Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids

Suspect and non-target screening (SNTS) methods are being promoted in order to decode the human exposome since a wide chemical space can be analysed in a diversity of human biofluids. However, SNTS approaches in the exposomics field are infra-studied in comparison to environmental or food monitoring studies. In this work, a comprehensive suspect screening workflow was developed to annotate exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Precisely, human urine, breast milk, saliva and ovarian follicular fluid were employed as samples and analysed by means of ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry (UHPLC-HRMS/MS). To automate the workflow, the "peak rating" parameter implemented in Compound Discoverer 3.3.2 was optimized to avoid time-consuming manual revision of chromatographic peaks. In addition, the presence of endogenous molecules that might interfere with the annotation of xenobiotics was carefully studied as the employment of inclusion and exclusion suspect lists. To evaluate the workflow, limits of identification (LOIs) and type I and II errors (i.e., false positives and negatives, respectively) were calculated in both standard solutions and spiked biofluids using 161 xenobiotics and 22 metabolites. For 80.3 % of the suspects, LOIs below 15 ng/mL were achieved. In terms of type I errors, only two cases were identified in standards and spiked samples. Regarding type II errors, the 7.7 % errors accounted in standards increased to 17.4 % in real samples. Lastly, the use of an inclusion list for endogens was favoured since it avoided 18.7 % of potential type I errors, while the exclusion list caused 7.2 % of type II errors despite making the annotation workflow less time-consuming.

Simulation study comparing analytical methods for single-item longitudinal patient-reported outcomes data

PURPOSE

Efficient analytical methods are necessary to make reproducible inferences on single-item longitudinal ordinal patient-reported outcome (PRO) data. A thorough simulation study was performed to compare the performance of the semiparametric probabilistic index models (PIM) with a longitudinal analysis using parametric cumulative logit mixed models (CLMM).

METHODS

In the setting of a control and intervention arm, we compared the power of the PIM and CLMM to detect differences in PRO adverse event (AE) between these groups using several existing and novel summary scores of PROs. For each scenario, PRO data were simulated using copula multinomial models. Comparisons were also exemplified using clinical trial data.

RESULTS

On average, CLMM provided substantially greater power than the PIM to detect differences in PRO-AEs between the groups when the baseline-adjusted method was used, and a small advantage in power when using the baseline symptom as a covariate.

CONCLUSION

Although the CLMM showed the best performance among analytical methods, it relies on assumptions difficult to verify and that might not be fulfilled in the real world, therefore our recommendation is the use of PIM models with baseline symptom as a covariate.

A credit-like rating system to determine the legitimacy of scientific journals and publishers

The predatory nature of a journal is in constant debate because it depends on multiple factors, which keep evolving. The classification of a journal as being predatory, or not, is no longer exclusively associated with its open access status, by inclusion or exclusion on perceived reputable academic indexes and/or on whitelists or blacklists. Inclusion in the latter may itself be determined by a host of criteria, may be riddled with type I errors (e.g., erroneous inclusion of a truly predatory journal in a whitelist) and/or type II errors (e.g., erroneous exclusion of a truly valid scholarly journal in a whitelist). While extreme cases of predatory publishing behavior may be clear cut, with true predatory journals displaying ample predatory properties, journals in non-binary grey zones of predatory criteria are difficult to classify. They may have some legitimate properties, but also some illegitimate ones. In such cases, it might be too extreme to refer to such entities as "predatory". Simply referring to them as "potentially predatory" or "borderline predatory" also does little justice to discern a predatory entity from an unscholarly, low-quality, unprofessional, or exploitative one. Faced with the limitations caused by this gradient of predatory dimensionality, this paper introduces a novel credit-like rating system, based in part on well-known financial credit ratings companies used to assess investment risk and creditworthiness, to assess journal or publisher quality. Cognizant of the weaknesses and criticisms of these rating systems, we suggest their use as a new way to view the scholarly nature of a journal or publisher. When used as a tool to supplement, replace, or reinforce current sets of criteria used for whitelists and blacklists, this system may provide a fresh perspective to gain a better understanding of predatory publishing behavior. Our tool does not propose to offer a definitive solution to this problem.

Optimizing peer review to minimize the risk of retracting COVID-19-related literature

Retractions of COVID-19 literature in both preprints and the peer-reviewed literature serve as a reminder that there are still challenging issues underlying the integrity of the biomedical literature. The risks to academia become larger when such retractions take place in high-ranking biomedical journals. In some cases, retractions result from unreliable or nonexistent data, an issue that could easily be avoided by having open data policies, but there have also been retractions due to oversight in peer review and editorial verification. As COVID-19 continues to affect academics and societies around the world, failures in peer review might also constitute a public health risk. The effectiveness by which COVID-19 literature is corrected, including through retractions, depends on the stringency of measures in place to detect errors and to correct erroneous literature. It also relies on the stringent implementation of open data policies.

Better define beta-optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies

The minimum detectable difference (MDD) is a measure of the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. It is a measure at a defined level of probability and a given variability of the data. It provides an indication for the robustness of statistically derived effect thresholds such as the lowest observed effect concentration (LOEC) and the no observed effect concentration (NOEC) when interpreting treatment-related effects on a population exposed to chemicals in semi-field studies (e.g., micro-/mesocosm studies) or field studies. MDD has been proposed in the guidance on tiered risk assessment for plant protection products in edge of field surface waters (EFSA Journal 11(7):3290, 2013), in order to better estimate the robustness of endpoints from such studies for taking regulatory decisions. However, the MDD calculation method as suggested in this framework does not clearly specify the power which is represented by the beta-value (i.e., the level of probability of type II error). This has implications for the interpretation of experimental results, i.e., the derivation of robust effect values and their use in risk assessment of PPPs. In this paper, different methods of MDD calculations are investigated, with an emphasis on their pre-defined levels of type II error-probability. Furthermore, a modification is suggested for an optimal use of the MDD, which ensures a high degree of certainty for decision-makers.