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AbstractThe demand for botanicals and natural substances in consumer products has increased in recent years. These substances usually contain proteins and these, in turn, can pose a risk for immunoglobulin E (IgE)-mediated sensitization and allergy. However, no method has yet been accepted or validated for assessment of potential allergenic hazards in such materials. In the studies here, a dual proteomic-bioinformatic approach is proposed to evaluate holistically allergenic hazards in complex mixtures of plants, insects, or animal proteins. Twelve commercial preparations of source materials (plant products, dust mite extract, and preparations of animal dander) known to contain allergenic proteins were analyzed by label-free proteomic analyses to identify and semi-quantify proteins. These were then evaluated by bioinformatics using AllerCatPro 2.0 (https://allercatpro.bii.a-star.edu.sg/) to predict no, weak, or strong evidence for allergenicity and similarity to source-specific allergens. In total, 4,586 protein sequences were identified in the 12 source materials combined. Of these, 1,665 sequences were predicted with weak or strong evidence for allergenic potential. This first-tier approach provided top-level information about the occurrence and abundance of proteins and potential allergens. With regards to source-specific allergens, 129 allergens were identified. The sum of the relative abundance of these allergens ranged from 0.8% (lamb’s quarters) to 63% (olive pollen). It is proposed here that this dual proteomic-bioinformatic approach has the potential to provide detailed information on the presence and relative abundance of allergens, and can play an important role in identifying potential allergenic hazards in complex protein mixtures for the purposes of safety assessments.

Foreign proteins are potentially immunogenic, and a proportion of these are able to induce immune responses that result in allergic sensitization. Subsequent exposure of sensitized subjects to the inducing protein can provoke a variety of allergic reactions that may be severe, or even fatal. It has therefore been recognized for some time that it is important to determine a priori whether a given protein has the potential to induce allergic responses in exposed subjects. For example, the need to assess whether transgene products expressed in genetically engineered crop plants have allergenic properties. This is not necessarily a straightforward exercise (as discussed elsewhere in this edition), but the task becomes even more challenging when there is a need to conduct an overall allergenicity safety assessment of complex mixtures of proteins in botanicals or other natural sources that are to be used in consumer products. This paper describes a new paradigm for the allergenicity safety assessment of proteins that is based on the use of AllerCatPro 2.0, a new version of a previously described web application model developed for the characterization of the allergenic potential of proteins. Operational aspects of AllerCatPro 2.0 are described with emphasis on the application of new features that provide improvements in the predictions of allergenic properties such as the identification of proteins with high allergenic concern. Furthermore, the paper provides a description of strategies of how AllerCatPro 2.0 can best be deployed as a screening tool for identifying suitable proteins as ingredients in consumer products as well as a tool, in conjunction with label-free proteomic analysis, for identifying and semiquantifying protein allergens in complex materials. Lastly, the paper discusses the steps that are recommended for formal allergenicity safety assessment of novel consumer products which contain proteins, including consideration and integration of predicted consumer exposure metrics. The article therefore provides a holistic perspective of the processes through which effective protein safety assessments can be made of potential allergenic hazards and risks associated with exposure to proteins in consumer products, with a particular focus on the use of AllerCatPro 2.0 for this purpose.

Abstract Background The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered. Methods The dynamic RMST curve using a mixture model is proposed in this paper to fully enhance the RMST method for survival analysis in clinical trials. It is constructed that the RMST difference or ratio is computed over a range of values to the restriction time τ which traces out an evolving treatment effect profile over time. Results This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples. Conclusions The RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve also allows ones for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may be used for determining an appropriate time point for an interim analysis, and the data monitoring committee (DMC) can use this evaluation tool for study recommendation.

Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles > 20 nm) across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies based on κ–Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of κ. The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.

