Explainable machine learning for predicting 30-day readmission in acute heart failure patients.

We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703-0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.

Association of weight-adjusted waist index with all-cause mortality among non-Asian individuals: a national population-based cohort study.

INTRODUCTION: The Weight-Adjusted Waist Index (WWI) is a new indicator of obesity that is associated with all-cause mortality in Asian populations. Our study aimed to investigate the linear and non-linear associations between WWI and all-cause mortality in non-Asian populations in the United States, and whether WWI was superior to traditional obesity indicators as a predictor of all-cause mortality. METHODS: We conducted a cohort study using data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES), involving 18,592 participants. We utilized Cox proportional hazard models to assess the association between WWI, BMI, WC, and the risk of all-cause mortality, and performed subgroup analyses and interaction tests. We also employed a receiver operating characteristics (ROC) curve study to evaluate the effectiveness of WWI, BMI, and WC in predicting all-cause mortality. RESULTS: After adjusting for confounders, WWI, BMI, and WC were positively associated with all-cause mortality. The performance of WWI, BMI, and WC in predicting all-cause mortality yielded AUCs of 0.697, 0.524, and 0.562, respectively. The data also revealed a U-shaped relationship between WWI and all-cause mortality. Race and cancer modified the relationship between WWI and all-cause mortality, with the relationship being negatively correlated in African Americans and cancer patients. CONCLUSIONS: In non-Asian populations in the United States, there is a U-shaped relationship between WWI and all-cause mortality, and WWI outperforms BMI and WC as a predictor of all-cause mortality. These findings may contribute to a better understanding and prediction of the relationship between obesity and mortality, and provide support for effective obesity management strategies.

Rheumatoid arthritis increases the risk of heart failure: results from the cross-sectional study in the US population and mendelian randomization analysis in the European population.

Objective: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease. Among its various complications, heart failure (HF) has been recognized as the second leading cause of cardiovascular death in RA patients. The objective of this study was to investigate the relationship between RA and HF using epidemiological and genetic approaches. Methods: The study included 37,736 participants from the 1999-2020 National Health and Nutrition Examination Survey. Associations between RA and HF in the US population were assessed with weighted multivariate logistic regression analysis. A two-sample Mendelian randomization (MR) analysis was employed to establish the causal relationship between the two variables. The primary analysis method utilized was inverse variance weighting (IVW). Additionally, horizontal pleiotropy and heterogeneity were assessed to account for potential confounding factors. In cases where multiple independent datasets were accessible during MR analysis, we combined the findings through a meta-analytical approach. Results: In observational studies, the prevalence of HF in combination with RA reached 7.11% (95%CI 5.83 to 8.39). RA was positively associated with an increased prevalence of HF in the US population [odds ratio (OR):1.93, 95% confidence interval (CI):1.47-2.54, P < 0.0001]. In a MR analysis utilizing a meta-analytical approach to amalgamate the results of the IVW method, we identified a significant causal link between genetically predicted RA and a heightened risk of HF (OR = 1.083, 95% CI: 1.028-1.141; P = 0.003). However, this association was not deemed significant for seronegative RA (SRA) (OR = 1.028, 95% CI: 0.992-1.065; P = 0.126). These findings were consistent across sensitivity analyses and did not indicate any horizontal pleiotropy. Conclusion: RA correlates with an elevated prevalence of HF within the US population. Furthermore, genetic evidence derived from European populations underscores a causal link between RA and the risk of HF. However this association was not significant in SRA.

Network pharmacology and molecular docking technology for exploring the effect and mechanism of high-dose vitamin c on ferroptosis of tumor cells: A review.

