RIPK3, a key mediator of necroptosis, has been implicated in the host defense against viral infection primary in immune cells. However, gene expression analysis revealed that RIPK3 is abundantly ...expressed not only in immune organs but also in the gastrointestinal tract, particularly in the small intestine. We found that orally inoculated
, a bacterial foodborne pathogen, efficiently spread and caused systemic infection in
-deficient mice while almost no dissemination was observed in wild-type mice.
infection activated the RIPK3-MLKL pathway in cultured cells, which resulted in suppression of intracellular replication of
Surprisingly,
infection-induced phosphorylation of MLKL did not result in host cell killing. We found that MLKL directly binds to
and inhibits their replication in the cytosol. Our findings have revealed a novel functional role of the RIPK3-MLKL pathway in nonimmune cell-derived host defense against
invasion, which is mediated through cell death-independent mechanisms.
In this work, we develop models and a fault detection and isolation (FDI) methodology for heating, ventilation and air conditioning (HVAC) systems that utilizes recurrent neural networks (RNN). The ...FDI design does not require the existence of plant fault history, mechanistic models or a set of expert rules to isolate faults. The key is to first use plant data to build predictive models and input/output estimators, and then embed them within FDI filters. A distributed FDI framework is designed consisting of local FDI (LFDI) schemes that communicate with each other for improved FDI. The distributed FDI framework enables diagnosis of multiple faults in different components of the HVAC system when a fault in one of the control components directly affects the other subsystems. The effectiveness of the proposed FDI scheme is shown via simulation examples on a simulation test bed, as well as using real data. The simulations revealed superior performance of the proposed FDI methodology over FDI approaches using subspace based models for both simulation and real data cases.
Trimethlyamine-N-oxide (TMAO) was recently identified as a promoter of atherosclerosis. Patients with CKD exhibit accelerated development of atherosclerosis; however, no studies have explored the ...relationship between TMAO and atherosclerosis formation in this group. This study measured serum concentrations and urinary excretion of TMAO in a CKD cohort (n=104), identified the effect of renal transplant on serum TMAO concentration in a subset of these patients (n=6), and explored the cross-sectional relationship between serum TMAO and coronary atherosclerosis burden in a separate CKD cohort (n=220) undergoing coronary angiography. Additional exploratory analyses examined the relationship between baseline serum TMAO and long-term survival after coronary angiography. Serum TMAO concentrations demonstrated a strong inverse association with eGFR (r(2)=0.31, P<0.001). TMAO concentrations were markedly higher in patients receiving dialysis (median interquartile range, 94.4 μM 54.8-133.0 μM for dialysis-dependent patients versus 3.3 μM 3.1-6.0 μM for healthy controls; P<0.001); whereas renal transplantation resulted in substantial reductions in TMAO concentrations (median min-max 71.2 μM 29.2-189.7 μM pretransplant versus 11.4 μM 8.9-20.2 μM post-transplant; P=0.03). TMAO concentration was an independent predictor for coronary atherosclerosis burden (P=0.02) and predicted long-term mortality independent of traditional cardiac risk factors (hazard ratio, 1.26 per 10 μM increment in TMAO concentration; 95% confidence interval, 1.13 to 1.40; P<0.001). In conclusion, serum TMAO concentrations substantially increase with decrements in kidney function, and this effect is reversed by renal transplantation. Increased TMAO concentrations correlate with coronary atherosclerosis burden and may associate with long-term mortality in patients with CKD undergoing coronary angiography.
Bleeding is the most common complication after percutaneous coronary intervention (PCI) and is associated with increased morbidity and health care costs. The incidence of bleeding-related mortality ...after PCI has not been described in a nationally representative population. Furthermore, the relationships among bleeding risk, bleeding site, and mortality are unclear.
To describe the association between bleeding events and in-hospital mortality after PCI and to estimate the adjusted population attributable risk (estimated as the proportion of mortality risk associated with bleeding events), risk difference, and number needed to harm (NNH) for bleeding-related in-hospital mortality after PCI.
Data from 3,386,688 procedures in the CathPCI Registry performed in the United States between 2004 and 2011 were analyzed. The population attributable risk was calculated after adjustment for baseline demographic, clinical, and procedural variables. To calculate the NNH for bleeding-related mortality, a propensity-matched analysis was performed.
In-hospital mortality.
There were 57,246 bleeding events (1.7%) and 22,165 in-hospital deaths (0.65%) in 3,386,688 PCI procedures. The adjusted population attributable risk for mortality related to major bleeding was 12.1% (95% CI, 11.4%-12.7%) in the entire CathPCI cohort. The propensity-matched population consisted of 56,078 procedures with a major bleeding event and 224 312 controls. In this matched cohort, major bleeding was associated with increased in-hospital mortality (5.26% vs 1.87%; risk difference, 3.39% 95% CI, 3.20%-3.59%; NNH = 29 95% CI, 28-31; P < .001). The association between major bleeding and in-hospital mortality was observed in all strata of preprocedural bleeding risk (low: 1.62% vs 0.17%; risk difference, 1.45% 95% CI, 1.13%-1.77%, NNH = 69 95% CI, 57-88, P < .001; intermediate: 3.27% vs 0.71%; risk difference, 2.56% 95% CI, 2.33%-2.79%, NNH = 39 95% CI, 36-43, P < .001; and high: 8.16% vs 3.45%; risk difference, 4.71% 95% CI, 4.35%-5.07%, NNH = 21 95% CI, 20-23, P < .001). Although both access-site and non-access-site bleeding were associated with increased in-hospital mortality (2.73% vs 1.87%; risk difference, 0.86% 95% CI, 0.66%-1.05%, NNH = 117 95% CI, 95-151, P < .001; and 8.25% vs 1.87%; risk difference, 6.39% 95% CI, 6.04%-6.73%, NNH = 16 95% CI, 15-17, P < .001, respectively), the NNH was lower for nonaccess bleeding.
