Background : Relation between the stability of dental implants (2)
The second phenomenon is contact osteogenesis, in which bone formation takes place from the implant surface toward the local bone. This osteogenesis consists of the early phase of osteogenic cell migration, osteoconduction, and de novo bone formation. The de novo bone formation at a solid surface has four stages. The first stage is secretion of the two noncollagenous proteins, osteopontin, and bone sialoprotein. The second stage is calcium phosphate nucleation, which consists of the calcium binding at the calcium binding sites of these proteins. The third stage is the crystal growth phase. The last stage of de novo bone formation is derived from collagen production and subsequent collagen mineralization. Finally, bone remodeling is the third phenomenon of implant-bone integration.
Osteocalcin (OC) is the most plentiful noncollagenous protein of the bone matrix. It is secreted from odontoblasts, osteocytes, and osteoblasts, in order to bind hydroxyapatite and calcium during matrix mineralization. It is one of the serological markers in the bone formation process. Numerous studies have shown increased OC levels in bone formation. However, increased OC level relates more to osteoid formation than matrix mineralization during bone formation. Alkaline phosphatase (ALP) is a membrane-bound glycoprotein. Its function is catalyzing the hydrolysis of phosphate monoesters at a basic pH level. Bone-specific alkaline phosphatase (BALP) is known to be involved in bone calcification. It is secreted by osteoblasts to provide a high phosphate concentration at the osteoblast cell surface during bone mineralization.
The measurement of implant stability is based on the clinical, histological, biomechanical, and biochemical approaches. The resonance frequency analysis (RFA), a noninvasive clinical implant stability measurement, has been used in many studies. Meta-analysis of 47 studies has revealed a statistically significant correlation between RFA and insertion torque. Numerous clinical studies have used the resonance frequency analysis technique on various implant designs to determine implant stability during the osseointegration period. Evaluation of peri-implant crevicular fluid (PICF), a noninvasive, clinical, biochemical approach has been used to assess and to predict the peri-implant tissue loss.
The purposes of this study were to examine the correlation between the stability of dental implants and bone formation markers during the healing period and to monitor the stability of dental implants during a 3-month period using the resonance frequency analysis method. The null hypothesis of the study is no correlation between the stability of dental implant and bone formation markers. Due to the three-thread-design of the implants, the authors also aim to measure the average implant stability quotient using RFA during the healing period.
Serial posts:
- Relation between the stability of dental implants and two biological markers
- Background : Relation between the stability of dental implants (1)
- Background : Relation between the stability of dental implants (2)
- Methods : Relation between the stability of dental implants (1)
- Methods : Relation between the stability of dental implants (2)
- Methods : Relation between the stability of dental implants (3)
- Methods : Relation between the stability of dental implants (4)
- Results : Relation between the stability of dental implants
- Discussion : Relation between the stability of dental implants (1)
- Discussion : Relation between the stability of dental implants (2)
- Discussion : Relation between the stability of dental implants (3)
- Reference : Relation between the stability of dental implants
- Table 1 Inclusion and exclusion criteria
- Table 2 Profile of patients
- Table 3 ISQ values according to gender and bone quality
- Table 4 Crevicular fluid volume
- Table 5 Crevicular fluid ALP and OC levels
- Figure 1. Timeline of the clinical study
- Figure 2. Change in the mean ISQ values over time
- Figure 3. Change in the median values
- Figure 4. Change in the median values of the ALP level over time
- Figure 5. Change in the median values of the OC level over time
- Figure 6. There were weakly significant and positive correlations
- Figure 7. Comparison between biomarker levels & ISQ values
- Figure 8. There were moderately significant and positive correlations