Vitamin‐D Insufficiency Leads to Interleukin‐10 Reduction in Peri‐Implant Tissues: A Case–Control Study
Bibliographic record
Abstract
OBJECTIVES: Vitamin D has been reported to be crucial for bone mineralization and to play a significant role in immune and inflammatory responses. Its deficiency has been stated to be highly prevalent and might alter osseointegration of dental implants. Successful osseointegration has been claimed to be a critical aspect of implant survival and the effects of vitamin D on implant osseointegration have not been well documented. This study aimed to evaluate bone markers and cytokine levels of patients with or without vitamin D insufficiency. MATERIAL AND METHODS: A total of 42 patients were included and divided into two groups: vitamin D insufficient (Group IN-S; n = 21) and vitamin D sufficient (Group S; n = 21). Besides clinical periodontal parameters and implant stability measurements, the levels of RANKL, OPG, osteocalcin (OC), calcium (Ca), tumor necrosis factor alpha (TNF-α), IL-1β, caspase-1 (CASP1), and IL-10 in bone biopsy from implant preparation sockets and peri-implant crevicular fluid (PICF) were determined by enzyme-linked immunosorbent assay (ELISA). The results were represented as concentration and total amount. RESULTS: PICF RANKL levels (both concentration and total amount) were higher in patients with Vitamin D insufficiency compared to sufficient controls (p < 0.05). Concentration and total amount of IL-10 were significantly lower in vitamin D insufficient participants than those of vitamin D sufficient group (p < 0.05). No differences were detected between the groups in terms of other parameters. Bone levels of all evaluated parameters also did not differ between the groups (p > 0.05). CONCLUSION: It may be concluded that a low serum level of vitamin D may affect peri-implant health through altering IL-10 and RANKL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".