A brain-tumor neural circuit controls breast cancer progression in mice.

Tumor burden, considered a common chronic stressor, can cause widespread anxiety. Evidence suggests that cancer-induced anxiety can promote tumor progression, but the underlying neural mechanism remains unclear. Here, we used neuroscience and cancer tools to investigate how the brain contributes to tumor progression via nerve-tumor crosstalk in a mouse model of breast cancer. We show that tumor-bearing mice exhibited significant anxiety-like behaviors and that corticotropin-releasing hormone (CRH) neurons in the central medial amygdala (CeM) were activated. Moreover, we detected newly formed sympathetic nerves in tumors, which established a polysynaptic connection to the brain. Pharmacogenetic or optogenetic inhibition of CeMCRH neurons and the CeMCRH→lateral paragigantocellular nucleus (LPGi) circuit significantly alleviated anxiety-like behaviors and slowed tumor growth. Conversely, artificial activation of CeMCRH neurons and the CeMCRH→LPGi circuit increased anxiety and tumor growth. Importantly, we found alprazolam, an antianxiety drug, to be a promising agent for slowing tumor progression. Furthermore, we show that manipulation of the CeMCRH→LPGi circuit directly regulated the activity of the intratumoral sympathetic nerves and peripheral nerve-derived norepinephrine, which affected tumor progression by modulating antitumor immunity. Together, these findings reveal a brain-tumor neural circuit that contributes to breast cancer progression and provide therapeutic insights for breast cancer.

Stable Isotope Ratio and Elemental Profile Combined with Support Vector Machine for Provenance Discrimination of Oolong Tea (Wuyi-Rock Tea).

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n = 99) collected from nine producing areas and non-Wuyi-Rock tea (n = 33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.