Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Background and Aims:Abdominal aortic aneurysm(AAA)is a common arterial dilation disease in vascular surgery,with aneurysm rupture being its most serious complication,often leading to fatal hemorrhage and posing a severe threat to patients'lives.Endovascular aneurysm repair(EVAR),due to its minimally invasive nature,safety,and rapid recovery,has become the preferred treatment for AAA.However,endoleak,a complication unique to EVAR,remains a major clinical challenge.Persistent endoleak can lead to sustained high pressure within the aneurysm sac,increasing the risk of continued expansion and rupture.It is one of the main causes of the high reintervention rate following EVAR.In particular,the treatment strategy for type Ⅱ endoleaks remains controversial.This study was conducted to evaluate the clinical value of selective sac embolization via the iliac approach combined with standard EVAR in managing intraoperative immediate endoleaks.Methods:The clinical data of AAA patients with a risk of endoleak who underwent standard EVAR at the First Hospital of China Medical University between March 2023 and September 2024 were retrospectively collected.Patients were divided into an intervention group(n=42)and a non-intervention group(n=32)based on whether selective sac embolization via the iliac approach was performed during operation.General clinical data,preoperative anatomical characteristics of the AAA,surgical details,and postoperative follow-up results were compared between the two groups.Results:There were no statistically significant differences between the two groups in terms of age,sex,anatomical features,rupture rate,or off-label use(all P>0.05).The technical success rate during surgery was 100%in both groups.One patient in the intervention group experienced transient sigmoid colon ischemia after operation,which resolved with conservative treatment.The mean follow-up period was(6.49±4.68)months.The proportions of aneurysm sac shrinkage,stability,and enlargement in the intervention group were 40.5%,57.1%,and 2.4%,respectively,compared to 59.4%,40.6%,and 0.0%in the non-intervention group,with no statistically significant differences(all P>0.05).The incidence of endoleak during follow-up was also comparable between the two groups(P>0.05).Conclusion:For intraoperative endoleaks during standard EVAR,selective sac embolization via the iliac approach is a technically simple and safe method that provides short-term outcomes comparable to those in patients without intraoperative endoleaks.Its long-term efficacy warrants further investigation through extended follow-up.

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Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Pancreatic cancer is a kind of highly malignant tumor with a low survival rate and poor prognosis. The effectiveness of gemcitabine as a first-line chemotherapy drug is limited; however, it can activate dendritic cells and improve antigen presentation which increase the sensitivity of tumor cell to immunotherapy. Although immunotherapy has made some advancements in cancer treatment, the therapeutic benefit of programmed cell death receptor 1/programmed death receptor-ligand 1 (PD-1/PD-L1) blockade therapy remains relatively low. The chemokine C-X-C chemokine ligand 12 (CXCL12) contributes to an immunosuppressive tumor microenvironment by recruiting immunosuppressive cells. The receptor C-X-C motif chemokine receptor 4 (CXCR4), highly expressed in various tumors including pancreatic cancer, plays a crucial role in tumor development and progression. In this study, the anti-tumor immune response of human peripheral blood mononuclear cell (hPBMC) was enhanced using the combination of BsNb PX4 (anti-PD-L1&CXCR4 bispecific nanobody) and gemcitabine. In a co-culture system of gemcitabine-pretreated hPBMCs with tumor cells, the BsNb PX4 synergized gemcitabine to improve the cytotoxic activity of hPBMCs against tumor cells. Flow cytometry analysis confirmed increased ratio of CD8⁺ to CD4⁺ T cells in combination treatment. In NOD/SCID mice bearing pancreatic cancer, the combination treatment exhibited more infiltration of CD8⁺ T cells into tumor tissues, contributing to an effective anti-tumor response. This study presents potential new therapies for the treatment of pancreatic cancer. Ethical approval was obtained for collection of hPBMC samples from the Local Ethics Committee of Shanghai Jiao Tong University. All animal experiments were approved by the Animal Ethic Committee of Shanghai Jiao Tong University (authorizing number: A2024246).

