Quantitative analysis of particulate matter release during orthodontic procedures: a pilot study.

Introduction Transmission of SARS-CoV-2 through aerosol has been suggested, particularly in the presence of highly concentrated aerosols in enclosed environments. It is accepted that aerosols are produced during a range of dental procedures, posing potential risks to both dental practitioners and patients. There has been little agreement concerning aerosol transmission associated with orthodontics and associated mitigation.Methods Orthodontic procedures were simulated in a closed side-surgery using a dental manikin on an acrylic model using composite-based adhesive. Adhesive removal representing debonding was undertaken using a 1:1 contra-angle handpiece (W&H Synea Vision WK-56 LT, Bürmoos, Austria) and fast handpiece with variation in air and water flow. The removal of acid etch was also simulated with the use of combined 3-in-1 air-water syringe. An optical particle sizer (OPS 3330, TSI Inc., Minnesota, USA) and a portable scanning mobility particle sizer (NanoScan SMPS Nanoparticle Sizer 3910, TSI Inc., Minnesota, USA) were both used to assess particulate matter ranging in dimension from 0.08 to 10 µm.Results Standard debonding procedure (involving air but no water) was associated with clear increase in the 'very small' and 'small' (0.26-0.9 µm) particles but only for a short period. Debonding procedures without supplementary air coolant appeared to produce similar levels of aerosol to standard debonding. Debonding in association with water tended to produce large increases in aerosol levels, producing particles of all sizes throughout the experiment. The use of water and a fast handpiece led to the most significant increase in particles. Combined use of the 3-in-1 air-water syringe did not result in any detectable increase in the aerosol levels.Conclusions Particulate matter was released during orthodontic debonding, although the concentration and volume was markedly less than that associated with the use of a fast handpiece. No increase in particulates was associated with prolonged use of a 3-in-1 air-water syringe. Particulate levels reduced to baseline levels over a short period (approximately five minutes). Further research within alternative, open environments and without air exchange systems is required.

An SVM-based method for assessment of transcription factor-DNA complex models.

BACKGROUND: Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models. RESULTS: We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models. CONCLUSIONS: A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models.

Protein-protein interface detection using the energy centrality relationship (ECR) characteristic of proteins.

Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.