Green prescription is a written prescription aimed at improving health by promoting physical activity and improving diet， with advantages such as high cost-effectiveness， strong feasibility， and minimal harm to patients. The theory of traditional Chinese medicine （TCM） green prescription integrates the health philosophy of "following rule of yin and yang， and adjusting ways to cultivating health"， the exercise philosophy of balancing yin-yang and the five elements， and the dietary philosophy of moderation and balance， which embody core TCM concepts such as treating disease before its onset and harmony between humans and nature. It has also developed traditional exercise practices like Tai Chi， Baduanjin， Wuqinxi， Yi-Gin-Ching， and Qigong， as well as dietary adjustments like medicated diet and herbal wines. However， it is believed that the TCM green prescription currently suffers from insufficient evidence-based research， low patient awareness and acceptance， and weak basic research. Based on this， it is proposed that large-sample clinical trials should be conducted in the future to improve the quality of evidence-based medicine， basic research can be carried out with the help of artificial intelligence and other methods in research design， the hospital information system （HIS） can be used for control at the implementation level， and publicity and patient education can be strengthened through the new media， so as to promote the development and application of the TCM green prescriptions in the field of global health treatment.

Cuproptosis, a recently identified form of regulated cell death triggered by excess intracellular copper, has emerged as a promising cytotoxic strategy for cancer therapy. However, the therapeutic efficacy of copper ionophores such as elesclomol (ES) is often hindered by cellular copper homeostasis mechanisms that limit copper influx and cuproptosis induction. To address this challenge, we developed a nanoagent utilizing outer membrane vesicle (OMV) derived from Akkermansia muciniphila (Akk) for co-delivery of antioxidant 1 copper chaperone (Atox1)-targeting siRNA and ES (siAtox1/ES@OMV) to tumors. In vitro, we demonstrated that Atox1 knockdown via siRNA significantly disrupted copper export mechanisms, resulting in elevated intracellular copper levels. Simultaneously, ES facilitated efficient copper influx and mitochondrial transport, leading to Fe-S cluster depletion, increased proteotoxic stress, and robust cuproptosis. In vivo, siAtox1/ES@OMV achieved targeted tumor delivery and induced pronounced cuproptosis. Furthermore, leveraging the immunomodulatory properties of OMVs, siAtox1/ES@OMV promoted T-cell infiltration and the activation of tumor-reactive cytotoxic T cells, enhancing tumor immune responses. The combination of siAtox1/ES-induced cuproptosis and immunogenic cell death synergistically suppressed tumor growth in both subcutaneous breast cancer and orthotopic rectal cancer mouse models. This study highlights the potential of integrating copper homeostasis disruption with a copper ionophore using an immunomodulatory OMV-based vector, offering a promising combinatorial strategy for cancer therapy.

Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular generation models have recently advanced drug discovery, they often overlook the complexity of biological and chemical factors, leaving room for improvement. In this study, we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties. Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. This method specifically aims to enhance BBB permeability (BBBp) while maintaining high-affinity substructures of KRAS inhibitors. To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. Additionally, we introduce two novel metrics, the knowledge-integrated reproduction score (KIRS) and the composite diversity score (CDS), to assess structural performance and biological relevance. Retrospective validation with KRAS inhibitors, AMG510 and MRTX849, demonstrates the framework's effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications. This study provides a robust framework for accelerating the structural enhancement of lead compounds, advancing the drug development process across diverse targets.

