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Fine particulate matter (PM₂.₅) exposure is associated with morbidity and mortality induced by respiratory diseases and increases the lung cancer risk. However, the mechanisms therein involved are not yet fully clarified. In this study, the PM₂.₅ suspensions at different dosages (0.375, 1.5, 6.0, and 24.0 mg/kg body weight) were respectively given to rats by the intratracheal instillation. The results showed that PM₂.₅ exposure induced inflammatory cell infiltration and hyperemia in the lung tissues and increased the inflammatory cell numbers in bronchoalveolar lavage fluid. Furthermore, PM₂.₅ significantly elevated the levels of pro-inflammatory mediators including tumor necrosis factor-α (TNF-α), interleukin (IL)-6, IL-1β, and intercellular adhesion molecule 1 (ICAM-1) and the expression of c-fos and c-jun in rat lungs exposed to higher dose of PM₂.₅. These changes were accompanied by decreases of activities of superoxide dismutase and increases of levels of malondialdehyde, inducible nitric oxide synthase, nitric oxide, cytochrome P450s, and glutathione S-transferase. The results implicated that acute exposure to PM₂.₅ induced pathologically pulmonary changes, unchained inflammatory and oxidative stress processes, activated metabolic enzyme activity, and enhanced proto-oncogene expression, which might be one of the possible mechanisms by which PM₂.₅ pollution induces lung injury and may be the important determinants for the susceptibility to respiratory diseases.

Since late 2022, dozens of YouTube channels focusing on a diverse array of topics related to language learning with generative AI tools such as ChatGPT have rapidly emerged. This study explores the implementations and perspectives of YouTube content creators who now constitute an increasingly important segment of the ecosystem of language teaching and learning. A mixed methods netnographic approach was employed, combining qualitative and quantitative techniques. A total of 140 videos were identified and analyzed, and an in-depth content analysis was conducted to uncover underlying themes. Four main categories of creators were identified: educators, learners, technology professionals, and e-learning providers. Educators, especially English and Japanese teachers, were the majority, followed by learners and technology field professionals. This study highlights the benefits, drawbacks, and concerns associated with the integration of AI tools in language learning. By examining this rapidly evolving phenomenon, the study contributes towards an understanding of the role and impact of generative AI tools in language education.

This research targeted the learning preferences, goals and motivations, achievements, challenges, and possibilities for life change of self-directed online learners who subscribed to the monthly OpenCourseWare (OCW) e-newsletter from MIT. Data collection included a 25-item survey of 1,429 newsletter subscribers; 613 of whom also completed an additional 15 open-ended survey items. The 25 close-ended survey findings indicated that respondents used a wide range of devices and places to learn for their self-directed learning needs. Key motivational factors included curiosity, interest, and internal need for self-improvement. Factors leading to success or personal change included freedom to learn, resource abundance, choice, control, and fun. In terms of achievements, respondents were learning both specific skills as well as more general skills that help them advance in their careers. Science, math, and foreign language skills were the most desired by the survey respondents. The key obstacles or challenges faced were time, lack of high quality open resources, and membership or technology fees. Several brief stories of life change across different age ranges are documented. Among the chief implications is that learning something new to enhance one's life or to help others is often more important than course transcript credit or a certificate of completion.

This study explores the integration of generative AI, specifically ChatGPT, in self-directed language learning (SDLL) as perceived by YouTube content creators. Through thematic analysis of in-depth interviews with 14 prominent online language educators with related YouTube videos on ChatGPT, the present study investigates: (1) their perceptions of GenAI as a tool for SDLL, (2) strategies that they recommend to effectively utilize ChatGPT for enhancing SDLL, and (3) the guidelines they suggest for fostering SDLL with AI. The findings show that YouTube-based language educators acknowledged ChatGPT as a vital tool for SDLL as it offers availability, versatility, and transformative potential. Besides linguistic benefits, ChatGPT enhances SDLL experiences by generating contextually relevant responses and fostering meaningful conversations and learner growth. The study highlights the importance of addressing ethical, pedagogical, and sociocultural factors when incorporating AI in SDL and educators’ critical role in facilitating learners’ navigation through the evolving landscape of SDL in the age of generative AI. The study contributes to refining online language learning models and comprehending the impact of generative AI on SDL.