To investigate the mechanism by which high-dose vitamin C (HVC) promotes ferroptosis in tumor cells via network pharmacology, vitamin C-related and ferroptosis-related targets were obtained from the PharmMapper and GeneCards databases, respectively, and their common targets were compared using the Venn diagram. Common targets were imported into the STRING database for protein-protein interaction analysis, and core targets were defined. Core targets were enriched for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways using the R language packages. A map of the core target-based interaction network and a map of the mechanism by which HVC regulates ferroptosis were constructed. A total of 238 vitamin C-related and 721 ferroptosis-related targets were identified, of which 21 targets were common to both. Furthermore, ALDOA, AHCY, LDHB, HSPA8, LGALS3, and GSTP1 were identified as core targets. GO enrichment analysis suggested that the main biological processes included the extrinsic apoptotic signaling pathway and pyruvate metabolic process. KEGG enrichment analysis suggested that HVC regulates ferroptosis mainly through the amino acid and carbohydrate metabolic pathways. The targets were validated by molecular docking. In conclusion, HVC may promote ferroptosis in tumor cells by regulating metabolic pathways, and there is a synergistic effect between HVC and type I ferroptosis inducers. Glycolysis-dependent tumors may be beneficial for HVC therapy. Our study provides a reference for further clinical studies on HVC antitumor therapy.

Maintaining ideal cardiovascular health is associated with higher serum anti-aging protein klotho in the middle-aged and older populations.

OBJECTIVES: Maintaining ideal cardiovascular health (CVH) is believed to have potential anti-aging benefits. The American Heart Association (AHA) recently updated the "Life's Essential 8 (LE8)" metrics to measure ideal CVH, but its connection with the anti-aging protein klotho is still unclear. We aimed to explore the relationship between ideal cardiovascular health and serum anti-aging protein klotho in a nationally representative US middle-aged and older population. DESIGN: A cross-sectional study. SETTING: The National Health and Nutrition Examination Survey (2007-2016). PARTICIPANTS: A total of 9457 middle-aged and older participants. MEASUREMENTS: Ideal CVH scores and their components were defined according to the guidelines set by the AHA. Serum klotho detected by enzyme-linked immunosorbent assay. Weighted multivariable linear regression and restricted cubic spline were employed to examine the association between CVH score and klotho. Subgroup analyses were conducted, stratified by age (40-59 and 60-79), sex (Male and Female), race (Mexican American, non-Hispanic White, non-Hispanic Black, and Others) and chronic kidney disease (Yes and No) in fully adjusted models. RESULTS: A total of 9457 middle-aged and older participants were included in this study, with a mean age of 55.27 ± 0.17 years. The mean serum klotho level in the population was 849.33 ± 5.39 pg/mL. After controlling for potential confounders, the LE8 score showed a positive correlation with serum klotho levels (ß: 1.32; 95% CI 0.73, 1.91), and a non-linear dose-response relationship was observed. Furthermore, we also discovered a positive relationship between health behaviors score and health factors score and serum klotho levels (ß: 0.48; 95% CI 0.07, 0.88 and ß: 1.05; 95% CI 0.54, 1.56, respectively), particularly a stronger correlation between health factors and serum klotho. In the subgroup analysis, we observed a significant interaction between LE8 score and sex and race. (P for interaction <0.05). CONCLUSIONS: LE8 and its subscale scores were positively associated with serum klotho levels in the middle-aged and older populations. Promoting the maintenance of ideal CVH can contribute to delaying the aging process.

Association between systemic inflammatory indicators with the survival of chronic kidney disease: a prospective study based on NHANES.

