Specifically, eligible patients will be treated surgically. In one group, injured brain tissue removed during decompression and resection will be preserved in a tissue preservation solution. In another group, brain tissue in the surgical trajectory during surgical resection of the brain lesion (for example, gliomas, meningiomas, schwannomas) will be also preserved in the solution. Then these tissues will be analyzed through high-throughput single-cell sequencing and bioinformatics analysis. In order to obtain the most changed biomarkers, we will pick up the top 10 cell subgroups with 10 expression biomarkers in each subgroup to compare them with the results analyzed in the mice brain tissue. The target biomarkers will be validated in the cerebrospinal fluid (CSF) and blood in patients with TBI for the patient's neurological status evaluation and outcome prediction. At the same time, the patient's pre- and postoperative clinical data (including, gender, age, GCS score, pupil light reflection, CT results, etc) will also be collected. A regression model will be generated to estimate the relationship between the patient's neurological status and these independent variables. In addition, the association between a patient's prognosis and these factors will also be analyzed. ROC curve will be used to compare our regression model to the established GCS score scale and Marshall CT grading.
Clinical evaluation is crucial in the clinical diagnosis, treatment, and prognosis prediction in patients with traumatic brain injury (TBI). However, the existing evaluation systems are not perfect, because many factors are not taken into account, for example, there is a lack of molecular diagnostic criteria for evaluating patients with TBI. We attempt to collect the patient's clinical data and combine it with neuroimaging, as well as molecular biomarkers generated by single-cell sequencing to assess their neurological status and outcome. The clinical and molecular data collection and analysis will be helpful to evaluate the patient's neurological condition and predict the patient's outcome more accurately.