Cytokine release syndrome following COVID-19 infection during treatment with nivolumab for cancer of esophagogastric junction carcinoma: a case report and review.

BACKGROUND: Cytokine release syndrome (CRS) is an acute systemic inflammatory syndrome characterized by fever and multiple organ failure, which is triggered by immunotherapy or certain infections. Immune checkpoint inhibitors rarely cause immune-related adverse event- cytokine release syndrome (irAE-CRS). This article presents a case report of irAE-CRS triggered by coronavirus disease 2019 (COVID-19). CASE PRESENTATION: A 60-year-old man with type 2 diabetes received nivolumab treatment for esophagogastric junction carcinoma and experienced two immune-related adverse events: hypothyroidism and skin disorder. Eleven days before his visit to our hospital, he had a fever and was diagnosed with COVID-19. Five days before his visit, he developed a fever again, along with general malaise, water soluble diarrhea, and myalgia of the extremities. On admission, the patient was in a state of multiple organ failure, and although the source of infection was unknown, a tentative diagnosis of septic shock was made. The patient's condition was unstable despite systemic management with antimicrobial agents, high-dose vasopressors, and intravenous fluids. We suspected CRS due to irAE (irAE-CRS) based on his history of nivolumab use. Steroid pulse therapy (methylprednisolone 1 g/day) was started, and the patient temporarily recovered. However, his respiratory condition worsened; consequently, he was placed on a ventilator and tocilizumab was added to the treatment. His muscle strength recovered to the point where he could live at home, and was subsequently discharged. CONCLUSION: In patients previously treated with immune checkpoint inhibitors, irAE-CRS should be considered as a differential diagnosis when multiple organ damage is observed in addition to inflammatory findings. It is recommended to start treatment with steroids; if the disease is refractory, other immunosuppressive therapies such as tocilizumab should be introduced as early as possible.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Effects of apple pomace-mixed silage on growth performance and meat quality in finishing pigs.

We measured the growth performance and meat quality of 10 crossbred (Yorkshire × Duroc × Landrace) neutered male pigs to evaluate the effects of apple pomace-mixed silage (APMS). The pigs were divided into two groups and were respectively fed the control feed and the AMPS ad libitum during the experiment. No difference was found in the finished body weight, average daily gain, carcass weight, back fat thickness or dressing ratio between the control and the AMPS treatments, but average dairy feed intake (dry matter) was significantly lower and feed efficiency was significantly higher using the APMS treatment (P < 0.05). With regard to meat quality, the APMS increased the moisture content but decreased the water holding capacity (P < 0.05) compared with the control treatment. Furthermore, the APMS affected the fatty acid composition of the back fat by increasing linoleic acid (C18:2n6), linolenic acid (C18:3) and arachidic acid (C20:0) levels, while decreasing palmitic acid (C16:0), palmitoleic acid (C16:1) and heptadecenoic acid (C17:1) levels, compared with the control treatment. These results indicate that feeding fermented apple pomace to finishing pigs increases the feed efficiency and affects the meat quality and fatty acid composition of back fat.