Although clinical trials of the pentavalent rotavirus vaccine (RotaTeq®, RV5) have demonstrated efficacy against RV gastroenteritis (RGE) in low and high-income settings, a clear correlate of protection or a measure of immune response that could predict efficacy has yet to be identified. This is the first time that immunogenicity data with both serum neutralized antibody (SNA) titers and anti-RV IgA titers from several clinical efficacy trials were pooled to provide a unique context for evaluating the correlation between immunogenicity and RGE risk or efficacy of RV5. The correlation between immunogenicity and RGE risk is evaluated with data at the individual subject level. The analyses show that higher Postdose 3 (PD3) G1 SNA titers are associated with lower odds of contracting any RGE. The correlation between immunogenicity and efficacy is assessed using aggregated population level data, which shows higher efficacy associated with higher PD3 G1 SNA geometric mean titer (GMT) ratio (between RV5 and placebo) and PD3 serum anti-RV IgA GMT ratio. Among high-income countries, efficacy plateaus over the range of PD3 G1 SNA GMT ratios and PD3 serum anti-RV IgA GMT ratios. From both individual- and population-level analyses, PD3 G1 SNA titers correlated most closely with the RGE risk or efficacy for RV5.

Background: Smoking prevalence among U.S. adults experiencing homelessness is ≥70 %. Interventions are needed to address persisting tobacco disparities. Methods: Adults who smoked combustible cigarettes (CC) daily (N=60) were recruited from an urban day shelter and randomly assigned to an e-cigarette switching intervention with or without financial incentives for carbon monoxide (CO)-verified CC abstinence (EC vs. EC+FI). All participants received an e-cigarette device and nicotine pods during the first 4 weeks post-switch; and those in the EC+FI group also received escalating weekly incentives for CC abstinence during the same period. Key follow-ups were conducted at 4- and 8-weeks post-switch. Results: Participants were predominantly male (75 %), 50 % were racially/ethnically minoritized, with an average age of 48.8 years. Descriptive analyses indicated that CC smoking abstinence rates among EC and EC+FI were 3.3 % vs. 13.3 % at 4 weeks (8.3 % overall) and 10.0 % vs. 13.3 % at 8 weeks (11.7 % overall) in the intent-to-treat analyses (missing considered smoking). Among those who completed follow-ups (51.7 % and 45.0 % at 4- and 8-weeks), CC abstinence rates in EC and EC+FI were 6.3 % vs. 26.7 % at 4 weeks (16.1 % overall) and 21.4 % vs. 30.8 % at 8 weeks (25.9 % overall). EC+FI participants reported fewer days of smoking, more days of e-cigarette use, and greater reductions in CO at 4-week follow-up. Most participants reported a high likelihood of switching to e-cigarettes (67.7 %). Conclusion: E-cigarette switching with financial incentives for CC cessation is a promising approach to tobacco harm reduction among adults accessing shelter services. Refinements are needed to improve engagement.

The amount, variability and quality of coarse woody debris (CWD) in an oak-dominated natural forest reserve in Austria were studied in 2006. The average volume of CWD (snags and logs) was 107.3 m3/ha, which accounted for 39% of the total living volume. Among the CWD, on average, 23.4 m3/ha (22%) were snags and 83.9 m3/ha (78%) were logs. According to quality aspects the CWD displayed a wide range of variation in tree species, tree size, stage of decay, and structural characteristics, creating a high diversity of CWD habitats for microorganisms. Among the three forest associations, the highest amount of CWD was found in the mesic Galio sylvatici-Carpinetum association. The results of this study are discussed as reference values for a close-to-nature management of oak-dominated broadleaved submontane forests emphasizing conservation management.