Background: systemic inflammation disorders were observed in chronic kidney disease (CKD). Whether the systemic inflammatory indicators could be optimal predictors for the survival of CKD remains less studied. Methods: In this study, participants were selected from the datasets of the National Health and Nutrition Examination Survey (NHANES) between 1999 to 2018 years. Four systemic inflammatory indicators were evaluated by the peripheral blood tests including systemic immune-inflammation index (SII, platelet*neutrophil/lymphocyte), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR). Kaplan-Meier curves, restricted cubic spline (RCS), and Cox regression analysis were used to evaluate the association between the inflammatory index with the all-cause mortality of CKD. Receiver operating characteristic (ROC) and concordance index (C-index) were used to determine the predictive accuracy of varied systemic inflammatory indicators. Sensitive analyses were conducted to validate the robustness of the main findings. Results: A total of 6,880 participants were included in this study. The mean age was 67.03 years old. Among the study population, the mean levels of systemic inflammatory indicators were 588.35 in SII, 2.45 in NLR, 133.85 in PLR, and 3.76 in LMR, respectively. The systemic inflammatory indicators of SII, NLR, and PLR were all significantly positively associated with the all-cause mortality of CKD patients, whereas the high value of LMR played a protectable role in CKD patients. NLR and LMR were the leading predictors in the survival of CKD patients [Hazard ratio (HR) =1.21, 95% confidence interval (CI): 1.07-1.36, p = 0.003 (3rd quartile), HR = 1.52, 95%CI: 1.35-1.72, p<0.001 (4th quartile) in NLR, and HR = 0.83, 95%CI: 0.75-0.92, p<0.001 (2nd quartile), HR = 0.73, 95%CI: 0.65-0.82, p<0.001 (3rd quartile), and = 0.74, 95%CI: 0.65-0.83, p<0.001 (4th quartile) in LMR], with a C-index of 0.612 and 0.624, respectively. The RCS curves showed non-linearity between systemic inflammatory indicators and all-cause mortality risk of the CKD population. Conclusion: Our study highlights that systemic inflammatory indicators are important for predicting the survival of the U.S. population with CKD. The systemic inflammatory indicators would add additional clinical value to the health care of the CKD population.

Functional Microorganisms Drive the Formation of Black-Odorous Waters.

Black-odorous waters are water bodies that are noticeably abnormal in color or emit unpleasant odors. River water pollution and ecological degradation have gradually emerged with urbanization and rapid economic development, and BOW has become frequent. The black-odorous evolution of urban water bodies is a serious environmental problem in many areas, posing a serious threat to both human health and the ecological environment. Functional microorganisms are closely related to the formation of black-odorous phenomena in water bodies, but the understanding of the mechanisms by which functional microorganisms influence the formation of BOW is very limited. In this study, water samples from the Guangdang River in Yantai, Shandong Province, China, were collected as the bacterial solution in the study, and how environmental factors and functional microorganisms affect the formation of black smelly water was investigated by artificially simulating black smelly water. The results indicated that different environmental factors have different effects on the formation of BOW. Anaerobic conditions accelerated the formation of BOW, and species diversity and species abundance were lowest under this condition. Hydraulic disturbance and nitrate effectively mitigated the BOW phenomenon, in which species diversity and species abundance were higher; controlling either of these variables was effective in mitigating the BOW phenomenon. Desulfobacterota played a key role in the formation of BOW, and reducing the proportion of Desulfobacterota in the microbial community could effectively improve the water quality. Possible directions of electron transfer in the process were hypothesized. This study contributes to identifying the biological driving factors for black-odorous evolution, presents insight for preventing BOW formation, and provides a scientific basis for subsequent BOW management.

New insights into the sustainable use of co-pyrolyzed dredged sediment for the in situ remediation of Cd polluted sediments in coastal rivers.

The remediation of Cd-polluted sediment in coastal rivers is essential because of its potential hazards to river and marine ecosystems. Herein, a co-pyrolysis product of contaminated dredged sediment (S@BC) was innovatively applied to cap and immobilize Cd-contaminated sediment in coastal rivers in situ, and their remediation efficiencies, mechanisms, and microbial responses were explored based on a 360 d incubation experiment. The results showed that although S@BC immobilization and capping restrained sediment Cd release to the overlying water, S@BC capping presented a high inhibitory efficiency (66.0% vs. 95.3% at 360 d). Fraction analysis indicated that labile Cd was partially transformed to stable fraction after remediation, with decreases of 0.5%- 32.7% in the acid-soluble fraction and increases of 5.0%- 182.8% in the residual fraction. S@BC immobilization and capping had minor influences on the sediment bacterial community structure compared to the control. S@BC could directly adsorb sediment mobile Cd (precipitation and complexation) to inhibit Cd release and change sediment properties (e.g., pH and cation exchange capacity) to indirectly reduce Cd release. Particularly, S@BC capping also promoted Cd stabilization by enhancing the sediment sulfate reduction process. Comparatively, S@BC capping was a priority approach for Cd-polluted sediment remediation. This study provides new insights into the remediation of Cd-contaminated sediments in coastal rivers.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .

Assessing Global, Regional, and National Time Trends and Associated Risk Factors of the Mortality in Ischemic Heart Disease Through Global Burden of Disease 2019 Study: Population-Based Study.

