Background: Psoriasis is a chronic inflammatory skin disorder typified by well-demarcated erythematous plaques with scales. While considered an immune-driven condition, its underlying molecular triggers remain insufficiently defined. Cold-inducible RNA-binding protein (CIRP), a stress-response protein, has recently been recognized as a damage-associated molecular pattern that can stimulate immune responses. Objective: This study aimed to explore the potential association between circulating CIRP levels and the clinical as well as histological characteristics of psoriasis. Methods: Serum CIRP concentrations were analyzed in 67 individuals diagnosed with psoriasis and 20 healthy controls. Relationships between CIRP expression and various clinical and histological indices were also examined. Results: Patients with psoriasis exhibited significantly elevated serum CIRP levels compared to healthy individuals. Although correlations were observed between CIRP and certain clinical and histological indicators, CIRP levels did not significantly differ based on disease severity (Psoriasis Area and Severity Index score), joint involvement, or nail changes. Conclusion Our findings support the notion that CIRP may be involved in the immunopathogenesis of psoriasis and could be considered a prospective target for therapeutic modulation.

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.

Imatinib mesylate (also known as Gleevec) is a selective tyrosine kinase inhibitor, primarily used for the treatment of chronic myeloid leukemia and gastrointestinal stromal tumors. Despite its effectiveness, the use of imatinib has been associated with various adverse skin reactions such as maculopapular rash, edema, and lichenoid or psoriasiform lesions. We report the case of a 71-year-old female presented with follicular hyperkeratotic papular eruption that affected her entire body. The lesions had developed 2 weeks ago. The patient had been diagnosed with a malignant gastrointestinal stromal tumor and had been receiving imatinib mesylate since 2013. Three weeks before the onset of the skin eruptions, the imatinib dosage was increased to 800 mg/d. Skin biopsies were performed on the chin and forearms. Based on the clinical and histopathological results, the patient was diagnosed with imatinib-induced keratosis pilaris. Following the discontinuation of imatinib and retinoid therapy, her skin condition markedly improved, and the lesions resolved within a few weeks. Herein, we report a case that highlights the association between imatinib mesylate and keratosis of the pilaris.

Imatinib mesylate (also known as Gleevec) is a selective tyrosine kinase inhibitor, primarily used for the treatment of chronic myeloid leukemia and gastrointestinal stromal tumors. Despite its effectiveness, the use of imatinib has been associated with various adverse skin reactions such as maculopapular rash, edema, and lichenoid or psoriasiform lesions. We report the case of a 71-year-old female presented with follicular hyperkeratotic papular eruption that affected her entire body. The lesions had developed 2 weeks ago. The patient had been diagnosed with a malignant gastrointestinal stromal tumor and had been receiving imatinib mesylate since 2013. Three weeks before the onset of the skin eruptions, the imatinib dosage was increased to 800 mg/d. Skin biopsies were performed on the chin and forearms. Based on the clinical and histopathological results, the patient was diagnosed with imatinib-induced keratosis pilaris. Following the discontinuation of imatinib and retinoid therapy, her skin condition markedly improved, and the lesions resolved within a few weeks. Herein, we report a case that highlights the association between imatinib mesylate and keratosis of the pilaris.