Kui was first recorded in The Rites of Zhou and is the earliest domesticated wild vegetable in China. In the Qi Min Yao Shu, Kui was called “the master of all vegetables” and has a long history of application in China. As a medicine, Kuizi was first recorded in Shen Nong’s Herbal Classic, which has a history of more than 2 000 years of medicinal use and a long history of clinical application. By researching the ancient and modern herbal literature, the first herbs texts of Kui were examined, various recorded texts, confused products and the history of the original medicinal use were clarified. It was concluded that the ancient herbal texts recorded the base plant of Kui as Malva verticillata L. belonging to family Malvaceae, which provided scientific basis for the development and utilization of Kui.

ObjectiveTo explore the effectiveness of feedforward control mode on improving occupational burnout among operating room nurses through theoretical research and clinical practice， with a view to promoting their physical and mental health and enhancing the quality of surgical nursing. MethodsA total of 440 operating room nurses from different regions， scales， and nursing experiences in Shaanxi Province from November 2023 to December 2023 were randomly divided into an experimental group and a control group， with 220 nurses in each group. While the control group received routine intervention measures， the experimental group introduced a feedforward control mode based on the control group， with “emotional exhaustion，” “depersonalization，”and“personal achievement” as observation indicators. ResultsThe incidence of occupational burnout in the experimental group was 11.4%， while that in the control group was 20.0%. The experimental group showed a significantly lower incidence than the control group （P=0.013）. ConclusionThe feedforward control mode can significantly alleviate nurses’ sense of job burnout， promote the improvement of surgical nursing quality， as well as continuously improve the scientific rigor， advancement， and humanistic nature of nursing services， which is conducive to building a harmonious and efficient nursing team. The spirit of teamwork， reflected in mutual support， mutual trust， and joint efforts for surgical success and patient health， has become an important component of nurses’ professional ethics.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy of the digestive tract that poses a significant threat to human health, with an incidence rate that continues to rise globally. Increasing research highlights the crucial role of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), in regulating ferroptosis and contributing to the malignant progression of ESCC. These ncRNAs influence the proliferation, apoptosis, and invasion capabilities of ESCC cells by modulating iron metabolism and redox balance. miRNAs can regulate cellular iron accumulation and oxidative stress by targeting ferroptosis-related genes; lncRNAs may indirectly affect iron metabolic pathways by competitively binding to miRNAs; circRNAs, through a sponge effect, may regulate the activity of miRNAs. This review systematically summarizes the mechanisms of ncRNAs-mediated regulation of ferroptosis in ESCC, focusing on molecular mechanisms, regulatory networks, and their specific roles in the ferroptosis process. Additionally, the potential of ncRNAs in ESCC diagnosis, prognosis assessment, and therapeutic intervention is discussed, aiming to provide new insights and targets for ferroptosis-based tumor therapy.
Ferroptosis/genetics* ; Humans ; Esophageal Neoplasms/physiopathology* ; Esophageal Squamous Cell Carcinoma ; MicroRNAs/physiology* ; RNA, Long Noncoding/physiology* ; RNA, Circular ; RNA, Untranslated/physiology*

Forsythia suspensa is a traditional Chinese medicine and a commonly used landscaping plant. Its dried fruit is used in medicine for its functions of clearing heat, removing toxins, reducing swelling, dissipating masses, and dispersing wind and heat. It possesses extremely high medicinal and economic value. However, the genetic differentiation and diversity of its wild populations remain unclear. In this study, chloroplast genome sequences were obtained from 15 wild individuals of F. suspensa using high-throughput sequencing technology. The sequence characteristics and intraspecific variations were analyzed. The results were as follows:(1) The full length of the F. suspensa chloroplast genome ranged from 156 184 to 156 479 bp, comprising a large single-copy region, a small single-copy region, and two inverted repeat regions. The chloroplast genome encoded a total of 132 genes, including 87 protein-coding genes, 37 tRNA genes, and 8 rRNA genes.(2) A total of 166-174 SSR loci, 792 SNV loci, and 63 InDel loci were identified in the F. suspensa chloroplast genome, indicating considerable genetic variation among individuals.(3) Population structure analysis revealed that F. suspensa could be divided into five or six groups. Both the population structure analysis and phylogenetic reconstruction results indicated significant genetic variation within the wild populations of F. suspensa, with no obvious correlation between intraspecific genetic differentiation and geographical distribution. This study provides new insights into the genetic diversity and differentiation within F. suspensa species and offers additional references for the conservation of species diversity and the utilization of germplasm resources in wild F. suspensa.
Genome, Chloroplast ; Forsythia/classification* ; Phylogeny ; Genetic Variation ; Chloroplasts/genetics* ; Microsatellite Repeats

Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.
Alzheimer Disease/diagnosis* ; Humans ; Neuroimaging/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging ; Deep Learning ; Machine Learning

Most current medical image segmentation models are primarily built upon the U-shaped network (U-Net) architecture, which has certain limitations in capturing both global contextual information and fine-grained details. To address this issue, this paper proposes a novel U-shaped network model, termed the Multi-View U-Net (MUNet), which integrates self-attention and multi-view attention mechanisms. Specifically, a newly designed multi-view attention module is introduced to aggregate semantic features from different perspectives, thereby enhancing the representation of fine details in images. Additionally, the MUNet model leverages a self-attention encoding block to extract global image features, and by fusing global and local features, it improves segmentation performance. Experimental results demonstrate that the proposed model achieves superior segmentation performance in coronary artery image segmentation tasks, significantly outperforming existing models. By incorporating self-attention and multi-view attention mechanisms, this study provides a novel and efficient modeling approach for medical image segmentation, contributing to the advancement of intelligent medical image analysis.
Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Algorithms ; Attention ; Coronary Vessels/diagnostic imaging* ; Diagnostic Imaging/methods*

Colorectal tumorigenesis generally progresses from adenoma to adenocarcinoma, accompanied by dynamic changes in the tumor microenvironment (TME). A randomized controlled trial has confirmed the efficacy and safety of Shen-Bai-Jie-Du decoction (SBJDD) in preventing colorectal tumorigenesis. However, the mechanism remains unclear. In this study, we employed single-cell RNA sequencing (scRNA-seq) to investigate the dynamic evolution of the TME and validated cell infiltration with multiplex immunohistochemistry and flow cytometry. Bulk RNA sequencing was utilized to assess the underlying mechanisms. Our results constructed the mutually verifiable single-cell transcriptomic atlases in Apc ^Min/+ mice and clinical patients. There was a marked accumulation of CCL22⁺ dendritic cells (DCs) and an enhanced immunosuppressive action, which SBJDD and berberine reversed. Combined treatment with cholesterol and lipopolysaccharide induced characteristic gene expression of CCL22⁺ DCs, which may represent "exhausted DCs". Intraperitoneal injection of these DCs after SBJDD treatment eliminated its therapeutic effects. TMEM131 derived CCL22⁺ DCs generation by TNF signaling pathway and may be a potential target of berberine in retarding colorectal tumorigenesis. These findings emphasize the role of exhausted DCs and the regulatory mechanisms of SBJDD and berberine in colorectal cancer (CRC), suggesting that the multi-component properties of SBJDD may help restore TME homeostasis and offer novel cancer therapy.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Discoidin domain receptor 1 (DDR1) is overexpressed in various tumors, such as triple-negative breast cancer (TNBC), and is rarely expressed in normal tissues. These characteristics make DDR1 a preferable target candidate for the construction of an antibody-drug conjugate (ADC) for targeted therapy. Here, we investigated the preparation and preclinical efficacy of DDR1-DX8951, an ADC that includes an anti-DDR1 monoclonal antibody conjugated to DX8951 by a cleavable Gly-Gly-Phe-Gly (GGFG) linker. The anti-DDR1 monoclonal antibody was coupled to DX8951 (i.e., DDR1-DX8951), producing the targeted therapy ADC. The antitumor activities of DDR1-DX8951 monotherapy or DDR1-DX8951 plus pembrolizumab were assessed in TNBC mouse models. DDR1-DX8951 can specifically target DDR1, be quickly internalized by TNBC cells, and reduce the viability of TNBC cells in vitro. The potent antitumor activity of DDR1-DX8951 was revealed in TNBC xenograft models. Importantly, our investigation demonstrated that DDR1-DX8951 plus pembrolizumab not only revealed the inhibitory efficacy on tumor growth and metastasis but also played an important role in improving the immunosuppressive tumor microenvironment (TME) of TNBC. Taken together, this investigation provides justification for large-sample studies to further assess the safety and efficacy of DDR1-DX8951 plus pembrolizumab for TNBC clinical trials.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.