Emergency medical services (EMS) care may be delayed when out-of-hospital cardiac arrest (OHCA) occurs in tall or large buildings. We hypothesized that larger building height and volume were related to a longer curb-to-defibrillator activation interval. We retrospectively evaluated 3,065 EMS responses to OHCA in a large city between 2003-13 that occurred indoors, prior to EMS arrival, and without prior deployment of a defibrillator. The two-tiered EMS system uses automated external defibrillator-equipped basic life support firefighters followed by paramedics dispatched from a single call center. We calculated three time intervals obtained from the computerized dispatch report and time-synchronized defibrillators: initial 911 call to address curb arrival by first unit (call-to-curb), curb arrival to defibrillator power on (curb-to-defib on), and the combined call-to-defib on interval. Building height and surface area were measured with a validated program based on aerial photography. Buildings were categorized by height as short (<25 ft), medium (26-64 ft) and tall (>64 ft). Volume was categorized as small (<60,000 ft(3)), midsize (60,000-1,202,600 ft(3)) and large (>1,202,600 ft(3)). Intervals were compared using the two-tailed Mann-Whitney test. EMS responded to 1,673 OHCA events in short, 1,134 in medium, and 258 in tall buildings. There was a 1.14 minute increase in median curb-to-defib on interval from 1.97 in short to 3.11 minutes in tall buildings (p < 0.01). Taller buildings, however, had a shorter call-to-curb interval (4.73 for short vs 3.96 minutes for tall, p < 0.01), such that the difference in call-to-defib on interval was only 0.27 minutes: 6.87 for short and 7.14 for tall buildings. A similar relationship was observed for small-volume compared to large-volume building: longer curb-to-AED (1.90 vs. 3.01 minutes, p < 0.01), but shorter call-to-curb (4.87 vs. 4.05, p < 0.01); the difference in call-to-defib on was 0.18 minutes. Both taller and larger-volume buildings had longer curb-to-AED intervals but shorter 911 call-to-curb arrival intervals. As a consequence, building height and volume had a modest overall relationship with interval from call to defibrillator application. These results do not support the hypothesis that either taller or larger-volume buildings need cause poorer outcomes in urban environments.
Abstract Introduction There is conflicting evidence whether high‐density lipoprotein cholesterol (HDL‐C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein ( CETP ) locus is associated with altered HDL‐C. We aimed to assess AD risk by genetically predicted HDL‐C. Methods Ten single nucleotide polymorphisms within the CETP locus predicting HDL‐C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR‐Egger. Results Based on 10 single nucleotide polymorphisms distinctly predicting HDL‐C in the CETP locus, we found that HDL‐C was not associated with risk of AD ( P > .7). Discussion Our study does not support the role of HDL‐C on risk of AD through HDL‐C altered by CETP . This study does not rule out other mechanisms by which HDL‐C affects risk of AD.
Delayed detection of LN associates with worse outcomes. There are conflicting recommendations regarding a threshold level of proteinuria at which biopsy will likely yield actionable management. This study addressed the association of urine protein:creatinine ratios (UPCR) with clinical characteristics and investigated the incidence of proliferative and membranous histology in patients with a UPCR between 0.5 and 1.A total of 275 SLE patients (113 first biopsy, 162 repeat) were enrolled in the multicentre multi-ethnic/racial Accelerating Medicines Partnership across 15 US sites at the time of a clinically indicated renal biopsy. Patients were followed for 1 year.At biopsy, 54 patients had UPCR <1 and 221 had UPCR ≥1. Independent of UPCR or biopsy number, a majority (92%) of patients had class III, IV, V or mixed histology. Moreover, patients with UPCR <1 and class III, IV, V, or mixed had a median activity index of 4.5 and chronicity index of 3, yet 39% of these patients had an inactive sediment. Neither anti-dsDNA nor low complement distinguished class I or II from III, IV, V or mixed in patients with UPCR <1. Of 29 patients with baseline UPCR <1 and class III, IV, V or mixed, 23 (79%) had a UPCR <0.5 at 1 year.In this prospective study, three-quarters of patients with UPCR <1 had histology showing class III, IV, V or mixed with accompanying activity and chronicity despite an inactive sediment or normal serologies. These data support renal biopsy at thresholds lower than a UPCR of 1.