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Diterpenoid ferruginol is a key intermediate in biosynthesis of active ingredients such as tanshinone and carnosic acid.However, the traditional process of obtaining ferruginol from plants is often cumbersome and inefficient. In recent years, the increasingly developing gene editing technology has been gradually applied to the heterologous production of natural products, but the production of ferruginol in microbe is still very low, which has become an obstacle to the efficient biosynthesis of downstream chemicals, such as tanshinone. In this study, miltiradiene was produced by integrating the shortened diterpene synthase fusion protein,and the key genes in the MVA pathway were overexpressed to improve the yield of miltiradiene. Under the shake flask fermentation condition, the yield of miltiradiene reached about(113. 12±17. 4)mg·L~(-1). Subsequently, this study integrated the ferruginol synthase Sm CYP76AH1 and Sm CPR1 to reconstruct the ferruginol pathway and thereby realized the heterologous synthesis of ferruginol in Saccharomyces cerevisiae. The study selected the best ferruginol synthase(Il CYP76AH46) from different plants and optimized the expression of pathway genes through redox partner engineering to increase the yield of ferruginol. By increasing the copy number of diterpene synthase, CYP450, and CPR, the yield of ferruginol reached(370. 39± 21. 65) mg·L~(-1) in the shake flask, which was increased by 21. 57-fold compared with that when the initial ferruginol strain JMLT05 was used. Finally, 1 083. 51 mg·L~(-1) ferruginol was obtained by fed-batch fermentation, which is the highest yield of ferruginol from biosynthesis so far. This study provides not only research ideas for other metabolic engineering but also a platform for the construction of cell factories for downstream products.
Saccharomyces cerevisiae/genetics* ; Diterpenes/metabolism* ; Metabolic Engineering ; Fermentation ; Abietanes

Traditional Chinese medicine(TCM) resources are the essential material foundation for the development of TCM. The national survey of TCM resources serves as a periodic summary of these resources, ensuring the continuity, prosperity, and development of TCM in China. Since 1949, four national surveys of TCM resources have been conducted. The fourth survey incorporated an investigation into traditional knowledge related to TCM resources, including the traditional medicinal knowledge of Chinese ethnic minorities, with the goal of systematically exploring, preserving, and inheriting this knowledge. This manuscript provides an overview of the basic findings from the first three national surveys of TCM resources, while also clarifying the concepts, categories, forms, carriers, and acquisition pathways of traditional knowledge related to TCM resources. A preliminary summary of the findings from traditional knowledge investigations reported in current literature is also presented. Based on the fourth survey, this manuscript emphasizes the urgency of developing public medical knowledge through empirically-based investigations, the excavation, and compilation of traditional knowledge. It also outlines the potential for conducting "precise" investigations based on first-hand data obtained from the survey, as well as facilitating the discovery and evaluation of new medicines using traditional knowledge related to ethnic minority medicinal practices. This manuscript is expected to provide valuable insights for promoting the health and industrial development of ethnic minority populations in the post-"survey" phase.
Humans ; Medicine, Chinese Traditional ; China/ethnology* ; Minority Groups ; Ethnicity ; Drugs, Chinese Herbal/therapeutic use* ; Health Knowledge, Attitudes, Practice/ethnology* ; Surveys and Questionnaires

The tumor microenvironment is a crucial factor in tumor occurrence and progression. The hypoxic microenvironment is widely present in tumor tissue and is a key endogenous factor accelerating tumor deterioration. The "blood stasis toxin" theory, as an emerging perspective in tumor research, is regarded as the unique "soil" in tumor progression from the perspective of traditional Chinese medicine(TCM) due to its dynamic evolution mechanism, which closely resembles the formation of the hypoxic microenvironment. Scientifically integrating TCM theories with the biological characteristics of tumors and exploring precise syndrome differentiation and treatment strategies are key to achieving comprehensive tumor prevention and control. This article focused on the hypoxic microenvironment of the tumor, elucidating its formation mechanisms and evolutionary processes and carefully analyzing the internal relationship between the "blood stasis toxin" theory and the hypoxic microenvironment. Additionally, it explored the interaction among blood stasis, toxic pathogens, and hypoxic environment and proposed micro-level prevention and treatment strategies targeting the hypoxic microenvironment based on the "blood stasis toxin" theory, aiming to provide TCM-based theoretical support and therapeutic approaches for precise regulation of the hypoxic microenvironment.
Humans ; Tumor Microenvironment/drug effects* ; Neoplasms/therapy* ; Animals ; Medicine, Chinese Traditional ; Disease Progression ; Drugs, Chinese Herbal