Breast cancer screening motivation among women: an application of self-determination theory.

BACKGROUND: Breast cancer is a major health concern worldwide, especially in Vietnam. This study aimed to explore women's motivation for and factors related to breast cancer screening. METHODS: A mixed-methods study was conducted in Danang, Vietnam, using a convergent parallel approach. This study utilized both quantitative and qualitative methods to gather the data. The quantitative approach involved surveys to assess motivation levels and related factors, including demographic information and experience with breast cancer screening. In-depth qualitative interviews were used to gain deeper insights into participants' perspectives and experiences related to breast cancer screening. RESULTS: The average motivation score for breast cancer screening was moderate (3.55 ± 0.55). Ethnicity, regular health check-ups, family history of breast cancer, receiving information about breast cancer, and women's health issues have direct relationships with breast cancer screening motivation. According to the qualitative data, three categories emerged: intrinsic motivation, external motivation/internalization, and amotivation. The individual, and sociocultural environmental factors impacted screening motivation. CONCLUSIONS: This study highlights the motivations behind breast cancer screening among women. Healthcare providers could use these findings to improve screening policies and guidelines and encourage more women to undergo regular screening, ultimately reducing the incidence of breast cancer in the community.

Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites.

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities.

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED: The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION: Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

Effects of Standardized Patient Simulation and Mobile Applications on Nursing Students' Clinical Competence, Self-Efficacy, and Cultural Competence: A Quasi-Experimental Study.

BACKGROUND: Simulation-based education has emerged as an effective approach in nursing education worldwide. We aimed to evaluate the effectiveness of a surgical nursing education program based on a simulation using standardized patients and mobile applications among nursing students. METHODS: A mixed-methods design with a quasi-experimental longitudinal approach and focus group interviews was employed. The data were collected from 130 third-year nursing students at three different time points who were equally divided into experimental and control groups. This study measured the level of clinical surgical nursing competence, self-efficacy in clinical performance, cultural competence, and satisfaction with simulation experience. Four focus group interviews were conducted using open-ended questions to explore the participants' perspectives on the course's efficacy and satisfaction. RESULTS: There were statistically significant differences in clinical surgical nursing competence (F = 8.68, p < 0.001), self-efficacy in clinical performance (F = 13.56, p < 0.001), and cultural competence (F = 10.35, p < 0.001) across time between the intervention and control groups. Student satisfaction with the simulation-based training was high, particularly regarding debriefing and reflection, with an overall mean satisfaction level of 4.25 (0.40). Students' perspectives regarding integrated hybrid training are categorized into three themes: educational achievement, dynamic learning experiences, and satisfaction and suggestion. CONCLUSION: Simulation-based learning provides a dynamic and immersive educational experience that enables undergraduate nursing students to develop and refine essential clinical skills while also fostering confidence and cultural competence.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information.

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

Development and Evaluation of Cultural Competence Course on Undergraduate Nursing Students in Vietnam.

Cultural competence is a crucial requirement of nursing to promote caring for patients with diverse backgrounds. The purpose of this study was to develop a cultural competence course and to evaluate the effects of the course on undergraduate nursing students in Vietnam. A concurrent triangulation mixed-methods study was adopted using quantitative and qualitative data sources. Sixty-six nursing students were recruited for the following groups: cultural competence course with field experience (n = 22), stand-alone cultural competence course (n = 22), and a control group (n = 22). The findings indicated that significant group by time interactions in total cultural competence score (F = 66.73, p < 0.001) were found. Participants' perceptions reflected on three categories: (a) journey to cultural competence, (b) satisfaction of cultural competence course, and (c) suggestions for improvements. No statistically significant differences between the two experimental groups were revealed, but "obtaining cultural experiences" and "expanding understanding of cultural competence through field experience" were immersed from participants having field experience. It is vital to expand cultural competency education into nursing curricula to enhance nursing students' perspective of culturally competent care.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation.

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock's historical time series data into a candlestick chart to elucidate patterns in the stock's movement. Finally, we integrated the stock's sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

The endemic Helicobacter pylori population in Southern Vietnam has both South East Asian and European origins.

BACKGROUND: The burden of Helicobacter pylori-induced gastric cancer varies based on predominant H. pylori population in various geographical regions. Vietnam is a high H. pylori burden country with the highest age-standardized incidence rate of gastric cancer (16.3 cases/100,000 for both sexes) in Southeast Asia, despite this data on the H. pylori population is scanty. We examined the global context of the endemic H. pylori population in Vietnam and present a contextual and comparative genomics analysis of 83 H. pylori isolates from patients in Vietnam. RESULTS: There are at least two major H. pylori populations are circulating in symptomatic Vietnamese patients. The majority of the isolates (~ 80%, 66/83) belong to the hspEastAsia and the remaining belong to hpEurope population (~ 20%, 17/83). In total, 66 isolates (66/83) were cagA positive, 64 were hspEastAsia isolates and two were hpEurope isolates. Examination of the second repeat region revealed that most of the cagA genes were ABD type (63/66; 61 were hspEastAsia isolates and two were hpEurope isolates). The remaining three isolates (all from hspEastAsia isolates) were ABC or ABCC types. We also detected that 4.5% (3/66) cagA gene from hspEastAsia isolates contained EPIYA-like sequences, ESIYA at EPIYA-B segments. Analysis of the vacA allelic type revealed 98.8% (82/83) and 41% (34/83) of the strains harboured the s1 and m1 allelic variant, respectively; 34/83 carried both s1m1 alleles. The most frequent genotypes among the cagA positive isolates were vacA s1m1/cagA + and vacA s1m2/cagA + , accounting for 51.5% (34/66) and 48.5% (32/66) of the isolates, respectively. CONCLUSIONS: There are two predominant lineages of H. pylori circulating in Vietnam; most of the isolates belong to the hspEastAsia population. The hpEurope population is further divided into two smaller clusters.

Comparative Studies on Patient Safety Culture to Strengthen Health Systems Among Southeast Asian Countries.

Patient safety is an important issue in health systems worldwide. A systematic review of previous studies on patient safety culture in Southeast Asian countries is necessary for South Korea's partnership with these countries, especially given South Korea's assistance in strengthening the health systems of these developing countries. Studies on patient safety culture in Southeast Asian countries, published in English and Thai languages, were retrieved from computerized databases using keywords through a manual search. Data extraction, quality assessment, and analyses were performed using several tools. The review included 21 studies conducted in Indonesia (n = 8), Thailand (n = 5), Malaysia (n = 3), Vietnam (n = 2), Singapore (n = 1), and the Philippines (n = 1). They were analyzed and categorized into 12 dimensions of safety culture, and differences in response rate or scores were identified compared to the mean of the dimensions. The heterogeneous of safety culture's situation among Southeast Asian countries, both in practice and in research, can be explained since patient safety policy and its application are not prioritized as much as they are in developed countries in the priority compared to the developed countries. However, Vietnam, Cambodia, Myanmar, and Laos are the priority countries for South Korea's official healthcare development assistance in the Southeast Asia region. Vietnam, for instance, is an economically transitioning country; therefore, consolidated patient safety improvement by inducing patient safety culture in the provincial and central health system as well as strengthening project formulation to contribute to health policy formation are needed for sustainable development of the partner countries' health systems. It is recommended that more evidence-based proactive project planning and implementation be conducted to integrate patient safety culture into the health systems of developing countries, toward health policy on patient safety and quality service for the attainment of sustainable development goals in South Korea's development cooperation.