Stereological Study on the Effect of Carnosine on of Purkinje Cells in the Cerebellum of Rats Exposed to 900 MHz Electromagnetic Field.

AIM: To evaluate the impact of carnosine on Purkinje neurons in rats exposed to a 900 Mhz electromagnetic field. MATERIAL AND METHODS: This study evaluated 24 rats divided into the following three different groups: a control group, a group exposed to the electromagnetic field, and a group that was injected with carnosine while being exposed to the electromagnetic field. The electromagnetic field group was exposed to a 900 Mhz electromagnetic field for an hour daily over 28 days. Thereafter, stereological analysis was performed histologically on cerebellar sections, and the number of Purkinje cells were counted. RESULTS: The electromagnetic field group had remarkably fewer Purkinje cell compared to control. The electromagnetic field group plus 20 mg of carnosine had significantly more total Purkinje cells compared to the electromagnetic field group (p < 0.05). CONCLUSION: The present study showed that electromagnetic field exposure decreases the number of Purkinje cell, whereas carnosine protected the cerebellum from neural damage induced by electromagnetic field exposure.

Bent Fiber Sensor for Preservative Detection in Milk.

A fiber optic sensor sensitive to refractive index changes of the outer region of the fiber cladding is presented. The sensor uses bent plastic optical fibers in different bending lengths to increase sensitivity. Measurements were made for low-fat milk, the refractive index of which is altered by some preservatives such as formaldehyde, hydrogen peroxide, and sodium carbonate. Concentrations of the preservatives in the milk were changed between 0% and 14.3% while the refractive indices occurred between 1.34550 and 1.35093 for the minimum (0%) and maximum (14.286%) concentrations of sodium carbonate, respectively. Due to bending-induced sensitivity, the sensor is able to detect refractive index changes less of than 0.4%. The results show that there is excellent linearity between the concentration and normalized response of the sensor.

Color regeneration from reflective color sensor using an artificial intelligent technique.

A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.