UNASSIGNED: Infection is the main cause of death for patients after allogeneic hematopoietic stem cell transplantation (HSCT). However, pathogen profiles still have not been reported in detail due to their heterogeneity caused by geographic region.
UNASSIGNED: To evaluate the performance of metagenomic next-generation sequencing (mNGS) and summarize regional pathogen profiles of infected patients after HSCT.
UNASSIGNED: From February 2021 to August 2022, 64 patients, admitted to the Department of Hematology of The First Hospital of Jilin University for HSCT and diagnosed as suspected infections, were retrospectively enrolled.
UNASSIGNED: A total of 38 patients were diagnosed as having infections, including bloodstream (n =17), pulmonary (n =16), central nervous system (CNS) (n =4), and chest (n =1) infections. Human betaherpesvirus 5 (CMV) was the most common pathogen in both bloodstream (n =10) and pulmonary (n =8) infections, while CNS (n =2) and chest (n =1) infections were mainly caused by Human gammaherpesvirus 4 (EBV). For bloodstream infection, Mycobacterium tuberculosis complex (n =3), Staphylococcus epidermidis (n =1), and Candida tropicalis (n =1) were also diagnosed as causative pathogens. Furthermore, mNGS combined with conventional tests can identify more causative pathogens with high sensitivity of 82.9% (95% CI 70.4-95.3%), and the total coincidence rate can reach up to 76.7% (95% CI 64.1-89.4%).
UNASSIGNED: Our findings emphasized the importance of mNGS in diagnosing, managing, and ruling out infections, and an era of more rapid, independent, and impartial diagnosis of infections after HSCT can be expected.

The identification of pathogens associated with respiratory symptoms other than the novel coronavirus disease 2019 (COVID-19) can be challenging. However, the diagnosis of pathogens is crucial for assessing the clinical outcome of patients. We comprehensively profiled pathogens causing non-COVID-19 respiratory symptoms during the 7th prevalent period in Gunma, Japan, using deep sequencing combined with a next-generation sequencer (NGS) and advanced bioinformatics technologies. The study included nasopharyngeal swabs from 40 patients who tested negative for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) using immuno-chromatography and/or quantitative reverse transcription polymerase chain reaction (qRT-PCR) methods. Comprehensive pathogen sequencing was conducted through deep sequencing using NGS. Additionally, short reads obtained from NGS were analyzed for comprehensive pathogen estimation using MePIC (Metagenomic Pathogen Identification Pipeline for Clinical Specimens) and/or VirusTap. The results revealed the presence of various pathogens, including respiratory viruses and bacteria, in the present subjects. Notably, human adenovirus (HAdV) was the most frequently detected virus in 16 of the 40 cases (40.0%), followed by coryneforms, which were the most frequently detected bacteria in 21 of the 40 cases (52.5%). Seasonal human coronaviruses (NL63 type, 229E type, HKU1 type, and OC43 type), human bocaviruses, and human herpesviruses (human herpesvirus types 1-7) were not detected. Moreover, multiple pathogens were detected in 50% of the subjects. These results suggest that various respiratory pathogens may be associated with non-COVID-19 patients during the 7th prevalent period in Gunma Prefecture, Japan. Consequently, for an accurate diagnosis of pathogens causing respiratory infections, detailed pathogen analyses may be necessary. Furthermore, it is possible that various pathogens, excluding SARS-CoV-2, may be linked to fever and/or respiratory infections even during the COVID-19 pandemic.

Identifying and differentiating bacteria based on their emitted volatile organic compounds (VOCs) opens vast opportunities for rapid diagnostics. Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an ideal technique for VOC-biomarker discovery because of its speed, sensitivity towards polar molecules and compound characterization possibilities. Here, an in vitro SESI-HRMS workflow to find biomarkers for cystic fibrosis (CF)-related pathogens P. aeruginosa, S. pneumoniae, S. aureus, H. influenzae, E. coli and S. maltophilia is described. From 180 headspace samples, the six pathogens are distinguishable in the first three principal components and predictive analysis with a support vector machine algorithm using leave-one-out cross-validation exhibited perfect accuracy scores for the differentiation between the groups. Additionally, 94 distinctive features were found by recursive feature elimination and further characterized by SESI-MS/MS, which yielded 33 putatively identified biomarkers. In conclusion, the six pathogens can be distinguished in vitro based on their VOC profiles as well as the herein reported putative biomarkers. In the future, these putative biomarkers might be helpful for pathogen detection in vivo based on breath samples from patients with CF.