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Applied Sciences
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Advanced Materials and Technology in Dental, Oral and Maxillofacial Health
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Advanced Materials and Technology in Dental, Oral and Maxillofacial Health

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Dentistry and Oral Sciences".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 7232

Special Issue Editor

Dr. Farah Asa'ad

E-Mail Website1 Website2
Guest Editor
Associate Professor, Department of Biomaterials, Institute of Clinical Sciences & Department of Oral Biochemistry, Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
Interests: epigenetics; extracellular vesicles; biomaterials; bone and periodontal tissue regeneration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Patients’ quality of life is highly dependent on dental, oral, and maxillofacial health. As such, healthcare providers aim to either maintain already-existing healthy conditions, or to restore diseased tissues and defects to the state of health.

Defects in the maxillofacial region can either be due to accidents or due to diseases such as oral/head and neck cancer and periodontitis, which is an oral disease characterized by irreversible bone destruction around natural teeth.

In recent years, the regeneration of defects in the maxillofacial region have witnessed major advancements and innovations, mainly in the terms of biomaterial fabrication using advanced technologies. These advancements are in line with the concept of personalized medicine, which is dedicated towards creating a personalized scaffold made of biomaterials with fine-tuned geometry that perfectly fit an individual defect, which would increase the success of the final regenerative outcomes due to the use of a scaffold that perfectly matches the defect morphology.

In this Special Issue, we welcome original articles (clinical, in vitro, and in vivo studies), reviews (narrative and systematic), and short communications that address the following topics focused on the regeneration of defects in the maxillofacial region, either due to accidents, oral/head and neck cancer, and periodontitis; 3D printing methods for the manufacturing of 3D printed scaffolds; as well as advances in biomaterial fabrication and coating, and their applications in personalized medicine.

Dr. Farah Asa'ad
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomaterials
  • 3D printing
  • scaffolds
  • personalized medicine
  • maxillofacial defects

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Published Papers (2 papers)

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Research

11 pages, 4797 KiB  
Article
Can Vitamin C Improve Proliferation and Viability of Smokers’ Gingival Fibroblasts on Collagen Membranes? An In Vitro Study
by Fahad Ali Alshehri
Appl. Sci. 2023, 13(19), 10828; https://doi.org/10.3390/app131910828 - 29 Sep 2023
Cited by 1 | Viewed by 1041
Abstract
Periodontal regeneration using a barrier membrane can be affected by several factors, including patient-related factors (such as smoking habits), surgical techniques, and type of barrier membrane. Smoking exposure has a negative impact on the periodontium due to its direct inhibition of gingival fibroblast [...] Read more.
Periodontal regeneration using a barrier membrane can be affected by several factors, including patient-related factors (such as smoking habits), surgical techniques, and type of barrier membrane. Smoking exposure has a negative impact on the periodontium due to its direct inhibition of gingival fibroblast function. Vitamin C is widely recognized as an antioxidant that can be used to mitigate the detrimental impact of smoking products on periodontal cells. This study aimed to investigate whether vitamin C could improve the proliferation and viability of gingival fibroblasts extracted from smoking and non-smoking donors and then cultured on non-crosslinked (CopiOs Pericardium) and crosslinked (BioMend) collagen membranes. To address this aim, human gingival fibroblasts were extracted from healthy periodontium of smoker patients (Group 1) and non-smoker patients (Group 2). The cells were cultivated and subsequently subcultured in a growth medium supplemented with the required nutrients. Subsequently, the medium at passage six was supplemented with vitamin C, i.e., at the start of the experiment. An evaluation of cell proliferation and viability was carried out using cell migration assays and AlamarBlue® assays for cells grown on BioMend and CopiOs Pericardium collagen membranes. Assessment of the morphology and attachment of gingival fibroblasts to the experimental collagen membranes was conducted using scanning electron microscopy (SEM). The viability and proliferation assessments of hGFs from the migration assay were evaluated using AlamarBlue®. The results exhibited significant fluorescent intensity of gingival fibroblasts on both membrane groups (BioMend and CopiOs Pericardium) in the smoker group compared to the non-smoker group (p < 0.05), which was interpreted to be the result of hGF metabolic activity and the exclusion of any cytotoxic effects, particularly from vitamin C addition. Vitamin C positively affected cells from the smoker group with statistically significant results in the BioMend group (Wilcoxon signed-rank test of p value < 0.05; p = 0.028). SEM images revealed the crosslinking pattern of the BioMend membrane and the non-crosslinked natural tissue structure of the CopiOs Pericardium membrane, which did not change regardless of whether the cultured smoker or non-smoker hGFs were treated with vitamin C. Small numbers of attached hGFs in membrane matrices in all samples, mainly in the peripheries, were observed. It can be concluded that the addition of vitamin C to collagen membranes in vitro seems to combat the adverse effects of smoking products on gingival fibroblasts. Full article
(This article belongs to the Special Issue Advanced Materials and Technology in Dental, Oral and Maxillofacial Health)
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14 pages, 2847 KiB  
Article
Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network
by Giulia Rubiu, Marco Bologna, Michaela Cellina, Maurizio Cè, Davide Sala, Roberto Pagani, Elisa Mattavelli, Deborah Fazzini, Simona Ibba, Sergio Papa and Marco Alì
Appl. Sci. 2023, 13(13), 7947; https://doi.org/10.3390/app13137947 - 6 Jul 2023
Cited by 12 | Viewed by 5666
Abstract
Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental [...] Read more.
Background and purpose: Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays. Methods: An instance segmentation model was trained using the architecture of a Mask Region-based Convolutional Neural Network (Mask-RCNN). The data for the training, validation, and testing were taken from the Tuft dental database (1000 panoramic dental radiographs). The number of the predicted label was 52 (20 deciduous and 32 permanent). The size of the training, validation, and test sets were 760, 190, and 70 images, respectively, and the split was performed randomly. The model was trained for 300 epochs, using a batch size of 10, a base learning rate of 0.001, and a warm-up multistep learning rate scheduler (gamma = 0.1). Data augmentation was performed by changing the brightness, contrast, crop, and image size. The percentage of correctly detected teeth and Dice in the test set were used as the quality metrics for the model. Results: In the test set, the percentage of correctly classified teeth was 98.4%, while the Dice score was 0.87. For both the left mandibular central and lateral incisor permanent teeth, the Dice index result was 0.91 and the accuracy was 100%. For the permanent teeth right mandibular first molar, mandibular second molar, and third molar, the Dice indexes were 0.92, 0.93, and 0.78, respectively, with an accuracy of 100% for all three different teeth. For deciduous teeth, the Dice indexes for the right mandibular lateral incisor, right mandibular canine, and right mandibular first molar were 0.89, 0.91, and 0.85, respectively, with an accuracy of 100%. Conclusions: A successful instance segmentation model for teeth identification in panoramic dental X-ray was developed and validated. This model may help speed up and automate tasks like teeth counting and identifying specific missing teeth, improving the current clinical practice. Full article
(This article belongs to the Special Issue Advanced Materials and Technology in Dental, Oral and Maxillofacial Health)
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