CAPA (COVID-19 associated pulmonary aspergillosis) is an important complication of COVID-19. It has been reported that the incidence of CAPA is as high as 19%e33% worldwide. However, its onset has not been reported in Japan. A 72-year-old Japanese man was diagnosed with COVID-19 and was transferred to our hospital due to deterioration of respiratory condition. Treatment with remdesivir, dexamethasone (DEXA), and antibiotics was performed under mechanical ventilation. Although the condition improved temporarily, a new shadow appeared in the lung, and Aspergillus fumigatus was cultured from sputum. The patient was clinically diagnosed with CAPA and treated with voriconazole. However, his progress deteriorated and he died. High-risk COVID-19 patients should be tested for Aspergillus to ensure early diagnosis of CAPA.

Objective Telephone triage service in emergency care has been introduced in many countries, and it is important to determine the effect of telephone triage service on the outcome of emergency patients. The aim of this study was to evaluate the effect of telephone triage service on the outcome of emergency patients using propensity score. Methods design, settings, and participants This was a retrospective study with a study period from January 2016 to December 2019. We included all patients transported by ambulances of the Osaka Municipal Fire Department during study period. Exposure Telephone triage service. Outcome measures and analysis The main outcome of this study was unfavorable outcome following use of the telephone triage service. In this study, unfavorable outcome was defined as patients who were admitted, transferred, or died after care in the emergency department. Propensity scores were calculated using a logistic regression model with 12 variables that were present before the telephone triage service was used or were indicative of the patient’s condition. Data analyses were not only propensity score matching but also a multivariable logistic regression model and regression model with propensity score as a covariate. Main results The number of patients eligible for analyses was 707 474. Of these patients, 8008 (1.0%) used the telephone triage services and 699 466 patients (99.0%) did not use it. The number of patients with an unfavorable outcome was 407 568 (57.6%) in the total cohort. Of them, 2305 patients (28.8%) used the telephone triage service and 297 601 patients (42.5%) did not use it. For propensity score matching, 8008 patients were matched from each group. Use of the telephone triage service was inversely associated with unfavorable outcome in a multivariate logistic regression model with propensity score as a covariate [adjusted odds ratio (OR) 0.874; 95% confidence interval (CI), 0.831–0.919] and propensity score matching (crude OR, 0.875; 95% CI, 0.818–0.936). Conclusions This study revealed that the use of the telephone triage service in Osaka city, Japan was associated with better outcomes of patients transported by ambulance.

Background GPT-4-based ChatGPT demonstrates significant potential in various industries; however, its potential clinical applications remain largely unexplored. Methods We employed the New England Journal of Medicine (NEJM) quiz "Image Challenge" from October 2021 to March 2023 to assess ChatGPT's clinical capabilities. The quiz, designed for healthcare professionals, tests the ability to analyze clinical scenarios and make appropriate decisions. We evaluated ChatGPT's performance on the NEJM quiz, analyzing its accuracy rate by questioning type and specialty after excluding quizzes which were impossible to answer without images. The NEJM quiz has five multiple-choice options, but ChatGPT was first asked to answer without choices, and then given the choices to answer afterwards, in order to evaluate the accuracy in both scenarios. Results ChatGPT achieved an 87% accuracy without choices and a 97% accuracy with choices, after excluding 16 image-based quizzes. Upon analyzing performance by quiz type, ChatGPT excelled in the Diagnosis category, attaining 89% accuracy without choices and 98% with choices. Although other categories featured fewer cases, ChatGPT's performance remained consistent. It demonstrated strong performance across the majority of medical specialties; however, Genetics had the lowest accuracy at 67%. Conclusion ChatGPT demonstrates potential for clinical application, suggesting its usefulness in supporting healthcare professionals and enhancing AI-driven healthcare.

A 73-year-old man previously treated with rituximab for his mucosa-associated lymphoid tissue lymphoma suffered a suboptimal humoral immune response against an acquired SARS-CoV-2 infection. A detailed serological description revealed discrepant antigen-specific humoral immune responses. The titer of spike-targeting, “viral-neutralizing” antibodies remained below the detection level, in contrast to the anti-nucleocapsid, “binding” antibody response, which was comparable in both magnitude and kinetics. Accordingly, viral neutralizability and clearance was delayed, leading to prolonged RNAemia and persistent pneumonia. The present case highlights the need to closely monitor this unique population of recipients of B-cell-targeted therapies for their neutralizing antibody responses against SARS-CoV-2.