(1) Background: International students with sufficient health literacy are better equipped to respond to public health emergencies and reduce any unintentional harm that may occur during such events. This study aims to assess the current status of health literacy among international students and investigate the factors that influence health literacy. (2) Methods: A cross-sectional study was conducted in Tokyo on international university students using a questionnaire consisting of the Communicative and Critical Health Literacy and eHealth Literacy Scales. The study analyzed 205 valid responses. Descriptive statistics were utilized to assess the level of health literacy, and linear regression was used to identify the association of socio-demographic characteristics and disease status with health and e-health literacy. (3) Results: Health literacy and e-health literacy were low in 48.29% and 47.29% of international students, respectively. The mean scores of CCHL items ranged from 3.13 to 3.26, while the mean scores of eHEALS items ranged from 3.33 to 3.49. Both health literacy and e-health literacy were better with unmarried status (p = 0.015), and e-health literacy was worse with higher age (p = 0.007). (4) Conclusions: Overall, international students’ health literacy and e-health literacy were at intermediate levels, with considerable room for improvement, and affected by certain student attributes.

Global healthcare systems have faced unprecedented strain due to the COVID-19 pandemic, with a profound impact on individuals with non-communicable diseases (NCDs), a scenario particularly pronounced in low-income countries like Nepal. This study aimed to understand the experiences of and challenges faced by patients with NCDs in Nepal during the pandemic, focusing on healthcare service availability and identifying factors affecting healthcare use, with the goal of being prepared for future emergencies. This study utilized a telephonic survey of 102 patients with NCDs and 10 qualitative interviews with healthcare providers in the Kavrepalanchok and Nuwakot districts of Nepal. We used mixed methods, with both qualitative and quantitative approaches. Specifically, multiple correspondence analysis, hierarchical cluster analysis, and classification tree analysis were used as exploratory methods. The study revealed that while 69.6% of the participants reported no difficulty in obtaining medication, other questions revealed that 58.8% experienced challenges in accessing routine medical care. Major barriers, such as fear of infection, unavailability of medicine in rural areas, and lack of transportation, were found through the qualitative interviews. Meanwhile, participants identified innovative strategies, such as telemedicine and community-based awareness programs, as potential facilitators for addressing barriers that arise during pandemic situations such as COVID-19. The COVID-19 pandemic exacerbated challenges in accessing healthcare services for patients with NCDs in Nepal. Our findings suggest the need to design and implement telemedicine services for patients with NCDs, as well as community-based programs that aim to improve health literacy, encourage healthy behavior, prevent development of NCDs, and ensure continuity of care during such crises, especially in countries with limited resources.

This study explored the living situations, financial conditions, religious obligations, and social distancing of Muslims during the COVID-19 pandemic. In total, 28 Muslim community members living in the Kanto region were recruited; 18 of them were included in in-depth qualitative interviews and 10 in two focus group interviews. The snowball method was used, and the questionnaires were divided into four themes. The audio/video interviews were conducted via Zoom, and NAVIO was used to analyse the data thematically. The major Muslim events were cancelled, and the recommended physical distancing was maintained even during the prayers at home and in the mosques. The Japanese government’s financial support to each person was a beneficial step towards social protection, which was highlighted and praised by every single participant. Regardless of religious obligations, the closing of all major mosques in Tokyo demonstrates to the Japanese community how Muslims are serious about adhering to the public health guidelines during the pandemic. This study highlights that the pandemic has affected the religious patterns and behaviour of Muslims from inclusive to exclusive in a community, and recounts the significance of religious commitments.

Background: Plasmodium falciparum has acquired resistance to artemisinin in Southeast Asia, with mutations in the P. falciparum Kelch-13 (Pfk13) gene associated with the resistance phenotype. The widespread use of Artemisinin-based combination therapy (ACT)s in Southeast Asia has led to the selection and spread of parasites carrying mutations in Pfk13. We characterised the allele diversity of Pfk13 and pfg377, an artemisinin-resistance neutral polymorphic gene, in parasite DNA extracted human blood from in southern Vietnam in 2003, 2012, 2015 and 2018. Method: This study was conducted in Bu Gia Map commune, Binh Phuoc province, Vietnam, from May 2018 to January 2019. Twenty-four samples from 2018 to 2019, 30 from 2003, 24 from 2012 and 32 from 2015 were analysed. Malaria-infected human blood was collected by finger-prick and used for molecular analysis. A nested-PCR targeting the small subunit ribosomal RNA gene was used for Plasmodium species identification, followed by amplification and nucleotide sequencing of Pfk13 and region 3 of pfg377. Archived blood samples collected in the same region in 2012 and 2015 were also analysed as above for comparison. Results: The genetic diversity of Pfk13 and pfg377 was lower in 2018–2019 compared to 2012 and 2015. The number of distinct Pfk13 mutants decreased from three in 2012 and 2015, P553L, V568G and C580Y, to one, C580Y in 2018–2019. In 2018–2019, the frequency of C580Y mutant strains was 71% (17/24 isolates). All samples were wild type in 2003. In 2012 and 2015, there were single-strain infections as well as co-infections with two mutant strains or with mutant and wild strains, whereas there were no co-infections in 2018. pfg377 allele diversity decreased from five alleles in 2012 to two alleles in 2018–2019. Conclusion: The genetic diversity of P. falciparum was reduced at the two genetic loci surveyed in this study, Pfk13 and pfg377. In the case of the former gene, we observed an increase in the prevalence of parasites carrying the C580Y gene, known to confer reduced susceptibility to ACTs. The reduction in the diversity of pfg377 may be linked to the clonal expansion of parasite strains carrying the C580Y mutation, leading to an overall reduction in parasite genetic diversity across the population.

A method that uses thermal images taken by UAVs is useful for outdoor search and rescue operations of a person in distress or imminent danger. To ensure saving lives under time constraints, it is necessary to improve the dependability of this method. A machine learning algorithm is used to analyze the thermal images. This involves preparing images of a heat source to be searched for as training data and test data, training a model, and using the pre-trained model to detect objects in newly captured unseen data. Dataset used for training can be selected from datasets which are publicly available on the web open sourced, or datasets that have been acquired by a user his or herself. This research is an empirical study on the intercomparison of thermal image recognition accuracy with different datasets.

If water surfaces are left untreated, mosquito larvae can develop into adult vectors within a few weeks, posing a significant risk of infectious disease. Consequently, technology for weekly on-site detection of mosquito larvae is required to control such situations effectively. Using autonomous UAV-captured images based on water surface mapping, this study establishes a technological foundation to detect mosquito larvae using deep learning, targeting automated larval source management for vector control. To enable local entomologists to build and adapt models using free, no-code cloud services, determining the minimum viable training dataset is essential. Therefore, we built an object detection model using Google Cloud Platform's Vertex AI with an extremely limited annotated dataset comprising 10 images and 371 annotations in total, of which 8 images (300 annotations) were allocated for training, to evaluate feasibility under resource-constrained conditions. The model was tested on two external datasets: controlled indoor images (JIHS, Japan) and field photographs from natural breeding sites (Malawi). Results demonstrated that while the model achieved high Recall (75.0–100.0%) and Precision (56.3–57.1%) in controlled environments (F1 0.643–0.727), Precision dropped substantially to 9.1–13.3% in turbid field conditions due to false positives from environmental noise, such as floating particles. These findings indicate that dataset diversification incorporating diverse backgrounds is essential for real-world deployment.