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.

Coronavirus disease 2019 (COVID-19), a multi-organ disease impacting the respiratory system and various organs, has recently been linked to diverse cutaneous manifestations. COVID toes, a cutaneous sign of COVID-19 infection, is relatively common in children and young adults, although its clear association with COVID-19 has not been widely reported. This report presents the case of a 1-year-old infant with COVID toes. The patient exhibited violaceous discoloration in the distal toes. Further, the patient exhibited no symptoms of COVID-19 infection and the enzyme-linked immunosorbent assay was negative for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2); therefore, the patient was initially diagnosed with frostbite. However, the infant’s condition deteriorated despite treatment with nonsteroidal anti-inflammatory drugs and a warm-water bath. After a skin biopsy and serum SARS-CoV-2 test, the patient was diagnosed with COVID toes and treated with systemic steroids, photobiomodulation therapy, and dressing. This case underscores the importance of recognizing chilblain-like lesions in pediatric patients during the COVID-19 pandemic, emphasizing the need for awareness of COVID toes among healthcare professionals.

DRESS syndrome is a severe cutaneous drug reaction characterized by fever, lymphadenopathy, hematologic abnormalities, multisystem involvement, and viral reactivation. Reslizumab, a monoclonal anti-interleukin-5 antibody, may be considered in the management of DRESS syndrome. In this study, we report two cases of DRESS syndrome successfully treated with reslizumab. A 30-year-old male, who had been taking allopurinol for a month presented to our clinic with generalized erythematous patches that appeared a week ago. Additionally, a 39-year-old female, who had been taking several medicines, including carbamazepine, was referred to our clinic with erythematous patches on her face, trunk, and extremities appearing 10 days ago. In both cases, based on the history of medication usage and European Registry of Severe Cutaneous Adverse Reactions to Drugs and Collection of Biological Samples criteria, DRESS syndrome was diagnosed. Eosinophilia and systemic symptoms aggravated despite intravenous corticosteroid administration, however, they improved after intravenous reslizumab injection. These cases indicate that DRESS syndrome, which could not be treated with corticosteroids, could be successfully managed with reslizumab.

Angiosarcoma is a rare yet aggressive tumor that originates from endothelial cells lining the blood or lymphatic vessels. Angiosarcoma is commonly associated with radiotherapy, often developing as a secondary cancer following irradiation. In this report, we present a case of a 66-year-old woman who underwent a lumpectomy with adjuvant radiotherapy for left breast cancer 9 years ago. She was referred to our clinic due to erythematous nodules and papules on the left breast that developed 10 months prior. A cutaneous biopsy revealed irregular, anastomosing vessels lined by crowded swollen endothelial cells, exhibiting nuclear atypia. The immunohistochemical stains for alpha-smooth muscle actin and CD31 were positive, while the ki-67 index was elevated. However, the stain was negative for human herpesvirus 8. Clinical and histopathological features were consistent with angiosarcoma associated with adjuvant radiotherapy. The patient underwent a left total mastectomy with sentinel lymph node biopsy. However, 18 months later, multiple bone metastases were noted on positron emission tomography-computed tomography, and the patient received palliative radiotherapy and supportive care.