Hydroxysafflor Yellow A

The modernization and globalization of traditional Chinese medicine (TCM) face challenges such as unclear active compounds and inadequate quality control. Taking Xuefu Zhuyu Oral Liquid (XZOL) as an example, this study proposed an artificial intelligence (AI) -driven strategy for active compounds discovery and non-destructive quality control. Firstly, the multi-wavelength fusion high-performance liquid chromatography (HPLC) fingerprints were constructed to comprehensively characterize the chemical composition of XZOL. Secondly, the pro-angiogenesis effects of XZOL were evaluated in a PTK787-induced intersegmental vessels (ISVs) injury zebrafish model. Then, spectrum-effect relationship models, incorporating gray relational analysis (GRA), partial least squares regression (PLSR), backpropagation artificial neural networks (BP-ANN), and convolutional neural networks (CNN), discovered seven pro-angiogenesis active compounds (Hydroxysafflor Yellow A, Paeoniflorin, Ferulic Acid, Narirutin, Naringin, Hesperidin, and Neohesperidin). Furthermore, the efficacy of these compounds was further validated through network pharmacology, molecular docking, and zebrafish. Finally, a rapid and non-destructive quality control system based on near infrared spectroscopy (NIRS) was established. This system effectively distinguished expired and normal samples by combining Hotelling T² and Distance to Model X (DModX) statistics of multivariate statistical process control (MSPC), and accurately predicted the content of above active compounds by CNN model integration with bidirectional long short-term memory (Bi-LSTM) and multi-head self-attention (MHSA) networks. This study underscores the potential of AI-driven strategy to enhance TCM standardization and global recognition by providing an active compounds-based holistic quality control strategy of TCM.

This study aimed to explore the impact of hydroxysafflor yellow A (HSYA) on the proliferation, cell cycle, and apoptosis of Jurkat cells, serving as a representative model of T-cell acute lymphoblastic leukemia (T-ALL), and to explore the underlying molecular mechanism of its action. HSYA was administered to Jurkat cells. Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay, and flow cytometry was utilized to analyze cell cycle distribution and apoptosis. The expression levels of Notch1, c-Myc, and Hes1 at both mRNA and protein levels were assessed employing reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot techniques. HSYA hinders cell growth in a manner dependent on both time and dosage. The IC₅₀ (half-maximal inhibitory concentration) for HSYA to inhibit Jurkat cells was 99.08 μmol/L, 77.81 μmol/L and 59.05 μmol/L at 24, 48, and 72 hours, respectively. HSYA induced G0/G1 cell cycle arrest in Jurkat cells in a concentration-dependent manner. After exposure to different concentration of HSYA for 48 hours there was a notable increase in G1 phase cells (F=48.588, df=2, P<0.001) accompanied by decrease in S phase cells (F=66.637, df=2, P<0.001). After 48 hours of HSYA treatment, apoptosis in the experimental group was significantly up-regulated compared with the control group (F=98.09, df=2, P<0.001). After 48 hours of HSYA treatment with varying concentrations (50 and 100 μmol/L), the Jurkat cells exhibited significantly lower expression levels of Notch1, c-Myc, and Hes1 mRNA as compared to the control group (F=298.361, df=2, P<0.001 for comparison of Notch1 expression; F=173.332, df=2, P<0.001 for comparison of c-Myc expression; F=126.563, df=2, P<0.001 for comparison of Hes1 expression). HSYA can play an anti-leukemia effect by inhibiting Notch1 signaling pathway in T-ALL, and can be as a potential candidate for the treatment of T-ALL.