Tracheobronchial adenoid cystic carcinoma （TACC） is a low-grade malignant tumor originating from the airway mucosa. Despite its slow progression，it is characterized by high invasiveness，frequent recurrence，and a strong tendency for metastasis. Preclinical studies have shown that vascular-targeted therapy holds significant potential. However，an effective systemic treatment for TACC has not been established yet. This study explored TACC from the perspective of "Feiji" in traditional Chinese medicine （TCM） as the starting point. It deeply investigated the mechanisms of abnormal collaterals and tumor vascular recruitment and further elaborated on the theoretical connection between abnormal collaterals and tumor vascular recruitment. Firstly，collateral hyperactivity led to disordered and erratic pulmonary collaterals. Their abnormal structures were similar to the disorderly and tortuous nature of tumor （pseudo）angiogenesis. This resulted in imbalances in the functions of circulation，perfusion，and reverse injection of the pulmonary collaterals，and then led to unrestrained collateral dysfunction and the accumulation of pathogenic factors. Secondly，the remodeling of the extracellular matrix （ECM） and epithelial-mesenchymal transition （EMT） in TACC were critical processes in vascular co-option （VCO），representing the micro-level manifestation of the displacement of nutrient and defense. During this process，ECM remodeling made TACC cells more likely to hijack normal blood vessels，creating a complex vascular microenvironment conducive to tumor growth. In terms of treatment，this study proposed a TCM strategy of "regulating collaterals to expel pathogenic factors and nourishing collaterals to strengthen the healthy Qi"，and listed potential TCM. These were intended to regulate the Qi and blood in the collaterals，repair the functions of abnormal collaterals，and intervene in the vascular recruitment process of TACC. Future research should focus on improving the TCM clinical syndrome characteristics of TACC. Through modern molecular biology techniques，it is necessary to deeply analyze the micro-level pattern of vascular recruitment in TACC. This would enrich the understanding of the profound connection between abnormal collaterals and tumor vascular recruitment，providing empirical evidence for TCM-targeted therapies for vascular recruitment in TACC.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.