Periodontal bacteria in the brain—Implication for Alzheimer's disease: A systematic review
Bibliographic record
Abstract
Periodontitis is a chronic non-communicable disease caused by a dysbiotic microbiota. Pathogens can spread to the bloodstream, colonize other tissues or organs, and favor the onset of other pathologies, such as Alzheimer's disease (AD). Pathogens could permanently or transiently colonize the brain and induce an immune response. Thus, we analyzed the evidence combining oral bacteria's detection in the brain, both in animals and humans affected with AD. This systematic review was carried out following the PRISMA guideline. Studies that detected oral bacteria at the brain level were selected. The search was carried out in the Medline, Latindex, SciELO, and Cochrane Library databases. SYRCLE tool and Newcastle-Ottawa Scale were used for the risk of bias assessment. 23 studies were selected according to the eligibility criteria. Infection with oral pathogens in animals was related to developing neuropathological characteristics of AD and bacteria detection in the brain. In patients with AD, oral bacteria were detected in brain tissues, and increased levels of pro-inflammatory cytokines were also detected. There is evidence of a microbiological susceptibility to develop AD when the most dysbiosis-associated oral bacteria are present. The presence of bacteria in the brain is related to AD's pathological characteristics, suggesting an etiological oral-brain axis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".