Search

Introduction: Drastic changes in food access during the COVID-19 pandemic may impact families' feeding practices, and eating behaviour. This study aimed to determine the association between maternal feeding practices, household food security status, and children's picky eating among Malaysian mothers. Methods: A cross-sectional study was conducted virtually among 102 mothers with children aged from 5 to 12 years old (January-June 2022). Self-administered questionnaires on sociodemographic, feeding practices, household food security and child-picky eating behaviour were used. The statistical associations for key variables were analysed. Results: Most participants were Chinese mothers (87.3%) aged 30-39 years old (52.0%) with tertiary education (52.0%) and from middle to high household income (64.7%). Restriction domain had the highest median (interquartile range) score of 3.63 (1.00). A quarter of the household was experiencing food insecurity and about 34.3% of the children were classified as 'picky eaters'. A significant difference was found for restrictive feeding practices and household food security status (3.75 (0.97) versus 3.13 (1.19), p=0.022), while mothers of picky eaters were more likely to pressure their children to eat compared to non-picky eaters (3.75 (0.75) versus 3.25 (1.00), p=0.01). Conclusion: Despite food secure status during the pandemic, two food parenting approaches (restriction and pressure to eat) were significantly associated with the household food security status and children's picky eating behaviour. While the additional environmental stress from the COVID-19 pandemic could be transient, there should be consideration of the long-term impact of maternal feeding practices on the health and nutritional status of growing children [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is growing at a fast pace and permeates many aspects of people’s daily lives. Medical students’ inclination towards AI in the medical field increases the probability of successful AI adoption and its value in the medical field. This study was conducted to evaluate medical AI readiness among undergraduate medical students. A cross-sectional study was conducted from March 2022 to April 2022 in a private medical institution in Malaysia. A non-probability purposive sampling method was used to enroll students and a questionnaire was distributed online via Google Forms. The questionnaire, captioned “Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)”, was used for data collection. The analysis included frequency tables, percentages, standard deviation, unpaired t-test, and analysis of variance (ANOVA) test. Out of 105 participants, 67.62% scored 53 to 83, followed by 24.76%, who scored 84 to 114, and 7.62%, who scored 22 to 52 on the medical AI readiness scale. The mean of the total score of medical AI readiness obtained was 75.04. There were significant correlations between age and study year with the ability, vision, and ethics domains of medical AI readiness. A significant association was observed between previous training with all four domains of medical AI readiness. Policymakers and the educational sector should set up more AI training centers to provide and introduce basic courses on AI. More AI courses should be provided to younger populations to engage in AI digital information earlier, thus enabling them to acquire more confidence in interacting with AI technology in the future. [ABSTRACT FROM AUTHOR]

He then expanded on the idea of regenerative tourism, where tourism innovations are promoted by embedding tourism practices within local communities and ecological processes that elevate human and non-human wellbeing (Bellato & Cheer, [1]). The 2nd International Conference on Responsible Tourism and Hospitality (ICRTH) was held at UCSI Hotel Kuching, Sarawak, Malaysia from 1 to 3 September 2022. The second roundtable session consisted of both international and local experts on eco-tourism where they highlighted the new possibilities and demands of eco-tourism in Asia. [Extracted from the article]

The diagnostic rate of 99mTc-MDP (methylene diphosphonate) bone scans for cancer bone metastasis is limited by image resolution and high similarity between images. This study aims to enhance the accuracy and precision of bone metastasis detection by applying fusion CNN (convolutional neural network) methods combined with artificial intelligence (AI) deep learning algorithms. In this retrospective study, a total of 1680 whole-body images were collected, including 1002 normal and 678 metastatic cases. Five popular CNN methods were used for transfer learning, extracting features from fully connected layers to create a new set of fusion features. The hyperparameters included batch size, epoch size, and learning rates (0.001). The dataset was divided into 70%, 20%, and 10% for training, validation, and testing of the CNN models, respectively. Machine learning models, such as SVM (support vector machine), Naive Bayes, and logistic regression, were trained using the fusion features. Classification performance was evaluated using accuracy, precision, and Kappa value. The fusion CNN combined with the Naive Bayes classifier achieved the highest accuracy, precision, and Kappa value of 0.982, 0.990, and 0.963, respectively, in the testing set compared to other methods. This study demonstrated that fusion CNN methods combined with machine learning techniques can achieve outstanding performance in classifying normal and bone metastasis images, enhancing the diagnostic potential of 99mTc-MDP bone scans. [ABSTRACT FROM AUTHOR]