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Background Incorporating cluster randomized trials (CRTs) into meta-analyses is challenging because appropriate standard errors of study estimates accounting for clustering are not always reported. Systematic reviews of CRTs often use a single constant external estimate of the intraclass correlation coefficient (ICC) to adjust study estimate standard errors and facilitate meta-analyses; an approach that fails to account for possible variation of ICCs among studies and the imprecision with which they are estimated. Using a large systematic review of the effects of diabetes quality improvement interventions, we investigated whether we could better account for ICC variation and uncertainty in meta-analyzed effect estimates by imputing missing ICCs from a posterior predictive distribution constructed from a database of relevant ICCs. Methods We constructed a dataset of ICC estimates from applicable studies. For outcomes with two or more available ICC estimates, we constructed posterior predictive ICC distributions in a Bayesian framework. For a selected continuous outcome, glycosylated hemoglobin (HbA1c), we compared the impact of incorporating a single constant ICC versus imputing ICCs drawn from the posterior predictive distribution when estimating the effect of intervention components on post treatment mean in a case study of diabetes quality improvement trials. Results Using internal and external ICC estimates, we were able to construct a database of 59 ICCs for 12 of the 13 review outcomes (range 1–10 per outcome) and estimate the posterior predictive ICC distribution for 11 review outcomes. Synthesized results were not markedly changed by our approach for HbA1c. Conclusion Building posterior predictive distributions to impute missing ICCs is a feasible approach to facilitate principled meta-analyses of cluster randomized trials using prior data. Further work is needed to establish whether the application of these methods leads to improved review inferences for different reviews based on different factors (e.g., proportion of CRTs and CRTs with missing ICCs, different outcomes, variation and precision of ICCs).

Background Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy. Methods Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures. Results Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias. Conclusions Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

In the past decade, behavioural science has gained influence in policymaking but suffered a crisis of confidence in the replicability of its findings. Here, we describe a nascent heterogeneity revolution that we believe these twin historical trends have triggered. This revolution will be defined by the recognition that most treatment effects are heterogeneous, so the variation in effect estimates across studies that defines the replication crisis is to be expected as long as heterogeneous effects are studied without a systematic approach to sampling and moderation. When studied systematically, heterogeneity can be leveraged to build more complete theories of causal mechanism that could inform nuanced and dependable guidance to policymakers. We recommend investment in shared research infrastructure to make it feasible to study behavioural interventions in heterogeneous and generalizable samples, and suggest low-cost steps researchers can take immediately to avoid being misled by heterogeneity and begin to learn from it instead. Behavioural science increasingly informs policy, but findings are not always replicated. Bryan et al. describe an emerging heterogeneity revolution. They recommend that researchers use heterogeneity in treatment effects to develop more robust theories of causality and strengthen the field.

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. Significance: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

Super-resolution fluorescence microscopy has become an important catalyst for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy, a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling off-state. Only emitters residing in a sub-diffraction volume around an intensity minimum are allowed to fluoresce, rendering them distinguishable from the nearby, but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers range in biological samples and is suitable for live imaging. Here, we review the working principle of STED and provide general guidelines for successful STED imaging. The strive for ever higher resolution comes at the cost of increased light burden. We discuss techniques to reduce light exposure and mitigate its detrimental effects on the specimen. These include specialized illumination strategies as well as protecting fluorophores from photobleaching mediated by high-intensity STED light. This opens up the prospect of volumetric imaging in cells and tissues with diffraction-unlimited resolution in all three spatial dimensions in living samples.

Identifying analytical interference is a challenge for the medical biologist in providing advice to the prescriber. Indeed, these analytical interferences often have deleterious consequences on the care of patients. Understanding their mechanisms and mastering corrective procedures is essential to limit these management errors. Faced with the many questions from clinicians in current practice, we propose an algorithm for managing a sample when interference is suspected.

