To establish a quantum dot immunochromatographic test strip for simultaneous detection of central nervous system-specific protein (S100 β) and neuron-specific enolase (NSE), thereby achieving combined detection of both biomarkers.

A double-antibody sandwich immunochromatographic assay was developed using S100β and NSE capture antibodies as the test line and species-specific secondary antibodies as the control line coated on the nitrocellulose membrane. Quantum dot bead-conjugated antibodies targeting S100β and NSE served as fluorescent probes. The preparation parameters of the strip were optimized, and its performance was systematically evaluated to develop a rapid diagnostic strip for mild traumatic brain injury (mTBI). The diagnostic efficacy of the strip was further validated using clinical samples.

The total assay time for combined detection of S100β and NSE was 17 min, with a limit of detection (LOD) of 0.1 ng/mL for S100β and 2.67 ng/mL for NSE. The inter-batch reproducibility of the strips was excellent (coefficient of variation, CV<15%). In clinical validation using 24 mTBI samples, the combined detection strip demonstrated a specificity of 90% and a sensitivity of 92.86%, outperforming single-marker detection. These results prove that the simultaneous quantification of S100β and NSE enhances diagnostic efficacy for mTBI patients.

A combined detection method for the biomarkers S100β and NSE in mTBI, based on quantum dot immunochromatography, has been preliminarily established. This method demonstrates promising clinical application prospects in the auxiliary diagnosis of mTBI.

To establish a technology for rapid recognition of fear emotions in order to facilitate the monitoring and interventions of fear emotions among personnel doing unusual jobs based on the fNIRSNet model.

Functional near infrared spectroscopy (fNIRS) was used to record the changes of cerebral blood oxygen activities in 50 subjects watching non-thrilling and thrilling videos, whose emotions were assessed based on Sel f-Assessment Manikin (SAM). Signals of blood oxygen in response to the two types of videos were labeled and preprocessed. Channels with significant differences were identified by calculating the Beta value. The features of data on fNIRS in the activated region of the brain where the channels were located were extracted. The fNIRSNet algorithm was used to establish a fear emotion recognition model, and the accuracy was evaluated using the 50-fold cross-validation method.

The SAM showed that fear emotions were induced, and the brain region activated by fear emotions was located in the medial prefrontal cortex. The fNIRSNet algorithm could help classify fear emotions with high precision by analyzing the data on the 20 s short time series, with an accuracy of 82.36%.

This technology for emotion recognition based on the fNIRSNet model is capable of rapid and accurate assessment of fear emotions, which can be used for related monitoring and interventions.