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Background: Medical students' white coats were found to harbor harmful organisms. This could be due to non-compliance to white coat hygiene measures. Therefore, we aim to develop and validate a questionnaire to assess the of knowledge, attitude, and practice (LAUNDERKAP) of white coat use among medical students in Malaysia., Methods: This study was conducted in 4 local medical schools. LAUNDERKAP was developed via literature review and had 3 domains: attitude, knowledge, practice. An expert panel assessed the content validity and clarity of wording. LAUNDERKAP was then piloted among 32 medical students. To test construct validity and internal consistency, 362 medical students were approached. Construct validity was assessed using exploratory factor analysis. Internal consistency was evaluated using Cronbach alpha for attitude and practice, while Kuder-Richardson 20 (KR-20) was used for knowledge., Results: A total of 319 of 362 students responded. Exploratory factor analysis extracted 1 factor each for attitude and knowledge respectively, and 3 factors for practice. Cronbach alpha for attitude was 0.843 while KR-20 for knowledge was 0.457. Cronbach alpha for practice ranged from 0.375 to 0.689. The final LAUNDERKAP contained 32-items (13 attitude, 9 knowledge, 10 practice)., Conclusions: LAUNDERKAP had adequate psychometric properties and can be used to assess KAP of medical students towards white coat use., (Copyright © 2022 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors., Methods: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020., Results: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali., Conclusion: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW., (© 2022. The Author(s).)

Rice is an important cereal and a staple food in many countries in the world. Climate change is a significant challenge that affects paddy production and threatens food security. However, research and development in this area continue to work to ensure the supply of rice fulfils the demands of the population. The study aims to analyse the transformation of international research power in trends in climate change that threaten food security (rice) worldwide. This study evaluates existing publications, especially research works from the period 1970 to 2020. The Web of Science database and the VOSviewer software were used together to generate a systematic analysis. A total of 1181 publications on climate change and paddy production were identified, written by 2249 authors from 56 countries. The highest number of publications was from China with 240 publications with 4609 citations, followed by India, with 225 publications and 2070 citations. Yield and adaptation are the most frequently used keywords that reflect this field's current significant research direction. Besides that, developing countries have received greater attention from researchers to focus on science, agriculture, climatology, and agriculture engineering as their domains. Therefore, socio-economic aspects should also be highlighted to raise awareness of the dangers of climate change and improve the farmers' economy by increasing paddy production. Attention was given by all countries globally, especially by researchers and stakeholders who need to plan holistic policies and strategies to encourage sustainable rice production and at the same time reduce the impact of climate change worldwide., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentation of the Schlemm's canal (SC) lumen in optical coherence tomography (OCT) scans of living mouse eyes. A novel convolutional neural network (CNN) for semantic segmentation grounded in a U-Net architecture was developed by incorporating a late fusion scheme, multi-scale input image pyramid, dilated residual convolution blocks, and attention-gating. 163 pairs of intensity and speckle variance (SV) OCT B-scans acquired from 32 living mouse eyes were used for training, validation, and testing of this CNN model for segmentation of the SC lumen. The proposed model achieved a mean Dice Similarity Coefficient (DSC) of 0.694 ± 0.256 and median DSC of 0.791, while manual segmentation performed by a second expert grader achieved a mean and median DSC of 0.713 ± 0.209 and 0.763, respectively. This work presents the first automatic method for segmentation of the SC lumen in OCT images of living mouse eyes. The performance of the proposed model is comparable to the performance of a second human grader. Open-source automatic software for segmentation of the SC lumen is expected to accelerate experiments for studying treatment efficacy of new drugs affecting intraocular pressure and related diseases such as glaucoma, which present as changes in the SC area., (Copyright © 2021 Elsevier Ltd. All rights reserved.)