Gang Chen

Ethnopharmacological relevance: Conventional antidepressant therapy necessitates prolonged medication, which can result in a plethora of adverse effects, including osteoporosis. Eucommiae Folium (EF) has also been demonstrated to alleviate depressive symptoms and osteoporosis. Gardeniae Fructus (GF) also has remarked antidepressant potential, which is often used together with EF as a herb pair in the clinics. This herb pair is highly valuable in synergistically and safely exerting antidepressant effects.Aim of the study: PACAP is a novel antidepressant target, via enhancing neuroplasticity. Here, we aimed to examine the synergistic effects of EF and GF (EG) on depression, and reveal mechanisms of PACAP and its downstream signaling.Materials and methods: The antidepressant effects of EG were confirmed in both CUMS and LPS depression models, with fluoxetine was used as a positive control. Network pharmacology analysis was utilized to reveal the associated mechanisms. The key signaling mechanisms were examined with immunoblotting and immunofluorescence, and further verified by knockdown of PACAP with virus-mediated RNAi. Results: EG exhibited synergistic efficacy in antidepressant response. EG alleviated depressive and anxiety symptoms in both the CUMS and LPS models, with effects comparable to fluoxetine. Network pharmacology analysis revealed the important role of PI3K/AKT/mTOR signaling pathway, which was verified in the hippocampus following EG treatment. Moreover, the expression of the potential upstream activator, PACAP, was also upregulated, in addition to enhanced the expressions of the synaptic proteins PSD95 and Synapsin-1. EG reduced the number of Iba-1+ microglial cells and their cell areas, and increased the number of BDNF+ cells in the hippocampus. Importantly, hippocampal PACAP knockdown blunted the antidepressant effects of EG and the PI3K/AKT/mTOR signaling.Conclusions: The herb pair of Eucommiae Folium and Gardeniae Fructus synergistically elicited antidepressant effects, likely via activating hippocampal PACAP and its downstream PI3K/AKT/mTOR signaling.

Background: Yueju Pill, a classic traditional Chinese medicine, shows antidepressant effects rapidly. However, biomarkers that can predict its treatment outcomes in major depressive disorder (MDD) are still lacking. Multimodal magnetic resonance imaging (MRI) offers a promising avenue to identify such biomarkers. Aims: This pilot study aimed to explore whether therapeutic responses to Yueju Pill could be predicted by MRI‐derived brain networks and to identify drug‐specific biomarkers in comparison to escitalopram, a mainstream antidepressant. Methods: We collected multimodal MRI data and blood samples from 28 outpatients with MDD from the Fourth People’s Hospital of Taizhou, who were randomly divided into two groups to receive either Yueju Pill (23 g/time/day) or escitalopram (10 mg, two times a day) for 4 days. Morphological and functional brain networks were constructed and used to predict individual changes in symptoms quantified by the 24‐item Hamilton Depression Scale (HAMD‐24) scores and serum brain‐derived neurotrophic factor (BDNF) levels. Results: After the treatment, both groups exhibited significant reductions in the HAMD‐24 scores, while only the Yueju Pill group showed significant increases in the BDNF levels. Gyrification Index‐based morphological networks predicted change rates of the HAMD‐24 scores in both groups, but sulcus depth‐based and cortical thickness‐based morphological networks predicted change rates of the HAMD‐24 scores and BDNF levels, respectively, only in the Yueju Pill group. Subnetwork analyses revealed that the visual network independently predicted the changes in both the HAMD‐24 scores (sulcus depth‐based networks) and BDNF levels (cortical thickness‐based networks) following Yueju Pill treatment. Conclusions: Morphological but not functional brain networks can predict symptom improvement and BDNF changes of patients with MDD after Yueju Pill treatment. Sulcus depth‐based and cortical thickness‐based morphological brain networks, particularly their visual subnetworks, might serve as Yueju Pill‐specific biomarkers for predicting the therapeutic responses. These findings have the potential to guide personalised therapy for patients with MDD early in the therapeutic process. Trial registration number: ChiCTR1900021114.

Cancer-related fatigue (CRF) is a common symptom in cancer patients, affecting their quality of life. Prunella vulgaris and its seed oil (PVSO) are edible-medicinal foods with bioactive compounds that may alleviate CRF. The present study aimed to test the effects of PVSO on CRF and underlying mechanisms. We found PVSO improved fatigue-like behaviors in both LPS-induced and tumor-bearing models. In the breast cancer tumor-bearing animals, PVSO reduced tumor growth, and suppressed serum and hypothalamic inflammatory cytokines (TNF-α, IL-1β, and IL-6) and NF-κB signaling. Furthermore, the hypothalamic corticotropin-releasing hormone (CRH) expression and serum CRH/cortisol levels dampened in tumor-bearing animals were normalized by PVSO. Our results suggest that PVSO exhibits potent anti-CRF effects likely via attenuation of cancer-induced hypothalamic inflammation and subsequent normalization of the dysregulated HPA axis. PVSO may be developed as a functional food for breast cancer-related fatigue.