Investigating DNA Damage Mechanism Induced by Monosodium Glutamate and Associated DNA Repair Cell Machinery: A Literature Review
Bibliographic record
Abstract
Introduction: Monosodium Glutamate (MSG) is a widely used food additive to enhance flavours. Though commonly used, MSG’s accumulation in the body can induce genomic instabilities. These genome instabilities are detectable through various methods such as Random Amplified Polymorphic DNA Polymerase Chain Reaction (RAPD-PCR) and comet assay. Additionally, cells can employ DNA repair mechanisms to ameliorate this damage. The objective of this review paper is to investigate the role of prolonged MSG ingestion in DNA damage, potential downstream diseases, and DNA repair mechanisms that cells undertake to counteract these effects, such as nucleotide excision repair (NER) and base excision repair (BER). Compounds such as vitamin C, green tea extract, and Allium sativum have been shown to ameliorate the health hazards of MSG by inhibiting oxidative stress, reducing apoptosis, and increasing antioxidant availability. Methods: This review focuses on MSG-induced DNA damage mechanisms including gene suppression, chromosomal disruption, and carcinogenic effects. We conducted a comprehensive literature review of 28 peer-reviewed articles published since 2000-present. Results: Studies show that MSG consumption may lead to the formation of Reactive Oxygen Species (ROS) and micronuclei (MN), which are known as biomarkers of carcinogenesis. Furthermore, the genomic instabilities that lead to this effect were identified through the use of RAPD-PCR and comet assays. These instabilities are mainly dealt with by endogenous repair machineries such as NER and BER. Also, researchers have identified many substances which act as preventative measures towards the potential harmful impacts of MSG. Discussion: Diseases, such as cancer and obesity, may be linked to chronic intake of MSG. The efficacy of the mentioned DNA damage detection methods will be discussed. Furthermore, the endogenous mechanisms of NER and BER are outlined in this review. Substances such as vitamin C, green tea extract, and Allium sativum aid to prevent genotoxic effects induced by MSG. Conclusion: Through this research, we hope to bring awareness to the harmful impacts of MSG on genome stability and its role in disease development. We aim to educate the public about the prevalent usage of MSG in the food industry and to inform individuals to take precautions in their food consumption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".