Cardiovascular calcification in chronic kidney disease: Risk factors and effect of α-keto acid tablets

Purpose: To investigate the effect of α-keto acid tablets, and risk factors for cardiovascular calcification in patients with chronic kidney disease (CKD).Methods: A total of 128 CKD patients were enrolled in this study. They were randomly assigned to study and control groups, each with 64 patients. Control patients received symptomatic treatment, while the study group patients received α-keto acid tablets plus. Indices of cardiovascular calcification, blood lipids and mineral metabolism were determined in the 2 groups of patients and compared. Risk factors for cardiovascular calcification were also analyzed.Results: After treatment, the two groups had decreased CACS scores and reduced serum FGF-23levels, with lower values in patients in the study group. Levels of Klotho and fetuin-A were significantly elevated after treatment, with higher values observed in study group patients. The degree of cardiovascular calcification was markedly lower in study group than that in controls. There was no significant difference in blood Ca level between the control and study groups before and after treatment. Logistic multivariate analysis demonstrated that hyperlipidemia, hyperphosphatemia, hypercalcemia, hypertension and diabetes put patients at risk for cardiovascular calcification.Conclusion: Compound α-keto acid tablets delay cardiovascular calcification in patients with CKD, and alleviate symptoms of related risk factors for cardiovascular calcification

Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children

AbstractBackgroundEarly warning scores (EWS) are designed to identify early clinical deterioration by combining physiologic and/or laboratory measures to generate a quantified score. Current EWS leverage only a small fraction of Electronic Health Record (EHR) content. The planned widespread implementation of EHRs brings the promise of abundant data resources for prediction purposes. The three specific aims of our research are: (1) to develop an EHR-based automated algorithm to predict the need for Pediatric Intensive Care Unit (PICU) transfer in the first 24h of admission; (2) to evaluate the performance of the new algorithm on a held-out test data set; and (3) to compare the effectiveness of the new algorithm's with those of two published Pediatric Early Warning Scores (PEWS).MethodsThe cases were comprised of 526 encounters with 24-h Pediatric Intensive Care Unit (PICU) transfer. In addition to the cases, we randomly selected 6772 control encounters from 62516 inpatient admissions that were never transferred to the PICU. We used 29 variables in a logistic regression and compared our algorithm against two published PEWS on a held-out test data set.ResultsThe logistic regression algorithm achieved 0.849 (95% CI 0.753–0.945) sensitivity, 0.859 (95% CI 0.850–0.868) specificity and 0.912 (95% CI 0.905–0.919) area under the curve (AUC) in the test set. Our algorithm's AUC was significantly higher, by 11.8 and 22.6% in the test set, than two published PEWS.ConclusionThe novel algorithm achieved higher sensitivity, specificity, and AUC than the two PEWS reported in the literature

IL-10 plays a central regulatory role in the cytokines induced by hepatitis C virus core protein and polyinosinic acid:polycytodylic acid

Hepatitis C virus (HCV) can cause persistent infection and chronic liver disease, and viral factors are involved in HCV persistence. HCV core protein, a highly conserved viral protein, not only elicits an immunoresponse, but it also regulates it. In addition, HCV core protein interacts with toll-like receptors (TLRs) on monocytes, inducing them to produce cytokines. Polyinosinic acid:polycytodylic acid (polyI:C) is a synthetic analogue of double-stranded RNA that binds to TLR3 and can induce secretion of type I IFN from monocytes. Cytokine response against HCV is likely to affect the natural course of infection as well as HCV persistence. However, possible effects of cytokines induced by HCV core protein and polyI:C remain to be investigated. In this study, we isolated CD14+ monocytes from healthy donors, cultured them in the presence of HCV core protein and/or polyI:C, and characterized the induced cytokines, phenotypes and mechanisms. We demonstrated that HCV core protein- and polyI:C-stimulated CD14+ monocytes secreted tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-10, and type I interferon (IFN). Importantly, TNF-α and IL-1β regulated the secretion of IL-10, which then influenced the expression of signal transducer and activator of transcription 1 (STAT1) and interferon regulatory factor 1 (IRF1) and subsequently the production of type I IFN. Interestingly, type I IFN also regulated the production of IL-10, which in turn inhibited the nuclear factor (NF)-κB subunit, reducing TNF-α and IL-1β levels. Therefore, IL-10 appears to play a central role in regulating the production of cytokines induced by HCV core protein and polyI:C

