Pneumocystis jirovecii Infection in autologous hematopoietic stem cell transplant recipients

The epidemiology of invasive fungal infections in transplant recipients

Transplant patients, including solid-organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) recipients, are exposed to various types of complications, particularly rejection. To prevent these outcomes, transplant recipients commonly receive long-term immunosuppressive regimens that in turn make them more susceptible to a wide array of infectious diseases, notably those caused by opportunistic pathogens. Among these, invasive fungal infections (IFIs) remain a major cause of mortality and morbidity in both SOT and HSCT recipients. Despite the continuing improvement in early diagnostics and treatments of IFIs, the management of these infections in transplant patients is still complicated. Here, we provide an overview concerning the most recent trends in the epidemiology of IFIs in SOT and HSCT recipients by describing the prominent yeast and mold species involved, the timing of post-transplant IFIs and the risk factors associated with their occurrence in these particularly weak populations. We also give special emphasis into basic research advances in the field that recently suggested a role of the global and long-term prophylactic regimen in orchestrating various biological disturbances in the organism and conditioning the emergence of the most adapted fungal strains to the particular physiological profiles of transplant patients.

Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT

Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely diagnosis of infectious fever and administration of antimicrobial drugs can effectively reduce patient mortality. In this study, serum samples were collected from 181 patients with HSCT with or without infection, as well as the clinical information. And more than 80 infectious-related microRNAs in the serum were selected according to the bulk RNA-seq result and detected in the 345 time-pointed serum samples by Q-PCR. Unsupervised clustering result indicates a close association between these microRNAs expression and infection occurrence. Compared to the uninfected cohort, more than 10 serum microRNAs were identified as the combined diagnostic markers in one formula constructed by the Random Forest (RF) algorithms, with a diagnostic accuracy more than 0.90. Furthermore, correlations of serum microRNAs to immune cells, inflammatory factors, pathgens, infection tissue, and prognosis were analyzed in the infection cohort. Overall, this study demonstrates that the combination of serum microRNAs detection and machine learning algorithms holds promising potential in diagnosing infectious fever after HSCT.