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Our understanding of COVID-19 vaccinations and their impact on health and mortality has evolved substantially since the first vaccine rollouts. Published reports from the original randomized phase 3 trials concluded that the COVID-19 mRNA vaccines could greatly reduce COVID-19 symptoms. In the interim, problems with the methods, execution, and reporting of these pivotal trials have emerged. Re-analysis of the Pfizer trial data identified statistically significant increases in serious adverse events (SAEs) in the vaccine group. Numerous SAEs were identified following the Emergency Use Authorization (EUA), including death, cancer, cardiac events, and various autoimmune, hematological, reproductive, and neurological disorders. Furthermore, these products never underwent adequate safety and toxicological testing in accordance with previously established scientific standards. Among the other major topics addressed in this narrative review are the published analyses of serious harms to humans, quality control issues and process-related impurities, mechanisms underlying adverse events (AEs), the immunologic basis for vaccine inefficacy, and concerning mortality trends based on the registrational trial data. The risk-benefit imbalance substantiated by the evidence to date contraindicates further booster injections and suggests that, at a minimum, the mRNA injections should be removed from the childhood immunization program until proper safety and toxicological studies are conducted. Federal agency approval of the COVID-19 mRNA vaccines on a blanket-coverage population-wide basis had no support from an honest assessment of all relevant registrational data and commensurate consideration of risks versus benefits. Given the extensive, well-documented SAEs and unacceptably high harm-to-reward ratio, we urge governments to endorse a global moratorium on the modified mRNA products until all relevant questions pertaining to causality, residual DNA, and aberrant protein production are answered., Competing Interests: Steve Kirsch is the founder of the Vaccine Safety Research Foundation or VSRF (vacsafety.org) but receives no income from this entity, (Copyright © 2024, Mead et al.)

Blood-brain barrier penetrating peptides (BBBPs) are short peptide sequences that possess the ability to traverse the selective blood-brain interface, making them valuable drug candidates or carriers for various payloads. However, the in vivo or in vitro validation of BBBPs is resource-intensive and time-consuming, driving the need for accurate in silico prediction methods. Unfortunately, the scarcity of experimentally validated BBBPs hinders the efficacy of current machine-learning approaches in generating reliable predictions. In this paper, we present DeepB3P3, a novel framework for BBBPs prediction. Our contribution encompasses four key aspects. Firstly, we propose a novel deep learning model consisting of a transformer encoder layer, a convolutional network backbone, and a capsule network classification head. This integrated architecture effectively learns representative features from peptide sequences. Secondly, we introduce masked peptides as a powerful data augmentation technique to compensate for small training set sizes in BBBP prediction. Thirdly, we develop a novel threshold-tuning method to handle imbalanced data by approximating the optimal decision threshold using the training set. Lastly, DeepB3P3 provides an accurate estimation of the uncertainty level associated with each prediction. Through extensive experiments, we demonstrate that DeepB3P3 achieves state-of-the-art accuracy of up to 98.31% on a benchmarking dataset, solidifying its potential as a promising computational tool for the prediction and discovery of BBBPs., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS., Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×., Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS., (© 2021. The Author(s).)

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

The Genome-Wide Association Working Group (GWAWG) is part of a large-scale effort by the MicroArray Quality Consortium (MAQC) to assess the quality of genomic experiments, technologies and analyses for genome-wide association studies (GWASs). One of the aims of the working group is to assess the variability of genotype calls within and between different genotype calling algorithms using data for coronary artery disease from the Wellcome Trust Case Control Consortium (WTCCC) and the University of Ottawa Heart Institute. Our results show that the choice of genotyping algorithm (for example, Bayesian robust linear model with Mahalanobis distance classifier (BRLMM), the corrected robust linear model with maximum-likelihood-based distances (CRLMM) and CHIAMO (developed and implemented by the WTCCC)) can introduce marked variability in the results of downstream case-control association analysis for the Affymetrix 500K array. The amount of discordance between results is influenced by how samples are combined and processed through the respective genotype calling algorithm, indicating that systematic genotype errors due to computational batch effects are propagated to the list of single-nucleotide polymorphisms found to be significantly associated with the trait of interest. Further work using HapMap samples shows that inconsistencies between Affymetrix arrays and calling algorithms can lead to genotyping errors that influence downstream analysis.

The Affymetrix GeneChip Human Mapping 500K array is common for genome-wide association studies (GWASs). Recent findings highlight the importance of accurate genotype calling algorithms to reduce the inflation in Type I and Type II error rates. Differential results due to genotype calling errors can introduce severe bias in case-control association study results. Using data from the Wellcome Trust Case Control Consortium, 1991 individuals with coronary artery disease (CAD) and 1500 controls from the UK Blood Services (NBS) were genotyped on the Affymetrix 500K array. Different batch sizes and compositions were used in the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) genotype calling algorithm to assess the batch effect on downstream association analysis. Results show that composition (cases and controls genotyped simultaneously or separate) and size (number of individuals processed by BRLMM at a time) can create 2-3% discordance in the results for quality control and statistical analysis and may contribute to the lack of reproducibility between GWASs. The changes in batch size are largely responsible for differential single-nucleotide polymorphism results, yet we observe evidence of an interactive effect of batch size and composition that contributes to discordant results in the list of significantly associated loci.

