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Modern radiotherapy delivers treatment plans optimised on an individual patient level, using CT-based 3D models of patient anatomy. This optimisation is fundamentally based on simple assumptions about the relationship between radiation dose delivered to the cancer (increased dose will increase cancer control) and normal tissue (increased dose will increase rate of side effects). The details of these relationships are still not well understood, especially for radiation-induced toxicity. We propose a convolutional neural network based on multiple instance learning to analyse toxicity relationships for patients receiving pelvic radiotherapy. A dataset comprising of 315 patients were included in this study; with 3D dose distributions, pre-treatment CT scans with annotated abdominal structures, and patient-reported toxicity scores provided for each participant. In addition, we propose a novel mechanism for segregating the attentions over space and dose/imaging features independently for a better understanding of the anatomical distribution of toxicity. Quantitative and qualitative experiments were performed to evaluate the network performance. The proposed network could predict toxicity with 80% accuracy. Attention analysis over space demonstrated that there was a significant association between radiation dose to the anterior and right iliac of the abdomen and patient-reported toxicity. Experimental results showed that the proposed network had outstanding performance for toxicity prediction, localisation and explanation with the ability of generalisation for an unseen dataset.

This study was conducted on impression technique on overdenture implants. The overdenture impression records the soft tissue supporting areas including retained roots or implants. Simultaneously with accurate positioning of the implant components for optimal distributions of functional loads, registering the soft tissue in functionally formed impression can be useful. The resultant master cast from this technique is accurate in terms of soft tissue detail, position of implant components, and relationship between soft tissue and implants.

Animal studies frequently are used in assessing potential human health effects from exposure to inhaled toxicants. Such studies also are used to investigate sensitive subpopulations such as children. Among other factors that influence the degree to which animal models are predictive of human effects in the delivered dose of the toxicant to the various regions of the respiratory tract. Because the rat is an obligatory nose breather, an understanding of the rat nasal-pharyngeal airway geometry is needed to relate exposures to delivered doses. In this study, the growth and development of the rat nasal-pharyngeal airway was studied at one-week intervals in male Fischer-344 rats from one to five weeks. Casts of an adult (60 day) and an aging(441 day) rat were included for comparison. Replica casts of the nasal-pharyngeal airway were made by injecting silicone rubber through the trachea, and sections in anterior-posterior positions were made for morphometric study. A simpler structure of the nasal-pharyngeal airway was found in the young rats. While the percentage of the airway composed of turbinates was similar at all ages, the surface area of the turbinates increased 7.7-fold between 7 and 60 days. Because of the simpler structure and smaller surface area in the young rat, extrathoracic clearance is probably less efficient, resulting in a higher delivered dose to the lung of a young rat than to that of an adult rat exposed to the same toxicant concentration.