BACKGROUND: Ischemic heart disease (IHD) is the leading cause of death among noncommunicable diseases worldwide, but data on current epidemiological patterns and associated risk factors are lacking. OBJECTIVE: This study assessed the global, regional, and national trends in IHD mortality and attributable risks since 1990. METHODS: Mortality data were obtained from the Global Burden of Disease 2019 Study. We used an age-period-cohort model to calculate longitudinal age curves (expected longitudinal age-specific rate), net drift (overall annual percentage change), and local drift (annual percentage change in each age group) from 15 to >95 years of age and estimate cohort and period effects between 1990 and 2019. Deaths from IHD attributable to each risk factor were estimated on the basis of risk exposure, relative risks, and theoretical minimum risk exposure level. RESULTS: IHD is the leading cause of death in noncommunicable disease-related mortality (118.1/598.8, 19.7%). However, the age-standardized mortality rate for IHD decreased by 30.8% (95% CI -34.83% to -27.17%) over the past 30 years, and its net drift ranged from -2.89% (95% CI -3.07% to -2.71%) in high sociodemographic index (SDI) region to -0.24% (95% CI -0.32% to -0.16%) in low-middle-SDI region. The greatest decrease in IHD mortality occurred in the Republic of Korea (high SDI) with net drift -6.06% (95% CI -6.23% to -5.88%), followed by 5 high-SDI nations (Denmark, Norway, Estonia, the Netherlands, and Ireland) and 2 high-middle-SDI nations (Israel and Bahrain) with net drift less than -5.00%. Globally, age groups of >60 years continued to have the largest proportion of IHD-related mortality, with slightly higher mortality in male than female group. For period and birth cohort effects, the trend of rate ratios for IHD mortality declined across successive period groups from 2000 to 2004 and birth cohort groups from 1985 to 2000, with noticeable improvements in high-SDI regions. In low-SDI regions, IHD mortality significantly declined in female group but fluctuated in male group across successive periods; sex differences were greater in those born after 1945 in middle- and low-middle-SDI regions and after 1970 in low-SDI regions. Metabolic risks were the leading cause of mortality from IHD worldwide in 2019. Moreover, smoking, particulate matter pollution, and dietary risks were also important risk factors, increasingly occurring at a younger age. Diets low in whole grains and legumes were prominent dietary risks in both male and female groups, and smoking and high-sodium diet mainly affect male group. CONCLUSIONS: IHD, a major concern, needs focused health care attention, especially for older male individuals and those in low-SDI regions. Metabolic risks should be prioritized for prevention, and behavioral and environmental risks should attract more attention to decrease IHD mortality.

Ratiometric electrochemical aptasensor based on split aptamer and Au-rGO for detection of aflatoxin M1.

A novel ratiometric electrochemical aptasensor based on split aptamer and Au-reduced graphene oxide (Au-rGO) nanomaterials was proposed to detect aflatoxin M1 (AFM1). In this work, Au-rGO nanomaterials were coated on the electrode through the electrodeposition method to increase the aptamer enrichment. We split the aptamer of AFM1 into 2 sequences (S1 and S2), where S1 was immobilized on the electrode due to the Au-S bond, and S2 was tagged with methylene blue (MB) and acted as a response signal. A complementary strand to S1 (CS1) labeled with ferrocene (Fc) was introduced as another reporter. In the presence of AFM1, CS1 was released from the electrode surface due to the formation of the S1-AFM1-S2 complex, leading to a decrease in Fc and an increase in the MB signal. The developed ratiometric aptasensor exhibited a linear range of 0.03 µg L-1 to 2.00 µg L-1, with a detection limit of 0.015 µg L-1 for AFM1 detection. The ratiometric aptasensor also showed a linear relationship from 0.2 µg L-1 to 1.00 µg L-1, with a detection limit of 0.05 µg L-1 in natural milk after sample pretreatment, indicating the successful application of the developed ratiometric aptasensor. Our proposed strategy provides a new way to construct aptasensors with high sensitivity and selectivity.

Suspended particulate matter affects the distribution and migration of heavy metals in the Yellow River.

