Objective To construct a targeted drug delivery system, Ang-BEVs@Dox, based on Angiopep-2 peptide-modified bacterial extracellular vesicles （BEVs） loaded with doxorubicin （Dox）, overcome the challenges of blood-brain barrier （BBB） penetration and systemic toxicity in chemotherapy for glioblastoma （GBM）, enhance drug targeting to brain tumors and reduce its toxic side effects. Methods BEVs derived from Escherichia coli were isolated using ultracentrifugation. The targeting ligand Angiopep-2, specific for the LRP-1 receptor, was conjugated onto the surface of BEVs to construct the targeted carrier （Ang-BEVs）. Dox was loaded into Ang-BEVs using low-frequency sonication to form Ang-BEVs@Dox. The physicochemical properties （morphology and size） of the carriers were characterized by transmission electron microscopy （TEM） and dynamic light scattering （DLS）. The BBB-penetrating capability, in vitro/in vivo anti-tumor efficacy, and biosafety of the system were evaluated using cellular uptake assays, 3D tumor spheroid models, and orthotopic tumor-bearing mouse models. Results ① Carrier characterization and in vitro efficacy: Ang-BEVs@Dox exhibited a particle size of approximately 100 nm and maintained structural stability after Dox loading. It significantly enhanced cellular uptake efficiency in U87MG cells and achieved deep penetration within 3D tumor spheroids. Cytotoxicity assays demonstrated synergistic anti-tumor effects between the BEVs and Dox in the Ang-BEVs@Dox system. ② In vivo targeting and anti-tumor efficacy: In orthotopic tumor-bearing mouse models, Ang-BEVs@Dox effectively penetrated the BBB and significantly inhibited tumor growth, extending the median survival time of tumor-bearing mice to 33.5 days （compared to 23.5 days in the blank control group, P<0.001）. Immunohistochemical analysis revealed significant suppression of the tumor cell proliferation marker Ki-67 and enhancement of the apoptosis marker TUNEL staining signals. ③ Biosafety: Major organs from mice in the Ang-BEVs@Dox treatment group showed no observable pathological damage, indicating good biosafety. Conclusion This study successfully constructed an Angiopep-2 peptide-modified engineered BEVs delivery system （Ang-BEVs@Dox）. Through Angiopep-2-mediated BBB penetration and tumor targeting, it significantly enhanced the accumulation and therapeutic efficacy of BEVs at the GBM site. This method combined efficient delivery, low systemic toxicity, and clinical translation potential, which provided an innovative solution to overcome the therapeutic bottleneck in GBM treatment.

WD repeat domain 1(WDR1)is a highly conserved cytoskeleton-associated protein that plays a crucial role in physiological processes such as actin cytoskeleton remodeling,dynamic regulation of intercellular junctions,cell division,and migration.WDR1 exhibits abnormal high ex-pression in various malignant tumors,including breast cancer,ovarian cancer,and thyroid cancer,and has been demonstrated to significantly promote the invasive and migratory capabilities of tumor cells,suggesting its important role in the malignant progression of tumors.Moreover,the expression level of WDR1 is closely related to the clinical prognosis of patients with multiple malignant tumors.Especially in patients with esophageal cancer and osteosarcoma,its high expression often indicates a poor overall survival rate.WDR1 can promote tumor initiation and progression by regulating the Wnt/β-Catenin signaling pathway and the Hippo-YAP signaling pathway.Meanwhile,its expression is also subject to multi-level regulation by transcription activation factors and long non-coding RNAs(lncR-NAs),thereby influencing the proliferation,migration,and other biological behaviors of tumor cells.Additionally,WDR1 can further drive the invasive growth and metastatic potential of tumors by regu-lating the epithelial-mesenchymal transition(EMT)process.This article aimed to systematically re-view the research progress in recent years regarding the biological functions and molecular mechanisms of WDR1 in tumor initiation and development,with a view to providing new theoretical foundations and research directions for the early diagnosis,prognosis assessment,and individualized treatment of clinical tumors.

Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

Objective To establish and verify a mature and stable glioblastoma(GBM)organoid model,so as to provide an accurate and personalized preclinical model for the research and treatment of GBM.Methods Fresh GBM tissues obtained through surgical procedures were initially processed,and then GBM stem cells(GSCs)were isolated using stem cell culture medium and were identified.Subsequently,GSCs were cultured in organoid culture medium for 3D cultivation,and GBM organoids were successfully obtained.The histological morphology of GBM organoids was observed by hematoxylin-eosin(H-E)staining;the stemness and similarity to the parental tumor were identified by immunofluorescence staining;and the in vivo tumorigenic ability of GBM organoids was identified by orthotopic tumorigenesis experiments in nude mice.Results A total of 7 GBM organoids were constructed from 9 human GBM samples,with a morphology resembling"neurosphere",and the average duration for organoid formation was 1 week.H-E staining results showed that the histological morphology of GBM organoids under high-power microscope was very similar to that of GBM tumor tissues;immunofluorescence staining results indicated that the GBM organoids possessed stemness characteristics and histological cellular similarity;and GBM organoids had a stronger tumorigenic ability compared to ordinary GBM cells in nude mice.Conclusion This study presents a stable and reliable method for constructing GBM organoids retaining the histological characteristics of the original GBM tissue,which providing new insights for future GBM research and clinical practice.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Cardiac insufficiency caused by myocardial iron overload(MIO)is a leading cause of death for patients with thalassemia and primary hemochromatosis,but easy to be missed or misdiagnosed due to the lack of typical clinical symptoms.Cardiac MR(CMR)has high tissue resolution without radiation,having become an important imaging method for evaluating MIO.The application progresses of CMR for assessing MIO were reviewed in this article.

Cardiac insufficiency caused by myocardial iron overload(MIO)is a leading cause of death for patients with thalassemia and primary hemochromatosis,but easy to be missed or misdiagnosed due to the lack of typical clinical symptoms.Cardiac MR(CMR)has high tissue resolution without radiation,having become an important imaging method for evaluating MIO.The application progresses of CMR for assessing MIO were reviewed in this article.

Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective To exploring the correlation between serum miR-195 and metabolic associated fatty liver disease(MAFLD)in type 2 diabetes mellitus(T2DM)patients.Methods A total of 79 patients with T2DM who were treated in the Endocrinology Department of Gansu Provincial People's Hospital were enrolled in this study from October 2022 to August 2023.They were divided into simple T2DM(n=37)and MAFLD group(MAFLD,n=42)according to whether they were complicated with MAFLD.Meanwhile,34 healthy individuals who underwent physical examinations were selected as the normal control(NC)group.Results Compared with the NC group,FPG,HbA1c and HOMA-IR increased,while HDL-C decreased in T2DM and MAFLD group(P<0.05).The TG and hs-CRP levels were higher in MAFLD group than in NC and T2DM group(P<0.05).The serum miR-195 expression decreased sequentially(P<0.05),while FAS increased sequentially in NC,T2DM,and MAFLD group(P<0.05).Spearman correlation analysis showed that miR-195 was negatively correlated with FPG,HOMA-IR,hs-CRP,and FAS(P<0.05).Multiple linear regression analysis showed that HOMA-IR and FAS were the influencing factors for serum miR-195.Conclusions Down-regulation of serum miR-195 expression in patients with T2DM combined with MAFLD may be by glucose and lipid metabolism,IR and inflammatory response.