Maternal neglect results in reduced telomerase activity and increased oxidative load in rats

A growing number of studies in humans have linked chronic stress, particularly during early life, to telomere shortening and increased oxidative stress. The effect of stress on telomerase activity, however, is understudied. Given the importance of telomere attrition in a wide range of diseases and immunosenescence, further research to elucidate the mechanisms by which stress alters telomere dynamics is required. However, animal studies are lacking, and it is not clear whether widely used stress models reliably mimic the accelerated telomere shortening observed humans. To this end, we evaluated the effect of maternal separation with early weaning (MSEW) on telomere length, telomerase activity, and oxidative load in rats. A total of 45 animals were used, (17 control: 3 males and 11 females and 28 MSEW: 11 males, 17 females), which were then sacrificed one year after birth. Importantly, we determined that telomerase activity measured in plasma was significantly decreased in the MSEW group, along with a non-significant reduction in telomere length from whole blood cells. We also examined the levels of three oxidative markers: plasma malondialdehyde, glutathione in erythrocytes, and plasma catalase activity. Malondialdehyde was found to be elevated in the plasma, indicating increased lipid peroxidation. Interestingly, while the antioxidant glutathione was upregulated, catalase activity remained unchanged. Our findings indicate that the rat MSEW model induces chronic changes to telomere dynamics and oxidative load and can capitulate long term aspects of human childhood stress.

A Custom-made Pupillometer System for Characterizing Pupillary Light Response

OBJECTIVES

This paper presents the design and construction of a viable pupillometer system and demonstrates its merits with extensive validation tests.

MATERIALS AND METHODS

A web camera was modified by removing its infrared filter and mounted on a chin rest. Light emitting diodes (LEDs) operating at infrared and visible spectra were integrated to provide background and light stimulus, respectively. The LEDs were controlled by a microprocessor board. Stimulation was presented using a periodic paradigm with variable period and duty cycle. Videos of both pupils were recorded at 30 frames/second and processed offline using software developed in-house. The overall system was validated with data gathered from individuals with healthy vision under different stimulation paradigms. Temporal variations in pupil size were determined and analyzed statistically.

RESULTS

The analysis revealed that the pupil sizes were accurately measured from the video frames provided that reflections from both infrared and visible lights remain outside the pupil. The system achieved moderate to excellent repeatability scores (87.8 and 86.8% for short 1 second and long 2 second pulses, respectively), which demonstrated its effectiveness and confirmed that it can be used reliably as a pupillometer.

CONCLUSION

The proposed pupillometer system produces useful, quantitative data characterizing pupillary light response. However, further development and implementation are needed to potentially turn it into a low-cost alternative for other studies involving the autonomic nervous system, cognitive function, drug metabolism, pain response, psychology, fatigue, and sleep disorders.

Machine learning aided diagnosis of hepatic malignancies through in vivo dielectric measurements with microwaves

In the past decade, extensive research on dielectric properties of biological tissues led to characterization of dielectric property discrepancy between the malignant and healthy tissues. Such discrepancy enabled the development of microwave therapeutic and diagnostic technologies. Traditionally, dielectric property measurements of biological tissues is performed with the well-known contact probe (open-ended coaxial probe) technique. However, the technique suffers from limited accuracy and low loss resolution for permittivity and conductivity measurements, respectively. Therefore, despite the inherent dielectric property discrepancy, a rigorous measurement routine with open-ended coaxial probes is required for accurate differentiation of malignant and healthy tissues. In this paper, we propose to eliminate the need for multiple measurements with open-ended coaxial probe for malignant and healthy tissue differentiation by applying support vector machine (SVM) classification algorithm to the dielectric measurement data. To do so, first, in vivo malignant and healthy rat liver tissue dielectric property measurements are collected with open-ended coaxial probe technique between 500 MHz to 6 GHz. Cole-Cole functions are fitted to the measured dielectric properties and measurement data is verified with the literature. Malign tissue classification is realized by applying SVM to the open-ended coaxial probe measurements where as high as 99.2% accuracy (F1 Score) is obtained.