G-protein-coupled estrogen receptor-30 (GPR30), also known as G-protein estrogen receptor-1 (GPER1), is a putative extranuclear estrogen receptor whose precise functions in the brain are poorly understood. Studies using exogenous administration of the GPR30 agonist, G1 suggests that GPR30 may have a neuroprotective role in cerebral ischemia. However, the physiological role of GPR30 in mediating estrogen (E2)-induced neuroprotection in cerebral ischemia remains unclear. Also unclear is whether GPR30 has a role in mediating rapid signaling by E2 after cerebral ischemia, which is thought to underlie its neuroprotective actions. To address these deficits in our knowledge, the current study examined the effect of antisense oligonucleotide (AS) knockdown of GPR30 in the hippocampal CA1 region upon E2-BSA-induced neuroprotection and rapid kinase signaling in a rat model of global cerebral ischemia (GCI). Immunohistochemistry demonstrated that GPR30 is strongly expressed in the hippocampal CA1 region and dentate gyrus, with less expression in the CA3 region. E2-BSA exerted robust neuroprotection of hippocampal CA1 neurons against GCI, an effect abrogated by AS knockdown of GPR30. Missense control oligonucleotides had no effect upon E2-BSA-induced neuroprotection, indicating specificity of the effect. The GPR30 agonist, G1 also exerted significant neuroprotection against GCI. E2-BSA and G1 also rapidly enhanced activation of the prosurvival kinases, Akt and ERK, while decreasing proapototic JNK activation. Importantly, AS knockdown of GPR30 markedly attenuated these rapid kinase signaling effects of E2-BSA. As a whole, the studies provide evidence of an important role of GPR30 in mediating the rapid signaling and neuroprotective actions of E2 in the hippocampus.

Research on emotion states based on physiological signals has attracted more and more attention. However, existing studies mainly focused on the classification accuracy tests based on different machine learning methods, without a comprehensive exploration for the inherent links between physiological features and emotion states. This pilot study aimed to investigate the differences of heart rate variability (HRV) indices between two opposite emotional states: happiness and sadness, to reveal the differences of autonomic nervous system activity under different emotional states. Forty-eight healthy volunteers were enrolled in the proposed study. Electrocardiography (ECG) signals were recorded under two emotion states with a random measurement order (first happiness then sadness or reverse). RR interval (RRI) time series were extracted from ECGs and multiple HRV indices, including time-domain (MEAN, SDNN, RMSSD and PNN50), frequency-domain (LFn, HFn and LF/HF) and nonlinear indices (SampEn and FuzzyMEn) were calculated. The results showed that experimental order had no significant impact on all HRV indices. Among all nine indices, six indices were identified having significant differences between happiness and sadness emotion states: MEAN (P<0.05), SDNN (P<0.01), three frequency-domain indices (all P<0.01) and FuzzyMEn (P<0.05), whereas RMSSD, PNN50 and SampEn were reported having no significant differences among the two emotional states. All nine indices except for SampEn had significant positive correlations (all P<0.01) for the two emotion states. All indices showed no HR-related or MAP-related changes for each emotional state except that four time-domain indices decreased with the increase of HR (P<0.01). It concluded that HRV indices had significant differences between happiness and sadness emotion states and the findings could help to better understand the inherent differences of cardiovascular time series between different emotion states in clinical practice.

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Aims This study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities. Methods Electrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study. Results A consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant ( p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals ( R = 0.24, p < 0.05), CSEn at 4 Hz ( R = 0.26, p < 0.05) or 2 Hz ( R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz ( R = 0.31, p < 0.01) or 2 Hz ( R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition. Conclusion The current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.