ChatGPT's ability to realistically mimic human conversation and its high level of ability to handle linguistic ambiguity opens new and exciting avenues in language learning. Building upon the technical affordances of ChatGPT, this study explores the perceptions of educational affordances when incorporating ChatGPT across languages discussed by language communities on YouTube and identifies best practices for its effective use in language education. Through inductive content analysis, this study discussed 18 languages categorized into four groups: (1) romanized languages with high resources, (2) non-romanized languages with high resources, (3) languages with medium resources, and (4) less frequently used languages. The findings reveal consensus that (a) ChatGPT is a valuable and remarkable tool for language teaching and, (b) learning and it cannot fully replace teachers, as humor, wit, and sympathy cannot be programmed. Two potentially significant issues or two gaps were identified and discussed: namely, the learning optimization gap and the knowledge comprehension gap.

Fine particulate matter (PM sub(2.5)) exposure is associated with morbidity and mortality induced by respiratory diseases and increases the lung cancer risk. However, the mechanisms therein involved are not yet fully clarified. In this study, the PM sub(2.5) suspensions at different dosages (0.375, 1.5, 6.0, and 24.0 mg/kg body weight) were respectively given to rats by the intratracheal instillation. The results showed that PM sub(2.5) exposure induced inflammatory cell infiltration and hyperemia in the lung tissues and increased the inflammatory cell numbers in bronchoalveolar lavage fluid. Furthermore, PM sub(2.5) significantly elevated the levels of pro-inflammatory mediators including tumor necrosis factor- alpha (TNF- alpha ), interleukin (IL)-6, IL-1 beta , and intercellular adhesion molecule 1 (ICAM-1) and the expression of c-fos and c-jun in rat lungs exposed to higher dose of PM sub(2.5). These changes were accompanied by decreases of activities of superoxide dismutase and increases of levels of malondialdehyde, inducible nitric oxide synthase, nitric oxide, cytochrome P450s, and glutathione S-transferase. The results implicated that acute exposure to PM sub(2.5) induced pathologically pulmonary changes, unchained inflammatory and oxidative stress processes, activated metabolic enzyme activity, and enhanced proto-oncogene expression, which might be one of the possible mechanisms by which PM sub(2.5) pollution induces lung injury and may be the important determinants for the susceptibility to respiratory diseases.

Issue Title: Alteration and element mobility at the microbe-mineral interface Fine particulate matter (PM2.5) exposure is associated with morbidity and mortality induced by respiratory diseases and increases the lung cancer risk. However, the mechanisms therein involved are not yet fully clarified. In this study, the PM2.5 suspensions at different dosages (0.375, 1.5, 6.0, and 24.0 mg/kg body weight) were respectively given to rats by the intratracheal instillation. The results showed that PM2.5 exposure induced inflammatory cell infiltration and hyperemia in the lung tissues and increased the inflammatory cell numbers in bronchoalveolar lavage fluid. Furthermore, PM2.5 significantly elevated the levels of pro-inflammatory mediators including tumor necrosis factor-[alpha] (TNF-[alpha]), interleukin (IL)-6, IL-1[beta], and intercellular adhesion molecule 1 (ICAM-1) and the expression of c-fos and c-jun in rat lungs exposed to higher dose of PM2.5. These changes were accompanied by decreases of activities of superoxide dismutase and increases of levels of malondialdehyde, inducible nitric oxide synthase, nitric oxide, cytochrome P450s, and glutathione S-transferase. The results implicated that acute exposure to PM2.5 induced pathologically pulmonary changes, unchained inflammatory and oxidative stress processes, activated metabolic enzyme activity, and enhanced proto-oncogene expression, which might be one of the possible mechanisms by which PM2.5 pollution induces lung injury and may be the important determinants for the susceptibility to respiratory diseases.