Comparative effectiveness studies, referred to here as "usual-care" trials, seek to compare current medical practices for the same medical condition. Such studies are presumed to be safe and involve only minimal risks. However, that presumption may be flawed if the trial design contains "unusual" care, resulting in potential risks to subjects and inaccurately informed consent. Three case studies described here did not rely on clinical evidence to ascertain contemporaneous practice. As a result, the investigators drew inaccurate conclusions, misinformed research participants, and subjects' safety was compromised. Before approving usual-care protocols, IRBs and scientific review committees should evaluate the quality and completeness of information documenting usual-care practices. Guidance from governmental oversight agencies regarding evidence-based documentation of current clinical practice could prevent similar occurrences in future usual-care trials. Accurate information is necessary to ensure that trials comply with government regulations that require minimizing research risks to subjects and accurate informed consent documents.

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

In this issue, Macklin and Natanson examine some of the controversies that arise in randomized clinical trials (RCTs). They are particularly concerned that researchers may misrepresent novel interv...

Trials that randomize patients to different “usual care” treatments play a crucial role in helping healthcare systems optimize the delivery of care. Yet, as illustrated by controversies surrounding...

According to Macklin and Natanson (2020), one reason unusual practices can be misidentified as usual care is that “instead of using pertinent, accurate information describing usual care, investigat...

When controversy over the SUPPORT study of extremely premature infants erupted six years ago, the research and bioethics community appeared split over the requirements of informed consent. Heated d...

Macklin and Natanson (2020) raise concerns surrounding the misrepresentation of usual care in three critical care studies. Emergency care research also yields examples of studies where claims of us...

The SUPPORT study of extremely premature newborns seems likely to go down as one of the most controversial studies of the 21st century (SUPPORT Study Group 2010). We previously suggested that the r...

Some researchers have their manuscripts screened for errors before they go to journals. Some researchers have their manuscripts screened for errors before they go to journals.

Quinones are becoming an essential tool for refractory organics treatment, while their quantification may be not well-considered. In this paper, two kinds of potential errors in quantification were evaluated in multiple pH conditions. They were derived from the coexistence of oxidized/reduced quinone species (Type I) and pH-sensitive feature (Type II), respectively. These errors would remarkably influence the accuracy of quantification while they haven't been emphasized. Thus, to elaborate the relationship between the two types of errors and the absorbance or pH conditions, three typical quinones [Anthraquinone-1-sulfonate (α-AQS), anthraquinone-2,6-disulfonate (AQDS) and lawsone] were selected and their acid dissociation coefficients (pKa) as well as UV-Vis spectra were determined. Results revealed that, for Type I, the relative error (RE) of α-AQS concentration would exceed the limit (5%) when reduced α-AQS was below 48% of total α-AQS. Similar results were found for lawsone. However, the RE can be eliminated by the equation established in this paper. For Type II, the pH-sensitive feature was related to the pKa values of quinones. Absorbances of α-AQS and lawsone would change remarkably with pH variation. Therefore, a model for correction was established. Analog data showed high consistency with experimental data [r = 0.995 (n = 25, p 0.01) and r = 0.997 (n = 36, p 0.01), for lawsone and α-AQS respectively]. Especially, the determination of AQDS concentrations was noticed to be pH-independent at 437 nm under pH 4.00 to 9.18 conditions. Based on these features, a comprehensive data solution was proposed for handling these errors.

Background: Cumulative Antibiotic Susceptibility Testing (CAST) plays a crucial role in providing knowledge about the evolution of bacterial resistance. The preparation of such report is however prone to many errors. This study investigated the variety of mistakes detected in the CAST of 9 Lebanese hospitals. Methods: Nine Lebanese hospitals were involved, where 21 different errors were looked for and analyzed. The total number of errors in each year was calculated and averaged according to the number of hospitals. Obtaining the average number of errors per hospital per year allowed the comparison of each hospital individually. Results: The average number of errors in 2008 was 38.75 and increased to 51.5 in 2012. The average number of errors then decreased to 37.89 by 2017. The most common error between 2008–2017 was the incoherent percentages. Superimposing these results allowed to determine if hospitals in general were following the trend of average errors. Some hospitals were constantly improving, others were making a variable number of errors over the years. Conclusion: The percentages of errors found here are alarming, urging therefore educating microbiologists on preparing CAST correctly. Future studies should aim to study the physician’s level of knowledge on the proper utilization of CAST. Keywords: CAST, Bacterial resistance, Antibiogram

