Noninvasive Digital Method for Determining Inflammation after Dental Implantation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Luminescence Spectroscopy
2.2. Patient Groups and Samples
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- Placement from 1 to 3 intraosseous implants in the mandible;
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- Men and women from 32 to 47 years of age;
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- All patients were non-smokers;
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- The absence of chronic diseases in patients at the stage of decompensation, foci of chronic infection in the oral cavity, and pregnancy.
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- Immediate implantation after removal of the corresponding teeth;
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- Guided bone regeneration;
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- Bad level of oral hygiene (more than 3.1 points of index values in the simplified oral hygiene index (OHI-S)).
3. Results
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Elani, H.W.; Starr, J.R.; Da Silva, J.D.; Gallucci, G.O. Trends in Dental Implant Use in the U.S.; 1999–2016, and Projections to 2026. J. Dent. Res. 2018, 97, 1424–1430. [Google Scholar] [CrossRef] [PubMed]
- Guglielmotti, M.B.; Olmedo, D.G.; Cabrini, R.L. Research on implants and osseointegration. Periodontology 2019, 79, 178–189. [Google Scholar] [CrossRef] [PubMed]
- Bosshardt, D.D.; Chappuis, V.; Buser, D. Osseointegration of titanium, titanium alloy and zirconia dental implants: Current knowledge and open questions. Periodontology 2017, 73, 22–40. [Google Scholar] [CrossRef] [PubMed]
- Smeets, R.; Henningsen, A.; Jung, O.; Heiland, M.; Hammächer, C.; Stein, J.M. Definition, etiology, prevention and treatment of peri-implantitis—a review. Head Face Med. 2014, 10, 34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nakamura, T. Dental MRI: A road beyond CBCT. Eur. Radiol. 2020, 30, 6389–6391. [Google Scholar] [CrossRef] [PubMed]
- Masthoff, M.; Gerwing, M.; Masthoff, M.; Timme, M.; Kleinheinz, J.; Berninger, M.; Heindel, W.; Wildgruber, M.; Schülke, C. Dental Imaging—A basic guide for the radiologist. Rofo 2019, 191, 192–198. [Google Scholar] [CrossRef] [Green Version]
- Izzetti, R.; Vitali, S.; Gabriele, M.; Caramella, D. Feasibility of a combination of intraoral UHFUS and CBCT in the study of peri-implantitis. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2019, 127, 89–94. [Google Scholar] [CrossRef]
- Insua, A.; Gañán, Y.; Macías, Y.; Garcia, J.A.; Rakic, M.; Monje, A. Diagnostic Accuracy of Cone Beam Computed Tomography in Identifying Peri-implantitis-Like Bone Defects Ex Vivo. Int. J. Periodontics Restor. Dent. 2021, 41, 223–231. [Google Scholar] [CrossRef]
- Russell, A.L. A system of classification and scoring for prevalence surveys of periodontal disease. J. Dent. Res. 1956, 35, 350–359. [Google Scholar] [CrossRef]
- Shour, I.; Massler, M. Survey of gingival disease using the PMA index. J. Dent. Res. 1948, 27, 727. [Google Scholar]
- Dhingra, K.; Vandana, K.L. Indices for measuring periodontitis: A literature review. Int. Dent. J. 2011, 61, 76–77. [Google Scholar] [CrossRef] [PubMed]
- Massler, M. The P-M-A index for the assessment of gingivitis. J. Periodontol. 1967, 38, 592–601. [Google Scholar] [CrossRef] [PubMed]
- Boutault, F.; Cadenat, H.; Hibert, P.J. Evaluation of gingival microcirculation by a laser-Doppler flowmeter: Preliminary results. J. Craniomaxillofac. Surg. 1989, 17, 105–109. [Google Scholar] [CrossRef]
- Cosoli, G.; Scalise, L.; Cerri, G.; Russo, P.; Tricarico, G.; Tomasini, E.P. Bioimpedancemetry for the assessment of periodontal tissue inflammation: A numerical feasibility study. Comput. Methods Biomech. Biomed. Engin. 2017, 20, 682–690. [Google Scholar] [CrossRef]
- Csempesz, F.; Vág, J.; Kerémi, B.; Györfi, A.; Fazekas, A. A szájüregi képletek keringésének vizsgálata lézer Doppler-áramlásméróvel humán egyedekben [Blood flow measurements in human oral tissues with laser Doppler flowmetry]. Fogorv. Szle. 2000, 93, 115–120. [Google Scholar]
- Kerdvongbundit, V.; Vongsavan, N.; Soo-Ampon, S.; Hasegawa, A. Microcirculation and micromorphology of healthy and inflamed gingivae. Odontology 2003, 91, 19–25. [Google Scholar] [CrossRef]
