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Rhinophyma is painless benign swelling due to hypertrophy of the sebaceous gland of the face and nose in particular. In a neglected case, it presented as ‘Proboscis-nose’. As it was hanging in front of nose it was compromising breathing ability of the patient during sleep. It was posing difficulty in placing normal size anstomical face-mask. We placed naso-pharyngeal airway under local anesthesia to retain nasal patency and largest size facemask (size-5) in a manner that it’s air filled cushion was kept deflated and wide mandibular arch was placed nasally to encase the swelling inside mask and then cushion was inflated to make it air tight fit on face. This helped us to get airtight fitting of the anatomical face mask and through nasopharyngeal airway allowed ventilation of the lungs during induction of general anesthesia before intubation. Rest of management and orotracheal intubation was achieved uneventful. We suggest use of nasal and oropharyngeal air way use and reverse uses of large facemask to attain bag-mask ventilation during induction of anesthesia in such cases.

Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH), organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN) comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN) to train and validate the data derived from Geographic Information System (GIS) spatial distribution grids. The neural network architecture with (6-20-1) pattern was able to predict the arsenic concentration with reasonable accuracy.

The spread of dengue fever depends mainly on the availability of favourable breeding sites for its mosquito vectors around human dwellings. To investigate if the various factors influencing breeding habitats can be mapped from space, dengue indices, such as the container index, the house index and the Breteau index, were calculated from Ministry of Public health data collected three times annually in Phitsanulok, Thailand between 2009 and 2011. The most influential factors were found to be temperature, humidity, rainfall, population density, elevation and land cover. Models were worked out using parameters mostly derived from freely available satellite images and fuzzy logic software with parameter synchronisation and a predication algorithm based on data mining and the Decision Tree method. The models developed were found to be sufficiently flexible to accommodate additional parameters and sampling data that might improve prediction of favourable breeding hotspots. The algorithm applied can not only be used for the prediction of near real-time scenarios with respect to dengue, but can also be applied for monitoring other diseases influenced by environmental and climatic factors. The multi-criteria model presented is a cost-effective way of identifying outbreak hotspots and early warning systems lend themselves for development based on this strategy. The proposed approach demonstrates the successful utilisation of remotely sensed images to map mosquito breeding habitats.