Detection of trace components in Xiangdan injection of Dalbergia odorifera based on microextraction and back-extraction along with bar-form-diagram strategy.

Xiangdan Injection are commonly used traditional Chinese medicine formulations for the clinical treatment of cardiovascular diseases. However, the trace components of Dalbergia odorifera in Xiangdan Injection pose a challenge for evaluating its quality due to the difficulty of detection. This study proposes a technology combining dispersive liquid-liquid microextraction and back-extraction (DLLME-BE) along with Bar-Form-Diagram (BFD) to address this issue. The proposed combination method involves vortex-mixing tetradecane, which has a lower density than water, with the sample solution to facilitate the transfer of the target components. Subsequently, a new vortex-assisted liquid-liquid extraction step is performed to enrich the components of Dalbergia odorifera in acetonitrile. The sample analysis was performed on HPLC-DAD, and a clear overview of the chemical composition was obtained by integrating spectral and chromatographic information using BFD. The combination of BFD and CRITIC-TOPSIS strategies was used to optimize the process parameters of DLLME-BE. The determined optimal sample pre-treatment process parameters were as follows: 200 µL extraction solvent, 60 s extraction time, 50 µL back-extraction solvent, and 90 s back-extraction time. Based on the above strategy, a total of 29 trace components, including trans-nerolidol, were detected in the Xiangdan Injection. This combination technology provides valuable guidance for the enrichment analysis of trace components in traditional Chinese medicines.

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition.

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.

Tenofovir alters the immune microenvironment of pregnant women with hepatitis B virus infection: Evidence from single-cell RNA sequencing.

BACKGROUND: Mother-to-child is the main route of the transmission of hepatitis B virus (HBV) infection. Tenofovir fumarate (TDF) antiviral treatment has become the most extensive choice worldwide. However, the effects of TDF treatment on the immune function of pregnant women remains unclear. Here we investigate the effect of TDF treatment on the immune microenvironment of pregnant women with HBV infection using single-cell RNA sequencing (scRNA-seq). METHODS: Three HBV-infected pregnant women were treated with TDF and six samples were collected before and after the treatment. In total, 68,200 peripheral blood mononuclear cells (PBMCs) were extracted for 10 × scRNA-seq. The cells were clustered using t-distributed stochastic neighbor embedding (t-SNE) and unbiased computational informatics analysis. RESULTS: The analysis identified four-cell subtypes, including T cells, monocytes, natural killer (NK) cells, and B cells, and unraveled the developmental trajectory and maturation of CD4+ T and CD8+ T cell subtypes. The cellular state and molecular features of the effector/memory T cells revealed a significant increase in the inflammatory state of CD4+ T cells and the cytotoxic characteristics of CD8+ T cells. Additionally, after TDF treatment, the monocytes showed a tendency for M1 polarization, and the cytotoxicity of NK cells was enhanced. Furthermore, the analysis of intercellular communication revealed the interaction of various subtypes of cells and the heterogeneous expression of key signal pathways. CONCLUSIONS: The findings of this study reveal significant differences in cellular subtypes and molecular characteristics of PBMCs of pregnant women with HBV infection before and after TDF treatment and demonstrate the recovery of immune response after treatment. These findings could help develop immune intervention measures to control HBV during pregnancy and the puerperium period.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

An intelligent extraction approach of feature information from three-dimensional DAD chromatogram for integrated quality control of traditional Chinese medicines: Gardenia jasminoides root as an example.

Traditional Chinese medicine (TCM) fingerprinting, which has the characteristics of holism and ambiguity, is a conventional strategy for the holistic quality control of TCMs. However, the fingerprinting of TCMs at the current stage generally adopts a single wavelength or few wavelengths, lacking the effective utilization of diode-array detector (DAD) chromatogram data. This study proposes an intelligent extraction approach of feature information from a three-dimensional DAD chromatogram to establish a novel bar-form-diagram (BFD) for integrated quality control of TCMs. The BFD was automatically established by the chromatographic and spectral information of a complex hybrid system in a DAD chromatogram. This covered the peak areas of target compositions at the optimal absorption wavelength. Taking 27 batches of Gardenia jasminoides root as samples, the BFD combined with chemometrics was applied for assessing the quality of samples completely, which improved the accuracy of origin classification using hierarchical cluster analysis, principal component analysis, soft independent modeling of class analogy and orthogonal partial least squares discriminant analysis. Single-wavelength fingerprinting and BFD used 23 and 38 common peaks as variables respectively, and the adjusted rand index results of the single wavelength and BFD were 0.559 and 0.819, respectively. Compared with the ergodic methods of each single wavelength, the peak recognition method in this study improved the operation speed from 180 s to 4 s and the computational complexity. The established BFD approach performed more abundant characteristic information of chemical components of TCMs and more accurate origin classification ability, and it had great advantages in the overall quality control of TCMs.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.

Mitogen-activated protein kinase phosphatase 1 is involved in tamoxifen resistance in MCF7 cells.

Tamoxifen resistance is a major clinical problem for ER-positive breast cancer, but the underlying mechanism is not completely elucidated. In the present study, we reported that mitogen-activated protein kinase (MAPK) phosphatase 1 (MKP-1), a member of the family of MKPs, is involved in tamoxifen resistance. We found that MKP1 expression increased in tamoxifen resistant MCF7 cells. To explore the possible role of MKP1 in tamoxifen resistance, siRNA targeting MKP1 was transfected into tamoxifen resistant MCF7 cells. To our surprise, knockdown of MKP-1 promoted cell death induced by tamoxifen. On the other hand, the MKP1 overexpressed MCF7 cell clone was established and MKP1 overexpression effectively attenuated MCF7 cell death induced by tamoxifen. In addition, we revealed that MKP1 inhibited tamoxifen­mediated JNK activation in tamoxifen resistant MCF7 and MCF7 cells, and by this mechanism MKP1 was able to inhibit tamoxifen-induced cell death. We also showed that combined appliaction of MKP1 inhibitor triptolide and tamoxifen can effectively increase tamoxifen sensitivity in tamoxifen resistant MCF7 cells. Collectively, our results indicated that MKP-1 can attenuate tamoxifen-induced cell death through inhibiting the JNK signal pathway, which represents a novel mechanism of tamoxifen resistance in MCF7 cells.