Phages, as obligate bacterial and archaeal parasites, constitute a virus group of paramount ecological significance due to their exceptional abundance and genetic diversity. These biological entities serve as critical regulators in Earth's ecosystems, driving biogeochemical cycles, energy fluxes, and ecosystem services across terrestrial and marine environments. Within soil microbiomes, phages function as microbial "dark matter," maintaining the soil-plant system balance through precise modulation of the microbial community structure and functional dynamics. Despite the growing research interests in soil phages in recent years, the proportion of such studies in environmental virology remains disproportionately low, which is primarily attributed to researchers' limited familiarity with the research methodologies for phage microecology, incomplete technical frameworks, and inherent challenges posed by soil environmental complexity. To address these challenges, this review synthesizes cutting-edge methodologies for soil phage investigation from four aspects: (1) tangential flow filtration (TFF)-based phage enrichment strategies; (2) integrated quantification approaches combining double-layer agar plating, epifluorescence microscopy, and flow cytometry; (3) multi-omics analytical pipelines leveraging metagenomics and viromics datasets; and (4) computational frameworks merging machine learning algorithms with eco-evolutionary theory for deciphering phage-host interaction networks. Through comparative analysis of methodological principles, technical merits, and application scopes, we establish a comprehensive workflow for soil phage research. Future research in this field should prioritize: (1) construction of soil phage resource libraries, (2) exploration of RNA phages based on transcriptomes, (3) functional characterization of unknown genes, and (4) deep integration and interaction validation of multi-omics data. This systematic methodological synthesis provides critical technical references for addressing fundamental challenges in characterizing soil phages regarding the community structure, functional potential, and interaction mechanisms with hosts.
Bacteriophages/physiology* ; Soil Microbiology ; Ecosystem ; Microbiota ; Metagenomics/methods*

Background: Autoimmune mechanisms have important roles in the pathogenesis of alopecia areata (AA). Objective: This study aimed to evaluate the exact biological and clinical importance of immunogenes in AA patients using bioinformatic methods. Methods: Five AA scalp gene expression profiles were obtained from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) between AA and control groups were identified. An immune-related gene diagnostic signature (IRGDS) was established by protein-protein interaction network analysis, least absolute shrinkage and selection operator and logistic regression analysis. Results: A total of 102 immune-related DEGs were identified. We developed an IRGDS composed of CD8A, CSF1R and CXCL10 for AA molecular pathological assessment and diagnosis (area under the receiver operating characteristic curve [AUC]=0.962). We also validated the diagnostic value of the IRGDS in an external cohort (AUC=0.955). Patients with high IRGDS scores presented with a higher abundance of immune cell infiltration and expression of genes associated with immune recruitment and immune activation, suggesting adverse biological alterations. Conclusion In our study, an IRGDS model with accurately diagnostic capacity for AA was established, and biological alterations were deciphered in AA. The IRGDS may be used as an auxiliary diagnostic marker for AA.

Objective To summarize the modeling methods and standards of thyroid cancer,to provide reference for the establishment of thyroid cancer animal model close to the clinical syndrome of Chinese and Western medicine,and to promote the progress of clinical diagnosis and treatment.Methods The modeling methods and characteristics of animal models of thyroid cancer in CNKI,Wanfang,Web of Science and PubMed databases were analyzed and summarized.The advantages and disadvantages were evaluated,and the coincidence degree with the clinical characteristics of traditional Chinese and Western medicine was analyzed.Results There are many modeling methods for thyroid cancer animal models,which are mainly divided into four types:spontaneous,induced,genetic engineering and transplantation models.Genetic engineering mouse models include transgenic models,gene knockout models,gene replacement models and the latest reported restricted mouse models.Among them,the genetic engineering mouse model and the transplanted mouse model have a high clinical coincidence,and the spontaneous and induced tumor model have a low clinical coincidence.Conclusion It is one of the important directions for future research on thyroid cancer to establish an animal model consistent with"TCM syndrome"and"Western medicine diagnostic criteria".

Objective:To explore the characteristics and typology of self-care behavior among patients with chronic heart failure (CHF), and analyze their influencing factors.Methods:A cross-sectional study was used. A total of 318 patients with CHF who were hospitalized in the Heart Center of Zhongshan Hospital, Fudan University from November 2022 to July 2023 were selected by continuous enrollment method. The General Information Questionnaire, Heart Failure Self-care Index Scale, Brief Illness Perception Questionnaire, Self-efficacy for Managing Chronic Disease 6-item Scale, Perceived Social Support Scale, Atlanta Heart Failure Knowledge Test-V2 and Self-Care Confidence Scale were used to investigate. Latent profile analysis was utilized to delineate the characteristics and subtypes of self-care behaviors in CHF patients and examine the influencing factors.Results:A total of 291 patients were included in this study, including 190 males and 101 females, aged 67 (61, 74) years old. The analysis identified three latent categories of self-care behaviors among CHF patients: 26 cases in high self-care group, 131 cases in moderate self-care with deficiencies in maintenance and symptom perception group, and 134 cases in low self-care group.Ordered multicategorical Logistic regression analysis revealed that age ( OR=1.023, 95% CI 1.001-1.046, P<0.05), self-care confidence ( OR=0.859, 95% CI 0.817-0.904, P<0.01), and social support ( OR=0.966, 95% CI 0.940-0.993, P<0.05) were the factors influencing the potential categories of self-care behavior in CHF patients. Conclusions:The study identifies distinct categorical characteristics of self-care behaviors in patients with CHF. Healthcare professionals can leverage these findings to identify the self-care behavior characteristics and influencing factors for each patient category at an early stage, thereby providing personalized and precise support strategies to help patients enhance self-care behaviors.

