Traumatic brain injury caused by blast shock waves represents a significant type of injury in modern warfare and civilian explosion accidents.Its high incidence and complexity have attracted a widespread attention,and the injury mechanism and cranial brain protection have become current research hotspots.This review first analyzes the dynamic load characteristics of blast shock waves and introduces the development and verification of cranial brain constitutive and finite element models to explore the mechanical responses of the cranial brain at tissue and cellular levels under blast waves and bullet impacts.Subsequently,the current state of research on injury mechanisms at tissue and cellular levels and cranial brain protection,is systematically summarized based on domestic and international studies.Finally,the current research challenges and future development directions are outlined,and the importance of interdisciplinary cooperation and innovation to promote the research and application transformation of blast-induced traumatic brain injury is emphasized.The findings provide a valuable reference for enhancing the comprehension of injury mechanism and fostering multi-disciplinary integration and protective helmet development.

Background and Aims:Radical resection is the only potentially curative treatment for intrahepatic cholangiocarcinoma(ICC),yet the high recurrence rate results in poor prognosis.In recent years,"textbook outcome"(TO)has been proposed as a comprehensive quality metric,but its association with prognosis remains unclear.This study aimed to analyze the risk factors influencing the achievement of TO after radical resection of ICC and to explore the relationship between TO and survival.Methods:A retrospective analysis was conducted on the clinical data of 180 ICC patients who underwent radical resection at the 904th Hospital of the Joint Logistic Support Force of the PLA between February 2018 and February 2023.Univariate and multivariate Logistic regression analyses were performed to identify risk factors associated with TO,and survival analysis was carried out using the Kaplan-Meier method and Log-rank test.Results:Of the 180 patients,66 achieved TO.Multivariate Logistic regression analysis indicated that preoperative total bilirubin＞22 μmol/L,preoperative CA19-9＞35 U/mL,maximum tumor diameter＞5 cm,poor tumor differentiation,microvascular invasion(MVI),and lymph node metastasis were independent risk factors for failing to achieve TO(all P＜0.05).Survival analysis demonstrated that patients who achieved TO had a significantly longer median survival compared with those who did not(36 months vs.16 months,P＜0.001).Conclusion:Preoperative total bilirubin＞22 μmol/L,preoperative CA19-9＞35 U/mL,maximum tumor diameter＞5 cm,poor tumor differentiation,MVI,and lymph node metastasis are independent risk factors for not achieving TO after radical resection of ICC.Patients who achieved TO exhibited markedly longer survival,suggesting that TO not only reflects perioperative treatment quality but also serves as an important prognostic indicator.Greater attention to these risk factors and optimization of perioperative management may improve the likelihood of achieving TO and enhance long-term outcomes.

Objective To investigate the predictive capacity of the Triglyceride-Glucose(TyG)index and the Triglyceride-Glucose-Body Mass Index(TyG-BMI)for the development of hyperuricemia(HUA)in a health examination population,and to identify suitable indicators as risk assessment tools for HUA.Methods This study ultimately included 12 004 participants from a health examination cohort.According to SUA levels,the partici-pants were categorized into a normal group(n=9 952)and a hyperuricemia(HUA)group(n=2 052).The TyG index and TyG-BMI index were calculated,and participants were further stratified into four groups(Q1—Q4)based on the quartiles of these indices.Binary logistic regression analysis was performed to assess the association between TyG,TyG-BMI,and HUA.The predictive value of TyG,TyG-BMI,and their combination for HUA was evaluated using Receiver Operating Characteristic(ROC)curves and the Area Under the Curve(AUC).Subgroup analyses were carried out by gender and age.Results The TyG and TyG-BMI indices were significantly elevated in the HUA group compared to the normal group.The prevalence of HUA was markedly higher in the TyG-Q4 and TyG-BMI-Q4 groups than in the other three corresponding quartile groups.Binary logistic regression analysis revealed a positive association between TyG,TyG-BMI levels,and the risk of HUA.The AUC values for predicting HUA using TyG,TyG-BMI,and their combination were 0.700,0.747,and 0.822,respectively.Specifically,for males,the AUC values were 0.641,0.674,and 0.709,respectively,whereas for females,they were 0.742,0.776,and 0.829,respectively.Among individuals younger than 60 years old,the AUC values were 0.716,0.759,and 0.835,respectively,while for those aged 60 years or older,the values were 0.614,0.645,and 0.731,respectively.Conclusions TyG and TyG-BMI are significantly associated with the risk of HUA.Specifically,TyG-BMI demon-strates superior predictive performance compared to TyG alone.Moreover,the combination of TyG and TyG-BMI further improves predictive accuracy,particularly among female and middle-aged or younger populations.

