How much nutrient reaches a stream: Insights from a hybrid model and implications for watershed nitrogen export and removal.

Excess nitrogen (N) discharged into streams and rivers degrades freshwater quality and threatens ecosystems worldwide. Land use patterns may influence riverine N export, yet the effect of location on N export and removal is not fully understood. We proposed a hybrid model to analyze N export and removal within the watersheds. The proposed model is satisfied for the riverine N modelling. The KGE and R2 are 0.75 and 0.72 in the calibration period which are 0.76 and 0.61 in the validation period. Human-impacted land use may modify the N yield in the watershed, and the net N export from built-up to the in-stream system was highest in the urbanized sub-watersheds (0.81), followed by the agricultural sub-watersheds (0.88), and forested sub-watersheds (0.96). Agricultural activities make a large contribution to the N exports in the watersheds, and the mean N input from the agricultural land use to in-stream were 2069-4353 kg km-2 yr-1. Besides, the excess inputs of N by overapplication of fertilizer and manure during the agricultural activities may increase legacy N in soil and groundwater. Biological processes for the riverine N removal may be controlled by the available substrate in the freshwater system, and temperature sensitivity of denitrification is highest in the flood seasons, especially for the human-impacted sub-watersheds. The riverine biological processes may be limited by other competitions. Our model results provide evidence that quantity and location of specific land use may control biogeochemistry within watersheds. We demonstrate the need to understand nutrient export and removal within watersheds by improving the representation of spatial patterns in existing watershed models, and we consider this study to be a new effort for the spatially explicit modeling to support land-use based N management in watersheds.

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization.

BACKGROUND: Acute myocardial infarction (AMI) with consequent heart failure is one of the leading causes of death in humans. The aim of this study was to develop a prediction model to identify heart failure risk in patients with AMI during hospitalization. METHODS: The data on hospitalized patients with AMI were retrospectively collected and divided randomly into modeling and validation groups at a ratio of 7:3. In the modeling group, the independent risk factors for heart failure during hospitalization were obtained to establish a logistic prediction model, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical value. Machine learning models with stacking method were also constructed and compared to logistic model. RESULTS: A total of 1875 patients with AMI were enrolled in this study, with a heart failure rate of 5.1% during hospitalization. The independent risk factors for heart failure were age, heart rate, systolic blood pressure, troponin T, left ventricular ejection fraction and pro-brain natriuretic peptide levels. The area under the curve (AUC) of the model in modeling group and validation group were 0.829 and 0.846, respectively. The calibration curve showed high prediction accuracy and the DCA curve showed good clinical value. The AUC value of the ensemble model by the stacking method in the validation group were 0.821, comparable to logistic prediction model. CONCLUSIONS: This model, combining laboratory and clinical factors, has good efficacy in predicting heart failure during hospitalization in AMI patients.

Elevated ApoB/apoA-1 is Associated with in-Hospital Mortality in Elderly Patients with Acute Myocardial Infarction.

Background: Apolipoprotein B/apolipoprotein A-1 (apoB/apoA-1) has been shown to be strongly associated with the risk of future cardiovascular disease, but the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with acute myocardial infarction(AMI) is inconclusive. Aim: To investigate the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with AMI. Methods: From December 2015 to December 2021, a total of 1495 elderly AMI patients (aged ≥ 60 years) with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Outcome was defined as all-cause mortality during hospitalization. Multivariate logistic regression and restricted spline cubic (RCS) models were used to evaluate the association between apoB/apoA-1 and in-hospital mortality risk, respectively. Receiver operating characteristic(ROC) curves were used to evaluate the predictive value of apoB/apoA-1 for in-hospital mortality events. Discordance analysis was performed when apoB/apoA-1 and LDL-C/HDL-C were not in concordance. Results: (1) A total of 128 patients (8.6%) died during hospitalization. Patients in the death group had higher apoB/apoA-1 than those in the non-death group, but lower apoA-1 levels than those in the non-death group, and the difference was statistically significant (P < 0.05); (2) Multivariate logistic regression analysis showed that apoB/apoA-1 was associated with the risk of in-hospital death in elderly AMI patients [Model 3 OR = 3.524 (1.622-7.659), P = 0.001]; (3) ROC curve analysis showed that apoB/apoA-1 (AUC = 0.572, P = 0.011) had some predictive value for the risk of in-hospital death in elderly AMI patients; (4) RCS models showed a linear dose-response relationship between apoB/apoA-1 and in-hospital death after adjusting for confounders (P for non-linearity = 0.762). Conclusion: ApoB/apoA-1 is associated with the risk of in-hospital death in elderly patients with AMI, and is superior to other blood lipid parameters and blood lipid ratio.