Errors-in-variables models in high-dimensional settings pose two challenges in application. First, the number of observed covariates is larger than the sample size, while only a small number of covariates are true predictors under an assumption of model sparsity. Second, the presence of measurement error can result in severely biased parameter estimates, and also affects the ability of penalized methods such as the lasso to recover the true sparsity pattern. A new estimation procedure called SIMulation-SELection-EXtrapolation (SIMSELEX) is proposed. This procedure makes double use of lasso methodology. First, the lasso is used to estimate sparse solutions in the simulation step, after which a group lasso is implemented to do variable selection. The SIMSELEX estimator is shown to perform well in variable selection, and has significantly lower estimation error than naive estimators that ignore measurement error. SIMSELEX can be applied in a variety of errors-in-variables settings, including linear models, generalized linear models, and Cox survival models. It is furthermore shown in the Supporting Information how SIMSELEX can be applied to spline-based regression models. A simulation study is conducted to compare the SIMSELEX estimators to existing methods in the linear and logistic model settings, and to evaluate performance compared to naive methods in the Cox and spline models. Finally, the method is used to analyze a microarray dataset that contains gene expression measurements of favorable histology Wilms tumors.

Dextran sulfate sodium (DSS) present in the tissues of DSS-treated laboratory animals inhibits quantitative real-time qPCR (RT-qPCR) and thus may be source of experimental errors. A recent systematic review concluded that the reporting of experimental method was insufficient in a majority of DSS studies and contributed to the poor reproducibility of experiments. Here we compared two DSS cleanup protocols applied to mouse tissue RNA preparations based on silica membrane spin column and lithium chloride precipitation. In absence of cleanup, exogenous DSS significantly inhibited reverse transcription and cDNA amplification at concentrations of 5 × 10−3 g/L and above during the quantification of IL8 mRNA levels in THP-1 macrophages. Silica membrane spin columns removed DSS from mouse RNA preparations and eliminated DSS-induced inhibition of qPCR. Mouse RNA isolated from DSS-treated tissues and purified with silica membrane spin columns was suitable for RT-qPCR and assessment of inflammatory biomarkers.

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .

Forensic science techniques are often used in criminal trials to infer the identity of theperpetrator of crime and jurors often find this evidence very persuasive. Unfortunately, two of the leading causes of wrongful convictions are forensic science testing errors and false or misleading forensic testimony (Saks & Koehler, 2005). Therefore, it is important to understand jurors pre- existing beliefs about forensic science, as these beliefs may impact how they evaluate forensic evidence in the courtroom. In this study, we examine people’s perceptions of the likelihood of error and human judgment involved at each stage of the forensic science process (i.e., collection, storage, testing, analysis, reporting, and presenting). In addition, we examine peoples’ perceptions of the accuracy of — and human judgment involved in — 16 different forensic techniques. We find that, in contrast to what would be expected by the CSI effect literature, participants believed that the process of forensic science involved considerable human judgment and was relatively error-prone. In addition, participants had wide-ranging beliefs about the accuracy of various forensic techniques, ranging from 65.18% (document analysis) up to 89.95% (DNA). For some forensic techniques, estimates were lower than that found in experimental proficiency studies, suggesting that our participants are more skeptical of certain forensic evidence than they need to be. Keywords: Forensic science, forensic evidence, accuracy, error rate, CSI effect.