Various formats of online discussion have proven valuable for enhancing learning and collaboration in distance and blended learning contexts. However, despite their capacity to reveal essential processes in collaborative inquiry, current mainstream analytical frameworks, such as the cognitive presence framework (Garrison, Anderson, & Archer, 2001) and the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997), have some methodological limitations. For example, they force individual discourse moves into preexisting fixed stages of the inquiry process. This requires a large inferential jump from interpretation of an individual discourse move to identification of a general social cognitive process. Such frameworks are also limited in their ability to recognize the relationships among discourse moves, thereby missing important information regarding interactivity and collaboration. This study develops a discourse method that highlights the relationships among discourse moves, with the goal of remedying the deficiency of current mainstream analytical methods. This method does not require making large inferential jumps in coding individual moves, since it only focuses on the relationship between two related segments in a discussion. A partial formative research method was used to develop the proposed method. The method developed combines two existing discourse analysis methods and includes three steps: (1) analysis of individual messages using rhetorical structure theory (Mann & Thompson, 1988) to reveal rhetorical and logical relationships between propositions within a message, (2) dynamic topic analysis (Herring & Nix, 1997) to identify the main threads in the online discussion, and (3) analysis of rhetorical structure of the identified threads. Rhetorical structure theory is adapted to meet the needs of analyzing multi-participant online discussion. The developed method was applied to two online discussions that took place in a graduate level course in a Midwestern university in the United States. This application demonstrated the ability of the developed method to assess the quality of collaborative inquiry in different discussions. The same discussion data were analyzed by Paulus (2003) using the Gunawardena et al. (1997) framework. Comparison of Paulus' analysis and that of the present study shows that their results support each other in terms of quality and level of collaboration of the inquiry.

The author of this study takes the perspective of a distance educator but mainly applies the theory of speech act analysis as the analytical tool. There are also other studies focused on a variety of issues such as cooperative learning (Harasim, Calvert, & Groeneboer, 1997), social presence and cognitive presence (Garrison, Anderson, & Archer, 1999), and so forth. For system designers, consideration for designing a discussion forum will include additional issues, such as what type of message labels can we build into the forum to facilitate students' discussion and problem solving in addition to the single subject line.

Allyl methyl trisulfide (AMTS) is one major lipid-soluble organosulfur compound of garlic. Previous studies have reported the potential therapeutic effect of garlic on acute lung injury (ALI) or its severe condition acute respiratory distress syndrome (ARDS), but the specific substances that exert the regulatory effects are still unclear. In this study, we investigate the protective effects of AMTS on lipopolysaccharide (LPS)-induced ALI mice and explored the underlying mechanisms. In vivo experiments, ICR mice were pretreated with 25–100 mg/kg AMTS for 7 days and followed by intratracheal instillation of LPS (1.5 mg/kg). The results showed that AMTS significantly attenuated LPS-induced deterioration of lung pathology, demonstrated by ameliorative edema and protein leakage, and improved pulmonary histopathological morphology. Meanwhile, the expression of inflammatory mediators and the infiltration of inflammation-regulation cells induced by LPS were also inhibited. In vitro experiments also revealed that AMTS could alleviate inflammation response and inhibit the exaggeration of macrophage M1 polarization in LPS-induced RAW264.7 cells. Mechanistically, we identified that AMTS treatment could attenuate the LPS-induced elevation of protein expression of p-IκBα, nuclear NF-κB-p65, COX2, iNOS, p-P38, p-ERK1/2, and p-JNK. Collectively, these data suggest that AMTS could attenuate LPS-induced ALI and the molecular mechanisms should be related to the suppression of the NF-κB and MAPKs pathways.