Gene polymorphism in oral health condition associated with coal dust exposure: a systematic review
Bibliographic record
Abstract
Introduction: South Kalimantan, known as the second largest coal supplier in Indonesia, is associated with the presence of significant quantities of coal dust in the environment. Coal dust contains various substances that are carcinogenic and cytotoxic. Direct contact of coal dust to the oral cavity is at risk of gene polymorphism resulting in the manifestation of diseases in the mouth. The study aimed to explore the relationship between gene polymorphisms affecting oral health conditions and coal dust exposure through a systematic review. Methods: A systematic search of PubMed, Google Scholar, Crossref, Scopus, Web of Science, Lens, and Semantic Scholar databases was conducted for English peer-reviewed articles (1/1/2004–15/9/2024) on oral-related gene polymorphisms from coal dust exposure in humans, animals, and cells. The review followed PRISMA 2020 guidelines, with a narrative synthesis of the findings. Bias was assessed using the Newcastle-Ottawa Scale (NOS), SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) and Quality Assessment Tool for In Vitro Studies (QUIN). Result: Overall 17 studies were included. From all 6.703 case-control, 136 in vivo participants and 60 cell samples showed that gene polymorphisms were more frequent in the coal dust-exposed group compared to the non-exposed group and the healthy group. Predisposing factors such smoking, length of time, and age contribute to triggering oral gene polymorphisms. The limitation of this research was the limited number of studies discussing gene polymorphisms due to coal dust exposure directly to the oral cavity, which affected the representativeness of the articles. Conclusion: There are polymorphisms in 11 oral-related genes (AhR, CYP1A1, GSTM1, GSTT1, hOGG1, IL6, IL1B, NQO1, TNF, TP53, and XRCC1) after coal dust exposure, presenting genotoxic and mutagenic potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".