Automated detection of medication administration errors in neonatal intensive care

AbstractObjectiveTo improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.MethodsWe developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.ResultsIn the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.ConclusionsAutomated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation

Brucella Dysregulates Monocytes and Inhibits Macrophage Polarization through LC3-Dependent Autophagy

Brucellosis is caused by infection with Brucella species and exhibits diverse clinical manifestations in infected humans. Monocytes and macrophages are not only the first line of defense against Brucella infection but also a main reservoir for Brucella. In the present study, we examined the effects of Brucella infection on human peripheral monocytes and monocyte-derived polarized macrophages. We showed that Brucella infection led to an increase in the proportion of CD14++CD16− monocytes and the expression of the autophagy-related protein LC3B, and the effects of Brucella-induced monocytes are inhibited after 6 weeks of antibiotic treatment. Additionally, the production of IL-1β, IL-6, IL-10, and TNF-α from monocytes in patients with brucellosis was suppressed through the LC3-dependent autophagy pathway during Brucella infection. Moreover, Brucella infection inhibited macrophage polarization. Consistently, the addition of 3-MA, an inhibitor of LC3-related autophagy, partially restored macrophage polarization. Intriguingly, we also found that the upregulation of LC3B expression by rapamycin and heat-killed Brucella in vitro inhibits M2 macrophage polarization, which can be reversed partially by 3-MA. Taken together, these findings reveal that Brucella dysregulates monocyte and macrophage polarization through LC3-dependent autophagy. Thus, targeting this pathway may lead to the development of new therapeutics against Brucellosis

Regulatory NK cells mediated between immunosuppressive monocytes and dysfunctional T cells in chronic HBV infection

Background and aims HBV infection represents a major health problem worldwide, but the immunological mechanisms by which HBV causes chronic persistent infection remain only partly understood. Recently, cell subsets with suppressive features have been recognised among monocytes and natural killer (NK) cells. Here we examine the effects of HBV on monocytes and NK cells. Methods Monocytes and NK cells derived from chronic HBV-infected patients and healthy controls were purified and characterised for phenotype, gene expression and cytokines secretion by flow cytometry, quantitative real-time (qRT)-PCR, ELISA and western blotting. Culture and coculture of monocytes and NK cells were used to determine NK cell activation, using intracellular cytokines staining. Results In chronic HBV infection, monocytes express higher levels of PD-L1, HLA-E, interleukin (IL)-10 and TGF-β, and NK cells express higher levels of PD-1, CD94 and IL-10, compared with healthy individuals. HBV employs hepatitis B surface antigen (HBsAg) to induce suppressive monocytes with HLA-E, PD-L1, IL-10 and TGF-β expression via the MyD88/NFκ B signalling pathway. HBV-treated monocytes induce NK cells to produce IL-10, via PD-L1 and HLA-E signals. Such NK cells inhibit autologous T cell activation. Conclusions Our findings reveal an immunosuppressive cascade, in which HBV generates suppressive monocytes, which initiate regulatory NK cells differentiation resulting in T cell inhibition

HCV core protein inhibits polarization and activity of both M1 and M2 macrophages through the TLR2 signaling pathway

