Comparison of saline infusion test and captopril challenge test in the diagnosis of Chinese with primary aldosteronism in different age groups.

Background: To explore the diagnostic accuracy and the optimal cutoff value between the saline infusion test (SIT) and captopril challenge test (CCT) [including the value and suppression of plasma aldosterone concentration (PAC)] for primary aldosteronism (PA) diagnosing. Methods: A total of 318 patients with hypertension were consecutively enrolled, including 126 patients with PA and 192 patients with essential hypertension (EH), in this observational study. The characteristics of patients and laboratory examinations were collected and compared. The comparison between SIT and CCT was carried by drawing the receiver operator characteristic curve (ROC) and calculating the area under the curve (AUC) to explore the diagnostic accuracy and the optimal cutoff value. Results: The average age was 51.59 ± 10.43 in the PA group and 45.72 ± 12.44 in the EH group (p<0.05). The optimal cutoff value was 10.7 ng/dL for post-CCT PAC, 6.8 ng/dL for post-SIT PAC, and 26.9% for suppression of post-CCT PAC. The diagnostic value of post-CCT PAC was the highest with 0.831 for the AUC and 0.552 for the Youden index. The optimal cutoff value for patients who were <50 years old was 11.5 ng/dL for post-CCT PAC and 8.4 ng/dL for post-SIT PAC. The suppression of post-CCT PAC turned to 18.2% for those of age 50 or older. Conclusion: Compared with SIT, CCT had a higher diagnostic value when post-CCT PAC was used as the diagnostic criterion in Chinese people, while the selection of diagnostic thresholds depended on patient age.

Association between uric acid and cardiac outcomes mediated by neutrophil-to-lymphocyte ratio in patients with left ventricular diastolic dysfunction and pulmonary hypertension.

To evaluate the association of uric acid (UA) with adverse outcomes and its potential mediator in patients with left ventricular diastolic dysfunction (LVDD) and pulmonary hypertension (PH). We retrospectively analyzed 234 patients with LVDD and PH. The baseline characteristics of patients with low UA (≤ 330 µmol/L) group were compared with high UA (> 330 µmol/L) group. Adverse outcomes included all-cause mortality, cardiac death and heart failure (HF) hospitalization. Their association with UA and the mediator were evaluated using Cox regression and mediation analysis. The mediation proportion was further quantified by the R mediation package. During a mean follow-up of 50 ± 18 months, there were 27 all-cause deaths, 18 cardiovascular deaths and 41 incidents of HF hospitalization. Multivariable Cox regression analysis showed UA was an independent risk factor of adverse outcomes in LVDD and PH patients, even after adjusting for age, sex, body mass index, medical histories, systolic blood pressure, fasting blood glucose, total cholesterol, triglyceride, eGFR, BNP and medications. The hazard ratios (HRs) for UA (per 10 µmol/L increase) were as below: for all-cause mortality, HR 1.143, 95% CI 1.069-1.221, P < 0.001; for cardiac death, HR 1.168, 95% CI 1.064-1.282, P = 0.001; for HF hospitalization, HR 1.093, 95% CI 1.035-1.155, P = 0.001. Neutrophil-to-lymphocyte ratio (NLR) played a partial mediation role in the association, and the mediation proportion for NLR on the UA-adverse outcomes were 21%, 19% and 17%, respectively. In patients of LVDD with PH, higher UA level was independently correlated with adverse outcomes. Furthermore, NLR partially mediated the effect of UA on the risk of all-cause mortality, cardiac death and HF hospitalization.

Effect of Neutrophil-to-Lymphocyte Ratio on Post-TAVR Mortality and Periprocedural Pulmonary Hypertension.

Aims: To evaluate the impact of neutrophil-to-lymphocyte ratio (NLR) on periprocedural pulmonary hypertension (PH) and 3-month all-cause mortality in patients with aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR) and to develop a nomogram for predicting the mortality for these patients. Methods and Results: 124 patients undergoing TAVR were categorized into three groups according to systolic pulmonary artery pressure (sPAP): Group I (no PH, n = 61) consisted of patients with no pre- and post-TAVR PH; Group II (improved PH, n = 35) consisted of patients with post-TAVR systolic pulmonary artery pressure (sPAP) decreased by more than 10 mmHg compared to pre-TAVR levels; and Group III (persistent PH, n = 28) consisted of patients with post-TAVR sPAP no decrease or less than 10 mmHg, or new-onset PH after the TAVR procedure. The risk of all-cause mortality within 3 months tended to be higher in Group II (11.4%) and Group III (14.3%) compared to Group I (3.3%) (P=0.057). The multinomial logistic regression analysis demonstrated a positive correlation between NLR and both improved PH (OR: 1.182, 95% CI: 1.036-1.350, P=0.013) and persistent PH (OR: 1.181, 95% CI: 1.032-1.352, P=0.016). Kaplan-Meier analysis revealed a significant association between higher NLR and increased 3-month all-cause mortality (16.1% vs. 3.1% in lower NLR group, P=0.021). The multivariable Cox regression analysis confirmed that NLR was an independent predictor for all-cause mortality within 3 months, even after adjusting for clinical confounders. A nomogram incorporating five factors (BNP, heart rate, serum total bilirubin, NLR, and comorbidity with coronary heart disease) was developed. ROC analysis was performed to discriminate the ability of the nomogram, and the AUC was 0.926 (95% CI: 0.850-1.000, P < 0.001). Conclusions: Patients with higher baseline NLR were found to be at an increased risk of periprocedural PH and all-cause mortality within 3 months after TAVR.

