Traditional Chinese medicine(TCM) processing is a unique and highly distinctive pharmaceutical technology in China. Utilizing modern scientific methods to elucidate the connotations of traditional processing theory and its effects is expected to facilitate the inheritance, development, innovation, and enhancement of TCM processing, and lead to more original research outcomes in the field of TCM. The breakthrough in TCM processing lies in the study of its underlying principles, and analyzing these principles involves researching the transformation mechanisms of chemical components and the biological effect mechanisms of the transformed components. This paper proposed the concept of "TCM processing chemical biology"(TCMPCB) for the first time. Under the guidance of TCM theory, the active components transformed during TCM processing were used as chemical tools to study their targets and molecular regulatory mechanisms, aiming to clarify the scientific principles by which TCM processing affected biological effects in the organism. The research findings also provided new directions for discovering novel active components, new lead compounds, creating new decoction pieces, and developing new TCM drugs. This paper provided a detailed introduction to the background, definition, research content, research ideas, research methods, and prospects of TCMPCB, with the aim of offering new research perspectives for analyzing the principles of TCM processing and providing new pathways for achieving the "four new and eight transformations" in TCM processing.
Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/methods* ; Humans ; Animals

Traditional Chinese medicine chemical biology(TCMCB), emerging as a pivotal discipline in the 21st century, integrates modern chemical and biological technologies to construct an innovative research system with distinctive features of traditional Chinese medicine(TCM). TCMCB proposes a research strategy that employs active components of TCM as molecular probes to identify direct targets and systematically analyze the complex biological mechanisms. Currently, TCMCB primarily achieves the optimization of active molecule structures in TCM and probe preparation through synthesis chemistry. Utilizing chemical proteomics, this approach facilitates target identification, integrates structural biology to analyze molecular interaction patterns, and combines molecular pharmacology with clinical medicine to validate biological regulatory mechanisms. This forms a methodological system that elucidates the material basis and action patterns of the efficacy of TCM from multiple dimensions. The research results provide molecular-level scientific evidence to clarify the complex and unique mechanisms of action of TCM, effectively promoting the establishment of quality standards, precision medication in traditional Chinese clinical practice, and the modernization and international promotion of TCM. Notably, TCMCB not only advances the modern scientific interpretation of TCM theories but also offers a unique cognitive perspective for modern life sciences through the discovery of new biological targets and modes of action, particularly demonstrating significant advantages in the study of regulatory mechanisms in complex disease systems. This article systematically reviews the research landscape of TCMCB over the past decade, focusing on its methodological role in advancing the modernization of TCM. In the future, TCMCB will prioritize building a technology system with independent intellectual property rights, continuously driving the innovative development of TCM theories and establishing a reference paradigm for life science research and new drug development.
Humans ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to pyruvate kinase M2 (PKM2)-dependent conventional caspase-3/gasdermin E (GSDME) cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-programmed death-1 (anti-PD-1). In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.
Pyroptosis/immunology* ; Humans ; Endoplasmic Reticulum/immunology* ; Animals ; Nogo Proteins/antagonists & inhibitors* ; Mice ; Cell Line, Tumor ; Xanthones/pharmacology* ; Neoplasms/pathology* ; Mice, Nude

Objective: To explore the causal link of cumulative ecological risk and future orientation with health risk behaviors among higher vocational college students, so as to provide reference for reducing and preventing health risk behaviors among higher vocational college students. Methods: A longitudinal follow up study was conducted on 612 students using convenience sampling from 2 vocational colleges in Hunan Province. The Cumulative Ecological Risk Scale, Future Orientation Scale, and Health Risk Behavior Scale were used during three follow up visits (T1: September 2022, T2: June 2023, T3: March 2024), and a cross lagged panel model was constructed to examine the longitudinal causal relationship of cumulative ecological risk, future orientation and health risk behaviors. Analysis of longitudinal intermediary effect between variables by Bootstrap. Results: The cumulative ecological risk scores of T1, T2 and T3 among higher vocational college students were (2.94±1.44,2.99±1.63,3.02±1.54), future orientation scores (40.49±4.71,41.51±5.72,41.06±4.35) and health risk behavior scores (3.73±2.01,3.49±2.00,3.23±2.00). The results of repeated measures ANOVA showed that the future orientation score of T2 was higher than that of T1, and the main effect of measurement time was statistically significant ( F=5.09，P＜0.01,η 2=0.02). The health risk behavior score of T1 was higher than that of T2, and the health risk behavior score of T2 was higher than that of T3, and the main effect of measurement time was statistically significant ( F=10.12，P＜0.01,η 2=0.03).The cross lagged model showed good adaptability, with χ 2/df =7.20 ( P <0.01), relative fitting indicators GFI=0.98, CFI=0.99, TLI=0.96, IFI=0.99, NFI =0.99, and absolute fitting indicator RMSEA =0.06. Among them, the T1, T2 cumulative ecological risk showed negatively predictive effects on T2, T3 future orientation ( β =-0.24, -0.47 ), and T1, T2 cumulative ecological risk positively predicted T2, T3 health risk behavior ( β =0.20, 0.24), while T1, T2 future orientation negatively predicted T2, T3 health risk behavior ( β =-0.25, -0.18) ( P ＜0.01). Bootstrap test analysis found that T2 future orientation had a longitudinal mediating effect ( β=0.04, P ＜0.01) on the T1 cumulative ecological risk and T3 health risk behavior. Conclusions The accumulation of ecological risk among higher vocational college students can positively predict health risk behaviors, while future orientation can negatively predict healthrisk behaviors. Moreover, future orientation plays a longitudinal mediating role between accumulated ecological risks and health risk behaviors.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

Natural products are bioactive components with me-dicinal value extracted from plants,animals and other natural sources.Historically,over half of innovative drugs have been de-rived from natural products or their derivatives.Identifying the molecular targets of these components in the human body is cru-cial for understanding their therapeutic mechanisms and develo-ping new drugs.Despite rapid advancements in target identifica-tion technologies,the elucidation of natural product targets still faces unique challenges.In recent years,researchers have devel-oped efficient methods for target identification by integrating chemical biology,proteomics,structural biology,and artificial in-telligence technologies,combined with molecular probe strate-gies.This article reviews the principles and practical applica-tions of current mainstream target identification techniques,pro-viding practical guidance for natural product research and drug development.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

Natural products are bioactive components with me-dicinal value extracted from plants,animals and other natural sources.Historically,over half of innovative drugs have been de-rived from natural products or their derivatives.Identifying the molecular targets of these components in the human body is cru-cial for understanding their therapeutic mechanisms and develo-ping new drugs.Despite rapid advancements in target identifica-tion technologies,the elucidation of natural product targets still faces unique challenges.In recent years,researchers have devel-oped efficient methods for target identification by integrating chemical biology,proteomics,structural biology,and artificial in-telligence technologies,combined with molecular probe strate-gies.This article reviews the principles and practical applica-tions of current mainstream target identification techniques,pro-viding practical guidance for natural product research and drug development.