Purpose: To report a case of peripapillary subretinal hemorrhage and vitreous hemorrhage after riding a roller coaster.Case summary: A 15-year-old female visited our clinic complaining of blurred vision in her left eye after repetitive roller coaster riding. The initial best-corrected visual acuity (BCVA) was 1.0 (right eye) and 0.4 (left eye). The light reflex, relative afferent pupillary defect, and intraocular pressure were within the normal range. On fundus examination, the patient was found to have a peripapillary subretinal hemorrhage, subhyaloid hemorrhage, and vitreous hemorrhage in her left eye. The BCVA of her left eye improved to 1.0 from 0.4 without any treatment after 2 weeks. The peripapillary subretinal hemorrhage and vitreous hemorrhage were completely absorbed after 7 months. Conclusions In the case of unexplained retinal hemorrhage in healthy patients without other retinal or systemic diseases, a detailed medical history should be collected to determine the possibility of disorders related to damages from riding a roller coaster.

BACKGROUND: Candidemia has increased with an increasing number of people in the high risk group and so has become more important. This study was conducted to investigate the isolation rate of Candida species from candidemia patients and the change in rate of antifungal resistance. METHODS: At a single tertiary care hospital, 1,120 blood cultures positive for Candida species from 1997 to 2016 were investigated according to date of culture, gender, age, and hospital department. RESULTS: During the investigation period, the number of candidemia patients increased from 14 in 1997 to 84 in 2016. The most common organism identified during the two decades was Candida albicans (40.8%), followed by Candida parapsilosis (24.1%), Candida tropicalis (13.2%), and Candida glabrata (12.8%). C. glabrata was relatively common in females (45.5%) compared to males. The age group 40-89 years was more frequently infected than other age groups, and the most frequent isolates according to age group were C. albicans in neonate (66.7%), C. parapsilosis in 1-9-year-olds (41.7%), and C. glabrata in those aged ≥60 years (range; 13.3%–20.0%). According to the visited departments, C. albicans, C. glabrata, and Candida haemulonii were more common in medical departments, while C. parapsilosis was more common in surgical departments. In the antifungal susceptibility test, a rising trend of azole resistance among C. albicans and C. glabrata was observed in recent years. CONCLUSION: In this study, it was confirmed that the isolation rate of Candida species in blood is different by age, gender, and hospital department, and the distribution of isolated Candida species changed over time. The resistance patterns of antifungal agents are also changing, and continuous monitoring and proper selection of antifungal agents are necessary.
Antifungal Agents ; Candida albicans ; Candida glabrata ; Candida tropicalis ; Candida* ; Candidemia* ; Danazol ; Drug Resistance, Fungal* ; Female ; Hospital Departments ; Humans ; Infant, Newborn ; Male ; Prevalence* ; Tertiary Healthcare*

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea.For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. Methods: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. Results: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81–0.9), KNN (AUROC, 0.89; 95% CI, 0.85–0.93), RF (AUROC, 0.86; 95% CI, 0.82–0.9) and BERT (AUROC, 0.82; 95% CI, 0.75–0.87) achieved excellent classification performance.Based on SHAP, we found “stress”, “pain score point”, “fever”, “breath”, “head” and “chest” were the important vocabularies for determining KTAS and symptoms. Conclusion We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality. Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency. Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency. Conclusions Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Curcumin is a well known natural polyphenol product isolated from the rhizome of the plant Curcuma longa, anti-inflammatory agent for arthritis by inhibiting synthesis of inflammatory prostaglandins. However, the mechanisms by which curcumin regulates the functions of chondroprogenitor, such as proliferation, precartilage condensation, cytoskeletal organization or overall chondrogenic behavior, are largely unknown. In the present report, we investigated the effects and signaling mechanism of curcumin on the regulation of chondrogenesis. Treating chick limb bud mesenchymal cells with curcumin suppressed chondrogenesis by stimulating apoptotic cell death. It also inhibited reorganization of the actin cytoskeleton into a cortical pattern concomitant with rounding of chondrogenic competent cells and down-regulation of integrin beta1 and focal adhesion kinase (FAK) phosphorylation. Curcumin suppressed the phosphorylation of Akt leading to Akt inactivation. Activation of Akt by introducing a myristoylated, constitutively active form of Akt reversed the inhibitory actions of curcumin during chondrogenesis. In summary, for the first time, we describe biological properties of curcumin during chondrogenic differentiation of chick limb bud mesenchymal cells. Curcumin suppressed chondrogenesis by stimulating apoptotic cell death and down-regulating integrin-mediated reorganization of actin cytoskeleton via modulation of Akt signaling.
Animals ; Anti-Inflammatory Agents, Non-Steroidal/*pharmacology ; Apoptosis/drug effects ; Cells, Cultured ; Chick Embryo ; Chondrogenesis/*drug effects ; Curcumin/*pharmacology ; Cytoskeleton/*drug effects/metabolism ; Limb Buds/*cytology ; Mesenchymal Stem Cells/cytology/*drug effects ; Proto-Oncogene Proteins c-akt/metabolism