The aerosol in the Amazon basin is dominated throughout the year by organic matter, for the most part soluble in water. In this modeling study, we show how the knowledge of water-soluble organic compounds (WSOC) and the associated physical and chemical properties (e.g. solubility, surface tension, dissociation into ions) affect the hygroscopic growth and activation of the aerosol in this area. The study is based on data obtained during the SMOCC field experiment carried out in Rondônia, Brazil, over a period encompassing the dry (biomass burning) season to the onset of the wet season (September to mid-November, 2002). The comparison of predicted and measured cloud condensation nuclei (CCN) number concentration shows that the knowledge of aerosol WSOC composition in terms of classes of compounds and of their relative molecular weights and acidic properties may be sufficient to predict aerosol activation, without any information on solubility. Conversely, the lack of knowledge on WSOC solubility leads to a high overestimation of the observed diameter growth factors (DGF) by the theory. Moreover, the aerosol water soluble inorganic species fail to predict both DGFs and CCN number concentration. In fact, this study shows that a good reproduction of the measured DGF and CCN concentration is obtained if the chemical composition of aerosol, especially that of WSOC, is appropriately taken into account in the calculations. New parameterizations for the computed CCN spectra are also derived which take into account the variability caused by chemical effects (surface tension, molecular composition, solubility, degree of dissociation of WSOC).

Non-small cell lung cancer (NSCLC) is characterized by multiple genetic alterations such as loss of heterozygosity (LOH), microsatellite instability (MSI), promoter hypermethylation and changes of miRNA expression. According to a field cancerization (FC) phenomenon the adjacent histologically normal tissue plays a role in tumor progression by triggering the transformation process. The aim of the study was the analysis of genetic alterations in tumor and adjacent tissue to determine the FC size and to reveal associations with clinico-morphological features of patients. The study group included 135 patients with NSCLC. From each patient 4 FFPE samples were analyzed: tumor, adjacent normal lung tissue at 2, 5, 10 cm. LOH/MSI analysis was evaluated by PCR using 7 microsatellite loci. Promoter hypermethylation in genes RASSF1A FHIT, DAPK1, CDH1, CD44, TIMP3, MGMT was investigated by methyl-sensitive PCR. The expression levels of miRNAs let-7a, miR-155, miR-205 were measured by real-time PCR. Our results demonstrated that LOH/MSI occurs only in tumor while promoter hypermethylation occurs also in adjacent tissue at 2, 5 cm, but not at 10 cm. The downregulation of let-7a, miR-155 in adjacent tissue is lower than in tumor. The levels of investigated miRNAs in adjacent tissue vary depending on tumor differentiation – in patients with differentiated tumors it is higher than in the group with poorly differentiated tumors. We postulate that FC size in NSCLC is at least 5 cm from tumor and includes only epigenetic but not structural (LOH/MSI) alterations. The evaluation of epigenetic changes in adjacent tissue (e.g., surgical margins) can potentially be used for postsurgical prognosis.

The heightened cardiovascular disease (CVD) risk observed among omnivores is thought to be linked, in part, to gut microbiota-dependent generation of trimethylamine-N-oxide (TMAO) from L-carnitine, a nutrient abundant in red meat. Gut microbial transformation of L-carnitine into trimethylamine (TMA), the precursor of TMAO, occurs via the intermediate γ-butyrobetaine (γBB). However, the interrelationship of γBB, red meat ingestion and CVD risks, as well as the gut microbial genes responsible for the transformation of γBB to TMA, are unclear. In the present study, we show that plasma γBB levels in individuals from a clinical cohort (n = 2,918) are strongly associated with incident CVD event risks. Culture of human faecal samples and microbial transplantation studies in gnotobiotic mice with defined synthetic communities showed that the introduction of Emergencia timonensis, a human gut microbe that can metabolize γBB into TMA, is sufficient to complete the carnitine → γBB → TMA transformation, elevate TMAO levels and enhance thrombosis potential in recipients after arterial injury. RNA-sequencing analyses of E. timonensis identified a six-gene cluster, herein named the γBB utilization (gbu) gene cluster, which is upregulated in response to γBB. Combinatorial cloning and functional studies identified four genes (gbuA, gbuB, gbuC and gbuE) that are necessary and sufficient to recapitulate the conversion of γBB to TMA when coexpressed in Escherichia coli. Finally, reanalysis of samples (n = 113) from a clinical, randomized diet, intervention study showed that the abundance of faecal gbuA correlates with plasma TMAO and a red meat-rich diet. Our findings reveal a microbial gene cluster that is critical to dietary carnitine → γBB → TMA → TMAO transformation in hosts and contributes to CVD risk.