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Potential risk of X-ray radiation from computed tomography (CT) has been a concern of the public. However, simply decreasing the dose will degrade quality of the CT images and compromise diagnostic performance. In this paper, we propose a noise learning generative adversarial network coupling with least squares, structural similarity and L1 losses for low-dose CT denoising. In our method, noise distributed in the input low-dose CT image is learned by the generator network and then subtracted from the input to generate the final denoised version. The denoised CT images are penalized by the least squares loss function, and they are pulled toward boundary of the decision even though they are classified as normal-dose CT. Least squares stabilize the training process without regularization. Structural similarity and L1 losses are utilized to keep textural details and sharpness of the denoised CT images respectively. Experiments and results show that our method can effectively suppress noise and remove artifacts compared with the state-ofthe-art methods. The texture statistical properties, which include mean, standard deviation, uniformity, and entropy, further confirm that the generated noise-reduced CT image is as close as to that of the normal-dose counterpart. INDEX TERMS Deep learning, generative adversarial network, least squares, low-dose CT, denoising.

Background5-hydroxytryptamine (5-HT) is one of the major neurotransmitters widely distributed in the CNS. Several 5-HT receptor subtypes have been identified in the spinal dorsal horn which act on both pre- and postsynaptic sites of excitatory and inhibitory neurons. However, the receptor subtypes and sites of actions as well as underlying mechanism are not clarified rigorously. Several electrophysiological studies have been performed to investigate the effects of 5-HT on excitatory transmission in substantia gelatinosa (SG) of the spinal cord. In the present study, to understand the effects of 5-HT on the inhibitory synaptic transmission and to identify receptor subtypes, the blind whole cell recordings were performed from SG neurons of rat spinal cord slices.ResultsBath applied 5-HT (50 μM) increased the frequency but not amplitudes of spontaneous inhibitory postsynaptic currents (sIPSCs) in 58% of neurons, and both amplitude and frequency in 23% of neurons. The frequencies of GABAergic and glycinergic mIPSCs were both enhanced. TTX (0.5 μM) had no effect on the increasing frequency, while the enhancement of amplitude of IPSCs was eliminated. Evoked-IPSCs (eIPSCs) induced by focal stimulation near the recording neurons in the presence of CNQX and APV were enhanced in amplitude by 5-HT. In the presence of Ba2+ (1 mM), a potassium channel blocker, 5-HT had no effect on both frequency and amplitude. A 5-HT2A receptor agonist, TCB-2 mimicked the 5-HT effect, and ketanserin, an antagonist of 5-HT2A receptor, inhibited the effect of 5-HT partially and TCB-2 almost completely. A 5-HT2C receptor agonist WAY 161503 mimicked the 5-HT effect and this effect was blocked by a 5-HT2C receptor antagonist, N-desmethylclozapine. The amplitudes of sIPSCs were unaffected by 5-HT2A or 5-HT2C agonists. A 5-HT3 receptor agonist mCPBG enhanced both amplitude and frequency of sIPSCs. This effect was blocked by a 5-HT3 receptor antagonist ICS-205,930. The perfusion of 5-HT2B receptor agonist had no effect on sIPSCs.ConclusionsOur results demonstrated that 5-HT modulated the inhibitory transmission in SG by the activation of 5-HT2A and 5-HT2C receptors subtypes located predominantly at inhibitory interneuron terminals, and 5-HT3 receptors located at inhibitory interneuron terminals and soma-dendrites, consequently enhanced both frequency and amplitude of IPSCs.

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Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.