USP5 promotes lipopolysaccharide-induced apoptosis and inflammatory response by stabilizing the TXNIP protein.

BACKGROUND: The role of thioredoxin-interacting protein (TXNIP) in lipopolysaccharide-induced liver injury in mice has been reported, but the underlying mechanisms are poorly understood. METHODS: We overexpressed deubiquitinase in cells overexpressing TXNIP and then detected the level of TXNIP to screen out the deubiquitinase regulating TXNIP; the interaction between TXNIP and deubiquitinase was verified by coimmunoprecipitation. After knockdown of a deubiquitinase and overexpression of TXNIP in Huh7 and HepG2 cells, lipopolysaccharide was used to establish a cellular inflammatory model to explore the role of deubiquitinase and TXNIP in hepatocyte inflammation. RESULTS: In this study, we discovered that ubiquitin-specific protease 5 (USP5) interacts with TXNIP and stabilizes it through deubiquitylation in Huh-7 and HepG2 cells after treatment with lipopolysaccharide. In lipopolysaccharide-treated Huh-7 and HepG2 cells, USP5 knockdown increased cell viability, reduced apoptosis, and decreased the expression of inflammatory factors, including NLRP3, IL-1ß, IL-18, ASC, and procaspase-1. Overexpression of TXNIP reversed the phenotype induced by knockdown USP5. CONCLUSIONS: In summary, USP5 promotes lipopolysaccharide-induced apoptosis and inflammatory response by stabilizing the TXNIP protein.

Experimental impacts of grazing on grassland biodiversity and function are explained by aridity.

Grazing by domestic herbivores is the most widespread land use on the planet, and also a major global change driver in grasslands. Yet, experimental evidence on the long-term impacts of livestock grazing on biodiversity and function is largely lacking. Here, we report results from a network of 10 experimental sites from paired grazed and ungrazed grasslands across an aridity gradient, including some of the largest remaining native grasslands on the planet. We show that aridity partly explains the responses of biodiversity and multifunctionality to long-term livestock grazing. Grazing greatly reduced biodiversity and multifunctionality in steppes with higher aridity, while had no effects in steppes with relatively lower aridity. Moreover, we found that long-term grazing further changed the capacity of above- and below-ground biodiversity to explain multifunctionality. Thus, while plant diversity was positively correlated with multifunctionality across grasslands with excluded livestock, soil biodiversity was positively correlated with multifunctionality across grazed grasslands. Together, our cross-site experiment reveals that the impacts of long-term grazing on biodiversity and function depend on aridity levels, with the more arid sites experiencing more negative impacts on biodiversity and ecosystem multifunctionality. We also highlight the fundamental importance of conserving soil biodiversity for protecting multifunctionality in widespread grazed grasslands.

The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem.

Emissions are essential for forecasting air quality and pollution control, but traditional emissions are often not real-time by the statistics of "bottom-up" approach due to high human resource demand. The four-dimensional variational method (4DVAR) and the ensemble Kalman filter (EnKF) are generally used to optimize emissions based on chemical transport models by assimilating observations. Although the two methods solve similar estimation problems, different functions have been developed to address the process of converting the emissions to concentrations. In this paper, we evaluated the performance of the 4DVAR and EnKF methods in optimizing SO2 emissions over China during 23-29 January 2020. The emissions optimized by the 4DVAR and EnKF methods showed a similar spatiotemporal distribution in most regions of China during the study period, suggesting that both methods are useful in reducing uncertainties in the prior emissions. Three forecast experiments with different emissions were conducted. Compared with the forecasts with prior emissions, the root-mean-square error of the forecasts with the emissions optimized by the 4DVAR and EnKF methods decreased by 45.7 % and 40.4 %. This indicates that the 4DVAR method was slightly more effective than the EnKF method in optimizing emissions and improves the accuracy of forecasts. Furthermore, it is found that the 4DVAR method performed better than the EnKF method when the spatial and/or temporal distribution of SO2 observations with strong local characteristics, The EnKF method showed a better performance for the condition of the large difference between prior emissions and real emissions. The results may help to design suitable assimilation algorithms for optimizing emissions and improving model forecasts. The advance data assimilation systems are beneficial for the understanding the effectiveness and value of emission inventories and air quality model.

Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method.

INTRODUCTION: Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance. METHODS: Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models. RESULT: A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5-50%, LY and GNB models both showed positive net benefits. CONCLUSION: The two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.