Background Hypothermia remains the best studied neuroprotectant. Despite extensive positive large and small animal data, side effects continue to limit human applications. Selective hypothermia is an efficient way of applying neuroprotection to the brain without the systemic complications of global hypothermia. However, optimal depth and duration of therapeutic hypothermia are still unknown. We analyzed a large animal cohort study of selective hypothermia for statistical relationships between depth or duration of hypothermia and the final stroke volume. Methods A cohort of 30 swine stroke subjects provided the dataset for normothermic and selective hypothermic animals. Hypothermic parameters including duration, temperature nadir, and an Area Under the Curve measurement for 34 and 30°C were correlated with the final infarct volumes measured by MRI and histology. Results Between group comparisons continue to demonstrate a reduction in infarct volume with selective hypothermia. Histologically-derived infarct volumes were 1.2 mm 3 smaller in hypothermia-treated pigs ( P = 0.04) and showed a similar, but non-significant reduction in MRI ( P = 0.15). However, within the selective hypothermia group, more intense cooling, as measured through increased AUC 34 and decreased temperature nadir was associated with larger infarct proportions by MRI [Pearson's r = 0.48 ( p = 0.05) and r = −0.59 ( p = 0.01), respectively]. Reevaluation of the entire cohort with quadratic regression demonstrated a U-shaped pattern, wherein the average infarct proportion was minimized at 515 degree-minutes (AUC34) of cooling, and increased thereafter. In a single case of direct brain tissue oxygen monitoring during selective hypothermia, brain tissue oxygen strongly correlated with brain temperature reduction over the course of selective hypothermia to 23°C. Conclusions In a large animal model of selective hypothermia applied to focal ischemia, there is a non-monotone relationship between duration and depth of hypothermia and stroke volume reduction. This suggests a limit to depth or duration of selective hypothermia for optimal neuroprotection. Further research is required to delineate more precise depth and duration limits for selective hypothermia.
ABSTRACT Technologies such as Cellular Indexing of Transcriptomes and Epitopes sequencing (CITE-seq) and RNA Expression and Protein sequencing (REAP-seq) augment unimodal single-cell RNA sequencing (scRNA-seq) by simultaneously measuring expression of cell-surface proteins using antibody derived oligonucleotide tags (ADT). These protocols have been increasingly used to resolve cellular populations that are difficult to infer from gene expression alone, and to interrogate the relationship between gene and protein expression at a single-cell level. However, the ADT-based protein expression component of these assays remains widely underutilized as a primary tool to discover and annotate cell populations, in contrast to flow cytometry which has used surface protein expression in this fashion for decades. Therefore, we hypothesized that computational tools used for flow cytometry data analysis could be harnessed and scaled to analyze ADT data. Here we apply Ozette Discovery™, a recently-developed method for flow cytometry analysis, to re-analyze a large (>400,000 cells) published COVID-19 CITE-seq dataset. Using the protein expression data alone, Ozette Discovery is able to identify granular, robust, and interpretable cellular phenotypes in a high-throughput manner. In particular, we identify a population of CLEC12A+CD11b+CD14- myeloid cells that are specifically expanded in patients with critical COVID-19, and can only be resolved by their protein expression profiles. Using the longitudinal gene expression data from this dataset, we find that early expression of interferon response genes precedes the expansion of this subset, and that early expression of PRF1 and GZMB within specific Ozette Discovery phenotypes provides a RNA biomarker of critical COVID-19. In summary, Ozette Discovery demonstrates that taking a protein-centric approach to cell phenotype annotation in CITE-seq data can achieve the potential that dual RNA/protein assays provide in mixed samples: instantaneous in silico flow sorting, and unbiased RNA-seq profiling. HIGHLIGHTS Ozette Discovery provides an alternative method for data-driven annotation of granular and homogeneous cell phenotypes in CITE-seq data using protein expression data alone. Our approach inherently accommodates for batch effects, and our novel background-normalization method improves the signal:noise ratio of these notoriously noisy protein measurements. While these subpopulations are not derived from RNA profiles, they have distinct and interpretable RNA signatures. We find a population of CLEC12A+CD11b+CD14- myeloid cells associated with critical COVID-19 severity that can only be identified by their protein profiles, and identify early expression of interferon response genes in a CD4 T cell subset as a predictor of CLEC12A+CD11b+CD14- cell expansion. Peforming differential expression analysis within our identified phenotypes reveals predictors of COVID-19 severity that are not found with coarser annotations.
Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.
Oxygen supplementation in preterm infants disrupts alveolar epithelial type 2 (AT2) cell proliferation through poorly understood mechanisms. Here, newborn mice are used to understand how hyperoxia stimulates an early aberrant wave of AT2 cell proliferation that occurs between Postnatal Days (PNDs) 0 and 4. RNA-sequencing analysis of AT2 cells isolated from PND4 mice revealed hyperoxia stimulates expression of mitochondrial-specific methylenetetrahydrofolate dehydrogenase 2 and other genes involved in mitochondrial one-carbon coupled folate metabolism and serine synthesis. The same genes are induced when AT2 cells normally proliferate on PND7 and when they proliferate in response to the mitogen fibroblast growth factor 7. However, hyperoxia selectively stimulated their expression via the stress-responsive activating transcription factor 4 (ATF4). Administration of the mitochondrial superoxide scavenger mitoTEMPO during hyperoxia suppressed ATF4 and thus early AT2 cell proliferation, but it had no effect on normative AT2 cell proliferation seen on PND7. Because ATF4 and methylenetetrahydrofolate dehydrogenase are detected in hyperplastic AT2 cells of preterm infant humans and baboons with bronchopulmonary dysplasia, dampening mitochondrial oxidative stress and ATF4 activation may provide new opportunities for controlling excess AT2 cell proliferation in neonatal lung disease.
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