Overview of the research work of Prof. Takashiba and the Department of Pathophysiology – Periodontal Science – research outputs and impact
Bibliographic record
Abstract
If periodontitis, or gum disease, is left untreated, it can lead to strokes, diabetes, heart disease and the onset of dementia. The alveolar bone is an area of the mouth that is particularly impacted by periodontal disease and periodontists have therefore been searching for a means of regenerating lost alveolar bone with periodontal tissue. Dr Shogo Takashiba, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences at Okayama University, is working to help patients retain the oral functions they require for nutrition and to develop means of managing infection and inflammation, thereby improving general health. In particular, he and his team are looking at preventing biofilm-related infection, evaluating infection and inflammation, and regenerating periodontal tissue. A key focus for the researchers is on producing cost-effective methods for treating periodontal disease and, as such, they are working to develop anti-biofilm reagents for long-term use considering emerging issue of antimicrobial resistance. Part of this work involves cetylpyridinium chloride hydrate (CPC) and Takashiba and the team have developed a strategy to prevent the development of biofilm by combining phosphorylated pullulan and CPC. They have obtained patents for their oral wash, rinse and ointments in Japan, China, the US, the EU and Canada and are now looking at how to produce these regenerative and anti-biofilm treatments on an industrial scale, which will enable the general public to access the innovative treatments.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.018 |
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".