Traumatic brain injury caused by blast shock waves represents a significant type of injury in modern warfare and civilian explosion accidents.Its high incidence and complexity have attracted a widespread attention,and the injury mechanism and cranial brain protection have become current research hotspots.This review first analyzes the dynamic load characteristics of blast shock waves and introduces the development and verification of cranial brain constitutive and finite element models to explore the mechanical responses of the cranial brain at tissue and cellular levels under blast waves and bullet impacts.Subsequently,the current state of research on injury mechanisms at tissue and cellular levels and cranial brain protection,is systematically summarized based on domestic and international studies.Finally,the current research challenges and future development directions are outlined,and the importance of interdisciplinary cooperation and innovation to promote the research and application transformation of blast-induced traumatic brain injury is emphasized.The findings provide a valuable reference for enhancing the comprehension of injury mechanism and fostering multi-disciplinary integration and protective helmet development.

Background and Aims:Radical resection is the only potentially curative treatment for intrahepatic cholangiocarcinoma(ICC),yet the high recurrence rate results in poor prognosis.In recent years,"textbook outcome"(TO)has been proposed as a comprehensive quality metric,but its association with prognosis remains unclear.This study aimed to analyze the risk factors influencing the achievement of TO after radical resection of ICC and to explore the relationship between TO and survival.Methods:A retrospective analysis was conducted on the clinical data of 180 ICC patients who underwent radical resection at the 904th Hospital of the Joint Logistic Support Force of the PLA between February 2018 and February 2023.Univariate and multivariate Logistic regression analyses were performed to identify risk factors associated with TO,and survival analysis was carried out using the Kaplan-Meier method and Log-rank test.Results:Of the 180 patients,66 achieved TO.Multivariate Logistic regression analysis indicated that preoperative total bilirubin＞22 μmol/L,preoperative CA19-9＞35 U/mL,maximum tumor diameter＞5 cm,poor tumor differentiation,microvascular invasion(MVI),and lymph node metastasis were independent risk factors for failing to achieve TO(all P＜0.05).Survival analysis demonstrated that patients who achieved TO had a significantly longer median survival compared with those who did not(36 months vs.16 months,P＜0.001).Conclusion:Preoperative total bilirubin＞22 μmol/L,preoperative CA19-9＞35 U/mL,maximum tumor diameter＞5 cm,poor tumor differentiation,MVI,and lymph node metastasis are independent risk factors for not achieving TO after radical resection of ICC.Patients who achieved TO exhibited markedly longer survival,suggesting that TO not only reflects perioperative treatment quality but also serves as an important prognostic indicator.Greater attention to these risk factors and optimization of perioperative management may improve the likelihood of achieving TO and enhance long-term outcomes.

Objective To investigate the predictive capacity of the Triglyceride-Glucose(TyG)index and the Triglyceride-Glucose-Body Mass Index(TyG-BMI)for the development of hyperuricemia(HUA)in a health examination population,and to identify suitable indicators as risk assessment tools for HUA.Methods This study ultimately included 12 004 participants from a health examination cohort.According to SUA levels,the partici-pants were categorized into a normal group(n=9 952)and a hyperuricemia(HUA)group(n=2 052).The TyG index and TyG-BMI index were calculated,and participants were further stratified into four groups(Q1—Q4)based on the quartiles of these indices.Binary logistic regression analysis was performed to assess the association between TyG,TyG-BMI,and HUA.The predictive value of TyG,TyG-BMI,and their combination for HUA was evaluated using Receiver Operating Characteristic(ROC)curves and the Area Under the Curve(AUC).Subgroup analyses were carried out by gender and age.Results The TyG and TyG-BMI indices were significantly elevated in the HUA group compared to the normal group.The prevalence of HUA was markedly higher in the TyG-Q4 and TyG-BMI-Q4 groups than in the other three corresponding quartile groups.Binary logistic regression analysis revealed a positive association between TyG,TyG-BMI levels,and the risk of HUA.The AUC values for predicting HUA using TyG,TyG-BMI,and their combination were 0.700,0.747,and 0.822,respectively.Specifically,for males,the AUC values were 0.641,0.674,and 0.709,respectively,whereas for females,they were 0.742,0.776,and 0.829,respectively.Among individuals younger than 60 years old,the AUC values were 0.716,0.759,and 0.835,respectively,while for those aged 60 years or older,the values were 0.614,0.645,and 0.731,respectively.Conclusions TyG and TyG-BMI are significantly associated with the risk of HUA.Specifically,TyG-BMI demon-strates superior predictive performance compared to TyG alone.Moreover,the combination of TyG and TyG-BMI further improves predictive accuracy,particularly among female and middle-aged or younger populations.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To investigate current status in knowledge,attitude and practice(KAP)of transitional care among the nurses in Shaanxi province,identify the influencing factors that hinder the transitional care and carry out training of transitional care for the nurses in different specialties in order to provide a theoretical basis for establishing a transitional care model for discharged patients.Methods A total of 2 288 nurses were selected to participate the survey from 41 hospitals in Shaanxi province by the stratified two-stage random sampling method.A self-designed questionnaire for general information,an obstacle factor scale,and a KAP of transitional care questionnaire were used as the survey instruments.The data acquired from the survey were processed by Cluster analysis to identify potential categories and the influencing factors.Results Totally 2085 nurses finished the study.Total score of KAP of transitional care among the nurses in Shaanxi province was found at(134.96±23.75),which was at a medium level.The scores of knowledge,attitude and practice were 77.24%,80.64%and 76.98%respectively.Cluster analysis showed that all the nurses could be categorised into either a lower scored group(n=1 596,76.5%)or a higher scored group(489,23.5%),with a best potential category number of 2 for the 2 085 nurses who participated the survey.There were statistically significant differences between the groups in terms of care training,telephone follow-up,network platform,outpatient service of nursing experts,family visits,patient club and hospital categories(all P＜0.05).Logistic regression analysis on even factors above showed that care training,telephone follow-up,network platform and patient club could significantly affect the potential categorisation of KAP of the nurses in transitional care(all P＜0.05).The main factors that hindered the transitional care included insufficient human resources(93.81%),insufficient supply of community services(93.57%)and lack of a standardised flowchart for transitional care(93.09%).Conclusions Levels of KAP about the transitional care are at a medium level among the nurses of 41 hospitals in Shaanxi province,which indicates that the nurses are active in attitude,but the knowledge and practice are required to be further improved and standardised.The results of cluster analysis indicate that it is necessary for nursing managers to carry out targeted trainings in transitional care for the nurses in different categories.This study provides a theoretical basis to establish a diversified transitional care mode for discharged patients in Shaanxi province.