Suspended particulate matter (SPM) is an important heavy metal transporter in water. As a well-known high-SPM river, its impact on the distribution and migration of heavy metals in the Yellow River (YR) deserves special attention. In this study, the spatial distributions of heavy metals in surface water and SPM of the YR were investigated. The results indicate that the concentrations (dissolved and particulate phases) and bioavailability (particulate phase) of most heavy metals were higher during the rainy season than during the dry season. A considerable proportion of heavy metals (>70 %) was transported by SPM and fine particles (clay) controlled the pollution status of heavy metals in the YR. This could lead to higher heavy metal concentrations in the SPM midstream and downstream during the rainy season and higher heavy metal concentrations in upstream during the dry season. Heavy metal adsorption experiments showed that specific combination methods (such as binding with carbonate) between Cd and SPM may cause SPM to act as a source of Cd midstream and downstream. This study provides a new perspective on the effects of SPM on heavy metal distribution and migration in the YR.

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA.

Background: Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM. Methods: The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells. Results: A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples. Conclusion: The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.

Analysis of the relationship between sleep-related disorder and systemic immune-inflammation index in the US population.

BACKGROUND: The association between sleep-related disorders and inflammation has been demonstrated in previous studies. The systemic immune-inflammation index (SII) is a novel inflammatory index based on leukocytes, but its relationship with sleep-related disorder is unclear. We aimed to investigate the relationship between sleep-related disorder and SII in a nationally representative nonhospitalized sample. METHODS: Data were obtained from the 2005-2008 National Health and Nutrition Examination Survey (NHANES). Exposure variables included self-reported sleep-related disorders, such as sleep duration, sleep problems, high risk of OSA, and daytime sleepiness. SII and other traditional markers of inflammation were considered as outcome variables, including platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR). Multiple linear regression models were employed to examine the correlation between sleep-related disorders and inflammatory markers. Subgroup interactions were analyzed using likelihood ratio tests, and nonlinear relationships were explored by fitting restricted cubic splines. RESULTS: A total of 8,505 participants were enrolled in this study. Overall, sleep-related disorders were found to have a stronger association with SII compared to the PLR and NLR. The results of multiple linear regression analysis revealed that participants who experienced sleep problems (ß: 21.421; 95% CI 1.484, 41.358), had symptoms of OSA (ß: 23.088; 95% CI 0.441, 45.735), and reported daytime sleepiness (ß: 30.320; 95% CI 5.851, 54.789) exhibited a positive association with higher SII. For the analysis of other inflammatory markers, we only found that daytime sleepiness was associated with increased NLR levels (ß: 0.081; 95% CI 0.002, 0.159). CONCLUSION: Sleep problems, symptoms of OSA, and daytime sleepiness were found to have a positive association with the SII in US adults. However, further prospective studies are necessary to establish whether there is a causal relationship between these factors.

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States.

Background and aims: Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death. Methods and results: Data (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C. Conclusion: Decreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

Progressive mechanical and structural changes in anterior cerebral arteries with Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease and the main cause for dementia. The irreversible neurodegeneration leads to a gradual loss of brain function characterized predominantly by memory loss. Cerebrovascular changes are common neuropathologic findings in aged subjects with dementia. Cerebrovascular integrity is critical for proper metabolism and perfusion of the brain, as cerebrovascular remodeling may render the brain more susceptible to pulse pressure and may be associated with poorer cognitive performance and greater risk of cerebrovascular events. The objective of this study is to provide understanding of cerebrovascular remodeling with AD progression. Anterior cerebral arteries (ACAs) from a total of 19 brain donor participants from controls and pathologically diagnosed AD groups (early-Braak stages I-II; intermediate-Braak stages III-IV; and advanced-Braak stages V-VI) were included in this study. Mechanical testing, histology, advanced optical imaging, and mass spectrometry were performed to study the progressive structural and functional changes of ACAs with AD progression. Biaxial extension-inflation tests showed that ACAs became progressively less compliant, and the longitudinal stress in the intermediate and advanced AD groups was significantly higher than that from the control group. With pathological AD development, the inner and outer diameters of the ACAs remained almost unchanged; however, histology study revealed progressive smooth muscle cell atrophy and loss of elastic fibers which led to compromised structural integrity of the arterial wall. Multiphoton imaging demonstrated elastin degradation at the media-adventitia interface, which led to the formation of an empty band of 21.0 ± 15.4 µm and 32.8 ± 9.24 µm in width for the intermediate and advanced AD groups, respectively. Furthermore, quantitative birefringence microscopy showed disorganized adventitial collagen with AD development. Mass spectrometry analysis provided further evidence of altered collagen content and other extracellular matrix (ECM) molecule and smooth muscle cell changes that were consistent with the mechanical and structural alterations. Collectively, our study provides understanding of the mechanical and structural cerebrovascular deterioration in cerebral arteries with AD, which may be related to neurodegenration and pathology in the brain.

