Fullerene-Filtered Light Spectrum and Fullerenes Modulate Emotional and Pain Processing in Mice

The most symmetric molecule, Buckminster fullerene C-60, due to its unique properties, has been intensively studied for various medical and technological advances. Minimally invasive and minimally toxic treatments hold great promise for future applications. With this in mind, this research exploited the physical properties of fullerene molecules for potential therapeutic effects. Pristine fullerenes have peak absorbance in the 380-500 nm range, making them an attractive violet-blue light filter. Since spectral quality of light can affect behavior, this research used resting state functional magnetic resonance imaging (rs fMRI) and behavioral testing to directly evaluate the effects of fullerene-filtered light on brain processing and behavior in mice. The same method was used to study if hydroxyl fullerene water complexes (3HFWC), with or without fullerene-filtered light, modulated brain processing. A month-long, daily exposure to fullerene-filtered light led to decreased activation of the brain area involved in emotional processing (amygdala). Water supplemented with 3HFWC resulted in an activation of brain areas involved in pain modulation and processing (periaqueductal gray), and decreased latency to first reaction when tested with a hot plate. The combination of fullerene-filtered light with 3HFWC in drinking water led to restored sensitivity to a hot plate and activation of brain areas involved in cognitive functions (prelimbic, anterior cingulate and retrosplenial cortex). These results uncovered the potential of fullerene-filtered light to impact emotional processing and modulate pain perception, indicating its further use in stress and pain management

Fullerene-Filtered Light Spectrum and Fullerenes Modulate Emotional and Pain Processing in Mice

The most symmetric molecule, Buckminster fullerene C-60, due to its unique properties, has been intensively studied for various medical and technological advances. Minimally invasive and minimally toxic treatments hold great promise for future applications. With this in mind, this research exploited the physical properties of fullerene molecules for potential therapeutic effects. Pristine fullerenes have peak absorbance in the 380-500 nm range, making them an attractive violet-blue light filter. Since spectral quality of light can affect behavior, this research used resting state functional magnetic resonance imaging (rs fMRI) and behavioral testing to directly evaluate the effects of fullerene-filtered light on brain processing and behavior in mice. The same method was used to study if hydroxyl fullerene water complexes (3HFWC), with or without fullerene-filtered light, modulated brain processing. A month-long, daily exposure to fullerene-filtered light led to decreased activation of the brain area involved in emotional processing (amygdala). Water supplemented with 3HFWC resulted in an activation of brain areas involved in pain modulation and processing (periaqueductal gray), and decreased latency to first reaction when tested with a hot plate. The combination of fullerene-filtered light with 3HFWC in drinking water led to restored sensitivity to a hot plate and activation of brain areas involved in cognitive functions (prelimbic, anterior cingulate and retrosplenial cortex). These results uncovered the potential of fullerene-filtered light to impact emotional processing and modulate pain perception, indicating its further use in stress and pain management

Rhodnius prolixus bug bloodsucking cell responses to infrared radiation in the Differential effects of ambient temperature on warm

International audienceDifferential effects of ambienttemperature on warm cell responses to infrared radiation in the bloodsuckingbug Rhodnius prolixus. J Neurophysiol 111: 1341–1349, 2014.First published January 8, 2014; doi:10.1152/jn.00716.2013.—Thermoreceptorsprovide animals with background information about thethermal environment, which is at least indirectly a prerequisite forthermoregulation and assists bloodsucking insects in the search fortheir host. Recordings from peg-in-pit sensilla and tapered hairs on theantennae of the bug Rhodnius prolixus revealed two physiologicallydifferent types of warm cells. Both types responded more strongly totemperature pulses produced by switching between two air streams atdifferent constant temperatures than to infrared radiation pulses employedin still air. In addition, both warm cells were better able todiscriminate small changes in air temperature than in infrared radiation.As convective and radiant heat determines the discharge, it isimpossible for a single warm cell to signal the nature of the stimulusunequivocally. Individual responses are ambiguous, not with regard totemperature change, but with regard to its source. We argue that thebugs use mechanical flow information to differentiate between pulsesof convective and radiant heat. However, if pulses of radiant heatoccur together with a constant temperature air stream, the mechanicalcues would not allow avoiding ambiguity that convective heat introducesinto radiant heat stimulation. In this situation, the warm cell inthe tapered hairs produced stronger responses than those in thepeg-in-pit sensilla. The reversal in the excitability of the two types ofwarm cells provides a criterion by which to distinguish the combinationof convective and radiant heat from the stimuli presented alone

Infrared detection without specialized infrared receptors in the bloodsuckingbug Rhodnius prolixus

International audienceInfrared detection withoutspecialized infrared receptors in the bloodsucking bug Rhodniusprolixus. J Neurophysiol 112: 1606–1615, 2014. First published June18, 2014; doi:10.1152/jn.00317.2014.—Bloodsucking bugs use infraredradiation (IR) for locating warm-blooded hosts and are able todifferentiate between infrared and temperature (T) stimuli. This paperis concerned with the neuronal coding of IR in the bug Rhodniusprolixus. Data obtained are from the warm cells in the peg-in-pitsensilla (PSw cells) and in the tapered hairs (THw cells). Both warmcells responded to oscillating changes in air T and IR with oscillationsin their discharge rates. The PSw cells produced stronger responses toT oscillations than the THw cells. Oscillations in IR did the reverse:they stimulated the latter more strongly than the former. The reversalin the relative excitability of the two warm cell types provides acriterion to distinguish between changes in T and IR. The existence ofstrongly responsive warm cells for one or the other stimulus in apaired comparison is the distinguishing feature of a “combinatorycoding” mechanism. This mechanism enables the information providedby the difference or the ratio between the response magnitudesof both cell types to be utilized by the nervous system in the neuralcode for T and IR. These two coding parameters remained constant,although response strength changed when the oscillation period wasaltered. To discriminate between changes in T and IR, two things areimportant: which sensory cell responded to either stimulus and howstrong was the response. The label warm or infrared cell may indicateits classification, but the functions are only given in the context ofactivity produced in parallel sensory cells

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset

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Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.</div