The combined signatures of G protein-coupled receptor family and immune landscape provide a prognostic and therapeutic biomarker in endometrial carcinoma.

G protein-coupled receptors (GPRs) are one of the largest surface receptor superfamilies, and many of them play essential roles in biological processes, including immune responses. In this study, we aim to construct a GPR- and tumor immune environment (TME-i)-associated risk signature to predict the prognosis of patients with endometrial carcinoma (EC). The GPR score was generated by applying univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression in succession. This involved identifying the differentially expressed genes (DEGs) in the Cancer Genome Atlas-Uterine Corpus Endometrioid Carcinoma (TCGA-UCEC) cohort. Simultaneously, the CIBERSORT algorithm was applied to identify the protective immune cells for TME score construction. Subsequently, we combined the GPR and TME scores to establish a GPR-TME classifier for conducting clinical prognosis assessments. Various functional annotation algorithms were used to conduct biological process analysis distinguished by GPR-TME subgroups. Furthermore, weighted correlation network analysis (WGCNA) was applied to depict the tumor somatic mutations landscapes. Finally, we compared the immune-related molecules between GPR-TME subgroups and resorted to the Tumor Immune Dysfunction and Exclusion (TIDE) for immunotherapy response prediction. The mRNA and protein expression of GPR-related gene P2RY14 were, respectively, validated by RT-PCR in clinical samples and HPA database. To conclude, our GPR-TME classifier may aid in predicting the EC patients' prognosis and immunotherapy responses.

A combined index constructed based on NLR and PLR is associated with in-hospital mortality risk in patients with acute myocardial infarction.

OBJECTIVE: To explore the validity of the neutrophil-to-lymphocyte ratio (NLR) combined with the platelet-to-lymphocyte ratio (PLR) in predicting the short-term prognosis of acute myocardial infarction (AMI). METHODS: We collected the data from a total of 3,246 clinical AMI patients hospitalized in the Second Affiliated Hospital of Dalian Medical University from December 2015 to December 2021. All patients underwent routine blood examination within 2 hours of admission. Outcome was defined as all-cause mortality during hospitalization. A total of 94 pairs of patients were generated by propensity score matching (PSM), and a combined NLR-based and PLR-based indicators was constructed according to receiver operating characteristic (ROC) curves and multivariate logistic regression analysis. RESULTS: We finally generated 94 pairs of patients by PSM, and analyzed NLR and PLR in those patients using ROC curves, and converted NLR (optimal cut-off = 5.094) and PLR (optimal cut-off = 165.413) into binary variables according to optimal cut-offs, defined as NLR grouping (5.094 vs. > 5.094, ≤ 5.094 = 0, > 5.094 = 1) and PLR grouping (165.413 vs. > 165.413, ≤ 165.413 = 0, > 165.413 = 1). We constructed a combined indicator (NLR grouping + PLR grouping) based on the results of multivariate logistic regression. Combined indicator has four conditions [Y1 = 0.887 (NLR grouping = 0; PLR grouping = 0); Y2 = 0.949 (NLR grouping = 0; PLR grouping = 1); Y3 = 0.972 (NLR grouping = 1; PLR grouping = 0); and Y4 = 0.988 (NLR grouping = 1; PLR grouping = 1)]. Univariate logistic regression showed that the risk of in-hospital death was significantly increased when the combined indicator of patients was in Y3 (OR = 4.968, 95% CI 2.215-11.141, P < 0.0001) and Y4 (OR = 10.473, 95% CI 4.610-23.793, P < 0.0001). Combined indicator constructed by NLR grouping and PLR grouping can better predict the risk of in-hospital mortality in AMI patients and help clinical cardiologists to more finely care for and treat these high-risk groups to improve their short-term prognostic outcomes.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio are Important Indicators for Predicting in-Hospital Death in Elderly AMI Patients.