The aim of this study is to investigate the immune effect of H9 subtype avian influenza virus(AIV)NP protein on mice and lay the foundation for the development of avian influenza vi-rus(AIV)vaccine.The H9N2 virus NP gene amplification product was cloned into the pET-32a expression vector,and the protein expression was verified by SDS-PAGE and Western blot,and the immune effect was evaluated by measuring the secretion of supernatant multicytokines in mouse splenocytes culture.The results showed that the total length of the coding region sequence of NP gene was 1 497 bp,NP recombinant proteins exist in both soluble and insoluble protein forms,and the specific bands were visible in Western blot.After immunizing mice,serum produces IgG-bind-ing antibodies with antibody titers of 1∶40 000.Compared with the control group,IL-2,IL-5 and IL-13 were significantly increased(P＜0.001),and the secretion of IL-6 was significantly increased compared with the control group.IL-4 and IL-12 p70 secretions were elevated compared with con-trols,but there was no significant difference.Compared with the control group,the secretions of IL-1β,IL-18,GM-CMF,TNF-α and IFN-γ were inhibited,but the difference was not significant(P＞0.05).The results showed that NP recombinant protein is a good immunogen,laying a foundation for in-depth research on influenza vaccine.

Objective:The classification model of prognosis of elderly cancer patients in rural areas of Shaanxi province was constructed based on machine learning algorithm, and its effectiveness was verified, providing reference for early prognosis recognition and intervention treatment.Methods:Using a multicenter, cross-sectional survey method and convenience sampling method, 597 elderly cancer patients in rural areas hospitalized in 9 different medical institutions (Shaanxi Cancer Hospital and its member units of specialty alliance) in Shaanxi Province from July to August 2022 were selected as the research objects, and a variable database of "basic information", "self-care ability", "symptoms", "comprehensive needs" and "quality of life" of elderly cancer patients in rural areas was established. Machine learning and statistical analysis were carried out to explore the important prognostic characteristics of elderly cancer patients in rural areas, and a prognostic classification model for elderly cancer patients in rural areas was constructed and verified.Results:The 597 valid questionnaires were ultimately collected.Among the 597 elderly cancer patients in rural areas, 207 were males and 390 were females, aged (69.56 ± 8.84) years. The results of cluster exploration showed that the prognosis of elderly cancer patients in rural areas was divided into three categories: good, medium and poor. The areas under the working characteristic curves of the subjects in good, medium and poor were 0.84, 0.79 and 0.69, respectively. The results of characteristic exploration showed that 10 indicators of "whether accompanied by metastasis", "distress", "sadness", "numbness", "eating", "walking", "fatigue", "forgetfulness", "fun of life" and "relationship with others" were important characteristic indicators of prognosis evaluation of elderly cancer patients in rural areas. There were statistically significant differences among the 10 important characteristic indicators in the three categories of prognosis of rural elderly cancer patients ( χ2=21.07, H values were 18.51-144.38, all P<0.01). There were statistically significant differences in the three categories of "comprehensive needs", "quality of life", "self-care ability" and "symptoms" ( H values were 519.40, 40.80, 103.69, all P<0.01). Conclusions:The construction and verification of a prognostic classification model for elderly cancer patients in rural areas based on machine learning algorithm can effectively explore the important characteristic indicators and prognostic classification of elderly cancer patients in rural areas, and provide basis and guidance for clinical medical staff to make individualized plans.