Reference management software (RMS) represents a cornerstone of modern academic writing and publishing. For decades, programs such as EndNote, Zotero, and Mendeley have played central roles in facilitating citation organization, bibliography formatting, and collaborative scholarship. Although each platform has introduced unique innovations, persistent limitations remain, particularly with respect to usability, accessibility, and accuracy. In parallel, the rise of generative artificial intelligence has introduced an unprecedented challenge: the inadvertent inclusion of fabricated or incorrect references mistakenly incorporated into manuscripts. This phenomenon has exposed a critical limitation of traditional RMS platforms, namely their inability to verify reference authenticity. Against this backdrop, new solutions have emerged. One such example is CiteWell (https://citewell.org/), an artificial intelligence (AI)-era RMS that introduces several notable innovations, including PubMed-integrated verification, an intuitive interface for new users, customizable journal-specific styles, and multilingual accessibility. This review provides a comprehensive historical overview of RMS, evaluates the strengths and weaknesses of major platforms, and positions emerging AI-based tools as a new paradigm that combines traditional reference management with essential safeguards for contemporary academic challenges.

Purpose: This study aimed to develop a digital multimedia-based educational intervention for hematopoietic stem cell (HSC) donors and to evaluate its effects on anxiety, knowledge, attitudes toward hematopoietic stem cell donation, and educational satisfaction. Methods: This quasi-experimental study employed a nonequivalent control group non-synchronized pretest-posttest design. Participants were 60 HSC donors admitted to a general hospital in Seoul, Republic of Korea, between April and December 2024. The participants were assigned to an experimental group (n=30) and a control group (n=30). The experimental group received a digital multimediabased educational intervention in addition to the usual education using a standardized handout, whereas the control group received the usual education only. The intervention was provided in three sessions: on the day of admission (20 minutes), on the day of stem cell collection (10 minutes), and on the day of discharge (10 minutes). Data were collected at admission and discharge using structured questionnaires. Anxiety was analyzed using generalized estimating equations (GEE), knowledge and attitudes using mixed ANOVA, and educational satisfaction using an independent t-test. Results: The experimental group showed significantly lower anxiety (p=.048) and higher knowledge levels (p=.005) than the control group. No significant differences were found between the groups in attitudes toward donation or educational satisfaction. Conclusion The digital multimedia-based educational intervention was effective in reducing anxiety and improving knowledge among HSC donors. This intervention may enhance donors’ understanding of the donation process and support a more positive donation experience. Further multicenter studies with larger samples and longitudinal follow-up measurements are needed to verify the long-term effects and generalizability of the intervention.

Objective: We investigated the distribution of AI viruses in fecal samples from wild bird habitats (and nearby poultry-farm areas) surveyed between September and March from 2019 to 2025 and identified associated epidemiological risk factors. Methods: Samples were screened for influenza A (M, H5, H7) genes using real-time reverse transcription polymerase chain reaction (PCR), subjected to virus isolation in embryonated chicken eggs, and subtyped by PCR and sequencing. Host species were identified through DNA barcoding. Relative risks (RRs) with 95% confidence intervals were estimated for province, month, and waterfowl density. Results: Overall prevalence of HPAI and low pathogenic AI (LPAI) virus was 0.10% and 3.21%, respectively. HPAI virus was continuously isolated since 2020–2021, except 2019– 2020, while LPAI prevalence steadily increased (3.01%–4.35%). Twelve hemagglutinin (H1–H12) subtypes were identified in 1,722 isolates, and H3 (16.5%) was the most prevalent, followed by H5 (11.1%) and H7 (5.2%). LPAI H5N3 (55.7%) and H7N7 (75.5%) were the predominant H5 and H7 subtypes, respectively. Detection was higher in western coastal provinces, and higher mallard/spot-billed duck density and sampling in September– December were associated with increased risk. Conclusions and Relevance: Continued surveillance of migratory-bird habitats can provide early warning of HPAIV incursions and support targeted biosecurity measures in high-risk regions and seasons.

Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Objective: The purpose of this study is to identify the types of pain changes that affect older Koreans, as well as their effects on depressive symptom. Methods: We analyzed the Korean Longitudinal Study of Aging data collected from 2010 to 2018. A data of total of 1,359 participants, aged 65 or older were used to estimate the change in pain. A latent growth model and growth mixture modeling were performed to estimate the overall change in pain and to categorize the types of pain changes. Results: The pain changes of older adults were classified into two categories: low-stable and high increasing. The depressive symptom showed a stronger relationship among the high-increasing type of pain than the low-stable type. The high-increasing type had a higher percentage of females, lower income, relatively low educational attainment, and a higher percentage of rural residents than the low-stable type. Conclusion The significance of this study is that it reiterated the importance of early pain diagnosis and intervention by identifying the types of pain changes in older adults and analyzing their effects on depressive symptoms. Therefore, it is especially important to pay attention to interventions that are designed to help vulnerable groups with a high risk of pain obtain effective pain management.

Japanese encephalitis virus (JEV) is a zoonotic arbovirus transmitted by mosquitoes and poses a significant public health concern. JEV is classified into 5 genotypes (G1–G5). Since 2010, JEV genotype 5 (G5) has been predominant in the Republic of Korea; however, a specific antigen for hemagglutination (HA) inhibition (HI) assays has been lacking. This study aimed to develop a novel antigen to enable genotype-specific HI antibody detection for JEV G5. The Sangju strain of JEV G5 was serially passaged 30 times in Vero and C6/36 cells, resulting in Sangju-30, which exhibited 2 envelope protein mutations and an HA titer of 8, increasing to 512 following polyethylene glycol 8000 concentration. The antigen demonstrated a strong correlation (R = 0.95, p < 0.05, 95% confidence interval, 6.3–31.9) with HI antibody levels in pig sera. These findings suggest that the Sangju-30 antigen is a viable alternative for JEV G5 serological monitoring, enhancing diagnostic accuracy and offering a replacement for traditional murine-derived antigen production.