This clinical trial compared the efficacy of doxycycline (DOX) in β-cyclodextrin (DOX)/βCD) with DOX- alone in gel on thirty-three subjects with periodontitis. Patients were randomized to group 1 GI; 10% DOX + scaling and root planning (SRP); group 2 (GII (10% DOX /β-CD + SRP), and group 3 (GIII; SRP). Gels were applied in GI and GII at baseline (T0) and 30 days later (T1). Periodontal Probing Depth (PPD), Clinical Attachment Level (CAL), Bleeding on Probing (BOP) and Visible Plaque Index (VPI) were evaluated at (T0), 30 days (T1) and 60 days after T0 (T2). Bone density was analyzed after 18 months (T3). GII showed the most significant reduction of PPD (2.62 mm; p <0.003), and greatest gain in CAL (2.54 mm p <0.003) at T2. BOP and the VPI had a strong reduction in all groups at T2 (p <0.05), both decreased by ≥5 times and 2 times, respectively, in all groups at T1. Bone density increased in all groups in radiographs (T3). The use of DOX encapsulated in β-CD gel with SRP resulted in significant improvements in clinical periodontal parameters; such molecular inclusion of doxycycline into β-CD in gel for local application is relatively simple and useful in dentistry.

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm⁻¹, 1013 cm⁻¹, 1002 cm⁻¹, 1004 cm⁻¹, and 1011 cm⁻¹ in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

Salivary analysis tool for disease diagnostics has garnered growing scientific interest. The key advantages and the wide range of components consistently associated with oral diseases were discussed. A variety of salivary diagnostic technologies are available and have contributed to strategies for periodontal, dental, and mucosal alterations. Advanced nucleic acid (RT-Polymerase chain reaction (PCR), qPCR), salivary proteome, metabolomic, and lipidomic profiling (enzyme-linked immunosorbent assay (ELISA), mass spectrometry, ATR-FTIR) have identified biomarkers related to the diagnosis and progression of periodontal disease. Salivary studies with dental caries focus on host factors and cariogenic pathways like salivary antimicrobial proteins, flow, and bacteria clearance, emphasizing salivary markers in susceptible individuals. Human and microbial salivary proteomes have offered insights into the molecular pathogenesis of Molar Incisor Hypomineralization (MIH) (mass spectrometry-bioinformatic tools), identifying biomarker proteins found only in MIH subjects. Infrared profile (ATR-FTIR spectroscopy) coupled with machine algorithms can differentiate amelogenesis imperfecta and control saliva samples. Using immunological assay (ELISA) and an ultra-deep proteomics approach, we identified biomarkers that provide a good picture of the oral environment in the context of oral mucositis, providing a valuable window into treatment and/or disease progression monitoring. The combination of the biofluid saliva with analytical analysis has brought numerous perspectives for applications in diagnosing and managing oral diseases.

Objectives: To describe the landscape of dental care provided by specialised centres for children and adolescents with rare diseases (RDs) in the state of Minas Gerais, southeastern Brazil. Methods: A retrospective cross‐sectional study was conducted involving individuals aged 0–18 years with a confirmed diagnosis of a RD who received care at five specialised dental centres. Data on the diagnosis, age at first dental appointment, frequency of annual visits and travel distance from home to treatment centre were analysed using descriptive and inferential statistics. Results: A total of 1057 individuals with 244 different RDs were identified. Most were boys (54.9%). The average age at the first dental appointment was 8.52 years. Haematological diseases were the most prevalent (38.9%). The average travel distance for treatment was 99.1 km, with individuals from the Jequitinhonha region traveling the farthest (526.3 km). The average number of annual dental visits was 2.4. Patients with craniofacial syndromes accessed care earlier (average: 3.6 years) and had more frequent follow‐up appointments (average: 4.8 visits/year). Significant regional disparities were found in age at first appointment ( p < 0.001), travel distance ( p < 0.001) and frequency of visits ( p = 0.002). Conclusions: Children and adolescents with RDs had delayed initiation of dental care, low follow‐up rates and substantial travel burdens. The concentration of specialised centres in the state capital underscores the need for policy reforms to improve equitable access, particularly for patients in remote areas.

A Amelogênese Imperfeita é uma doença rara de interesse na Odontologia definida como uma anormalidade genética que envolve o esmalte de todos os dentes, de ambas as dentições, com ampla variabilidade genotípica e fenotípica. Devido à necessidade de melhor compreensão das bases moleculares da Amelogênese Imperfeita, propôs-se um estudo das alterações genéticas em indivíduos com esse fenótipo, em parceria da Faculdade de Odontologia com o ICB-UFMG. Utilizamos Sequenciamento do Exoma Completo (WES) e análise de bioinformática, com objetivo de compreender o espectro de variantes genéticas de famílias cujos membros apresentam Amelogênese Imperfeita. As estratégias são investigar o padrão de herança dos indivíduos afetados, os genes e as variantes genéticas envolvidas e o impacto da variante na proteína, além de tentar estabelecer uma correlação entre o genótipo com o fenótipo, realizar o aconselhamento genético e identificar possíveis casos sindrômicos.

Amelogenesis imperfecta (AI) encompasses a group of conditions characterized by abnormalities in the development or function of tooth enamel. Clinical manifestations include different forms and degrees of enamel frailty, associated with sensitivity, tooth fractures, stains, abnormal tooth morphology, missing teeth, etc. AI is genetically heterogeneous, with over 70 genes associated with autosomal dominant, autosomal recessive, X-linked, and oligogenic inheritance. Objective To identify genetic variants associated with AI in a single family. Methodology We describe the clinical findings of a family affected by AI, composed of five individuals: four affected (the father and three daughters) and one unaffected (the mother). The observed segregation pattern suggests a dominant, X-linked inheritance. Genetic variants were screened using whole-exome sequencing. The initial bioinformatic analysis was conducted using Qiagen QCI, and variants were selected based on their presence in all four affected family members and absence in the unaffected mother. Search terms included “amelogenesis imperfecta,” “tooth,” and “enamel.” Several types of software were used to classify variants according to pathogenicity. Results Candidate variants were identified in six genes. Three of these variants were detected in autosomal genes: NM_031889.3(ENAM):c.1726T>C (p.F576L), NM_022168.4(IFIH1):c.1764dupA, (p.A589fs*21), and NM_032383.5(HPS3):c.1897A>T (p.M633L). Three variants were detected in X-linked genes: NM_006150.5(PRICKLE3):c.8C>G (p.A3G), NM_004484.4(GPC3):c.584A>G (p.N195S), and NM_152787.5(TAB3):c.1936G>A (p.V646M). None of these variants were classified as pathogenic or likely pathogenic in AI. Discussion Among the identified genes, only ENAM has previously been associated with AI; however, IFIH1, PRICKLE3, and GPC3 are associated with dental/enamel development. The relatively high number of candidate genes and variants detected may reflect an oligogenic component already proposed for AI. Conclusions This study provides a set of new candidate genes and genetic variants for AI. Despite sharing the same variants, AI-affected family members show considerable phenotypic variant, suggesting the involvement of non-shared genetic or environmental factors.