Alleviatory effect of isoquercetin on benign prostatic hyperplasia via IGF-1/PI3K/Akt/mTOR pathway

We evaluated the effect of isoquercetin(quercetin-O-3-glucoside-quercetin,IQ)as a functional component of Abeliophyllum disistichum Nakai ethanol extract(ADLE)on prostate cell proliferation and apoptosis and its effects on the IGF-1/PI3K/Akt/mTOR pathway in benign prostatic hyperplasia(BPH).Metabolites in ADLE were analyzed using UHPLC-qTOF-MS and HPLC.IQ was orally administered(1 or 10 mg/kg)to a testosterone propionate-induced BPH rat model,and its effects on the prostate weight were evaluated.The effect of IQ on androgen receptor(AR)signaling was analyzed in LNCaP cells.Whether IGF-1 and IQ affect the IGF-1/PI3K/Akt/mTOR pathway in BPH-1 cells was also examined.The metabolites in ADLE were identified and quantified,which confirmed that ADLE contained abundant IQ(20.88 mg/g).IQ significantly reduced the prostate size in a concentration-dependent manner in a BPH rat model,and significantly decreased the expression of AR signaling factors in the rat prostate tissue and LNCaP cells in a concentration-dependent manner.IQ also inhibited the PI3K/AKT/mTOR pathway activated by IGF-1 treatment in BPH-1 cells.In BPH-1 cells,IQ led to G0/G1 arrest and suppressed the expression of proliferation factors while inducing apoptosis.Thus,IQ shows potential for use as a pharmaceutical and nutraceutical for BPH.

Dual-Task Contrastive Meta-Learning for Few-Shot Cross-Domain Emotion Recognition

Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.