No abstract

Feather keratin is a biomass generated in excess from various livestock industries. With appropriate processing, it holds potential as a green source for degradable biopolymer that could potentially replace current fossil fuel based materials. Several processing methods have been developed, but the use of ultrasonication has not been explored. In this study, we focus on (i) comparing and optimizing the dissolution process of turkey feather keratin through sonication and conventional processes, and (ii) generating a biodegradable polymer material, as a value added product, from the dissolved keratin that could be used in packaging and other applications. Sonication of feather keratin in pure ionic liquids (ILs) and a mixture containing ILs and different co-solvents was conducted under different applied acoustic power levels. It was found that ultrasonic irradiation significantly improved the rate of dissolution of feather keratin as compared to the conventional method, from about 2 h to less than 20 min. The amount of ILs needed was also reduced by introducing a suitable co-solvent. The keratin was then regenerated, analyzed and characterized using various methods. This material holds the potential to be reused in various appliances.

Breast cancer (BC) infection, which is peculiar to women, brings about the high rate of deaths among women in every part of the world. The early investigation of BC has minimized the severe effects of cancer as compared to the last stage diagnosis. Doctors for diagnostic tests usually suggest the medical imaging modalities like mammograms or biopsy histopathology (Hp) images. However, Hp image analysis gives doctors more confidence to diagnose BC as compared to mammograms. Many studies used Hp images to develop BC classification models to assist doctors in early BC diagnosis. However, these models lack better and reliable results in terms of reporting multiple performance evaluation metrics. Therefore, the goal of this study is to create a reliable, more accurate model that consumes minimum resources by using transfer learning based convolution neural network model. The proposed model uses the trained model after fine tuning, hence requires less number of images and can show better results on minimum resources. BreakHis dataset, which is available publicly has been employed in overall experiments in this research. BreakHis dataset is separated into training, testing, and validation for the experimentation. In addition, the dataset for training was augmented followed by stain normalization. By using the concept of transfer learning (TL), AleNext was retained after fine-tuning the last layer for binary classification like benign and malignant. Afterward, preprocessed images are fed into the TL based model for training. The model training was performed many times by changing the hyper-parameters randomly until the minimum validation loss was achieved. Now the trained model was used for feature extraction. The extracted features were further evaluated by using six ML classifiers (i.e. softmax, Decision tree, Naïve Bayes, Linear discriminant analysis, Support vector machine, k-nearest neighbor) through five performance measures such as precision, F-measure, accuracy, specificity, and sensitivity for experimental evaluation. The softmax has outperformed among all classifiers. Furthermore, to reduce the wrong prediction, a misclassification reducing (MR) algorithm was developed. After using the MR algorithm the proposed model produced better and reliable results. The observed accuracy, specificity, sensitivity, precision and F measure are 81.25%, 77.47%, 82.49%, 91.70%, and 86.80% respectively. These results show that the proposed TL based model along with misclassification reduction algorithm produced comparable results to the current baseline models. Hence, the expected model could serve as a second opinion for BC classification in any healthcare center.

BackgroundThe accessibility to radiotherapy facilities may affect the willingness to undergo treatment. We sought to quantify the distance and travel time of Malaysian population to the closest radiotherapy centre and to estimate the megavoltage unit (MV)/million population based on the regions.Materials & methodsData for subdistricts in Malaysia and radiotherapy services were extracted from Department of Statistics Malaysia and Directory of Radiotherapy Centres (DIRAC). Data from DIRAC were validated by direct communication with centres. Locations of radiotherapy centres, distance and travel time to the nearest radiotherapy were estimated using web mapping service, Google Map.ResultsThe average distance and travel time from Malaysian population to the closest radiotherapy centre were 82.5km and 83.4mins, respectively. The average distance and travel were not homogenous; East Malaysia (228.1km, 236.1mins), Central (14.4km, 20.1mins), East Coast (124.2km, 108.8mins), Northern (42.9km, 42.8mins) and Southern (36.0km, 39.8mins). The MV/million population for the country is 2.47, East Malaysia (1.76), Central (4.19), East Coast (0.54), Northern (2.40), Southern (2.36). About 25% of the population needs to travel >100 km to get to the closest radiotherapy facility.ConclusionOn average, Malaysians need to travel far and long to reach radiotherapy facilities. The accessibility to radiotherapy facilities is not equitable. The disparity may be reduced by adding centres in East Malaysia and the East Coast.

Lung cancer is the second most fatal cancer worldwide and ranks at the eighth place in the overall reported deaths in Malaysia [1]. Almost 90% of lung cancer patients are both active and passive smokers. As early symptoms of lung cancer are commonly unnoticeable, most of the diagnosis of these patients are found out when they are at stage 3 or 4. Therefore, early screening is highly anticipated. Lung cancer screening can be carried out through computed tomography (CT) scan, sputum cytology and biopsy [2].