BackgroundRapid maxillary expansion (RME), indicated in the treatment of maxillary deficiency directs high forces to maxillary basal bone and to other adjacent skeletal bones. The aim of this study is to (i) evaluate stress distribution along craniofacial sutures and (ii) study the displacement of various craniofacial structures with rapid maxillary expansion therapy by using a Finite Element model.MethodsAn analytical model was developed from a dried human skull of a 12 year old male. CT scan images of the skull were taken in axial direction parallel to the F-H plane at 1 mm interval, processed using Mimics software, required portion of the skull was exported into stereo-lithography model. ANSYS software was used to solve the mathematical equation. Contour plots of the displacement and stresses were obtained from the results of the analysis performed.ResultsAt Node 47005, maximum X-displacement was 5.073 mm corresponding to the incisal edge of the upper central incisor. At Node 3971, maximum negative Y-displacement was -0.86 mm which corresponds to the anterior zygomatic arch, indicating posterior movement of craniofacial complex. At Node 32324, maximum negative Z-displacement was -0.92 mm representing the anterior and deepest convex portion of the nasal septum; indicating downward displacement of structures medial to the area of force application.ConclusionsPyramidal displacement of maxilla was evident. Apex of pyramid faced the nasal bone and base was located on the oral side. Posterosuperior part of nasal cavity moved minimally in lateral direction and width of nasal cavity at the floor of the nose increased, there was downward and forward movement of maxilla with a tendency toward posterior rotation. Maximum von Mises stresses were found along midpalatal, pterygomaxillary, nasomaxillary and frontomaxillary sutures.

In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) bioALBERT an effective domain-specific language model trained on large-scale biomedical corpora designed to capture biomedical context-dependent NER. We adopted a selfsupervised loss used in ALBERT that focuses on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction techniques to lower memory consumption and increase the training speed in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.

Patients with chronic pain are frequently excluded from trials because of comorbidities. More inclusive studies will have better generalizability considering differences between excluded and included patients.

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