Mycotic aneurysm is an uncommon disease which could be fatal without appropriate treatment. Although standard therapy for mycotic aneurysms consists of resection of the infected aorta and in situ graft replacement, some treat with endovascular stent-grafting because patients may not tolerate graft replacement due to underlying diseases. There are 6 more reported cases of mycotic aneurysm caused by Edwardsiella tarda. With the exception of our case, all underwent resection and debridement of the infected aorta or vascular prosthesis. Herein we report the first case ever of mycotic aneurysm caused by E. tarda, successfully treated with stenting and suppressive antibiotic therapy without resection of the infected aorta. A 65-year-old Japanese woman with cirrhosis and hepatocellular carcinoma complained of fatigue. Her work up revealed a ruptured aneurysm of the descending aorta. She went through endovascular stent-graft placement. Edwardsiella tarda grew from blood cultures, which led to the diagnosis of mycotic aneurysm. Edwardsiella tarda is a Gram negative bacillus which rarely causes infections in humans. In the case of bacteremia, its mortality is reported to be very high and all reported cases with mycotic aneurysm caused by E. tarda ended up with resection of the infected aorta. Our case shows that in the case of mycotic aneurysm caused by E. tarda, endovascular stent-graft placement could be an alternative to in situ graft replacement.

Our antimicrobial pharmacist-led intervention included: (a) a structured review of antibiotic prescriptions; (b) educating prescribers on antimicrobial therapy; (c) monthly reporting of department-level rates of blood sampling for culture. Daily review began in May 2018 and was discontinued after 10 months; however, the other interventions were conducted throughout the study period. This study aimed to evaluate the sustained impact of pharmacist’s interventions on antimicrobial therapy and clinical outcomes between the baseline (May–December 2017), intervention (May–December 2018), and post-intervention (May–December 2019) periods. The rate of blood culture collections before starting antipseudomonal agent therapy was significantly increased from the baseline to post-intervention periods (71% vs. 85%, p < 0.001). Antipseudomonal agent therapy was more frequently de-escalated in the post-intervention period than in the baseline period (73% vs. 54%, p = 0.038). Total use of antipseudomonal agents was reduced from the baseline to intervention periods and persisted during the post-intervention period (50.5 vs. 41.8 and 42.6 DDD per 1000 patient-days, p = 0.016 and p = 0.022, respectively). During the study period, there were significant reductions in the incidence of hospital-acquired Clostridioides difficile infection (1.12, 0.54, and 0.51 per 10,000 patient-days, respectively, p = 0.031) and 30-day mortality with bacteremia (19%, 18%, and 12%, respectively, p = 0.005). Our pharmacist-led interventions sustainably achieved appropriate antimicrobial therapy and improved clinical outcomes.

We retrospectively reviewed patients with Corynebacterium striatum bacteremia treated with daptomycin. All 11 isolates were initially susceptible to daptomycin, but the emergence of daptomycin nonsusceptibility during treatment and clinical failure occurred in 36% and 45% of patients, respectively.

Introduction. Timely and accurate diagnosis of bacterial infections enables early administration of appropriate antimicrobial treatment and improved outcomes. Hypothesis/Gap Statement. The accuracy of computer-aided diagnosis (CAD) for identifying organisms on urine Gram stains has not been compared with that of microbiology specialists (MS). Aim. To compare the interpretation of urine Gram-stain results by MS and a CAD app designed using artificial intelligence. Methodology. Urine specimens from patients with urinary tract infections were used and collected at two tertiary hospitals between 1 April and 31 December 2022. Using non-inferiority analysis to assess whether CAD was non-inferior to expert interpretation, CAD-predicted microscopic findings of the Gram-stained slide generated from iPhone camera images from two hospitals were compared with those from ten MS. A total of 153 images were taken from each hospital, and CAD interpreted a total of 306. The primary endpoint was the prediction accuracy based on the morphology of the Gram-stained bacteria. Results. The accuracy (95% confidence interval) of MS and CAD predictions was 83.0% (81.6%–84.3%) and 87.9% (83.7%–91.3%), respectively, with a difference of –4.93% (–8.43% to –0.62%) indicating non-inferiority of CAD. Conclusion. CAD was non-inferior to MS predictions for identifying Gram-stained pathogens; therefore, CAD was suggested to have the potential for guiding empirical antibiotic selection in patients with urinary tract infections.

