Journal of Indian Society of Periodontology最新文献

Aims and objectives: Initial research suggested a potential link between titanium particles and peri-implantitis. Titanium's susceptibility to fretting, a process that generates microscopic debris, and its passive layer's breakdown in inflammatory environments contribute to the release of titanium ions. However, the full impact of these particles on implant health remains unclear. Therefore, the aim of the study is to evaluate the effect of different concentrations of titanium nanoparticles on the cytotoxicity and matrix metalloproteinase-8 expression in human periodontal ligament fibroblasts (HPDLfs).

Materials and methods: To investigate the effects of titanium (Ti) on HPDLfs, a standard Ti solution was diluted to create various concentrations (0.001, 0.1, 1, and 100 ppm). HPDLFs were cultured in media containing these Ti concentrations, and their viability was assessed after 24 and 48 h. Based on these cytotoxicity results, optimal Ti concentrations (0.01, 0.1, and 10 ppm) were selected for further analysis. The expression of matrix metalloproteinase-8 (MMP-8) was measured using quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) to evaluate the impact of Ti on HPDLfs.

Results: Exposure to titanium (Ti) reduced the viability of HPDLFs in a concentration-and time-dependent manner. At 100 ppm, more than 50% cell death was observed. qRT-PCR indicated a significant up-regulation of MMP-8 expression. However, ELISA analysis did not reveal any significant changes in MMP-8 levels.

Conclusion: Within the study's limitations, the cytotoxic and immunological responses of HPDLFs to titanium (Ti) are directly linked to the concentration and duration of exposure. Although no significant differences in MMP-8 release were observed across varying TiO2 concentrations, a decrease in cell viability was associated with higher TiO2 levels.

Background: Early identification of periodontal disease, mainly in diabetic patients, is crucial for the prognosis and treatment planning. Biochemical signaling during periodontal disease involves the production of cytokines. Chemokines belong to the group of chemotactic cytokines that are able to activate and migrate a variety of cells, like bone cells and leukocytes, which stimulate inflammatory events. Chemotactic chemokines, monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein-1 alpha (MIP-1α) are secreted by different types of cells. These salivary biomarkers might be of use in assessing periodontal disease presence, risk and progression, especially in diabetic patients.

Materials and methods: Thirteen milliliters of unstimulated whole saliva were collected from five groups: Systemically and periodontally healthy individuals, gingivitis patients without diabetes, periodontitis patients without diabetes, periodontitis patients with diabetes, and periodontally healthy diabetic individuals. The samples were then centrifuged at 2600 rpm for 15 min at 4°C, and the supernatant was kept at - 80°C until additional analysis. The enzyme-linked immunosorbent assay was used to examine the amounts of MCP-1 and MIP-1α in the saliva.

Results: Levels of MCP-1 and MIP-1α were significantly higher in periodontitis with diabetes group (P = 0.000) than in other groups. Receiver operating curve showed 95% specificity and 100% sensitivity for MCP-1 and MIP-1α, respectively, in detecting periodontitis, especially in diabetic individuals."

Conclusion: The present study illustrates that salivary biomarkers MCP-1 and MIP-1α might be promising biomarkers in differentiating periodontal disease from health, especially in diabetic individuals.

A frenum is a thin fold of mucous membrane that attaches the lips to the alveolar mucosa and the periosteum. The frenum provides stability and supports the lips and the tongue. Normally, the terminal end of the frenum is seen till the alveolar mucosa or the gingiva. However, in some individuals, the frenum extends to or beyond the interdental papilla. Such an aberrant or high frenular attachment compromises plaque control, orthodontic treatment, denture retention, and esthetics. Therefore, surgical correction of the high frenal attachment is recommended either by completely removing the frenum (frenectomy) or repositioning it (frenotomy). Frenectomy or frenotomy can be performed either with a surgical blade, electrocautery, or lasers. Frenectomy using a surgical blade is considered to be more invasive compared to other methods, as it involves mild-to-moderate bleeding, the formation of large surgical wounds, and the chances of scarring during healing. Hence, many modifications to the conventional frenectomy technique have been explored to provide a simpler, faster, and minimally invasive surgical approach. The present case series highlights a novel frenectomy technique involving a modification of the paralleling technique for frenal correction.

Background: A common and persistent inflammatory condition impacting the supportive structures of teeth, periodontal disease presents notable challenges in dental healthcare. It leads to various clinical issues, including the loss of clinical attachment, increased pocket depth, and tooth mobility. The global prevalence of periodontitis is substantial, with an estimated 20%-50% of the world's population affected, particularly in developing countries. Furthermore, periodontitis often culminates in tooth loss, affecting overall health and the quality of life, particularly in aging populations. Early intervention and accurate prediction of tooth loss are crucial for improving oral health outcomes. Conventional prognostic models have their constraints in sensitivity, prompting the exploration of alternative approaches. Machine learning, an evolving field in artificial intelligence, has gained prominence in various domains, including healthcare. In this study, we examined the potential of machine learning to predict tooth loss based on diverse parameters, including age, systemic diseases (such as diabetes and hypertension), grades of tooth mobility, oral hygiene habits, and more.