Identification of early biomarkers of transcriptomics in alveolar macrophage for the prognosis of intubated ARDS patients.

BACKGROUND: Currently, the rate of morbidity and mortality in acute respiratory distress syndrome (ARDS) remains high. One of the potential reasons for the poor and ineffective therapies is the lack of early and credible indicator of risk prediction that would help specific treatment of severely affected ARDS patients. Nevertheless, assessment of the clinical outcomes with transcriptomics of ARDS by alveolar macrophage has not been performed. METHODS: The expression data GSE116560 was obtained from the Gene Expression Omnibus databases (GEO) in NCBI. This dataset consists of 68 BAL samples from 35 subjects that were collected within 48 h of ARDS. Differentially expressed genes (DEGs) of different outcomes were analyzed using R software. The top 10 DEGs that were up- or down-regulated were analyzed using receiver operating characteristic (ROC) analysis. Kaplan-Meier survival analysis within two categories according to cut-off and the value of prediction of the clinical outcomes via DEGs was verified. GO enrichment, KEGG pathway analysis, and protein-protein interaction were also used for functional annotation of key genes. RESULTS: 24,526 genes were obtained, including 235 up-regulated and 292 down-regulated DEGs. The gene ADORA3 was chosen as the most obvious value to predict the outcome according to the ROC and survival analysis. For functional annotation, ADORA3 was significantly augmented in sphingolipid signaling pathway, cGMP-PKG signaling pathway, and neuroactive ligand-receptor interaction. Four genes (ADORA3, GNB1, NTS, and RHO), with 4 nodes and 6 edges, had the highest score in these clusters in the protein-protein interaction network. CONCLUSIONS: Our results show that the prognostic prediction of early biomarkers of transcriptomics as identified in alveolar macrophage in ARDS can be extended for mechanically ventilated critically ill patients. In the long term, generalizing the concept of biomarkers of transcriptomics in alveolar macrophage could add to improving precision-based strategies in the ICU patients and may also lead to identifying improved strategy for critically ill patients.

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method. Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis. Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.

Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application.

With an increasing number of air quality monitoring stations installed around the Chinese mainland, high-resolution aerosol observations become available, allowing improvements in air pollution monitoring and aerosol forecasting. However, the multi scales (especially small-scale) information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method (3DVAR). This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps, two-scale-3DVAR (TS-3DVAR), to improve the effectiveness of assimilating high-resolution observations. In TS-3DVAR, the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples. The data assimilation (DA) analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field's multi-scale characteristics, which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting. Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) and 10.0 µm (PM10) from the surface air quality monitoring stations from November 01 to November 30, 2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy. The results showed that TS-3DVAR better constrained both large-scale and small-scale, especially the spatial wavelengths in a range of 54-216 km and those above 351 km. The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70% and 35.33% higher than those of 3DVAR. As a result, the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field, and thereby the forecasting capability for PM2.5. In the initial chemical field, the 30-day average correlation coefficient (Corr) of PM2.5 of TS-3DVAR was 0.052 (6.12%) higher than that of 3DVAR, and the root mean square error (RMSE) of TS-3DVAR was 3.446 µg m-3 (16.4%) lower than that of 3DVAR. For the forecasting capability for PM2.5 mass concentration, the 30-day average Corr of TS-3DVAR during the 0-24 hour forecast period was 0.025 (5.08%) higher than that of 3DVAR, and the average RMSE was 2.027 µg m-3 (4.85%) lower. The positive effect of TS-3DVAR on the improvement of forecasting capability can last for more than 24 h.

Association Between Previous Stroke and Severe COVID-19: A Retrospective Cohort Study and an Overall Review of Meta-Analysis.

Objective: The objective of this study was to investigate the association between previous stroke and the risk of severe coronavirus disease 2019 (COVID-19). Methods: We included 164 (61.8 ± 13.6 years) patients with COVID-19 in a retrospective study. We evaluated the unadjusted and adjusted associations between previous stroke and severe COVID-19, using a Cox regression model. We conducted an overall review of systematic review and meta-analysis to investigate the relationship of previous stroke with the unfavorable COVID-19 outcomes. Results: The rate of severe COVID-19 in patients with previous stroke was 28.37 per 1,000 patient days (95% confidence interval [CI]: 10.65-75.59), compared to 3.94 per 1,000 patient days (95% CI: 2.66-5.82) in those without previous stroke (p < 0.001). Previous stroke was significantly associated with severe COVID-19 using a Cox regression model (unadjusted [hazard ratio, HR]: 6.98, 95% CI: 2.42-20.16, p < 0.001; adjusted HR [per additional 10 years]: 4.62, 95% CI: 1.52-14.04, p = 0.007). An overall review of systematic review and meta-analysis showed that previous stroke was significantly associated with severe COVID-19, mortality, need for intensive care unit admission, use of mechanical ventilation, and an unfavorable composite outcome. Conclusion: Previous stroke seems to influence the course of COVID-19 infection; such patients are at high risk of severe COVID-19 and might benefit from early hospital treatment measures and preventive strategies.

