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Background: Nasopharyngeal cancer (NPC) patients have high risk of cachexia. It is important to notice cachexia parameters in the beginning of their treatment in order to predict and anticipate unexpected outcomes. This study aimed to describe the profile of advanced stage NPC patient receiving first chemotherapy, especially in cachexia parameters and dietary intakes. Methods: A cross-sectional study was conducted involving advanced stage of NPC patients who would receive their first chemotherapy aged 20-70 years. Subjects with distant metastatic, acute infection, history of past chemotherapy or radiotherapy, were excluded. Parameters of cachexia being studied were inflammation markers (NLR and CRP level), anorexia and fatigue scores, body composition (fat and muscle mass) and hand grip strength (HGS), meanwhile dietary intakes were energy and protein. Frequency distribution, descriptive statistic, and Pearson correlation test were used in data analyses. Results: Forty-three subjects were participated. Average age was48.1±9.8 years old and 63% were males. NLR and CRP level were high, i.e.4.8±5.0 and 13.3±20.6 mg/dL, respectively. Anorexia and fatigue scores were 32.4±9 and 35.9±11.8, respectively, with 70% subjects were anorexic and 30% subjects experienced severe fatigue. Less than 20% were malnourished but 35% subjects had central obesity and 37% subjects classified to have very high fat mass. Most subjects had low muscle mass (77%) and classified to have weak HGS (77%). Energy and protein intakes were 26.7±8.9 kcal/kgBW/d and 1±0.4 g/kg BW/d respectively. Some variables had strong correlation (r≥0.80), i.e. BMI with visceral fat (r=0.94), fat with muscle mass (r=-0.89), energy with protein intakes (r=0.80), and moderate correlation (r=0.60–0.79) between anorexia and fatigue scores. Conclusions: Advanced stage of NPC patients receiving first chemotherapy had high inflammation status, anorexia and fatigue. Many subjects had low muscle mass and weak HGS. BMI with visceral fat, fat with muscle mass, energy with protein intake, were strongly correlated. Bangladesh Journal of Medical Science Vol. 21 No. 02 April’22 Page : 302-310

The pandemic hits the world today has made educators and parents moved to develop attractive English learning media for young learners to keep them excited while learning from home. Android smartphone devices are currently popularly used for interaction media between young learners and teachers during learning from home. From this background, researchers developed English learning media using Augmented Reality that can be easily accessed from Android smartphone for young learners. This research focuses on creating learning media and English quizzes based on Android using Augmented Reality (AR) technology. The English materials used in this research are taken from Thematic English Learning 1 book from chapter 1 to chapter 5 and focused on enriching student's vocabulary. The AR application was made using Unity with the addition tool named Vuforia SDK. It is used to detect an image target so that the AR objects can be displayed throughout the screen. Vuforia SDK detects image target by comparing captured images with those stored in the database. This research resulted 24 threedimensional AR objects, 6 two-dimensional AR objects which are ready to be used as English learning media and 54 quizzes which are feasibly to evaluate students' skills after learning English through AR objects.

Brain tumors are a type of disease in the form of lumps of meat that grow in the brain. In differentiating brain tumor tissue from normal tissue become a difficulty caused by the same colors are an obstacle in seeing brain tumors using MRI images. Accuracy is needed in analyzing brain tumors. However, currently, radiographers (radiologists) still analyze the results of manual MRI images of brain tumors. Therefore we need a method that is able to segment MRI images precisely and automatically, with the aim of obtaining faster and more accurate image segmentation of brain tumors so that we can know the percentage of brain tumors found in the brain. To overcome difficulties when segmenting brain tumors in separating brain tumor tissue from other tissues such as normal brain tissue, cerebrospinal fluid, fat, and edema, a learning-based system method that will carry out the training process uses Haar training to narrow the MRI image so that it is more focused on the part of the head object. Then median filtering is performed to maintain the edge of the image on the MRI image. Then the segmentation process using the thresholding method is run, then repeated to take the largest area. Segmentation of brain is carried out by marking the brain area and the area outside the brain using the DAS method and then cleaning the skull using the cropping method. In this research, 12 images of MRI brain tumors were used. The results of segmentation compared to area of the brain tumor and area of the brain tissue. The system obtains a calculation of the tumor area having an average error of 10,5%.