Transcriptome

This study adopted a prospective, single-center, open-label design, aiming to evaluate the efficacy of the RTI clinical outcome prediction model in predicting clinical outcomes in subjects with acute respiratory tract infections. In this study, the subjects were divided into the community-acquired pneumonia group and the sepsis group, including one baseline visit, one visit on the 7th day, and one visit on the 28th day. During the research process, blood samples will be collected at the corresponding visiting points for the validation of the predictive model.

Febrile illness is a common condition and it is crucial to have an early triage of patients according to various aetiologies to enable appropriate treatment. Currently, most screening/diagnostic tests target the detection of pathogens, while only a few assays aim to understand the host response, and they are mostly based on a measurement of serum proteins (e.g. CRP or procalcitonin). Recently, blood transcriptome has been explored to differentiate bacterial and viral infections. However, gene expression in blood represents a composite score of gene expression of all the component cell-types present in the sample. Here, we propose to develop a rapid test that can determine gene expressions of a specified single cell type in peripheral blood (e.g., monocytes or granulocytes) as a host response biomarker to differentiate three major categories of infections that are bacterial, viral, and tuberculosis The assay is called Direct Leukocyte Single cell-type transcript abundance (TA) assay (DIRECT LS-TA) as it can directly determine the gene expression of a specified single cell-type among various other leukocyte populations directly in a peripheral blood sample. Such results signify the nature of host response according to 3 or more axes (Type I or Type II interferon signaling response or pro-inflammatory cytokine signaling) And it can be used to indicate the type of underlying infection (viral, bacterial, or active tuberculosis).

Bagaev et al. have identified four tumor microenvironment (TME) subtypes that are conserved across diverse cancers and correlated with immunotherapy response in melanoma, bladder, and gastric cancers. They provided a visual tool revealing the TME subtypes integrated with targetable genomic alterations, which provided a planetary view of each tumor that can aid in oncology clinical decision making. We aim to use this tool to prospectively analyse the biopsy specimens of osteosarcomas to identify their TME subtypes so as to deliver appropriate treatment strategy if these osteosarcomas experience disease progression afterwards. We will compare the past sequencing date stored in PKUPH bank so as to compare the event-free survival(EFS) of these patients to check the Superiority of this method later.