Molecular dynamics-based study of the modification mechanism of asphalt by graphene oxide.

CONTEXT: Graphene oxide(GO) has been widely used in asphalt modification due to its excellent properties. To reveal the interaction effect between GO and asphalt materials, the microscopic behavior and molecular structure changes of asphalt and GO/asphalt were investigated by molecular dynamics (MD) simulations. Mean square displacement (MSD) results showed that GO significantly inhibited the diffusion of molecules of asphalt components. Radial distribution function (RDF) results that GO destroys the original sol-type structure of asphalt. Simultaneously, GO adsorbed resins at low-temperature, adsorbed asphaltenes at high-temperature, and dispersed as a dispersed phase in the light components. The concentration of the dispersed phase in the asphalt colloidal structure was increased and the mutual attraction was enhanced. This improves the deformation resistance at high temperature, but weakens the ductility at low temperatures. METHODS: To investigate the mechanism of action of GO-modified asphalt, the asphalt model and the GO/asphalt composite system model were constructed using the Amorphous Cell module in Materials Studio 2020 software. Subsequently, molecular dynamics simulations of the GO/asphalt composite system were performed using the Forcite module, while the interactions between atoms and molecules were described using the COMPASS II force field.

Strength deterioration prediction of pervious concrete in sulfate and dry-wet cycle environments utilizing ultrasonic velocity.

Strength is a crucial performance indicator for evaluating the durability of pervious concrete (PC). However, there are few models for estimating the remaining strength of in-service PC in sulfate and dry-wet cycle circumstances. Even though there are already direct detection methods for strength, nondestructive testing methods are still worth additional research. This paper aims to give a calculation model for the residual strength of PC under corrosion conditions based on ultrasonic methods, which is economical and convenient for engineering applications. The apparent morphological, compressive strength, and ultrasonic velocity of PC against sulfate and dry-wet cycle attack were examined. The results highlight that the primary cause of the macroscopic mechanical deterioration is the worsening in interface strength. Furthermore, the compressive strength and ultrasonic wave velocity of PC followed the same trends during sulfate and dry-wet cycles, increasing first and subsequently decreasing. Additionally, using the curve-fitting approach, an empirical model of strength deterioration based on ultrasonic velocity was developed and validated utilizing experimental data, demonstrating that the proposed model could more accurately define the strength progression. The results can provide an effective calculation method for monitoring the residual strength of PC pavement engineering in a corrosive environment.

Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma.

Objectives: Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods: Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results: Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708-0.820), 0.775 (95% CI: 0.728-0.823), and 0.756 (95% CI: 0.715-0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions: This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.

A prediction model for massive hemorrhage in trauma: a retrospective observational study.

BACKGROUND: Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma. METHODS: Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use. RESULTS: A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01-1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98-0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87-0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95-0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48-3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21-2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875-0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679-0.757). Model 1 was deployed as a public web tool ( http://82.156.217.249:8080/ ). CONCLUSIONS: Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.

[Research on grading prediction model of traumatic hemorrhage volume based on deep learning].

OBJECTIVE: To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume. METHODS: A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system. RESULTS: Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95%CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95%CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95%CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN∗ model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95%CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO2), Total CO2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN∗ model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. CONCLUSIONS: Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Application of Atomic Force Microscopy as Advanced Asphalt Testing Technology: A Comprehensive Review.

In the past three decades, researchers have engaged in the relationship between the composition, macro performance, and microstructure of asphalt. There are many research results in the use of atomic force microscopy (AFM) to study the microstructure and related mechanisms of asphalt. Based on previous studies, the performance of asphalt from its microstructure has been observed and analyzed, and different evaluation indices and modification methods have been proposed, providing guidance toward improving the performance of asphalt materials and benefiting potential applications. This review focuses on the typical application and analysis of AFM in the study of the aging regeneration and modification properties of asphalt. Additionally, this review introduces the history of the rheological and chemical testing of asphalt materials and the history of using AFM to investigate asphalt. Furthermore, this review introduces the basic principles of various modes of application of AFM in the microstructure of asphalt, providing a research direction for the further popularization and application of AFM in asphalt or other materials in the future. This review aims to provide a reference and direction for researchers to further popularize the application of AFM in asphalt and standardize the testing methods of AFM. This paper is also helpful in further exploring the relationship between the microstructure and macro performance of asphalt.

