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Objectives: Introduction Fractal analysis (FA) is a computational method used to quantify the complex trabecular structure of bone. While FA has been widely applied in dentistry, there are challenges in standardizing the technique due to factors such as image resolution, region of interest (ROI) selection, and image processing. This study aimed to investigate the impact of the direction of ROI selection (DROIS) on fractal dimension (FD) calculations., Methods: Panoramic radiographs of 226 individuals aged 20-35 years were analyzed. ROIs were selected on the mandibular condyle, angular region, and mental region, and oriented at 0°, 22.5°, 45°, and 67.5° angles. FD was calculated using the box-counting method in ImageJ. The Friedman test and Wilcoxon signed-rank test were used for statistical analysis., Results: The FD values differed significantly between the angled ROI groups in all three regions (Friedman test, p < 0.0001). Pairwise comparisons showed significant differences in FD between most ROI orientations, except between 22.5° and 67.5° in the angular region., Conclusions: DROIS is an important factor that should be considered in FA studies to ensure reliable and reproducible FD values. Appropriate methodological choices can help account for the influence of DROIS on FD calculations.., Competing Interests: Declarations Conflict of interest There is no conflict of interest among authors. Ethical approval All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Aims: Benralizumab, a humanized, afucosylated monoclonal antibody against the interleukin 5 receptor, α subunit, causes rapid depletion of eosinophils by antibody-dependent cellular cytotoxicity. We investigated the pharmacokinetic and pharmacodynamic effects of benralizumab in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) from the phase III OSTRO trial., Methods: Patients received a placebo or 30 mg of benralizumab by subcutaneous injection every 8 weeks (first three doses every 4 weeks) to week 48; a subset of patients continued in an extended follow-up period to assess treatment durability to week 80. Serum benralizumab concentrations and blood eosinophil and basophil counts were assessed to week 80. Biomarker assessments were performed on nasal polyp tissue biopsies at week 56 and nasal lining fluid at weeks 24 and 56 to examine changes in immune cells and inflammatory mediators., Results: Among 185 patients in this analysis, 93 received benralizumab. Serum benralizumab concentrations reached a steady state by week 24 (median concentration 385.52 ng mL -1 ); blood eosinophils were almost fully depleted and blood basophils were reduced between weeks 16 and 56. Nasal polyp tissue eosinophils decreased with benralizumab from 57.6 cells mm -2 at baseline to 0 cells mm -2 at week 56 (P < .001 vs placebo), and tissue mast cells were numerically reduced. In nasal lining fluid, eosinophil-derived neurotoxin was significantly reduced at weeks 24 and 56 (P < .001) and interleukin-17 at week 56 (P < .05) with benralizumab., Conclusion: Benralizumab treatment led to rapid, sustained, nearly complete depletion of eosinophils from blood and nasal polyp tissue in patients with CRSwNP., (© 2024 AstraZeneca. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Objective: The aim of this study is to evaluate the knowledge and awareness levels of patients who have at least one impacted tooth and who had previously applied to the dentist., Study Design: This study was conducted in patients aged 15 years and older who applied to Ordu University Faculty of Dentistry for routine examination and agreed to fill out the questionnaire form. A total of 325 people participated in the survey conducted to determine the awareness of patients applying to the faculty of dentistry about their existing impacted teeth. A Pearson's chi-square test was used for hypothesis testing when expected frequencies exceeded 5., Results: It was determined that 56.9 % (185) of the participants were aware of their existing teeth, while 43.1 % (140) were not aware. When the patients were evaluated according to the institutions they had visited, it was seen that the group who were most aware of the presence of impacted tooth were those who apply to the faculty of dentistry (74.4 %). The rate of being informed by dentists in the institutions that they had previously visited was higher in patients with university or post-university graduates (p < 0.05). The most common information given by the dentists to the patients about their impacted dental problems was that the tooth should be followed up (40.4 %), while the removal of the tooth constituted 28.4 % of the information given., Conclusion: This study showed that although patients are aware of their existing impacted teeth, their level of knowledge about the risks it may pose is low. For a healthy oral care and health, patients should be adequately informed about impacted teeth., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights., (© 2023. The Author(s).)

Objectives: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer., Methods: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively., Results: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists., Conclusion: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry., (© 2021. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns that generate high-dimensional outputs, combined with machine learning, to achieve distributed information encoding and decoding. Our approach exploits a critical property of many natural pattern-formation systems: in repeated realizations, each initial configuration generates similar but not identical output patterns due to randomness in the patterning process. However, for sufficiently small randomness, different groups of patterns that arise from different initial configurations can be distinguished from one another. Modulating the pattern-generation and machine learning model training can tune the tradeoff between encoding capacity and security. We further show that this strategy is scalable by implementing the encoding and decoding of all characters of the standard English keyboard., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Bitewing radiographic imaging is an excellent diagnostic tool for detecting caries and restorations that are difficult to view in the mouth, particularly at the molar surfaces. Labeling radiological images by an expert is a labor-intensive, time-consuming, and meticulous process. A deep learning-based approach has been applied in this study so that experts can perform dental analyzes successfully, quickly, and efficiently. Computer-aided applications can now detect teeth and number classes in bitewing radiographic images automatically. In the deep learning-based approach of the study, the neural network has a structure that works according to regions. A region-based automatic segmentation system that segments each tooth using masks to help to assist analysis as given to lessen the effort of experts. To acquire precision and recall on a test dataset, Intersection Over Union value is determined by comparing the model's classified and ground-truth boxes. The chosen IOU value was set to 0.9 to allocate bounding boxes to the class scores. Mask R-CNN is a method that serves as instance segmentation and predicts a pixel-to-pixel segmentation mask when applied to each Region of Interest. The tooth numbering module uses the FDI notation, which is widely used by dentists, to classify and number dental items found as a result of segmentation. According to the experimental results were reached 100% precision and 97.49% mAP value. In the tooth numbering, were obtained 94.35% precision and 91.51% as an mAP value. The performance of the Mask R-CNN method used has been proven by comparing it with other state-of-the-art methods., (Copyright © 2022 Elsevier Ltd. All rights reserved.)