Nutrients in overlying water affect the environmental behavior of heavy metals in coastal sediments.

Excessive nutrients in aquatic ecosystems are the main driving factors for eutrophication and water quality deterioration. However, the influence of nutrients in overlying water on sediment heavy metals is not well understood. In this study, the effects of nitrate nitrogen (NO3-N) addition and phosphate addition in the overlying water on the environmental behaviors of chromium (Cr), copper (Cu), and cadmium (Cd) in coastal river sediments were investigated. Fresh estuary sediments and synthetic saltwater were used in microcosm studies conducted for 13 d. To determine the biological effect, unsterilized and sterilized treatments were considered. The results showed that the diffusion of Cr and Cu was inhibited in the unsterilized treatments with increased NO3-N. However, under the NO3-N sterilized treatments, Cr and Cu concentrations in the overlying water increased. This was mostly related to changes in the microbial regulation of dissolved organic carbon and pH in the unsterilized treatments. Further, in the unsterilized treatments, NO3-N addition considerably increased the concentrations of the acid-soluble (Cr, Cu, and Cd increased by 5%-8%, 29%-41%, and 31%-42%, respectively) and oxidizable (Cr, Cu, and Cd increased by 10%, 5%, and 14%, respectively) fractions. Additionally, compared with that in the unsterilized treatments, Cu and Cd concentrations in P-3 treatments decreased by 7% and 63%, respectively. By producing stable metal ions, microorganisms reduced the amount of unstable heavy metals in the sediment and heavy metal concentration in the overlying water, by considerably enhancing the binding ability of phosphate and heavy metal ions. This study provides a theoretical basis for investigating the coupling mechanisms between heavy metals and nutrients.

Association of magnesium sulfate use with mortality in critically ill patients with sepsis: a retrospective propensity score-matched cohort study.

BACKGROUND: Trials have demonstrated lower rates of acute kidney injury in critically ill patients receiving magnesium supplementation, but they have yielded conflicting results regarding mortality. METHODS: This is a retrospective cohort study based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with sepsis were included in the analysis. The exposure was magnesium sulfate use during ICU stay. The primary outcome was 28-day all-cause mortality. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders. RESULTS: The pre-matched and propensity score-matched cohorts included 10 999 and 6052 patients, respectively. In the PSM analysis, 28-day all-cause mortality rate was 20.2% (611/3026) in the magnesium sulfate use group and 25.0% (757/3026) in the no use group. Magnesium sulfate use was associated with lower 28-day all-cause mortality (hazard ratio [HR], 0.70; 95% CI, 0.61-0.79; P<0.001). Lower mortality was observed regardless of baseline serum magnesium status: for hypomagnesaemia, HR, 0.64; 95% confidence interval (CI), 0.45-0.93; P=0.020; for normomagnesaemia, HR, 0.70; 95% CI, 0.61-0.80; P<0.001. Magnesium sulfate use was also associated with lower ICU mortality (odds ratio [OR], 0.52; 95% CI, 0.42-0.64; P<0.001), lower in-hospital mortality (OR, 0.65; 95% CI, 0.55-0.77; P<0.001), and renal replacement therapy (OR, 0.67; 95% CI, 0.52-0.87; P=0.002). A sensitivity analysis using the entire cohort also demonstrated lower 28-day all-cause mortality (HR, 0.62; 95% CI, 0.56-0.69; P<0.001). CONCLUSIONS: Magnesium sulfate use was associated with lower mortality in critically ill patients with sepsis. Prospective studies are needed to verify this finding.

