Electrical Neural Stimulation and Simultaneous in Vivo Monitoring with Transparent Graphene Electrode Arrays Implanted in GCaMP6f Mice

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

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Developing Fluoromodule-based Probes for in vivo Monitoring the Bacterial Infections and Antibiotic Responses

Talanta ◽

10.1016/j.talanta.2021.122610 ◽

2021 ◽

pp. 122610

Author(s):

Xiang Wang ◽

Qinghua Wang ◽

Qingyang Zhang ◽

Xiaowan Han ◽

Shengnan Xu ◽

...

Keyword(s):

Bacterial Infections ◽

In Vivo Monitoring

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FSMP-06. IN VIVO MONITORING OF LDHA EXPRESSION IN GLIOBLASTOMA USING QUANTITATIVE EXCHANGED-LABEL TURNOVER 1H MRS TECHNIQUE

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab024.070 ◽

2020 ◽

Vol 3 (Supplement_1) ◽

pp. i17-i17

Author(s):

Puneet Bagga ◽

Laurie Rich ◽

Mohammad Haris ◽

Neil Wilson ◽

Mitch Schnall ◽

...

Keyword(s):

Magnetic Resonance ◽

Lactate Production ◽

Deuterium Labeling ◽

1H Mrs ◽

In Vivo Monitoring ◽

Lactate Turnover ◽

Crucial Information ◽

Specialized Hardware

Abstract Most cancers, including glioblastomas (GBMs), rely extensively on glycolysis to support growth, proliferation, and survival. A hallmark of this elevated glycolysis is overexpression of Lactate dehydrogenase-A (LDHA) protein leading to increased uptake of glucose and overproduction of lactate. Various clinical trials using LDHA as a target for diagnosis and treatment have yielded encouraging results. However, in vivo monitoring of LDHA expression has been challenging due to either requirement of administration of radioactive substrates or specialized hardware. In this presentation, we will demonstrate a new method-quantitative exchanged-label turnover MRS (QELT, or simply qMRS)-that increases the sensitivity of magnetic resonance-based metabolic mapping without the requirement for specialized hardware. qMRS relies on the administration of deuterated (2H-labeled) substrates to track the production of downstream metabolites. Since 2H is invisible on 1H MRS, replacement of 1H with 2H due to metabolic turnover leads to an overall reduction in 1H MRS signal for the corresponding metabolites. We applied our qMRS technique to monitor the rate of lactate production in a preclinical GBM model. Infusion of [6,6’-2H2]glucose led to downstream deuterium labeling of lactate, thereby resulting in a reduction in the 1.33 ppm lactate-CH3 peak on 1H MRS over time. The subtraction of post-administration 1H MR spectra from the pre-infusion spectra aided in the determination of the kinetics of the lactate turnover. We believe that the detection and quantification of lactate production kinetics may provide crucial information regarding tumor LDHA expression non-invasively in GBMs without requiring biopsies. Hence, qMRS is expected to open up new opportunities to probe LDHA expression differences in a variety of gliomas, including GBMs and astrocytomas. This method takes advantage of the universal availability and ease of implementation of 1H MRS on all clinical and preclinical magnetic resonance scanners.

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