Antibody production is one of the primary mechanisms for recovery from coronavirus disease 2019 (COVID-19). It is speculated that massive clonal expansion of B cells, which can produce clinically meaningful neutralizing antibodies, occurs in patients who recover on the timing of acquiring adaptive immunity. To evaluate fluctuations in clonal B cells and the size of the clones, we chronologically assessed the B-cell receptor (BCR) repertoire in three patients with COVID-19 who recovered around 10 days after symptom onset. We focused on the three dominant clonotypes (top 3) in each individual. The percentage frequencies of the top 3 clonotypes increased rapidly and accounted for 27.8 % on day 9 in patient 1, 10.4 % on day 12 in patient 2, and 10.8 % on day 11 in patient 3, respectively. The frequencies of these top 3 clonotypes rapidly decreased as the patients’ clinical symptoms improved. Furthermore, BCR network analysis revealed that accumulation of clusters composed of similar complementarity-determining region 3 (CDR3) sequences were rapidly formed, grew, and reached their maximum size around 10 days after symptom onset. BCR repertoire analysis revealed that a massive surge of some unique BCRs occurs during the acquisition of adaptive immunity and recovery. The peaks were more prominent than expected. These results provide insight into the important role of BCRs in the recovery from COVID-19 and raise the possibility of developing neutralizing antibodies as COVID-19 immunotherapy.

Background/Objectives: Gram staining is an essential diagnostic technique used for the rapid identification of bacterial and fungal infections, playing a pivotal role in clinical decision-making, especially in point-of-care (POC) settings. Manual staining, while effective, is labor-intensive and prone to variability, relying heavily on the skill of laboratory personnel. Current automated Gram-staining systems are primarily designed for high-throughput laboratory environments, limiting their feasibility in decentralized healthcare settings such as emergency departments and rural clinics. This study aims to introduce and evaluate the Point-of-Care Gram Stainer (PoCGS^®), a compact, automated device engineered for single-slide processing, addressing challenges related to portability, standardization, and efficiency in POC applications. Methods: The PoCGS^® device was developed to emulate expert manual staining techniques through features such as methanol fixation and programmable reagent application. A comparative evaluation was performed using 40 urine samples, which included both clinical and artificial specimens. These samples were processed using PoCGS^®, manual staining by skilled experts, and manual staining by unskilled personnel. The outcomes were assessed based on microbial identification concordance, the staining uniformity, presence of artifacts, and agreement with the culture results. Statistical analyses, including agreement rates and quality scoring, were conducted to compare the performance of PoCGS^® against manual staining methods. Results: PoCGS^® achieved a 100% concordance rate with expert manual staining in terms of microbial identification, confirming its diagnostic accuracy. However, staining quality parameters such as the uniformity and presence of artifacts showed statistically significant differences when compared to skilled and unskilled personnel. Despite these limitations, PoCGS^® demonstrated a comparable performance regarding artifact reduction and agreement with the culture results, indicating its potential utility in POC environments. Challenges such as fixed processing times and limited adaptability to varying specimen characteristics were identified as areas for further improvement. Conclusions: The study findings suggest that PoCGS^® is a reliable and valuable tool for microbial identification in POC settings, with a performance comparable to skilled manual staining. Its compact design, automation, and ease of use make it particularly beneficial for resource-limited environments. Although improvements in staining uniformity and background clarity are required, PoCGS^® has the potential to standardize Gram staining protocols and improve diagnostic turnaround times. Future developments will focus on optimizing staining parameters and expanding its application to other clinical sample types, ensuring robustness and broader usability in diverse healthcare settings.