Objective: The research sought to determine the prevalence of errata for drug trial publications that are included in systematic reviews, their potential value to reviews, and their accessibility via standard information retrieval methods.Methods: The authors conducted a retrospective review of included studies from forty systematic reviews of drugs evaluated by the Canadian Agency for Drugs and Technologies in Health (CADTH) Common Drug Review (CDR) in 2015. For each article that was included in the systematic reviews, we conducted searches for associated errata using the CDR review report, PubMed, and the journal publishers’ websites. The severity of errors described in errata was evaluated using a three-category scale: trivial, minor, or major. The accessibility of errata was determined by examining inclusion in bibliographic databases, costs of obtaining errata, time lag between article and erratum publication, and correction of online articles.Results: The 40 systematic reviews included 127 articles in total, for which 26 errata were identified. These errata described 38 errors. When classified by severity, 6 errors were major; 20 errors were minor; and 12 errors were trivial. No one database contained all the errata. On average, errata were published 211 days after the original article (range: 15–1,036 days). All were freely available. Over one-third (9/24) of online articles were uncorrected after errata publication.Conclusion: Errata frequently described non-trivial errors that would either impact the interpretation of data in the article or, in fewer cases, impact the conclusions of the study. As such, it seems useful for reviewers to identify errata associated with included studies. However, publication time lag and inconsistent database indexing impair errata accessibility.

Critics of the use of advanced biotechnologies in the agri-food sector (“New Breeding Techniques”, comprising CRISPR) demand a strict regulation of any such method, even more severe than rules applied to so-called “Genetically Modified Organisms” (i.e. recombinant DNA processes and products). But their position is unwarranted, since it relies on faulty arguments. While most life scientists have always explained that the trigger for regulation should be the single product and its phenotypic traits, opponents insist that the target should be certain biotech processes. The antagonists maintain that NBTs are inherently risky: this belief is exactly the opposite of a long-standing, overwhelming scientific consensus. NBTs involve unpredictable effects, but it is the same for the results of any other technique. The critics wrongly equate “unintended” with “harmful” and misunderstand two meanings of “risk”: the “risk” of not achieving satisfactory results does not automatically translate into health or environment “risks”. Generic claims that allergenic or toxic properties are a hidden danger of outcomes from NBTs are unsubstantiated – as they would be for traditional techniques. Among several errors, we criticize the misuse of the Precautionary principle, a misplaced alarm about “uncontrolled spreading” of genetically engineered cultivars and the groundless comparison of (hypothetical) agricultural products from NBTs with known toxic substances. In order to “save” traditional techniques from “GMO”-like regulations, while calling for the enforcement of similar sectarian rules for the NBTs, the dissenters engage in baseless, unscientific distinctions. Important and necessary socio-economic, ethical and legal considerations related to the use of agri-food biotechnologies (older and newer) are outside the scope of this paper, which mostly deals with arguments from genetics, biology, and evolutionary theory that are provided by those who are suspicious of NBTs. Yet, we will provide some hints on two additional facets of the debate: the possible motivations for certain groups to embrace views which are utterly anti-scientific, and the shaky regulatory destiny of NBTs in the European Union.

Traditional epitranscriptomics relies on capturing a single RNA modification by antibody or chemical treatment, combined with short-read sequencing to identify its transcriptomic location. This approach is labor-intensive and may introduce experimental artifacts. Direct sequencing of native RNA using Oxford Nanopore Technologies (ONT) can allow for directly detecting the RNA base modifications, although these modifications might appear as sequencing errors. The percent Error of Specific Bases (%ESB) was higher for native RNA than unmodified RNA, which enabled the detection of ribonucleotide modification sites. Based on the %ESB differences, we developed a bioinformatic tool, epitranscriptional landscape inferring from glitches of ONT signals (ELIGOS), that is based on various types of synthetic modified RNA and applied to rRNA and mRNA. ELIGOS is able to accurately predict known classes of RNA methylation sites (AUC > 0.93) in rRNAs from Escherichiacoli, yeast, and human cells, using either unmodified in vitro transcription RNA or a background error model, which mimics the systematic error of direct RNA sequencing as the reference. The well-known DRACH/RRACH motif was localized and identified, consistent with previous studies, using differential analysis of ELIGOS to study the impact of RNA m6A methyltransferase by comparing wild type and knockouts in yeast and mouse cells. Lastly, the DRACH motif could also be identified in the mRNA of three human cell lines. The mRNA modification identified by ELIGOS is at the level of individual base resolution. In summary, we have developed a bioinformatic software package to uncover native RNA modifications.

