Single-Appointment Fabrication of Interim Immediate Denture: A Clinical Report

Objective: An immediate complete denture is fabricated before the extraction of all teeth. It has several advantages such as preservation of esthetics, muscular tone, normal speech and reduction of post-operative pain. This report describes a method of using patient’s current fixed partial denture (FPD) for single-appointment construction of interim immediate denture.Case: We used patient’s existing maxillary FPD for single-appointment fabrication of an interim immediate denture; which was delivered to the patient after the extraction of his remaining maxillary teeth.Conclusion: Within a short time, an interim immediate denture can be fabricated for patients to preserve occlusion, vertical facial height and facial appearance until the fabrication of final prosthesis

An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis

Teachers’ use of Descriptive Assessment methods in primary school mathematics education in Iran

International audienc

Efficacy of different doses of ketamine as a bolus in major depressive disorder

Background: Major depressive disorder is a severe, heterogeneous, common medical illness and a leading cause of disability throughout the world that poses a significant public health issue. Previous studies have shown rapid antidepressant effects following a single administration of ketamine. This study aimed to assess the impact of route of administration and dose of ketamine for the reduction of depressive symptoms and compare the effects of different doses and methods. Methods: A double-blind clinical controlled trial was done on 100 patients with a primary diagnosis of major depressive disorder who were assigned into two groups of 50 subjects at a dose of 0.5 mg/kg and 0.75 mg/kg ketamine and each group was divided into two groups of 25 subjects following a single dose of intravenous bolus and infusion of ketamine. The patient’s severity of depression was evaluated with Hamillton Depression Rating Scale and Beck Depression Inventory scores after 2 days, 7 days, 30 days and 60 days of ketamine administration, then the results were compared between groups. Results: According to Hamilton and Beck score, the treatment response in investigated patients was 64% and 60%, respectively. Conclusions: These data suggest that ketamine effect is related to drug dose and type of administration. The dose of 0.75 mg/kg of ketamine is more effective than 0.5 mg/kg and a bolus injection of low-dose ketamine (0.5 mg/kg) is more effective than infusion and in high-dose ketamine (0.75 mg/kg), there was no difference between the methods of drug administration

Dusty Air Pollution is Associated with an Increased Risk of Allergic Diseases in Southwestern Part of Iran

Concerns have been raised about the adverse impact of dusty air pollution (DAP) on human health. The aim of this study was to find the association between dusty air pollution based on air quality index (AQI) and the risk of allergic diseases in southwestern provinces of Iran, with assessing cytokine profiles and lymphocyte immunophenotypes. In this case control study 148 individuals participated. The sampling was done in hazardous condition (AQI >300) as the case and clean air (AQI <50) as the control. We measured cytokine production by using ELISA method and phenotypes of T-lymphocytes (CD4+ and CD8+), CD19+ B-lymphocytes, CD25+, CD4+ CD25+ cells by FACSort flow cytometer. The mean serum level of IL-4 (33.4±2.9 vs 0.85± 0.65 pg/dl) and IL-13 (15.1±4.4 vs. 0.12±0.7 pg/dl) in the subjects exposed to ambient DAP was increased significantly compared to the individuals in the clean air condition. Also, CD19+ B-lymphocytes (12.6± 4.9 vs 8.9±3.2%) and CD4+ CD25+ cell count (13.6± 4.6 vs 7.7± 3.8%) in peripheral blood were increased significantly in subjects exposed to ambient DAP compared with the controls. The result of our study suggested that ambient DAP affected immune system in a way that might lead to allergic diseases in the population

4D-Precise: learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

Background and Objective In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series. Methods We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation. Results The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo. Conclusions Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series