Strategies and Applications of Graphene and Its Derivatives-Based Electrochemical Sensors in Cancer Diagnosis.

Graphene is an emerging nanomaterial increasingly being used in electrochemical biosensing applications owing to its high surface area, excellent conductivity, ease of functionalization, and superior electrocatalytic properties compared to other carbon-based electrodes and nanomaterials, enabling faster electron transfer kinetics and higher sensitivity. Graphene electrochemical biosensors may have the potential to enable the rapid, sensitive, and low-cost detection of cancer biomarkers. This paper reviews early-stage research and proof-of-concept studies on the development of graphene electrochemical biosensors for potential future cancer diagnostic applications. Various graphene synthesis methods are outlined along with common functionalization approaches using polymers, biomolecules, nanomaterials, and synthetic chemistry to facilitate the immobilization of recognition elements and improve performance. Major sensor configurations including graphene field-effect transistors, graphene modified electrodes and nanocomposites, and 3D graphene networks are highlighted along with their principles of operation, advantages, and biosensing capabilities. Strategies for the immobilization of biorecognition elements like antibodies, aptamers, peptides, and DNA/RNA probes onto graphene platforms to impart target specificity are summarized. The use of nanomaterial labels, hybrid nanocomposites with graphene, and chemical modification for signal enhancement are also discussed. Examples are provided to illustrate applications for the sensitive electrochemical detection of a broad range of cancer biomarkers including proteins, circulating tumor cells, DNA mutations, non-coding RNAs like miRNA, metabolites, and glycoproteins. Current challenges and future opportunities are elucidated to guide ongoing efforts towards transitioning graphene biosensors from promising research lab tools into mainstream clinical practice. Continued research addressing issues with reproducibility, stability, selectivity, integration, clinical validation, and regulatory approval could enable wider adoption. Overall, graphene electrochemical biosensors present powerful and versatile platforms for cancer diagnosis at the point of care.

Progressive Mechanical and Structural Changes in Anterior Cerebral Arteries with Alzheimer's Disease.

Alzheimer disease (AD) is a neurodegenerative disease and the main cause for dementia. The irreversible neurodegeneration leads to a gradual loss of brain function characterized predominantly by memory loss. Cerebrovascular changes are common neuropathologic findings in aged subjects with dementia. Cerebrovascular integrity is critical for proper metabolism and perfusion of the brain, as cerebrovascular remodeling may render the brain more susceptible to pulse pressure and may be associated with poorer cognitive performance and greater risk of cerebrovascular events. The objective of this study is to provide understanding of cerebrovascular remodeling with AD progression. A total of 28 brain donor participants with human anterior cerebral artery (ACA) from controls and pathologically diagnosed AD groups (early - Braak stages I-II; intermediate - Braak stages III-IV; and advanced - Braak stages V-VI) were included in this study. Mechanical testing, histology, advanced optical imaging, and mass spectrometry were performed to study the progressive structural and functional changes of ACAs with AD progression. Biaxial extension-inflation tests showed that ACAs became progressively less compliant, and the longitudinal stress in the intermediate& advanced AD groups was significantly higher than that from the control group. With pathological AD development, the inner and outer diameter of ACA remained almost unchanged; however, histology study revealed progressive smooth muscle cell atrophy and loss of elastic fibers which led to compromised structural integrity of the arterial wall. Multiphoton imaging demonstrated elastin degradation at the media-adventitia interface, which led to the formation of an empty band of 21.0 ± 15.4 µm and 32.8 ± 9.24 µm in width for the intermediate& advanced AD groups, respectively. Furthermore, quantitative birefringence microscopy showed disorganized adventitial collagen with AD development. Mass spectrometry analysis provided further evidence of altered collagen content and other extracellular matrix (ECM) molecule and smooth muscle cell changes that were consistent with the mechanical and structural alterations. Collectively, our study provides understanding of the mechanical and structural cerebrovascular deterioration in cerebral arteries with AD, which may be related to neurodegenration and pathology in the brain.