Six-month follow-up of functional status in discharged patients with coronavirus disease 2019.

BACKGROUND: The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a 6-month follow-up of functional status of COVID-19 survivors. METHODS: We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a 6-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 4 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a 6-month follow-up after discharge. RESULTS: We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At 6-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95% CI 1.33-12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95% CI 1.06-10.81, p = 0.039). CONCLUSIONS: A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a 6-month follow-up; intervention strategies are required.

The predictive value of serum level of cystatin C for COVID-19 severity.

To investigate the potential prognostic value of Serum cystatin C (sCys C) in patients with COVID-19 and determine the association of sCys C with severe COVID-19 illness. We performed a retrospective review of medical records of 162 (61.7 ± 13.5 years) patients with COVID-19. We assessed the predictive accuracy of sCys C for COVID-19 severity by the receiver operating characteristic (ROC) curve analysis. The participants were divided into two groups based on the sCys C cut-off value. We evaluated the association between high sCys C level and the development of severe COVID-19 disease, using a COX proportional hazards regression model. The area under the ROC curve was 0.708 (95% CI 0.594-0.822), the cut-off value was 1.245 (mg/L), and the sensitivity and specificity was 79.1% and 60.7%, respectively. A multivariable Cox analysis showed that a higher level of sCys C (adjusted HR 2.78 95% CI 1.25-6.18, p = 0.012) was significantly associated with an increased risk of developing a severe COVID-19 illness. Patients with a higher sCys C level have an increased risk of severe COVID-19 disease. Our findings suggest that early assessing sCys C could help to identify potential severe COVID-19 patients.

Identification of transcriptomics biomarkers for the early prediction of the prognosis of septic shock from pneumopathies.

BACKGROUND: Identifying the biological subclasses of septic shock might provide specific targeted therapies for the treatment and prognosis of septic shock. It might be possible to find biological markers for the early prediction of septic shock prognosis. METHODS: The data were obtained from the Gene Expression Omnibus databases (GEO) in NCBI. GO enrichment and KEGG pathway analyses were performed to investigate the functional annotation of up- and downregulated DEGs. ROC curves were drawn, and their areas under the curves (AUCs) were determined to evaluate the predictive value of the key genes. RESULTS: 117 DEGs were obtained, including 36 up- and 81 downregulated DEGs. The AUC for the MME gene was 0.879, as a key gene with the most obvious upregulation in septic shock. The AUC for the THBS1 gene was 0.889, as a key downregulated gene with the most obvious downregulation in septic shock. CONCLUSIONS: The upregulation of MME via the renin-angiotensin system pathway and the downregulation of THBS1 through the PI3K-Akt signaling pathway might have implications for the early prediction of prognosis of septic shock in patients with pneumopathies.

Does pre-existent physical inactivity have a role in the severity of COVID-19?

BACKGROUND AND AIMS: Physical inactivity is considered an important lifestyle factor for overweight and cardiovascular disease. We aimed to investigate the association between pre-existent physical inactivity and the risk of severe coronavirus disease 2019 (COVID-19). METHODS: We included 164 (61.8 ± 13.6 years) patients with COVID-19 who were admitted between 15 February and 14 March 2020 in this retrospective study. We evaluated the association between pre-existent physical inactivity and severe COVID-19 using a logistic regression model. RESULTS: Of 164 eligible patients with COVID-19, 103 (62.8%) were reported to be physically inactive. Univariable logistic regression analysis showed that physical inactivity was associated with an increased risk of severe COVID-19 [unadjusted odds ratio (OR) 6.53, 95% confidence interval (CI) 1.88-22.62]. In the multivariable regression analysis, physical inactivity remained significantly associated with an increased risk of severe COVID-19 (adjusted OR 4.12, 95% CI 1.12-15.14) after adjustment for age, sex, stroke, and overweight. CONCLUSION: Our data showed that pre-existent physical inactivity was associated with an increased risk of experiencing severe COVID-19. Our findings indicate that people should be encouraged to keep physically active to be at a lower risk of experiencing a severe illness when COVID-19 infection seems unpredicted.The reviews of this paper are available via the supplemental material section.

