCE scores of lymphocytes with those of fibroblasts or comparison of scores on fibroblasts from different individuals could not be done. In practice, to compare samples or individuals, it suffices to score the SCE on a limited number of chromosomes (e.G., the A group) of 50 metaphases.     

Water Structure in the Gramicidin A Transmembrane Channel

Abstract

The interaction energy and the structure of water molecules either inside the Gramicidin A transmembrane channel or at its two extremities is examined with the use of iso-energy maps and Monte Carlo simulations. The shape of the channel as experienced by water is analyzed in detail. Variations in the hydration structure due to the presence of a Na+ ion placed at several positions along the channel are simulated, analyzed and discussed. Preliminary data on Li+ and K+ interacting with Gramicidin A and the system of water molecules are reported. The Gramicidin A atomic coordinates have been taken from Urry's recent papers.     

Does P-Cresylglucuronide Have the Same Impact on Mortality as Other Protein-Bound Uremic Toxins?

Background

Uremic toxins are emerging as important, non-traditional cardiovascular risk factors in chronic kidney disease (CKD). P-cresol has been defined as a prototype protein-bound uremic toxin. Conjugation of p-cresol creates p-cresylsulfate (PCS) as the main metabolite and p-cresylglucuronide (PCG), at a markedly lower concentration. The objective of the present study was to evaluate serum PCG levels, determine the latter’s association with mortality and establish whether the various protein-bound uremic toxins (i.e. PCS, PCG and indoxylsulfate (IS)) differed in their ability to predict mortality.

Methodology/Principal Findings

We studied 139 patients (mean ± SD age: 67±12; males: 60%) at different CKD stages (34.5% at CKD stages 2–3, 33.5% at stage 4–5 and 32% at stage 5D). A recently developed high-performance liquid chromatography method was used to assay PCG concentrations. Total and free PCG levels increased with the severity of CKD. During the study period (mean duration: 779±185 days), 38 patients died. High free and total PCG levels were correlated with overall and cardiovascular mortality independently of well-known predictors of survival, such as age, vascular calcification, anemia, inflammation and (in predialysis patients) the estimated glomerular filtration rate. In the same cohort, free PCS levels and free IS levels were both correlated with mortality. Furthermore, the respective predictive powers of three Cox multivariate models (free PCS+other risk factors, free IS+other risk factors and free PCS+other risk factors) were quite similar - suggesting that an elevated PCG concentration has much the same impact on mortality as other uremic toxins (such as PCS or IS) do.

Conclusions

Although PCG is the minor metabolite of p-cresol, our study is the first to reveal its association with mortality. Furthermore, the free fraction of PCG appears to have much the same predictive power for mortality as PCS and IS do.     

Clinical Evaluation of Rega 8: An Updated Genotypic Interpretation System That Significantly Predicts HIV-Therapy Response

Introduction

Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.

Materials & Methods

9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.

Results

Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76–1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69–1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80–1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.

Conclusion

Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.     

Effects of Wild Pig Disturbance on Forest Vegetation and Soils

In North America, wild pigs (Sus scrofa; feral pigs, feral swine, wild boars) are a widespread exotic species capable of creating large-scale biotic and abiotic landscape perturbations. Quantification of wild pig environmental effects has been particularly problematic in northern climates, where they occur only recently as localized populations at low densities. Between 2016 and 2017, we assessed short-term (within ~2 yrs of disturbance) effects of a low-density wild pig population on forest features in the central Lower Peninsula of Michigan, USA. We identified 16 8-ha sites using global positioning system locations from 7 radio-collared wild pigs for sampling. Within each site, we conducted fine-scale assessments at 81 plots and quantified potential disturbance by wild pigs. We defined disturbance as exposure of overturned soil, often resulting from rooting behavior by wild pigs. We quantified ground cover of plants within paired 1-m² frames at each plot, determined effects to tree regeneration using point-centered quarter sampling, and collected soil cores from each plot. We observed less percent ground cover of native herbaceous plants and lower species diversity, particularly for plants with a coefficient of conservatism ≥5, in plots disturbed by wild pigs. We did not observe an increase in colonization of exotic plants following disturbance, though the observed prevalence of exotic plants was low. Wild pigs did not select for tree species when rooting, and we did not detect any differences in regeneration of light- and heavy-seeded tree species between disturbed or undisturbed plots. Magnesium and ammonium content in soils were lower in disturbed plots, suggesting soil disturbance accelerated leaching of macronutrients, potentially altering nitrogen transformation. Our study suggested that disturbances by wild pigs, even at low densities, alters short-term native herbaceous plant diversity and soil chemistry. Thus, small-scale exclusion of wild pigs from vulnerable and rare plant communities may be warranted. © 2020 The Wildlife Society.     

Opportunistic Predation of Wild Turkey Nests by Wild Pigs

Wild pigs (Sus scrofa; i.e., feral hogs, feral swine) are considered an invasive species in the United States. Where they occur, they damage agricultural crops and wildlife habitat. Wild pigs also depredate native wildlife, particularly ground-nesting bird species during nesting season. In areas inhabited by wild turkeys (Meleagris gallopavo), nest destruction caused by wild pigs may affect recruitment. There is debate whether wild pigs actively seek ground-nesting bird nests or depredate them opportunistically. To address this debate, in 2016 we examined the movements of wild pigs relative to artificial wild turkey nests (i.e., control [no artificial nests], moderate density [12.5–25 nests/km²], and high density [25–50 nests/km²]) throughout the nesting season (i.e., early, peak, and late) in south-central Texas, USA. We found no evidence that wild pigs learned to seek and depredate wild turkey nests relative to nest density or nesting periods. Despite wild pigs being important nest predators, depredation was not a functional response to a pulsed food resource and can only be associated with overlapping densities of wild pigs and nests. Protecting reproductive success of wild turkeys will require reducing wild pig densities in nesting habitat prior to nesting season. © 2019 The Wildlife Society.     

Intraoperative multispectral and hyperspectral label‐free imaging: A systematic review of in vivo clinical studies

Multispectral and hyperspectral imaging (HSI) are emerging optical imaging techniques with the potential to transform the way surgery is performed but it is not clear whether current systems are capable of delivering real‐time tissue characterization and surgical guidance. We conducted a systematic review of surgical in vivo label‐free multispectral and HSI systems that have been assessed intraoperatively in adult patients, published over a 10‐year period to May 2018. We analysed 14 studies including 8 different HSI systems. Current in‐vivo HSI systems generate an intraoperative tissue oxygenation map or enable tumour detection. Intraoperative tissue oxygenation measurements may help to predict those patients at risk of postoperative complications and in‐vivo intraoperative tissue characterization may be performed with high specificity and sensitivity. All systems utilized a line‐scanning or wavelength‐scanning method but the spectral range and number of spectral bands employed varied significantly between studies and according to the system's clinical aim. The time to acquire a hyperspectral cube dataset ranged between 5 and 30 seconds. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of intraoperative in‐vivo label‐free HSI but further work is needed to fully integrate it into the current surgical workflow.      

Functional proteome analysis of the banana plant (Musa spp.) using de novo sequence analysis of derivatized peptides

We report the use of chemical derivatization with MALDI-MS/MS analysis for de novo sequence analysis. Using three frequently used homology-based search algorithms, we were able to identify more than 40 proteins from banana, a nonmodel plant with unsequenced genome. Furthermore, this approach allowed the identification of different isoforms. We also observed that the identification score obtained varied according to the position of the peptide sequences in the query using the MS-Blast algorithm.