Serum-based Raman spectroscopic diagnosis of blast-induced brain injury in a rat model

Meilan Ge; Yuye Wang; Tong Wu; Haibin Li; Chuanyan Yang; Tunan Chen; Hua Feng; Degang Xu; Jianquan Yao

doi:10.1364/BOE.495285

Serum-based Raman spectroscopic diagnosis of blast-induced brain injury in a rat model

Biomed Opt Express. 2023 Jun 23;14(7):3622-3634. doi: 10.1364/BOE.495285. eCollection 2023 Jul 1.

Authors

Meilan Ge^{1

2}, Yuye Wang^{1

2}, Tong Wu³, Haibin Li^{1

2}, Chuanyan Yang⁴, Tunan Chen⁴, Hua Feng⁴, Degang Xu^{1

2}, Jianquan Yao^{1

2}

Affiliations

¹ School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
² Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China.
³ School of Marine Science and Technology, Tianjin University, Tianjin 300072, China.
⁴ Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China.

Abstract

The diagnosis of blast-induced traumatic brain injury (bTBI) is of paramount importance for early care and clinical therapy. Therefore, the rapid diagnosis of bTBI is vital to the treatment and prognosis in clinic. In this paper, we reported a new strategy for label-free bTBI diagnosis through serum-based Raman spectroscopy. The Raman spectral characteristics of serum in rat were investigated at 3 h, 24 h, 48 h and 72 h after mild and moderate bTBIs. It has been demonstrated that both the position and intensity of Raman characteristic peaks exhibited apparent differences in the range of 800-3000cm^-1 compared with control group. It could be inferred that the content, structure and interaction of biomolecules in the serum were changed after blast exposure, which might help to understand the neurological syndromes caused by bTBI. Furthermore, the control group, mild and moderate bTBIs at different times (a total of 9 groups) were automatically classified by combining principal component analysis and four machine learning algorithms (quadratic discriminant analysis, support vector machine, k-nearest neighbor, neural network). The highest classification accuracy, sensitivity and precision were up to 95.4%, 95.9% and 95.7%. It is suggested that this method has great potential for high-sensitive, rapid, and label-free diagnosis of bTBI.