Efficacy of angiotensin receptor neprilysin inhibitor in Asian patients with refractory hypertension.

Sacubitril/valsartan, simultaneously inhibits neprilysin and angiotensin II receptor, showed an effect in reducing blood pressure (BP). The authors aimed to study whether it can be used as an antihypertensive agent in patients with refractory hypertension who have already been treated. A total of 66 Chinese patients with refractory hypertension were enrolled. Patients received sacubitril/valsartan  200 instead of angiotensin II receptor blocker or angiotensin converting enzyme inhibitor while other agents continued. If BP was uncontrolled after 4 weeks, sacubitril/valsartan was increased to 400 mg. The BP reduction was evaluated by office BP and ambulatory BP monitoring after 8-week treatment. The baseline office BP and mean arterial pressure (MAP) were 150.0/95.0 mmHg and 113.3 mmHg. BP and MAP reduced to 130.6/83.2 mmHg and 99.0 mmHg at week 8. Office BP and MAP reductions were 19.4/11.8 mmHg and 14.3 mmHg at endpoint (all p < .001). The 24-h, daytime and nighttime ambulatory BP were 146.2/89.1, 148.1/90.3, and 137.5/83.7 mmHg, respectively at baseline, and BP reduced to 129.6/79.8, 130.6/81.1, and 121.7/75.8 mmHg, respectively at week 8. The 24-h, daytime and nighttime ambulatory BP reductions were 16.6/9.3, 17.5/9.2, and 15.8/7.9 mmHg, respectively at endpoint (all p < .001). Sacubitril/valsartan significantly reduced office and ambulatory BP in refractory hypertension patients. Our study provided new evidence for sacubitril/valsartan in refractory hypertension.

Early warning model for death of sepsis via length insensitive temporal convolutional network.

Sepsis is a life-threatening systemic syndrome characterized by various biological, biochemical, and physiological abnormalities. Due to its high mortality, identifying sepsis patients with high risk of in-hospital death early and accurately will help doctors make optimal clinical decisions and reduce the mortality of sepsis patients. In this paper, we propose a length insensitive TCN-based model to predict sepsis patient's death risk in the future k hours, which is the first work for sepsis death risk early warning model only based on vital signs time series to our best knowledge. Furthermore, we design residual connections between temporal residual blocks to improve the prediction performance and stability especially on short input sequences. We validate and evaluate our model on two freely-available datasets, i.e., MIMIC-IV and eICU, from which 16,520 and 29,620 patients are selected respectively. The experiment results show that our model outperforms LSTM and other machine learning methods, as it has the highest sensitivity and Youden index in almost all cases. Meanwhile, the Youden index of the TCN-based model only slightly decreases by 0.0233 and 0.0307 when the time range of the input sequence changes from 24 to 4 h for k equal to 6 and 12, respectively.

Fatigue Resistance and Cracking Mechanism of Semi-Flexible Pavement Mixture.

Semi-flexible pavement (SFP) is widely used in recent years because of its good rutting resistance, but it is easy to crack under traffic loads. A large number of studies are aimed at improving its crack resistance. However, the understanding of its fatigue resistance and fatigue-cracking mechanism is limited. Therefore, the semi-circular bending (SCB) fatigue test is used to evaluate the fatigue resistance of the SFP mixture. SCB fatigue tests under different temperature values and stress ratio were used to characterize the fatigue life of the SFP mixture, and its laboratory fatigue prediction model was established. The distribution of various phases of the SFP mixture in the fracture surface was analyzed by digital image processing technology, and its fatigue cracking mechanism was analyzed. The results show that the SFP mixture has better fatigue resistance under low temperature and low stress ratio, while its fatigue resistance under other environmental and load conditions is worse than that of asphalt mixture. The main reason for the poor fatigue resistance of the SFP mixture is the poor deformation capacity and low strength of grouting materials. Furthermore, the performance difference between grouting material and the asphalt binder is large, which leads to the difference of fatigue cracking mechanism of the SFP mixture under different conditions. Under the fatigue load, the weak position of the SFP mixture at a low temperature is asphalt binder and its interface with other materials, while at medium and high temperatures, the weak position of the SFP mixture is inside the grouting material. The research provides a basis for the calculation of the service life of the SFP structure, provides a reference for the improvement direction of the SFP mixture composition and internal structure.