Objective:To assess the current state of care for Severe Postpartum Hemorrhage(SPPH)in refer-ral centers and non-referral centers,and to propose enhanced strategies for the regional prevention and manage-ment of SPPH.Methods:The clinical data of patients with SPPH,defined as postpartum blood loss≥1500 ml or transfusion of blood products≥1000 ml,in two districts of Beijing from January 2021 to June 2023 were retrospec-tively analyzed.A total of 201 cases of SPPH were included and they were divided into 125 cases in the referral center group and 76 cases in the non-referral center group based on whether they were city level referral centers.The clinical characteristics between these two groups were compared.Furthermore,a stratified analysis was con-ducted using a Logistic regression model to identify the risk factors associated with massive postpartum hemor-rhage,defined as postpartum hemorrhage≥4000 ml,transfusion requirements exceeding suspended red blood cells(RBC)>10 U and(or)plasma>1000 ml.Results:Analysis of cases presenting with SPPH between the two study groups showed that patients in the referral center group exhibited advanced maternal age,smaller gestation-al weeks at delivery and a higher proportion of high-risk factors compared to those in the non-referral center group,and the difference was statistically significant(P<0.05).The primary cause of SPPH in the referral center group was placental factors,while uterine atony was identified as the main factor in the non-referral center group,and this difference was statistically significant(P<0.05).Additionally,within the non-referral center group,there was a higher amount of blood loss during cesarean section,lower proportion of B-Lynch suture/vascular suture ligation,and higher proportion of uterine packing(P<0.05).Furthermore,compared to the referral center group,there were significantly higher incidences of plasma transfution volume,return to operating room for further inter-vention or exploratory laparotomy procedures after initial delivery and complications related to postpartum hemor-rhage observed in the non-referral center group(P<0.05).Moreover,it was noted that there were more cases of massive postpartum hemorrhagic disease reported in the non-referral center group than in the referral center group(P<0.05).In massive postpartum hemorrhage cases analyzed,referring centers had a higher percentage of patients presenting with multiple high-risk factors for postpartum hemorrhage during pregnancy when compared to non-referring centers(71.4%vs.33.3%,P<0.05).Placental factors accounted for majority causes leading to hemorrhage within referring centers(57.1%),whereas both uterine atony and placental factors played major roles within non-referring centers′cases(42.9%,28.6%).The multivariate Logistic regression analysis revealed that non-referral center delivery(aOR 3.47,95%CI 1.40-9.18)and a history of multiple intrauterine operations(aOR 12.63,95%CI 1.24-131.30)were identified as significant risk factors for massive postpartum hemor-rhage.Conclusions:The outcomes of high-risk pregnant women referral management in the region exhibit an im-provement,necessitating the reinforcement of training in non-referral midwifery institutions regarding identification of high-risk factors,surgical suture techniques,and comprehensive SPPH management to avert excessive bleed-ing and blood transfusion.

There are different views on the theory of “spleen governs time”， which is still a hot spot in the study of Zangxiang （藏象） theory. Based on Zangxiang time-space view， it is found that the thinking mode of the spleen governing time theory follows space-time logic. It is believed that the different time views of the spleen governing time are all formed based on the space view that the spleen belongs to earth and resides in the center， and the zang time theory is developed with the unified time and space logic. Guided by Zangxiang time-space view， the origin of the spleen belonging to earth and residing in the center is traced， and the theoretical connotation and its clinical application of spleen governing time under different time-space logic are explored with reference to the four season and five zang theory， five season and five zang theory， six season and six zang theory， and eight season and eight zang theory.