Acute brain injury (ABI) affects up to one-third of patients using mechanical circulatory support (MCS). In venoarterial extracorporeal membrane oxygenation (VA ECMO), ABI incidence (11%–40%) has not improved in two decades. Conversely, improvements in left ventricular assist devices (LVADs) have reduced the incidence of stroke, although it remains a major complication (10%–30%). The failure of MCS to ensure adequate cerebral protection may impair cerebrovascular autoregulation (CVAR) and disrupt microcirculatory function affected by reduced pulsatility, endothelial injury, acute perturbations in partial pressure of arterial carbon dioxide (PaCO2), and cerebral venous congestion. Here, we review evidence demonstrating that these factors alter microcirculatory dynamics and CVAR, thereby contributing to ABI through shared mechanistic pathways. Current methods for assessing CVAR are reviewed, including invasive indices such as the pressure reactivity index (PRx) from intracranial pressure monitoring and noninvasive metrics such as the cerebral oximetry index (COx) from near-infrared spectroscopy or flow-velocity correlations from transcranial Doppler. Each method is limited by feasibility, signal artifacts, and inter-modality variability. Our review identifies three priority areas for cerebral protection in MCS: preservation of pulse pressure, cautious titration of PaCO2, and integration of CVAR-informed blood pressure management. Preliminary evidence suggests that very low pulse pressure, rapid carbon dioxide correction, and persistent microcirculatory impairment are each associated with ABI risk. Future investigations should focus on validating bedside tools to assess CVAR and microcirculatory integrity, and on determining whether physiological targets derived from these measures can improve neurological outcomes in patients using MCS.

Objective:To develop a machine learning prediction model based on peripheral blood indicators for assessing 30-day mortality risk in elderly patients diagnosed with hemophagocytic lymphohistiocytosis (HLH).Methods:A retrospective cohort study was conducted, enrolling elderly patients (age≥65 years) diagnosed HLH at the First Affiliated Hospital of Nanjing Medical University between January 1, 2015, and November 30, 2023. Demographic characteristics, clinical manifestations, and laboratory parameters at admission were collected. The study included 204 elderly HLH patients with a median age of 70 (68-75) years, comprising 134 males (65.69%) and 70 females (34.31%). Using computer-generated random numbers, the data was randomly divided into the training and validation cohorts at a 7∶3 ratio. Based on 30-day survival outcomes, patients in the training cohort were categorized into the death and survivor groups. Predictive variables were screened through univariate analysis and the Boruta algorithm, with prediction models constructed using 11 machine learning algorithms. Model performance was evaluated using the following metrics: area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, calibration curve, and decision curve analysis. SHAP analysis was employed for model interpretation.Results:Comparison between the death and survivor groups in the training cohort identified 25 significant indicators ( P<0.05) through univariate analysis. Boruta algorithm-based screening further identified nine predictive variables: urea, ferritin, creatinine (CREA), D-dimer (D-D), platelet (PLT), activated partial thromboplastin time (APTT), aspartate aminotransferase (AST), creatine kinase (CK), and alanine aminotransferase (ALT). Among the 11 algorithms, the top five models by AUC in the training cohort were: XGBoost(AUC=1.000), AdaBoost(AUC=1.000), GBDT(AUC=1.000), DT(AUC=0.967), and RF(AUC=0.945). In the validation cohort, the top five performers by AUC were: RF(AUC=0.812), LR(AUC=0.792), LightGBM(AUC=0.769), AdaBoost(AUC=0.746), and GBDT(AUC=0.742). Thus, the RF model demonstrated optimal performance. SHAP analysis indicated urea as the most significant contributor to prediction outcomes. Conclusion:A machine learning model based on routine laboratory indicators can accurately predict the 30-day mortality risk in elderly HLH patients.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

Objective:To develop a machine learning prediction model based on peripheral blood indicators for assessing 30-day mortality risk in elderly patients diagnosed with hemophagocytic lymphohistiocytosis (HLH).Methods:A retrospective cohort study was conducted, enrolling elderly patients (age≥65 years) diagnosed HLH at the First Affiliated Hospital of Nanjing Medical University between January 1, 2015, and November 30, 2023. Demographic characteristics, clinical manifestations, and laboratory parameters at admission were collected. The study included 204 elderly HLH patients with a median age of 70 (68-75) years, comprising 134 males (65.69%) and 70 females (34.31%). Using computer-generated random numbers, the data was randomly divided into the training and validation cohorts at a 7∶3 ratio. Based on 30-day survival outcomes, patients in the training cohort were categorized into the death and survivor groups. Predictive variables were screened through univariate analysis and the Boruta algorithm, with prediction models constructed using 11 machine learning algorithms. Model performance was evaluated using the following metrics: area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, calibration curve, and decision curve analysis. SHAP analysis was employed for model interpretation.Results:Comparison between the death and survivor groups in the training cohort identified 25 significant indicators ( P<0.05) through univariate analysis. Boruta algorithm-based screening further identified nine predictive variables: urea, ferritin, creatinine (CREA), D-dimer (D-D), platelet (PLT), activated partial thromboplastin time (APTT), aspartate aminotransferase (AST), creatine kinase (CK), and alanine aminotransferase (ALT). Among the 11 algorithms, the top five models by AUC in the training cohort were: XGBoost(AUC=1.000), AdaBoost(AUC=1.000), GBDT(AUC=1.000), DT(AUC=0.967), and RF(AUC=0.945). In the validation cohort, the top five performers by AUC were: RF(AUC=0.812), LR(AUC=0.792), LightGBM(AUC=0.769), AdaBoost(AUC=0.746), and GBDT(AUC=0.742). Thus, the RF model demonstrated optimal performance. SHAP analysis indicated urea as the most significant contributor to prediction outcomes. Conclusion:A machine learning model based on routine laboratory indicators can accurately predict the 30-day mortality risk in elderly HLH patients.

OBJECTIVE: To compare the expression of matrix metalloproteinase-9 and tissue inhibitor of matrix metalloproteinase-1 of nasopharyngeal carcinoma at Xi'an and Shenzhen area. METHOD: The protein expression of MMP 9 and TIMP 1 were studied by immunohistochemistry staining in 21 cases nasopharyngeal carcinoma of Xi'an and 22 cases of Shenzhen. RESULT: The expression positive rates of MMP-9 in nasopharyngeal carcinoma tissues at Xi'an and Shenzhen were 61.90% and 68.18%. The positive rates of TIMP-1 in nasopharyngeal carcinoma tissues at two regions were 28.57% and 31.82%. The expression between MMP-9 and TIMP-1 were negative relationship either Xi'an or Shenzhen. The expression of MMP-9 was positively correlated to lymph node metastasis, and TIMP-1 was no relationship with lymph node metastasis. The expression of MMP-9 and TIMP-1 was no difference between Xi'an and Shenzhen. CONCLUSION The expression of MMP-9 and TIMP-1 in nasopharyngeal carcinoma 2 tissues was no difference between Xi'an and Shenzhen. Increase of MMP-9 play an important role in progression and metastasis of nasopharyngeal carcinoma.
Adolescent ; Adult ; Aged ; Carcinoma ; Carcinoma, Squamous Cell ; metabolism ; pathology ; Child ; China ; Female ; Humans ; Male ; Matrix Metalloproteinase 9 ; metabolism ; Middle Aged ; Nasopharyngeal Carcinoma ; Nasopharyngeal Neoplasms ; metabolism ; pathology ; Tissue Inhibitor of Metalloproteinase-1 ; metabolism ; Young Adult