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The search to date for accurate protein biomarkers in acute ischemic stroke has taken into consideration the stage and/or the size of infarction, but has not accounted for the site of stroke. In the present study, multiple reaction monitoring using labeled reference peptide (LRP) following laser capture microdissection (LCM) is used to identify site-specific protein biomarker candidates. In middle cerebral artery occlusion (MCAO) rat models, both intact and infarcted brain tissue was collected by LCM, followed by on-film digestion and semi-quantification using triple-quadrupole mass spectrometry. Thirty-four unique peptides were detected for the verification of 12 proteins in both tissue homogenates and LCM-captured samples. Six insoluble proteins, including neurofilament light polypeptide (NEFL), alpha-internexin (INA), microtubule-associated protein 2 (MAP2), myelin basic protein (MBP), myelin proteolipid protein (PLP) and 2′,3′-cyclic-nucleotide 3′-phosphodiesterase (CNP), were found to be site-specific. Soluble proteins, such as neuron-specific enolase (NSE) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1), and some insoluble proteins, including neurofilament heavy polypeptide (NEFH), glial fibrillary acidic protein (GFAP), microtubule-associated protein tau (MAPT) and tubulin β-3 chain (TUBB3), were found to be evenly distributed in the brain. Therefore, we conclude that some insoluble protein biomarkers for stroke are site-specific, and would make excellent candidates for the design and analysis of relevant clinical studies in the future.

Clinically-informative biomarkers of ischemic stroke are needed for rapid diagnosis and timely treatment. In the present study, APOA1 unique peptide (APOA1-UP), a novel peptide biomarker, was identified and quantified by multiple reaction monitoring (MRM) using labeled reference peptide (LRP). Serum samples of 94 patients in the ischemic stroke group and 37 patients in the non-stroke group were analyzed for the levels of total APOA1-UP, low density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). Median ratio of total APOA1-UP/LRP was 2.14 (interquartile range, 0.40) in the non-stroke group and 1.32 (0.44) in the ischemic stroke group (p < 0.0001). The serum level of total APOA1-UP was independently correlated with the presence of ischemic stroke by multivariate logistic regression analysis (p < 0.0001). From the receiver operating characteristic (ROC) curve, the area under the curve (AUC) was 0.9750 and the optimal cutoff value of the serum APOA1-UP level was 1.80, which yielded a sensitivity of 90.63% and a specificity of 97.14%. The diagnostic efficiency of HDL-C was lower, with an AUC of 0.7488. Therefore, the serum level of APOA1-UP is a diagnostic biomarker candidate for ischemic stroke in the early stage.

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A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.