Hepatitis C virus (HCV) establishes persistent infection in most infected patients, and eventually causes chronic hepatitis, cirrhosis, and hepatocellular carcinoma in some patients. Monocytes and macrophages provide the first line of defense against pathogens, but their roles in HCV infection remains unclear. We have reported that HCV core protein (HCVc) manipulates human blood-derived dendritic cell development. In the present study, we tested whether HCVc affects human blood-derived monocyte differentiating into macrophages. Results showed that HCVc inhibits monocyte differentiation to either M1 or M2 macrophages through TLR2, associated with impaired STATs signaling pathway. Moreover, HCVc inhibits phagocytosis activity of M1 and M2 macrophages, M1 macrophage-induced autologous and allogeneic CD4+ T cell activation, but promotes M2 macrophage-induced autologous and allogeneic CD4+ T cell activation. In conclusion, HCVc inhibits monocyte-derived macrophage polarization via TLR2 signaling, leading to dysfunctions of both M1 and M2 macrophages in chronic HCV infected patients. This may contribute to the mechanism of HCV persistent infection, and suggest that blockade of HCVc might be a novel therapeutic approach to treating HCV infection

Hepatitis C virus core protein triggers expansion and activation of CD4+CD25+ regulatory T cells in chronic hepatitis C patients

CD4+CD25+FoxP3+ regulatory T cells (Tregs) are increased in patients with chronic hepatitis C, which may contribute to the sustained suppression of hepatitis C virus (HCV)-specific T-cell responses and viral persistence in HCV-infected individuals. We postulated that HCV core protein (HCVc) directly contributes to the expansion of Tregs in HCV-infected patients, and we provide evidence to support this hypothesis in the report. Peripheral blood mononuclear cells (PBMCs) and sera were collected from 87 treatment-naïve chronic HCV-infected patients, CD4+CD25+ Tregs were measured by flow cytometry, and HCV RNA and HCVc levels were detected using qPCR and enzyme-linked immunosorbent assay (ELISA), respectively. CD4+, CD8+, CD4+CD25+ and CD4+CD25− T cells were purified from healthy donors and cultured with recombinant HCVc and Toll-like receptor (TLR) ligands. Flow cytometry was used to analyze cell proliferation, and ELISA was performed to measure cytokine production. In the 87 chronic HCV-infected patients, HCVc showed a significant correlation with HCV RNA and CD4+CD25+ Tregs. Mechanistic studies showed that HCVc, together with anti-CD3 antibody, augmented CD4+CD25+ Treg proliferation, but inhibited CD4+CD25− T-cell proliferation and IFN-γ production, in a dose-dependent and Treg-dependent manner. Moreover, unlike the TLR3 ligand (poly I:C) and the TLR4 ligand (lipopolysaccharide, LPS), the TLR2 ligand (lipoteichoic acid, LTA) and HCVc both inhibited TCR-induced CD4+ T-cell proliferation and IFN-γ secretion in a Treg-dependent manner. These data indicate that HCVc, like other TLR2 ligands, triggers CD4+CD25+ Treg activation and expansion to inhibit host immune responses, which may play a critical role in viral persistence in HCV-infected patients

Two Evolutionary Histories in the Genome of Rice: the Roles of Domestication Genes

Genealogical patterns in different genomic regions may be different due to the joint influence of gene flow and selection. The existence of two subspecies of cultivated rice provides a unique opportunity for analyzing these effects during domestication. We chose 66 accessions from the three rice taxa (about 22 each from Oryza sativa indica, O. sativa japonica, and O. rufipogon) for whole-genome sequencing. In the search for the signature of selection, we focus on low diversity regions (LDRs) shared by both cultivars. We found that the genealogical histories of these overlapping LDRs are distinct from the genomic background. While indica and japonica genomes generally appear to be of independent origin, many overlapping LDRs may have originated only once, as a result of selection and subsequent introgression. Interestingly, many such LDRs contain only one candidate gene of rice domestication, and several known domestication genes have indeed been “rediscovered” by this approach. In summary, we identified 13 additional candidate genes of domestication

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic