A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.

Augmented Reality in Supporting Healthcare and Nursing Independent Learning: Narrative Review.

New advances in technology have brought challenges and opportunities for education and instructional methods. Compared with traditional education, the increased use of technology-enhanced blended learning in healthcare and nursing education requires students to take more responsibility for their learning. The use of advanced technology has resulted in independent learning skills becoming increasingly important. Many studies have reported a positive correlation between independent learning and success rates in an e-learning environment. This paper focuses on the potential contribution of augmented reality, which superimposes layers of virtual content on real physical objects. The paper initially presents a narrative literature review to identify augmented reality's strengths and challenges in facilitating independent learning and highlights several potential approaches for utilizing augmented reality in nursing education. However, it also reveals a lack of studies integrating augmented reality and independent learning theories such as self-regulated learning. The paper then addresses this gap by proposing a new learning approach to support independent learning.

Nitrate contamination in drinking water and colorectal cancer: Exposure assessment and estimated health burden in New Zealand.

BACKGROUND: Epidemiological evidence in multiple jurisdictions has shown an association between nitrate exposure in drinking water and an increased risk of colorectal cancer (CRC). OBJECTIVE: We aimed to review the extent of nitrate contamination in New Zealand drinking water and estimate the health and financial burden of nitrate-attributable CRC. METHODS: We collated data on nitrate concentrations in drinking water for an estimated 85% of the New Zealand population (∼4 million people) who were on registered supplies. We estimated nitrate levels for the remaining population (∼600,000 people) based on samples from 371 unregistered (private) supplies. We used the effective rate ratio from previous epidemiological studies to estimate CRC cases and deaths attributable to nitrate in drinking water. RESULTS: Three-quarters of New Zealanders are on water supplies with less than 1 mg/L NO3-N. The population weighted average for nitrate exposure for people on registered supplies was 0.49 mg/L NO3-N with 1.91% (95%CI 0.49, 3.30) of CRC cases attributable to nitrates. This correlates to 49.7 cases per year (95%CI 14.9, 101.5) at a cost of 21.3 million USD (95% 6.4, 43.5 million USD). When combining registered and unregistered supplies, we estimated 3.26% (95%CI 0.84, 5.57) of CRC cases were attributable to nitrates, resulting in 100 cases (95%CI 25.7, 171.3) and 41 deaths (95%CI 10.5, 69.7) at a cost of 43.2 million USD (95%CI 10.9, 73.4). CONCLUSION: A substantial minority of New Zealanders are exposed to high or unknown levels of nitrates in their drinking water. Given the international epidemiological studies showing an association between cancer and nitrate ingestion from drinking water, this exposure may cause an important burden of preventable CRC cases, deaths, and economic costs. We consider there is sufficient evidence to justify a review of drinking water standards. Protecting public health adds to the strong environmental arguments to improve water management in New Zealand.

Quantifying the nitrate levels in bottled water in New Zealand.

OBJECTIVE: There is growing epidemiological evidence linking nitrate contamination to adverse health outcomes. Health concerns may drive consumers towards bottled water, however, nitrate levels in bottled water are not readily available. METHODS: We tested water samples from the 10 most popular brands using a TriOS OPUS UV optical nitrate sensor. RESULTS: Overall, all bottled water brands tested returned nitrate levels below 4.4 mg/L NO3. CONCLUSIONS: The growing health concerns associated with nitrate contamination suggest that increased reporting of water quality is required. IMPLICATIONS FOR PUBLIC HEALTH: Mandatory reporting of water quality laboratory reports by bottled water producers would improve transparency to consumers and help public health researchers track potential threats to water quality as new evidence emerges.