Ribosomal frameshifting and misreading of mRNA in COVID-19 vaccines produces “off-target” proteins and immune responses eliciting safety concerns: Comment on UK study by Mulroney et al.
Bibliographic record
Abstract
We comment on the study by Mulroney et al.(1) entitled: “N1-methylpseudouridylation of mRNA causes +1 ribosomal frameshifting.” The study found evidence in mice and humans for the misreading of the modRNA contained within the Pfizer COVID-19 vaccine to inadvertently produce “off-target” proteins capable of eliciting “off-target” immune responses. The authors propose that these novel proteins are the result of ribosomal frameshifting occasioned by the substitution of N1-methyl pseudouridine. The authors state that the “error prone” code is a safety concern with a “huge potential to be harmful” and that “it is essential that these therapeutics are designed to be free from unintended side-effects.”The findings reveal a developmental and regulatory failure to ask fundamental questions that could affect the safety and effectiveness of these products. According to WHO guidelines for mRNA vaccines, (2) manufacturers should provide details of “unexpected ORFs”(Open Reading Frames). The formation of these off-target proteins is not disclosed in the package insert for COMIRNATY.The finding that unintended proteins may be produced as a result of vaccination is sufficient cause for regulators to conduct full risk assessments of past or future harms that may have ensued. Given that this study was conducted under the auspices of the United Kingdom Government, we must assume UK regulators, manufacturers, and international regulatory agencies, including FDA, were apprised of the data many months ago. We await their account of what steps they have taken to investigate why the formation of off-target proteins was not discovered sooner, what toxic effects they may have caused and what steps they are taken to prevent harm in the future and to inform the public of these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.046 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".