Assessing the impact of dental and periodontal statuses on the salivary microbiome: a global oral health scale
Bibliographic record
Abstract
Very few 16S rRNA-based studies have conducted a simultaneous analysis to identify the impact of various dental and periodontal parameters and determine which of them have the greatest repercussion for the salivary microbiota. Consequently, this study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental, periodontal and global oral disease. Our global oral health scale was used to produce a convenience sample of 81 patients from 270 who were initially recruited. These subjects were assigned the following grades: 47 had a periodontal grade (PG) of 0 and dental grades (DGs) between 0-3, and 46 had a DG of 0 and PGs between 0-3. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 x 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the Phyloseq, DESeq2 and Microbiome packages. The impact on the salivary microbiota of the different DGs, PGs and global oral grades (GGs) was investigated in relation to: 1) indicators of alpha diversity and the structure of the bacterial community; and 2) the composition of the core microbiome and the results of differential abundance tests. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or global oral disease, especially in high grades. The non-specific microbiome core contains a greater number of more abundant species than the specific core of a particular dental or periodontal condition (health or pathology). The number of taxa in the salivary microbiota with differential abundances between the DGs, PGs or GGs represents, at most, a quarter of the bacterial community and are mainly non-core species. Supragingival dental parameters influence the microbiota`s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that global oral health has on salivary microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".