Mesial temporal lobe epilepsy (MTLE) is one of the most common refractory epilepsies and is usually accompanied by a range of brain pathological changes, such as neuronal injury and astrocytosis. Naïve astrocytes are readily converted to cytotoxic reactive astrocytes (A1) in response to inflammatory stimulation, suppressing the polarization of A1 protects against neuronal death in early central nervous system injury. Our previous study found that pro‐inflammatory cytokines and miR‐132‐3p (hereinafter referred to as “miR‐132”) expression were upregulated, but how miR‐132 affected reactive astrocyte polarization and neuronal damage during epilepsy is not fully understood. Here, we aimed to explore the effect and mechanism of miR‐132 on A1 polarization. Our results confirmed that A1 markers were significantly elevated in the hippocampus of MTLE rats and IL‐1β‐treated primary astrocytes. In vivo, knockdown of miR‐132 by lateral ventricular injection reduced A1 astrocytes, neuronal loss, mossy fiber sprouting, and remitted the severity of status epilepticus and the recurrence of spontaneous recurrent seizures. In vitro, the neuronal cell viability and axon length were reduced by additional treatment with A1 astrocyte conditioned media (ACM), and downregulation of astrocyte miR‐132 rescued the inhibition of cell activity by A1 ACM, while the length of axons was further inhibited. The regulation of miR‐132 on A1 astrocytes may be related to its target gene expression. Our results show that interfering with astrocyte polarization may be a breakthrough in the treatment of refractory epilepsy, which may extend to the research of other astrocyte polarization‐mediated brain injuries.
Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to generate pseudo labels for the noisy training set. These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model. Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging. Moreover, a single parameter shared by all slots and all instances may be suboptimal. To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter. Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions. These functions are trained in a meta-learning manner by taking the validation set as meta data. Experimental results demonstrate that all three schemes can achieve competitive performance. Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4.
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