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Debate is a universally acknowledged competency for its vital role in fostering essential skills such as analytical reasoning, eloquent communication, and persuasive argument construction. This is relevant in both formal educational settings like classrooms and informal venues such as after-school clubs. Traditional debate training methods often face challenges in facilitating personalized learning, primarily due to the difficulty of securing a debate partner. However, the emerging field of generative AI offers a promising alternative. This study evaluates the effectiveness of DEBO, a debate education application that utilizes ChatGPT, an advanced language model by OpenAI. Focusing on university students actively involved in a debate club, six volunteers tested DEBO and participated in interviews to provide insights. The findings reveal that DEBO notably enhances divergent thinking and features a comprehensive analytics dashboard for in-depth performance analysis. However, there are few limitations including occasional shortcomings in debate quality, minor inaccuracies, and the need for faster response times. The study underscores the transformative potential of advanced language models like ChatGPT in reshaping debate education, while also identifying areas for further improvement.

Chronic respiratory disease is among the most common non-communicable diseases, and particulate materials (PM) are a major risk factor. Meanwhile, evidence of the relationship between the physicochemical characteristics of PM and pulmonary toxicity mechanism is still limited. Here, we collected particles (CPM) from the air of a port city adjacent to a cement factory, and we found that the CPM contained various elements, including heavy metals (such as arsenic, thallium, barium, and zirconium) which are predicted to have originated from a cement plant adjacent to the sampling site. We also delivered the CPM intratracheally to mice for 13 weeks to investigate the pulmonary toxicity of inhaled CPM. CPM-induced chronic inflammatory lesions with an increased total number of cells in the lung of mice. Meanwhile, among inflammatory mediators measured in this study, levels of IL-1β, TNF-α, CXCL-1, and IFN-γ were elevated in the treated group compared with the controls. Considering that the alveolar macrophage (known as dust cell) is a professional phagocyte that is responsible for the clearance of PM from the respiratory surfaces, we also investigated cellular responses following exposure to CPM in MH-S cells, a mouse alveolar macrophage cell line. CPM inhibited cell proliferation and formed autophagosome-like vacuoles. Intracellular calcium accumulation and oxidative stress, and altered expression of pyrimidine metabolism- and olfactory transduction-related genes were observed in CPM-treated cells. More interestingly, type I-LC3B and full-length PARP proteins were not replenished in CPM-treated cells, and cell cycle changes, apoptotic and necrotic cell death, and caspase-3 cleavage were not significantly detected in cells exposed to CPM. Taken together, we conclude that dysfunction of alveolar macrophages may contribute to CPM-induced pulmonary inflammation. In addition, given the possible transformation of heart tissue observed in CPM-treated mice, we suggest that further study is needed to clarify the systemic pathological changes and the molecular mechanisms following chronic exposure to CPM.

Debate is a universally acknowledged competency for its vital role in fostering essential skills such as analytical reasoning, eloquent communication, and persuasive argument construction. This is relevant in both formal educational settings like classrooms and informal venues such as after-school clubs. Traditional debate training methods often face challenges in facilitating personalized learning, primarily due to the difficulty of securing a debate partner. However, the emerging field of generative AI offers a promising alternative. This study evaluates the effectiveness of DEBO, a debate education application that utilizes ChatGPT, an advanced language model by OpenAI. Focusing on university students actively involved in a debate club, six volunteers tested DEBO and participated in interviews to provide insights. The findings reveal that DEBO notably enhances divergent thinking and features a comprehensive analytics dashboard for in-depth performance analysis. However, there are few limitations including occasional shortcomings in debate quality, minor inaccuracies, and the need for faster response times. The study underscores the transformative potential of advanced language models like ChatGPT in reshaping debate education, while also identifying areas for further improvement.

Physical activity reduces the risk of colon cancer, but its prognostic impact after cancer diagnosis remains unclear. To evaluate the association between post-diagnosis activity and cause-specific mortality, we reconstructed a colorectal cancer patient cohort from the 2009–16 Korean National Health Insurance Service (NHIS) database. Subgroup analyses were performed by treatment group. In total, 27,143 colon cancer patients and 16,453 rectal cancer patients were included in the analysis (mean follow-up, 4.3 years; median 4.0 years). In the surgically treated group, a high level of activity (the weighted sum of the frequencies for walking, moderate, and vigorous activity greater than or equal to 3 times/week) was inversely associated with all-cause mortality (colon cancer: HR, 0.79; 95% CI, 0.72 to 0.88; rectal cancer: HR, 0.75; 95% CI, 0.66 to 0.86) and colorectal cancer-specific mortality (colon cancer: HR, 0.85; 95% CI, 0.76 to 0.97; rectal cancer: HR, 0.77; 95% CI, 0.66 to 0.90). No significant results were shown for cardiovascular disease-specific mortality. No association was shown in patients who received chemoradiotherapy without surgery. The present study may provide evidence for post-diagnosis physical activity as a prognostic factor in colorectal cancer, particularly in surgically treated early-stage patients.

To date, documentation of two doses of measles-containing vaccine (MCV) has been accepted as confirmation of measles immunity among healthcare workers (HCWs). However, we encountered measles in an HCW who had received two doses of MCV. A patient with measles was admitted to our hospital. Among 62 exposed HCWs, one nurse who had previously received two doses of MCV was shown to be negative for anti-measles immunoglobulin G (IgG), and was confirmed to have measles 14 days after exposure. Based on this experience, we suggest that all HCWs should be tested for anti-measles IgG to confirm their immunity to measles. [ABSTRACT FROM AUTHOR]