Biology subjects

Cong, F.

Publications and source records attributed to Cong, F..

4 recordsLinked to original sources

bioRxiv2020

Remdesivir Metabolite GS-441524 Efficiently Inhibits SARS-CoV-2 Infection in Mouse Model

The outbreak of coronavirus disease 2019 (COVID-19) rapidly spreads across worldwide and becomes a global pandemic. Remdesivir is the only COVID-19 treatment approved by U.S. Food and Drug Administration (FDA); however, its effectiveness is still under questioning as raised by the results of a large WHO Solidarity Trial. Herein, we report that the parent nucleotide of remdesivir, GS-441524, potently inhibits the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Vero E6 and other cells. It exhibits good plasma distribution and longer half-life (t1/2=4.8h) in rat PK study. GS-441524 is highly efficacious against SARS-CoV-2 in AAV-hACE2 transduced mice and murine hepatitis virus (MHV) in mice, reducing the viral titers in CoV-attacked organs, without noticeable toxicity. Given that GS-441524 was the predominant metabolite of remdesivir in the plasma, the anti-COVID-19 effect of remdesivir may partly come from the effect of GS-441524. Our results also supported that GS-441524 as a promising and inexpensive drug candidate in the treatment of COVID-19 and future emerging CoVs diseases.

pharmacology and toxicology↗

bioRxiv2020

SingleChannelNet: A model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (CC-SHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better over-all accuracy and Cohens kappa (CCSHS: 90.2%-86.5%, Sleep-EDF: 86.1%-80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

bioengineering↗

bioRxiv2020

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. The selection of regions of evoked EROs tends to be subjectively based on the previous studies and the visual inspection of grand averaged time-frequency representations (TFRs) which causes some missing or redundant information. Meanwhile, the evoked EROs cannot be fully extracted via the conventional time-frequency analysis (TFA) method because they are sometimes overlapped with each other or with artifacts in time, frequency, and space domains to some extent. Hence, these shortcomings may pose some challenges to investigate the related neuronal processes. A data-driven approach was introduced to fill the gaps as below: extracting the temporal and spatial components of interest simultaneously by principal component analysis and Promax rotation and projecting them to the electrode field to correct their variance and polarity indeterminacy, calculating the TFRs of the back-projected components, and determining the regions of interest objectively using the edge detection algorithm. We performed this novel approach and the conventional TFA method in analyzing both a synthetic dataset and an actual ERP dataset in a two-factor simple gambling paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic dataset results indicated that N2-theta and P3-delta oscillations were detected using the proposed approach, but, by comparison, only one oscillation was obtained via the conventional TFA method. Furthermore, the actual ERP dataset results of P3-delta for our approach revealed that it was sensitive to the waiting time (which also was found in the previous reports) but not for that of the conventional TFA method. This study manifested that the proposed approach can objectively extract evoked EROs, which allows a better understanding of the modulations of the oscillatory responses.

neuroscience↗

bioRxiv2019

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

neuroscience↗