Exploring the oral‐gut microbiota during thyroid cancer: Factors affecting the thyroid functions and cancer development
Bibliographic record
Abstract
Thyroid cancer (TC) is categorized into papillary, follicular, medullary, and anaplastic. The TC is increasing in several countries, including China, the United States, the United Kingdom, Canada, France, Australia, Germany, Japan, Spain, and Italy. Thus, this review comprehensively covers the factors that affect thyroid gland function, TC types, risk factors, and symptoms. Lifestyle factors (such as nutrient consumption and smoking) and pollutants (such as chemicals and heavy metals) increased the thyroid-stimulating hormone (TSH) levels which are directly related to TC prevalence. The conventional and recent TC treatments are also highlighted. The role of the oral and gut microbiota as well as the application of probiotics on TC are also discussed. The variations in the composition of oral and gut microbes influence the thyroid function indirectly through alteration in metabolites (such as short-chain fatty acids) that are eminent for cellular energy metabolism. Maintenance of healthy gut and oral microbiota can help in regulating thyroid function by regulating iodine uptake. Oral or gut microbial dysbiosis can be considered as an early diagnosis factor or TC marker. High TSH during TC can increase the oral microbial diversity while disrupting the high ratio of Firmicutes and Bacteroidetes in the gut. Supplementation of probiotics as an adjuvant in TC treatment is beneficial. However, needs more extensive research to explore the direct effect of probiotics on thyroid function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".