In a large registry of patients undergoing PCI, postprocedural bleeding events were associated with increased risk of in-hospital mortality, with an estimated 12.1% of deaths related to bleeding complications.
Objective:
Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention ...(CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them.
Methods:
We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ).
Results:
These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73.
Conclusion:
The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics.
•A FDI framework for sensor and actuator fault detection and isolation in VAV boxes of HVAC systems.•Diagnosing multiple faults and faults that their effect gets masked by the controller.•A safe ...parking strategy with energy saving capability for handling stuck dampers.
This work presents an integrated framework for fault detection and isolation (FDI) and fault tolerant control (FTC) of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems. To this end, first a statistical model based FDI framework is designed using existing techniques such as principal component analysis (PCA) and joint angle analysis as a benchmark for comparison. Then a novel linear causal model based framework for FDI of multiple actuator and multiple sensor faults is designed and implemented and shown to possess superior FDI capabilities compared to the statistical model based framework. Finally, a safe parking strategy is designed and the ensuing energy savings for the case of stuck dampers demonstrated.
Mycoplasma spp. are a major cause of mastitis, arthritis and pneumonia in cattle, and have been associated with reproductive disorders in cows. While culture is the traditional method of ...identification the use of PCR has become more common. Several investigators have developed PCR protocols to detect M. bovis in milk, yet few studies have evaluated other sample types or other important Mycoplasma species. Therefore the objective of this study was to develop a multiplex PCR assay to detect M. bovis, M. californicum and M. bovigenitalium, and evaluate its analytical performance against traditional culture of bovine milk, semen and swab samples. The PCR specificity was determined and the limit of detection evaluated in spiked milk, semen and swabs. The PCR was then compared to culture on 474 field samples from individual milk, bulk tank milk (BTM), semen and swab (vaginal, preputial, nose and eye) samples. Specificity analysis produced appropriate amplification for all M. bovis, M. californicum and M. bovigenitalium isolates. Amplification was not seen for any of the other Mollicutes or eubacterial isolates. The limit of detection of the PCR was best in milk, followed by semen and swabs. When all three Mycoplasma species were present in a sample, the limit of detection increased. When comparing culture and PCR, overall there was no significant difference in the proportion of culture and PCR positive samples. Culture could detect significantly more positive swab samples. No significant differences were identified for semen, individual milk or BTM samples. PCR identified five samples with two species present. Culture followed by 16S-23S rRNA sequencing did not enable identification of more than one species. Therefore, the superior method for identification of M. bovis, M. californicum and M. bovigenitalium may be dependent on the sample type being analysed, and whether the identification of multiple target species is required.
The potential for cardiotoxicity is carefully evaluated for pharmaceuticals, as it is a major safety liability. However, environmental chemicals are seldom tested for their cardiotoxic potential. ...Moreover, there is a large variability in both baseline and drug-induced cardiovascular risk in humans, but data are lacking on the degree to which susceptibility to chemically-induced cardiotoxicity may also vary. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes have become an important in vitro model for drug screening. Thus, we hypothesized that a population-based model of iPSC-derived cardiomyocytes from a diverse set of individuals can be used to assess potential hazard and inter-individual variability in chemical effects on these cells. We conducted concentration-response screening of 134 chemicals (pharmaceuticals, industrial and environmental chemicals and food constituents) in iPSC-derived cardiomyocytes from 43 individuals, comprising both sexes and diverse ancestry. We measured kinetic calcium flux and conducted high-content imaging following chemical exposure, and utilized a panel of functional and cytotoxicity parameters in concentration-response for each chemical and donor. We show reproducible inter-individual variability in both baseline and chemical-induced effects on iPSC-derived cardiomyocytes. Further, chemical-specific variability in potency and degree of population variability were quantified. This study shows the feasibility of using an organotypic population-based human in vitro model to quantitatively assess chemicals for which little cardiotoxicity information is available. Ultimately, these results advance in vitro toxicity testing methodologies by providing an innovative tool for population-based cardiotoxicity screening, contributing to the paradigm shift from traditional animal models of toxicity to in vitro toxicity testing methods.
•Cardiotoxicity information is lacking for most environmental chemicals.•Cardiovascular disease risks vary greatly across the population.•We demonstrated that hiPSC-derived cardiomyocytes can address these gaps.•Potency and population variability in cardiotoxicity varied across chemicals.•This population-based model can substantially improve chemical safety evaluations.
A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and ...manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes-Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.
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