Objectives Because observational studies often use imperfect measurements, results are prone to misclassification errors. We used as a motivating example the possible teratogenic risks of antiemetic agents in pregnancy since a large observational study recently showed that first-trimester exposure to doxylamine-pyridoxine was associated with significantly increased risk of congenital malformations as a whole, as well as central nervous system defects, and previous observational studies did not show such associations. A meta-analysis on this issue was carried out with the aim to illustrate how differential exposure and outcome misclassifications may lead to uncertain conclusions. Methods Medline, searched to October 2019 for full text papers in English. Summary Odds Ratios (ORs) with confidence intervals (CIs) were calculated using random-effect models. Probabilistic sensitivity analyses were performed for evaluating the extension of differential misclassification required to account for the exposure-outcome association. Results Summary ORs were 1.02 (95 % CI, 0.92–1.15), 0.99 (0.82–1.19) and 1.25 (1.08–1.44) for overall congenital, cardiocirculatory, and central nervous system malformations respectively. By assuming exposure and outcome bias factor respectively of 0.95 (i.e., newborns with congenital defects had exposure specificity 5% lower than healthy newborns) and 1.12 (i.e., exposed newborns had outcome sensitivity 12 % higher than unexposed newborns), summary OR of central nervous system defects became 1.13 (95 % CI, 0.99–1.29) and 1.17 (95 % CI, 0.99–1.38). Conclusion Observational investigations and meta-analyses of observational studies need cautious interpretations. Their susceptibility to several, often sneaky, sources of bias should be carefully evaluated.

Background: Experimental treatments pass through various stages of development. If a treatment passes through early-phase experiments, the investigators may want to assess it in a late-phase randomised controlled trial. An efficient way to do this is adding it as a new research arm to an ongoing trial while the existing research arms continue, a so-called multi-arm platform trial. The familywise type I error rate is often a key quantity of interest in any multi-arm platform trial. We set out to clarify how it should be calculated when new arms are added to a trial some time after it has started. Methods: We show how the familywise type I error rate, any-pair and all-pairs powers can be calculated when a new arm is added to a platform trial. We extend the Dunnett probability and derive analytical formulae for the correlation between the test statistics of the existing pairwise comparison and that of the newly added arm. We also verify our analytical derivation via simulations. Results: Our results indicate that the familywise type I error rate depends on the shared control arm information (i.e. individuals in continuous and binary outcomes and primary outcome events in time-to-event outcomes) from the common control arm patients and the allocation ratio. The familywise type I error rate is driven more by the number of pairwise comparisons and the corresponding (pairwise) type I error rates than by the timing of the addition of the new arms. The familywise type I error rate can be estimated using Šidák’s correction if the correlation between the test statistics of pairwise comparisons is less than 0.30. Conclusions: The findings we present in this article can be used to design trials with pre-planned deferred arms or to add new pairwise comparisons within an ongoing platform trial where control of the pairwise error rate or familywise type I error rate (for a subset of pairwise comparisons) is required.

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.

