EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search.

Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings' mental activities and can be served as an objective biomarker for diagnosing MDD.Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels.Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition.Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.

Icaritin ameliorates extracellular microparticles-induced inflammatory pre-metastatic niche via modulating the cGAS-STING signaling.

The concept of the inflammatory pre-metastatic niche (PMN) provides a new and promising direction for the prevention and treatment of metastasis. The excessive activation of the GAS-STING signaling leads to augmented metastasis by promoting the formation of the inflammatory PMN. In this study, tumor-derived microparticles (MP) were used to establish the PMN model both in vitro and in vivo, and pro-inflammatory mediators were also employed to evaluate the effects of Icaritin (ICT). It was demonstrated that ICT could inhibit the pulmonary metastasis of B16BL6 melanoma cells in mice via interfering with PMN. The phosphorylation and dimerization of STING and its downstream signaling TBK1-IFNß were proved to be diminished in the presence of ICT. Furthermore, we revealed that ICT suppressed the generation of pro-inflammatory PMN through conferring the inactivation of the STING signaling pathway. CETSA and DARTS assay also confirmed that STING tended to be a target for the action of ICT. Collectively, our findings highlight a new binding mechanism between STING and ICT for the inhibition of transduction of the STING signaling pathway, suggesting that pharmacological or therapeutic intervention of the STING-TBK1-IFNß singling axis may serve as an effective strategy to prevent the progression of inflammatory PMN and lung metastasis.