- Scardina, G.A.; Ruggieri, A.; Messina, P. Oral microcirculation observed in vivo by videocapillaroscopy: A review. J. Oral Sci. 2009, 51, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Kourkoumelis, N.; Balatsoukas, I.; Moulia, V.; Elka, A.; Gaitanis, G.; Bassukas, I.D. Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation. Int. J. Mol. Sci. 2015, 16, 14554–14570. [Google Scholar] [CrossRef] [Green Version]
- Bachmann, L.; Zezell, D.M.; Ribeiro, A.C.; Gomes, L.; Ito, A.S. Fluorescence Spectroscopy of Biological Tissues—A Review. Appl. Spectrosc. Rev. 2006, 41, 575–590. [Google Scholar] [CrossRef]
- Pavlova, I.; Weber, C.R.; Schwarz, R.A.; Williams, M.D.; Gillenwater, A.M.; Richards-Kortum, R. Fluorescence spectroscopy of oral tissue: Monte Carlo modeling with site-specific tissue properties. J. Biomed. Opt. 2009, 14, 014009. [Google Scholar] [CrossRef]
- Richards-Kortum, R.; Sevick-Muraca, E. Quantitative optical spectroscopy for tissue diagnosis. Annu. Rev. Phys. Chem. 1996, 47, 555–606. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moncada, B.; Castillo-Martínez, C.; Arenas, E.; León-Bejarano, F.; Ramírez-Elías, M.G.; González, F.J. Raman spectroscopy analysis of the skin of patients with melasma before standard treatment with topical corticosteroids, retinoic acid, and hydroquinone mixture. Skin Res. Technol. 2016, 22, 170–173. [Google Scholar] [CrossRef]
- Hanchanale, V.S.; Rao, A.R.; Das, S. Raman spectroscopy and its urological applications. Indian J. Urol. 2008, 24, 444–450. [Google Scholar] [CrossRef] [PubMed]
- Cui, X.; Zhao, Z.; Zhang, G.; Chen, S.; Zhao, Y.; Lu, J. Analysis and classification of kidney stones based on Raman spectroscopy. Biomed. Opt. Epress 2018, 9, 4175–4183. [Google Scholar] [CrossRef] [PubMed]
- Artemyev, D.N.; Kukushkin, V.I.; Avraamova, S.T.; Aleksandrov, N.S.; Kirillov, Y.A. Using the Method of “Optical Biopsy” of Prostatic Tissue to Diagnose Prostate Cancer. Molecules 2021, 26, 1961. [Google Scholar] [CrossRef]
- Ntziachristos, V. Fluorescence Imaging. In Encycl. Diagn. Imaging; Baert, A.L., Ed.; Springer: Berlin/Heidelberg, Germany, 2008; pp. 723–726. [Google Scholar]
- Maitra, D.; Bragazzi Cunha, J.; Elenbaas, J.S.; Bonkovsky, H.L.; Shavit, J.A.; Omary, M.B. Porphyrin-Induced Protein Oxidation and Aggregation as a Mechanism of Porphyria-Associated Cell Injury. Cell. Mol. Gastroenterol. Hepatol. 2019, 8, 535–548. [Google Scholar] [CrossRef] [Green Version]
- Petritskaya, E.N.; Kulikov, D.A.; Rogatkin, D.A.; Guseva, I.A.; Kulikova, P.A. Use of fluorescence spectroscopy for diagnosis of hypoxia and inflammatory processes in tissue. J. Opt. Technol. 2015, 82, 810–814. [Google Scholar] [CrossRef]
- Greene, J.C.; Vermillion, J.R. The simplified oral hygiene index. J. Am. Dent. Assoc. 1964, 68, 7–13. [Google Scholar] [CrossRef]
- Zakharov, V.P.; Bratchenko, I.A.; Myakinin, O.O.; Artemyev, D.N.; Khristoforova, Y.A.; Kozlov, S.V.; Moryatov, A.A. Combined Raman spectroscopy and autofluoresence imaging method for in vivo skin tumor diagnosis. Proc. SPIE—Int. Soc. Opt. Eng. 2014, 9198, 919804. [Google Scholar]
- Zakharov, V.P.; Bratchenko, I.A.; Artemyev, D.N.; Myakinin, O.O.; Khristoforova, Y.A.; Vrakova, M.G. Skin neoplasm diagnostics using combined spectral method in visible and near infrared regions. In Proceedings of the 2015 International Conference on BioPhotonics, Florence, Italy, 20–22 May 2015; pp. 108–111. [Google Scholar]
- Zakharov, V.P.; Bratchenko, I.A.; Artemyev, D.N.; Myakinin, O.O.; Khristoforova, Y.A.; Kozlov, S.V.; Moryatov, A.A. Combined autofluorescence and Raman spectroscopy method for skin tumor detection in visible and near infrared regions. Progress in Biomedical Optics and Imaging. Proc. SPIE 2015, 9537, 95372H. [Google Scholar]
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Prikule, D.V.; Kukushkin, V.I.; Prikuls, V.F. Noninvasive Digital Method for Determining Inflammation after Dental Implantation. Biophysica 2022, 2, 412-416. https://doi.org/10.3390/biophysica2040036
Prikule DV, Kukushkin VI, Prikuls VF. Noninvasive Digital Method for Determining Inflammation after Dental Implantation. Biophysica. 2022; 2(4):412-416. https://doi.org/10.3390/biophysica2040036
Chicago/Turabian StylePrikule, Diana V., Vladimir I. Kukushkin, and Vladislav F. Prikuls. 2022. "Noninvasive Digital Method for Determining Inflammation after Dental Implantation" Biophysica 2, no. 4: 412-416. https://doi.org/10.3390/biophysica2040036
APA StylePrikule, D. V., Kukushkin, V. I., & Prikuls, V. F. (2022). Noninvasive Digital Method for Determining Inflammation after Dental Implantation. Biophysica, 2(4), 412-416. https://doi.org/10.3390/biophysica2040036