From 1980 to 1991, Kyriacou, Hall, and collaborators (KH) reported that the

Objectives The present systematic review aimed to perform an in‐depth analysis of the different features of retracted publications in the dental field. Material and methods This review has been recorded in the PROSPERO database (CRD42017075634). Two independent reviewers performed an electronic search (Pubmed, Retraction Watch) for retracted articles in dental literature up to December 31, 2018. Results 180 retracted papers were identified, the first published in 2001. Retractions increased by 47% in the last four‐year period (2014–2018), when compared with 2009–2013 (94 and 64 retracted publications, respectively). Author misconduct was the most common reason for retraction (65.0%), followed by honest scientific errors (12.2%) and publisher‐related issues (10.6%). The majority of retracted research was conducted in Asia (55.6%), with 49 papers written in India (27.2%). 552 researchers (89%) are listed as authors in only one retracted article, while 10 researchers (1.6%) are present in five or more retracted publications. Retracted articles were cited 530 times after retraction: the great majority of these citations (89.6%) did not consider the existence of the retraction notice and treated data from retracted articles as reliable. Conclusions Retractions in dental literature have constantly increased in recent years, with the majority of them due to misconduct and fraud. The publication of unreliable research has many negative consequences. Studies derived from such material are designed on potentially incorrect bases, waste funds and resources, and most importantly, increase risk of incorrect treatment for patients. Citation of retracted papers represents a major issue for the scientific community.

Local error estimates can be used to improve the success of models in molecular replacement., Model quality assessment programs estimate the quality of protein models and can be used to estimate local error in protein models. ProQ3D is the most recent and most accurate version of our software. Here, it is demonstrated that it is possible to use local error estimates to substantially increase the quality of the models for molecular replacement (MR). Adjusting the B factors using ProQ3D improved the log-likelihood gain (LLG) score by over 50% on average, resulting in significantly more successful models in MR compared with not using error estimates. On a data set of 431 homology models to address difficult MR targets, models with error estimates from ProQ3D received an LLG of >50 for almost half of the models 209/431 (48.5%), compared with 175/431 (40.6%) for the previous version, ProQ2, and only 74/431 (17.2%) for models with no error estimates, clearly demonstrating the added value of using error estimates to enable MR for more targets. ProQ3D is available from http://proq3.bioinfo.se/ both as a server and as a standalone download.

A novel zoonotic coronavirus outbreak is spreading all over the world. This pandemic disease has now been defined as novel coronavirus disease 2019 (COVID-19), and is sustained by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As the current gold standard for the etiological diagnosis of SARS-CoV-2 infection is (real time) reverse transcription polymerase chain reaction (rRT-PCR) on respiratory tract specimens, the diagnostic accuracy of this technique shall be considered a foremost prerequisite. Overall, potential RT-PCR vulnerabilities include general preanalytical issues such as identification problems, inadequate procedures for collection, handling, transport and storage of the swabs, collection of inappropriate or inadequate material (for quality or volume), presence of interfering substances, manual errors, as well as specific aspects such as sample contamination and testing patients receiving antiretroviral therapy. Some analytical problems may also contribute to jeopardize the diagnostic accuracy, including testing outside the diagnostic window, active viral recombination, use of inadequately validated assays, insufficient harmonization, instrument malfunctioning, along with other specific technical issues. Some practical indications can hence be identified for minimizing the risk of diagnostic errors, encompassing the improvement of diagnostic accuracy by combining clinical evidence with results of chest computed tomography (CT) and RT-PCR, interpretation of RT-PCR results according to epidemiologic, clinical and radiological factors, recollection and testing of upper (or lower) respiratory specimens in patients with negative RT-PCR test results and high suspicion or probability of infection, dissemination of clear instructions for specimen (especially swab) collection, management and storage, together with refinement of molecular target(s) and thorough compliance with analytical procedures, including quality assurance.

