Search

Objective To explore the characteristics and their significance of peripheral blood lymphocyte subsets in patients with active tuberculosis(ATB), based on a relatively large sample. Methods Clinical data were retrospectively collected from patients with ATB hospitalized in Peking Union Medical College Hospital (June 2012-January 2022) and the healthy population matched with age and sex (March 2019-May 2022). The characteristics of changes in peripheral blood lymphocyte subsets in ATB patients were analyzed using the healthy population as controls, and group comparisons were made between ATB patients with different modes of diagnosis and sites of involvement. Results A total of 212 ATB patients and 200 healthy participants were included. ATB patients consisted of 82 cases (38.7%) without etiological evidence and 130 cases(61.3%) with etiological evidence (77 with pulmonary tuberculosis, 21 with extrapulmonary tuberculosis, and 32 with pulmonary and extrapulmonary tuberculosis). Compared to healthy population, ATB patients had significantly lower counts and percentages of multiple peripheral blood lymphocyte subsets indicators, significantly higher percentage in T lymphocyte, CD8+T lymphocyte as well as higher memory CD4+T lymphocyte/CD4+T lymphocyte ratio and CD38+CD8+T lymphocyte/CD8+T lymphocyte ratio(all P < 0.05). Lymphocyte, B-cell, natural killer (NK) cell, T-lymphocyte, CD4+T-lymphocyte, and CD8+T-lymphocyte counts were lower in patients with pathogenetically confirmed ATB than in those with clinically confirmed ATB (all P < 0.05). There were no significant differences in the counts of memory CD4+T lymphocytes, naïve CD4+T lymphocytes, CD28+T lymphocytes and activated CD8+T lymphocyte subpopulations and their percentages in patients with different tuberculosis infection sites (all P > 0.05). Compared to pulmonary tuberculosis alone, patients with pulmonary and extrapulmonary tuberculosis had lower B-cell counts[23(10, 69)cells/μL vs. 73(25, 133)cells/μL, P=0.003] and patients with extrapulmonary tuberculosis alone had higher percentages of NK cells[16.7%(10.8%, 23.6%) vs. 10.6% (5.3%, 17.4%), P=0.042]. Conclusion ATB patients manifested a reduction in peripheral blood lymphocyte subset, indicating remarkable dysfunction of immune system.

Objective: To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery., Methods: All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP)., Results: A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio (OR) = 1.062, 95% confidence interval (95%CI) was 1.030-1.100, P = 0.005], obesity (OR = 2.234, 95%CI was 1.900-2.640, P < 0.001), male (OR = 0.858, 95%CI was 0.794-0.928, P = 0.049), diabetes (OR = 1.820, 95%CI was 1.680-1.980, P < 0.001) and emergency admission (OR = 1.278, 95%CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95%CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95%CI was 0.75-0.80), which was significantly higher than that of Logistic regression model (P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221)., Conclusions: Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.

Objective: To evaluate the diagnostic efficiency of a T-cell interferon-γ release assay based on recombinant Mycobacterium tuberculosis (MTB) 11kD protein for diagnosing tuberculosis., Methods: This prospective study enrolled inpatients with suspected tuberculosis at PUMCH to examine the diagnostic sensitivity, specificity, predictive value (PV) and likelihood ratio (LR) of T-cell interferon-γ release assays based on recombinant MTB-11kD protein, early antigenic target-6 (ESAT-6) and culture filtrate protein-10 (CFP-10) peptides (T-SPOT. TB). Test results were compared with final clinical and microbiological diagnoses., Results: Among 151 inpatients with suspected tuberculosis, 33 (21.9%) were microbiologically or clinically diagnosed as tuberculosis and 107 (70.9%) ruled out. And the remaining 11 (7.3%) patients were clinically indeterminate. The sensitivities of recombinant MTB-11kD-ELISPOT assay and T-SPOT. TB were 60.6%and 81.8%respectively (P = 0.057).Whereas, the specificity of recombinant MTB-11kD-ELISPOT assay was significantly higher than that of T-SPOT. TB (84.1%vs 72.9%, P = 0.046). The parallel testing increased the sensitivity to 84.9% and serial testing increased the specificity to 86.9%., Conclusion: The recombinant MTB-11kD-ELISPOT assay is more specific than T-SPOT. TB. And its combination with T-SPOT. TB may improve the diagnostic efficiency for tuberculosis.

We introduced the method of computer simulation in the studies of gravitational physiology. Based on work of Melchior (1994), we developed a mathematical model that can be used to stimulate cardiovascular responses to orthostatic stress (lower body negative pressure, LBNP). The model includes 7 sub-models: the redistribution of blood, the filling of left ventricle, left ventricle working, peripheral circulation, control of heart rate (HR), control of peripheral resistance and control of venous tone. Then we simulated the changes of blood pressure (BP) and heart rate during lower body negative pressure, and the results agreed well with the results of our human experiment. By using the developed model, we also simulated the effects of hypovolemia on the BP, HR and shock index during orthostatic stress. The simulation results indicate that the cardiovascular responses to orthostatic stress change significantly when the decrease of blood volume is more than 15% of the total blood volume. However, if the amount of the decrease of blood volume is less than 5% of the total blood volume, HR and BP could be maintained in normal range by the regulation of baroreflex during LBNP. Our simulation results suggest that hypovolemia may be the main cause of orthostatic intolerance induced by weightlessness.