Materials and methods: Data from 200 patients were collected, categorized by gender, age, and mobility grades, with 45 having diabetes, 36 with hypertension, and the remaining free of these systemic diseases. The Orange machine learning tool was employed to analyze these data. The free and open-source data visualization and machine learning platform offers user-friendly visual programming for predictive modeling and data analysis.

Results: This study showed that machine learning models produced highly accurate predictions, with an area under the curve of 1.000 for several algorithms, such as Naive Bayes, AdaBoost, Random Forest, and Neural Network. Accuracy, precision, recall, and specificity values consistently exceeded 95%, demonstrating the potential of machine learning in predicting tooth loss.

Conclusion: By analyzing comprehensive datasets, machine learning models can enhance the accuracy and objectivity of tooth loss prediction. While challenges remain, such as data quality and privacy concerns, integrating machine learning algorithms in dentistry can revolutionize dental healthcare, improve patient outcomes, and reshape the future of periodontics.

Background: Medical students often exhibit limited awareness of periodontitis as a significant risk factor for cardiovascular diseases.

Aim: This study aimed to evaluate the knowledge, attitudes, and practices (KAPs) of medical students regarding periodontitis as a cardiovascular risk factor and their approaches to managing patients requiring dental care.

Materials and methods: A cross-sectional study was conducted among 501 medical students in Central Gujarat using a structured, self-administered questionnaire comprising 23 questions. Descriptive and inferential statistics, including t-tests, one-way analysis of variance, post hoc tests, Fisher's Exact Test, and Pearson's correlation, were applied.

Results: Postgraduate students demonstrated significantly higher knowledge scores than undergraduates (P < 0.01), with no statistically significant differences in attitudes or practices. A weak or negligible correlation was observed between KAPs (knowledge-practice: r = -0.013; attitude-practice: r = -0.006).

Conclusion: Periodontitis is an important yet under-recognized cardiovascular risk factor. Its inclusion in medical curricula, alongside other established risk factors, is warranted. Improved interdisciplinary education may enhance the quality of care for cardiovascular patients with periodontal disease.

Background: Periodontal therapy aims to eliminate the periopathogens involved in disease progression to restore the lost form and function of the tooth-supporting structures. To overcome the drawbacks of conventional treatment, several adjunctive treatment modalities have been successfully used to treat moderate-to-severe periodontal diseases. The present study aims to comparatively evaluate the efficacy of locally delivered tetracycline fibers and antimicrobial photodynamic therapy (PDT) as an adjunct to nonsurgical periodontal treatment, focusing on the possibility of improving the clinical parameters and reducing the microbial load postoperatively.

Materials and methods: Twenty-two patients with Stage II Grade B periodontitis were divided into two groups: Group A: scaling and root planing (SRP) + locally delivered tetracycline fibers and Group B: SRP + antimicrobial PDT using indocyanine green as photosensitizer. The following clinical parameters were assessed at baseline, 3 months, and 6 months: Gingival index, modified sulcular bleeding index, clinical attachment level, and probing pocket depth. Subgingival plaque samples were obtained for microbial analysis of Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Prevotella intermedia at the following periods.

Results: There was significant improvement in clinical and microbiological parameters in both the groups at 3 months and 6 months postoperatively. Microbial recolonization was noted in both the groups, albeit without reaching statistical significance with respect to P. gingivalis and A. actinomycetemcomitans at 6 months postoperatively.

Conclusion: From the results obtained, it can be concluded that both local drug delivery and antimicrobial PDT can be used as an effective adjunctive treatment to SRP. However, supportive periodontal care is required to avoid microbial recolonization.

Background: The efficacy of autogenous tooth bone graft (auto-TBG) is affected by many determinants, such as sterilization protocols and the expression of bone-inducing proteins such as bone morphogenetic protein (BMP) present in the graft.

Aim: This in vitro study was conducted to compare the efficacy of different sterilization protocols and their impact on the osteogenic potential of auto-TBG.

Materials and methods: Ninety-eight auto-TBG samples were obtained from 10 extracted teeth that were prepared, ground, and sorted with the help of a dentin grinder. Seventy samples were partially demineralized with the standard recommended techniques, and 28 samples were kept nondemineralized (ND) for comparison. All the processed samples were subjected to different sterilization protocols namely Group A (gamma radiation), Group B (autoclave), Group C (chemical sterilization): Subgroup a (chlorhexidine), Subgroup b (povidone-iodine), Subgroup c (lactic acid), Subgroup d (sodium hydroxide), and Group D (normal saline). Sterilized auto-TBG samples were subjected to microbiological and biochemical analysis.

Results: All sterilization protocols could effectively eliminate the microbial growth except normal saline. Mean BMP-2 concentration (pg/mL) was found as 141.91 (chlorhexidine) >134.50 (sodium hydroxide) >134.17 (lactic acid) >133.83 (povidone-iodine) >122.83 (gamma radiation) >121.81 (autoclave) >119.80 (normal saline).

Conclusion: Comparatively, the chemical method of sterilization showed a higher mean concentration of BMP-2. In addition, all the samples of demineralized auto-TBG particles showed significantly higher mean BMP-2 concentration as compared to ND samples.