In 2015, we conducted a randomized controlled trial (RCT) in primary care to evaluate if posters and pamphlets dispensed in general practice waiting rooms enhanced vaccination uptake for seasonal influenza. Unexpectedly, vaccination uptake rose in both arms of the RCT whereas public health data indicated a decrease. We wondered if the design of the trial had led to a Hawthorne effect (HE). Searching the literature, we noticed that the definition of the HE was unclear if stated. Our objectives were to refine a definition of the HE for primary care, to evaluate its size, and to draw consequences for primary care research. We designed a Preferred Reporting Items for Systematic reviews and Meta-Analyses review and meta-analysis between January 2012 and March 2022. We included original reports defining the HE and reports measuring it without setting limitations. Definitions of the HE were collected and summarized. Main published outcomes were extracted and measures were analyzed to evaluate odds ratios (ORs) in primary care. The search led to 180 records, reduced on review to 74 for definition and 15 for quantification. Our definition of HE is "an aware or unconscious complex behavior change in a study environment, related to the complex interaction of four biases affecting the study subjects and investigators: selection bias, commitment and congruence bias, conformity and social desirability bias and observation and measurement bias." Its size varies in time and depends on the education and professional position of the investigators and subjects, the study environment, and the outcome. There are overlap areas between the HE, placebo effect, and regression to the mean. In binary outcomes, the overall OR of the HE computed in primary care was 1.41 (95% CI: [1.13; 1.75]; I 2 = 97%), but the significance of the HE disappears in well-designed studies. We conclude that the HE results from a complex system of interacting phenomena and appears to some degree in all experimental research, but its size can considerably be reduced by refining study designs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Berkhout, Berbra, Favre, Collins, Calafiore, Peremans and Van Royen.)

Tissue cultures of immortalized human cells, also known as established cell lines, are broadly accessible and cost-efficient tools for biomedical research. We here review potential genetic sources of systematic error in cell line experiments due to clonal evolution in vitro. In particular, the authors highlight alterations in telomere function over prolonged culture and population bottlenecks, respectively, as two commonly overlooked phenomena that can result in significant alterations in cell line genotypes over just one or a few passages in vitro. These alterations may include changes in mutation status of oncogenes and large scale chromosomal imbalances. We introduce a simple list of factors to be avoided in order to reduce the risk of data misinterpretation due to clonal evolution, including unacknowledged in vitro selection pressures, prolonged culture per se, harsh population size reductions, experiments at early phases after establishment, and the employment of cell lines not sufficiently analyzed by high resolution genetic techniques.

Background: It is of interest to know whether early pregnancy exposure to phenols such as bisphenol-A (BPA) or triclosan (TCS) negatively impacts birth weight outcomes. Exposure to these chemicals is widespread in the Canadian population but obtaining accurate measurements of average exposure is difficult because these chemicals are rapidly excreted from the body, causing body levels to fluctuate both within and between days, as observed in a recent Canadian study (P4). This measurement error can attenuate the estimated effects of exposures. Methods: Data from two Canadian cohort studies, the Plastics and Personal-care Products use in Pregnancy (P4) Study and the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, such that all participants with complete BPA or TCS exposure and outcome data were used (MIREC n = 1822, P4 n = 68). We used regression calibration to correct for the attenuating effects of exposure measurement error when modeling the effect of first trimester BPA or TCS exposure on four birth weight outcomes: birth weight (BW), low birth weight (LBW), small for gestational age (SGA) and large for gestational age (LGA). Specific gravity, time of day, and time since last urine void were also controlled in the analysis. Results: TCS exposure has a marginally significant association with SGA only with odds ratio 0.87 and 95% confidence interval (0.74, 1.00). It also has a marginally significant association with LGA in male offspring with odds ratio 1.11 and 95% confidence interval (1.00, 1.25). The effects of BPA on the four birth outcomes were insignificant. Conclusions: Increased TCS exposure during pregnancy is marginally associated with decreased odds of having SGA offspring. It is possibly associated with decreased BW in males and decreased odds of LBW, though these associations were not present in measurement error corrected models. TCS is possibly associated with increased odds in male offspring of being LGA, though this relationship was not present in models not corrected for measurement error. The study finds no significant effects of BPA on birth weight outcomes, which may be due to more severe measurement error in a single observation of BPA. Keywords: Measurement error, Regression calibration, Intra-class correlation coefficient, Gestational age, Bisphenol A, Triclosan