Prevalence and outcomes of re-positive nucleic acid tests in discharged COVID-19 patients.

The prevalence and outcomes of patients who had re-activation of coronavirus disease 2019 (COVID-19) after discharge remain poorly understood. We included 126 consecutively confirmed cases of COVID-19 with 2-month follow-up data after discharge in this retrospective study. The upper respiratory specimen using a reverse-transcription polymerase chain reaction test of three patients (71 years [60-76]) were positive within 11-20 days after their discharge, with an event rate of 19.8 (95%CI 2.60-42.1) per 1,000,000 patient-days. Moreover, all re-positive patients were asymptomatic. Our findings suggest that few recovered patients may still be virus carriers even after reaching the discharge criteria.

The effect of vascular risk factor burden on the severity of COVID-19 illness, a retrospective cohort study.

BACKGROUND: Patients with cardiovascular comorbidities are at high risk of poor outcome from COVID-19. However, how the burden (number) of vascular risk factors influences the risk of severe COVID-19 disease remains unresolved. Our aim was to investigate the association of severe COVID-19 illness with vascular risk factor burden. METHODS: We included 164 (61.8 ± 13.6 years) patients with COVID-19 in this retrospective study. We compared the difference in clinical characteristics, laboratory findings and chest computed tomography (CT) findings between patients with severe and non-severe COVID-19 illness. We evaluated the association between the number of vascular risk factors and the development of severe COVID-19 disease, using a Cox regression model. RESULTS: Sixteen (9.8%) patients had no vascular risk factors; 38 (23.2%) had 1; 58 (35.4%) had 2; 34 (20.7%) had 3; and 18 (10.9%) had ≥4 risk factors. Twenty-nine patients (17.7%) experienced severe COVID-19 disease with a median (14 [7-27] days) duration between onset to developing severe COVID-19 disease, an event rate of 4.47 per 1000-patient days (95%CI 3.10-6.43). Kaplan-Meier curves showed a gradual increase in the risk of severe COVID-19 illness (log-rank P < 0.001) stratified by the number of vascular risk factors. After adjustment for age, sex, and comorbidities as potential confounders, vascular risk factor burden remained associated with an increasing risk of severe COVID-19 illness. CONCLUSIONS: Patients with increasing vascular risk factor burden have an increasing risk of severe COVID-19 disease, and this population might benefit from specific COVID-19 prevention (e.g., self-isolation) and early hospital treatment measures.

Significance of Early Postoperative Arterial Lactic Acid, Inferior Vena Cava Variability, and Central Venous Pressure in Hypovolemic Shock.

INTRODUCTION: Up to one-third of patients admitted to the ICU are in circulatory shock, and early recognition of the condition is vital if subsequent tissue injuries are to be avoided. We would like to know what role the arterial lactic acid, inferior vena cava variability, and CVP (central venous pressure) play in the early stages of shock. METHODS: This is a retrospective study of patients who underwent surgical resuscitation in the Department of Critical Care Medicine. We use the ROC (receiver-operating characteristic) curve to evaluate the significance of each indicator in the diagnosis. For correlation analysis between groups, we first use linear regression for processing and then analysis with correlation. RESULTS: The ROC curve analysis shows that the area under the curve of the lactic acid group was 0.9272, the area under the curve of the inferior vena cava variability group was 0.8652, and the area under the curve of the CVP group was 0.633. Correlation analysis shows that the inferior vena cava variability and arterial lactic acid Pearson's r = 0.2863 and CVP and arterial lactic acid Pearson's r = 0.0729. CONCLUSION: The diagnostic value of arterial lactate is still very high and can still be used as an early warning indicator to help clinicians be alert to the microcirculatory disorders that have emerged quietly. The degree of inferior vena cava variability is linearly related to arterial lactic acid and can also be used as a reference indicator for early evaluation of shock. The diagnostic value of CVP is obviously lower.

Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models.

Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R(2) and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R(2)=0.83) with an APE of 19%, whereas it was relatively poor in spring (R(2)=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.