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

The mechanisms underlying defence reactions to a pathogen attack, though well studied in crop plants, are poorly understood in conifers. To analyze changes in gene transcript abundance in Pinus sylvestris L. root tissues infected by Heterobasidion annosum (Fr.) Bref. s.l., a cDNA microarray containing 2109 ESTs from P. taeda L. was used. Mixed model statistical analysis identified 179 expressed sequence tags differentially expressed at 1, 5 or 15 days post inoculation. In general, the total number of genes differentially expressed during the infection increased over time. The most abundant group of genes up-regulated upon infection coded for enzymes involved in metabolism (phenylpropanoid pathway) and defence-related proteins with antimicrobial properties. A class III peroxidase responsible for lignin biosynthesis and cell wall thickening had increased transcript abundance at all measurement times. Real-time RT-PCR verified the microarray results with high reproducibility. The similarity of the expression profiling pattern observed in this pathosystem to those documented in crop pathology suggests that angiosperms and gymnosperms use similar genetic programs in responding to invasive growth by microbial pathogens.

In the absence of specific high-affinity agonists and antagonists, it has been difficult to define the target genes and biological responses attributable to many of the orphan nuclear receptors (ONRs). Indeed, it appears that many members of this receptor superfamily are not regulated by classical small molecules but rather their activity is controlled by interacting cofactors. Motivated by this finding, we have developed an approach to genetically isolate specific receptor-cofactor pairs in cells, allowing us to define the biological responses attributable to each complex. This is accomplished by using combinatorial peptide phage display to engineer the receptor interacting domain of each cofactor such that it interacts selectively with one nuclear receptor. In this study, we describe the customization of PGC-1alpha and its use to study the biology of the estrogen-related receptor alpha (ERRalpha) in cultured liver cells.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

We have previously identified a family of novel androgen receptor (AR) ligands that, upon binding, enable AR to adopt structures distinct from that observed in the presence of canonical agonists. In this report, we describe the use of these compounds to establish a relationship between AR structure and biological activity with a view to defining a rational approach with which to identify useful selective AR modulators. To this end, we used combinatorial peptide phage display coupled with molecular dynamic structure analysis to identify the surfaces on AR that are exposed specifically in the presence of selected AR ligands. Subsequently, we used a DNA microarray analysis to demonstrate that differently conformed receptors facilitate distinct patterns of gene expression in LNCaP cells. Interestingly, we observed a complete overlap in the identity of genes expressed after treatment with mechanistically distinct AR ligands. However, it was differences in the kinetics of gene regulation that distinguished these compounds. Follow-up studies, in cell-based assays of AR action, confirmed the importance of these alterations in gene expression. Together, these studies demonstrate an important link between AR structure, gene expression, and biological outcome. This relationship provides a firm underpinning for mechanism-based screens aimed at identifying SARMs with useful clinical profiles.

Background: Variation in gene expression among cells in a population is often considered as noise produced from gene transcription and post-transcription processes and experimental artifacts. Most studies on noise in gene expression have emphasized a few well-characterized genes and proteins. We investigated whether different cell-arresting methods have impacts on the maximum expression levels (amplitudes) of a cell cycle related gene., Results: By introducing random noise, modeled by a von Mises distribution, to the phase angle in a sinusoidal model in a cell population, we derived a relationship between amplitude and the distribution of noise in maximum transcription time (phase). We applied our analysis to Whitfield's HeLa cell cycle data. Our analysis suggests that among 47 cell cycle related genes common to the 2nd experiment (thymidine-thymidine method) and the 4th experiment (thymidine-nocodazole method): (i) the amplitudes of CDC6 and PCNA, which are expressed during G1/S phase, are smaller in the 2nd experiment than in the 4th, while the amplitude of CDC20, which is expressed during G2/M phase, is smaller in the 4th experiment; and (ii) the two cell-arresting methods had little impact on the amplitudes of the other 43 genes in the 2nd and 4th experiments., Conclusion: Our analysis suggests that procedures that arrest cells in different stages of the cell cycle differentially affect expression of some cell cycle related genes once the cells are released from arrest. The impact of the cell-arresting method on expression of a cell cycle related gene can be quantitatively estimated from the ratio of two estimated amplitudes in two experiments. The ratio can be used to gauge the variation in the phase/peak expression time distribution involved in stochastic transcription and post-transcriptional processes for the gene. Further investigations are needed using normal, unperturbed and synchronized HeLa cells as a reference to compare how many cell cycle related genes are directly and indirectly affected by various cell-arresting methods.

Consistency and coherence of gene expression data across multiple sites depends on several factors such as platform (oligo, cDNA, etc.), environmental conditions at each laboratory, and data quality. The Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute consortium on the application of genomics to mechanism-based risk assessment is investigating these factors by comparing high-density gene expression data sets generated on two sets of RNA from methapyrilene (MP) experiments conducted at Abbott Laboratories and Boehringer-Ingelheim Pharmaceuticals, Inc. using a single platform (Affymetrix Rat Genome U34A GeneChip) at seven different sites. This article focuses on the evaluation of data quality and statistical models that facilitate the comparison of such data sets at the probe level. We present methods for exploring and quantitatively assessing differences in the data, with the principal goal being the generation of lists of site-insensitive genes responsive to low and high doses of MP. A combination of numerical and graphical techniques reveals important patterns and partitions of variability in the data, including the magnitude of the site effects. Although the site effects are significantly large in the analysis results, they appear to be primarily additive and therefore can be adjusted in the statistical calculations in a way that does not bias conclusions regarding treatment differences.

This bibliography lists all published methodological works (statistical methodology, implementation methodology, review papers, descriptions of software) in the area of population pharmacokinetics/pharmacodynamics up to 1993 and all published works describing applications of population pharmacokinetics/pharmacodynamics up to 1992.