Aim: To investigate the role of neutrophil-to-lymphocyte ratio(NLR) and platelet-to-lymphocyte(PLR) in predicting the risk of in-hospital mortality in elderly acute myocardial infarction(AMI) patients. Methods: This study was a single-center, retrospective and observational study. From December 2015 to December 2021, a total of 1550 elderly patients (age ≥ 60 years) with AMI with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Routine blood tests were performed on admission, and NLR and PLR were calculated based on neutrophil, platelet, and lymphocyte counts. Outcome was defined as all-cause mortality during hospitalization. Cox regression and restricted spline cubic(RCS) models were used to evaluate the association of NLR and in-hospital mortality risk and the association of PLR with in-hospital mortality risk, respectively. Results: (1) A total of 132 (8.5%) patients died during hospitalization. From the results of blood routine, the white blood cell, neutrophil, NLR and PLR in the death group were higher than those in the non-death group, while the lymphocyte was lower than that in the non-death group, and the difference was statistically significant (P < 0.05). (2) The results of receiver operating characteristic(ROC) curves analysis showed that the predictive ability of NLR (AUC = 0.790) for in-hospital death was better than that of PLR (AUC = 0.637). (3) Multivariate Cox proportional regression hazard models showed that high NLR was associated with the risk of in-hospital mortality in elderly AMI patients (HR = 3.091, 95% CI 2.097-4.557, P < 0.001), while high PLR was not. (4) RCS models showed a nonlinear dose-response relationship between NLR and in-hospital death (P for nonlinear = 0.0007). Conclusion: High NLR (> 6.69) is associated with the risk of in-hospital mortality in elderly patients with AMI and can be an independent predictor of poor short-term prognosis in elderly patients with AMI.

Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies.

Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related to models used and data input. In this study, random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) models are developed with three sampling frequency datasets (i.e., 4-hourly, daily, and weekly) and five conventional indicators (i.e., water temperature (WT), hydrogen ion concentration (pH), electrical conductivity (EC), dissolved oxygen (DO), and turbidity (TUR)) as surrogates to individually estimate riverine total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH4+-N) in a small-scale coastal watershed. The results show that the RF model outperforms the SVM and BPNN machine learning models in terms of estimative performance, which explains much of the variation in TP (79 ± 1.3%), TN (84 ± 0.9%), and NH4+-N (75 ± 1.3%), when using the 4-hourly sampling frequency dataset. The higher sampling frequency would help the RF obtain a significantly better performance for the three nutrient estimation measures (4-hourly > daily > weekly) for R2 and NSE values. WT, EC, and TUR were the three key input indicators for nutrient estimations in RF. Our study highlights the importance of high-frequency data as input to machine learning model development. The RF model is shown to be viable for riverine nutrient estimation in small-scale watersheds of important local water security.

A Meta-Analysis of Prognostic Factors in Patients with Posterior Circulation Stroke after Mechanical Thrombectomy.

BACKGROUND: Posterior circulation stroke is characterized by poor prognosis because its optimal thrombolysis "time window" is always missed. After mechanical thrombectomy (MT), the recanalization rate of posterior circulation obstruction is significantly increased, but prognosis remains poor. To best manage patients, prognostic factors are needed to inform MT triaging after posterior circulation stroke. METHODS: A systematic literature search was done for the period through April 2020. Studies included those with posterior circulation stroke cases that underwent MT. The primary outcome measure in this study was the modified Rankin Scale on day 90. RESULTS: No outcome differences were found in gender, atrial fibrillation, smoking, and coronary artery disease (OR = 1.07, 95% CI: 0.90-1.28; OR = 1.02, 95% CI: 0.82-1.26; OR = 1.26, 95% CI: 0.94-1.68; and OR = 0.84, 95% CI: 0.58-1.22, respectively). Hypertension, diabetes mellitus, and previous stroke correlated with poorer prognosis (OR = 0.61, 95% CI: 0.48-0.77; OR = 0.60, 95% CI: 0.50-0.73; and OR = 0.74, 95% CI: 0.55-0.99, respectively). However, hyperlipidemia correlated with better prognosis (OR = 1.28, 95% CI: 1.04-1.58). CONCLUSION: Our analysis indicates that hypertension, diabetes mellitus, or previous stroke correlate with poorer outcomes. Intriguingly, hyperlipidemia correlates with better prognosis. These factors may help inform triage decisions when considering MT for posterior circulation stroke patients. However, large, multicenter, randomized controlled trials are needed to validate these observations.