Frazierlindgren2443

Most fitting functions perform well, but the quantity used for dose quantification determines over- or under-valuation of dose in the long term. Edge effects and the influence of opaque templates need to be well understood, to allow optimization of methodology to the intended purpose.

The proposed method allows practical and simultaneous digitization of up to ten small irradiated film samples, with an experimental uncertainty of 1%.

The proposed method allows practical and simultaneous digitization of up to ten small irradiated film samples, with an experimental uncertainty of 1%.

The most effective agent for the third-line treatment of advanced/metastatic gastric cancer (AGC) has not yet been determined. The aim of this network meta-analysis is to compare the relative efficacy and tolerability of third-line treatments for AGC.

We conducted a comprehensive literature review of randomised clinical trials (RCTs) using four electronic databases. Overall survival (OS), progression-free survival (PFS), objective response rate (ORR)and adverse events (AEs) were used as efficacy or tolerability outcomes. A Bayesian network meta-analysis with a random-effects model was used.

Seven RCTs involving 2601 patients and nine treatments were included. The results suggested that 1mg/kg nivolumab (nivolumab1)+3mg/kg ipilimumab (ipilimumab3) (hazard ratio [HR] 0.59, 95% credible interval [Crl] 0.38-0.91) was the most effective treatment, followed by nivolumab (HR 0.63, 95% Crl 0.50-0.79), for prolonging OS. Regorafenib (HR 0.40, 95% Crl 0.28-0.58) was most likely to improve PFS, followed by apatinib (HR 0.45, 95% Crl 0.33-0.60). Nivolumab1+ipilimumab3 and nivolumab were better at improving ORR, whereas nivolumab1+ipilimumab3 had the highest toxicity based on the AEs. For benefit-risk ratio, nivolumab, apatinib or regorafenib appeared to be the best options. Chemotherapy or two different dose combinations of nivolumab and ipilimumab were ranked as the next options because of poor tolerability, despite good efficacy.

Immunotherapy (nivolumab) or antiangiogenic agents (regorafenib and apatinib) are associated with benefits for benefit-risk ratio as third-line monotherapy. This study might serve as a guideline to aid in the selection of third-line treatments for AGC.

Immunotherapy (nivolumab) or antiangiogenic agents (regorafenib and apatinib) are associated with benefits for benefit-risk ratio as third-line monotherapy. This study might serve as a guideline to aid in the selection of third-line treatments for AGC.Exposure to endocrine disrupting chemicals is an important public health concern although only a few endocrine disruption chemicals have been identified so far. To speed up their identification, in silico toxicological models appear to be the most appropriate, since the potential endocrine disruption of a large number of compounds can be estimated in a short time. In this study three in silico models (Endocrine disruptome software, VirtualToxLab and COSMOS KNIME) have been used. In silico predictions of the endocrine disruption potential of biocidal active substances have been made and predictions then compared with the available in vitro experimental binding affinities to androgen, estrogen, glucocorticoid and thyroid receptors. The chosen models had similar accuracies (around 60%), while differences were shown between the models in specificity and sensitivity. VirtualToxLab was the most balanced model. Additionally, three combined models were prepared and evaluated. As expected, the majority rule approach model was more accurate and balanced. However, the positive consensus rule model, that improved the specificity of predictions (≥80% for all studied nuclear receptors) was more applicable. This reduction of false positive predictions is especially useful in the search for positive (active) compounds. On the other hand, the novel negative consensus rule model improved the specificity of prediction (≥80% for all studied nuclear receptors), giving good predictions of negative (inactive) compounds that can be excluded from further testing. The results obtained by these combined models have great added value, since they can significantly reduce further experimental testing.The effects of ambient fine particulate matter (PM2.5) exposure on blood pressure have been widely reported. However, there remains uncertainty regarding the underlying roles of particulate matter components. We aimed to investigate the association between ambient PM2.5 exposure and blood pressure, as well as the potential effects of trace metal(loid)s, in a repeated-measurement study that enrolled women of childbearing age. Our study included 35 participants from Hebei Province, China, each of whom was visited for five times. During each visit, we conducted questionnaire surveys, measured blood pressure, and collected blood. The daily PM2.5 exposure of participants was estimated according to their residential addresses using a spatiotemporal model that combined monitoring data with satellite measurements and chemical-transport model simulations. This model was used to calculate average PM2.5 concentrations in 1, 3, 7, 15, 30, and 60 days prior to each visit. Serum concentrations of various trace metal(loid)s were measured. A linear mixed-effects model was used to investigate associations among study variables. Overall, the mean (standard deviation) 60 days PM2.5 concentration over all five visits was 108.1(43.3) μg/m3. PM2.5 concentration was positively associated with both systolic and diastolic blood pressures. Likewise, ambient PM2.5 concentration was positively associated with serum concentrations of manganese and arsenic, and negatively associated with serum concentrations of nickel, tin, and chromium. Only the serum concentration of molybdenum was negatively associated with systolic blood pressure. We concluded that ambient PM2.5 exposure may contribute to elevated blood pressure, potentially by interfering with internal intake of various metal(loid)s in the human body.In multi-elemental compound-specific isotope analysis the lambda (Λ) value expresses the isotope shift of one element versus the isotope shift of a second element. In dual-isotope plots, the slope of the regression lines typical reveals the footprint of the underlying isotope effects allowing to distinguish degradation pathways of an organic contaminant molecule in the environment. While different conventions and fitting procedures are used in the literature to determine Λ, it remains unclear how they affect the magnitude of Λ. Here we generate synthetic data for benzene δ2H and δ13C with two enrichment factors εH and εC using the Rayleigh equation to examine how different conventions and linear fitting procedures yield distinct Λ. Fitting an error-free data set in a graph plotting the δ2H versus δ13C overestimates Λ by 0.225%⋅εH/εC, meaning that if εH/εCis larger than 22, Λ is overestimated by more than 5%. Tamoxifen order The correct fitting of Λ requires a natural logarithmic transformation of δ2H versus δ13C data. Using this transformation, the ordinary linear regression (OLR), the reduced major-axis (RMA) and the York methods find the correct Λ, even for large εH/εC.