Piezo-Optical Transducers in High Sensitive Strain Measurements

New piezo-optical sensors based on the piezo-optical effect for high sensitive mechanical stress measurements have been proposed and developed. The piezo-optical method provides the highest sensitivity to strains compared to sensors based on any other physical principles. Piezo-optical sensors use materials whose parameters practically not change under load or over time, therefore piezo-optical sensors are devoid of the disadvantages inherent in strain-resistive and piezoelectric sensors, such as hysteresis, parameters degradation with time, small dynamic range, low sensitivity to strains, and high sensitivity to overloads. Accurate numerical simulation and experimental investigations of the piezo-optical transducer output signal formation made it possible to optimize its design and show that the its gauge factor is two to three orders of magnitude higher than the gauge factors of sensors of other types. The cruciform shape of the transducer photoelastic element made it possible to significantly increase the stresses in its working area at a given external force. Combining compactness, reliability, resistance to overloads, linearity and high sensitivity, in terms of the all set of these parameters, piezo-optical sensors significantly surpass the currently widely used strain-resistive, piezoelectric and fiber-optic sensors and open up new, previously inaccessible, possibilities in the tasks of measuring power loads

Terahertz Time-Domain Spectroscopy of Blood Serum for Differentiation of Glioblastoma and Traumatic Brain Injury

The possibility of the differentiation of glioblastoma from traumatic brain injury through blood serum analysis by terahertz time-domain spectroscopy and machine learning was studied using a small animal model. Samples of a culture medium and a U87 human glioblastoma cell suspension in the culture medium were injected into the subcortical brain structures of groups of mice referred to as the culture medium injection groups and glioblastoma groups, accordingly. Blood serum samples were collected in the first, second, and third weeks after the injection, and their terahertz transmission spectra were measured. The injection caused acute inflammation in the brain during the first week, so the culture medium injection group in the first week of the experiment corresponded to a traumatic brain injury state. In the third week of the experiment, acute inflammation practically disappeared in the culture medium injection groups. At the same time, the glioblastoma group subjected to a U87 human glioblastoma cell injection had the largest tumor size. The THz spectra were analyzed using two dimensionality reduction algorithms (principal component analysis and t-distributed Stochastic Neighbor Embedding) and three classification algorithms (Support Vector Machine, Random Forest, and Extreme Gradient Boosting Machine). Constructed prediction data models were verified using 10-fold cross-validation, the receiver operational characteristic curve, and a corresponding area under the curve analysis. The proposed machine learning pipeline allowed for distinguishing the traumatic brain injury group from the glioblastoma group with 95% sensitivity, 100% specificity, and 97% accuracy with the Extreme Gradient Boosting Machine. The most informative features for these groups’ differentiation were 0.37, 0.40, 0.55, 0.60, 0.70, and 0.90 THz. Thus, an analysis of mouse blood serum using terahertz time-domain spectroscopy and machine learning makes it possible to differentiate glioblastoma from traumatic brain injury