National trends in hospital, long-term care and outpatient Acinetobacter baumannii resistance rates

Introduction: Acinetobacter baumannii is a top-priority pathogen of the World Health Organization (WHO) and the Centers for Disease Control (CDC) due to antibiotic resistance. Gap Statement: Trends in A. baumannii resistance rates that include community isolates are unknown. Aim: Identify trends in A. baumannii resistance rates across the Veterans Affairs (VA) Healthcare System, including isolates from patients treated in hospitals, long-term care facilities and outpatient clinics nationally. Methodology: We included A. baumannii clinical cultures collected from VA patients from 2010 to 2018. Cultures were categorized by location: VA medical centers (VAMCs), long-term care (LTC) units [community living centers (CLCs)], or outpatient. We assessed carbapenem resistance, multidrug resistance (MDR) and extensive drug resistance (XDR). Time trends were assessed with Joinpoint regression. Results: We identified 19 376 A. baumannii cultures (53% VAMCs, 4% CLCs, 43% outpatient). Respiratory cultures were the most common source of carbapenem-resistant (43 %), multidrug-resistant (49 %) and extensively drug-resistant (21 %) isolates. Over the study period, the number of A. baumannii cultures decreased significantly in VAMCs (11.9% per year). In 2018, carbapenem resistance was seen in 28% of VAMC isolates and 36% of CLC isolates, but only 6% of outpatient isolates, while MDR was found in 31% of VAMC isolates and 36% of CLC isolates, but only 8 % of outpatient isolates. Carbapenem-resistant, multidrug-resistant and extensively drug-resistant A. baumannii isolates decreased significantly in VAMCs and outpatient clinics over time (VAMCs: by 4.9, 7.2 and 6.9%; outpatient: by 11.3, 10.5 and 10.2% per year). Resistant phenotypes remained stable in CLCs. Conclusion: In the VA nationally, the prevalence of A. baumannii is decreasing, as is resistance. Carbapenem-resistant and multidrug-resistant A. baumannii remain common in VAMCs and CLCs. The focus of infection control and antimicrobial stewardship efforts to prevent transmission of resistant A. baumannii should be in hospital and LTC settings

Clinical and genetic risk factors for biofilm-forming Staphylococcus aureus

The molecular and clinical factors associated with biofilm-forming methicillin-resistant Staphylococcus aureus (MRSA) are incompletely understood. Biofilm production for 182 MRSA isolates obtained from clinical culture sites (2004 to 2013) was quantified. Microbiological toxins, pigmentation, and genotypes were evaluated, and patient demographics were collected. Logistic regression was used to quantify the effect of strong biofilm production (versus weak biofilm production) on clinical outcomes and independent predictors of a strong biofilm. Of the isolates evaluated, 25.8% (47/182) produced strong biofilms and 40.7% (74/182) produced weak biofilms. Strong biofilm-producing isolates were more likely to be from multilocus sequence typing (MLST) clonal complex 8 (CC8) (34.0% versus 14.9%; P = 0.01) but less likely to be from MLST CC5 (48.9% versus 73.0%; P = 0.007). Predictors for strong biofilms were spa type t008 (adjusted odds ratio [aOR], 4.54; 95% confidence interval [CI], 1.21 to 17.1) and receipt of chemotherapy or immunosuppressants in the previous 90 days (aOR, 33.6; 95% CI, 1.68 to 673). Conversely, patients with high serum creatinine concentrations (aOR, 0.33; 95% CI, 0.15 to 0.72) or who previously received vancomycin (aOR, 0.03; 95% CI, 0.002 to 0.39) were less likely to harbor strong biofilm-producing MRSA. Beta-toxin-producing isolates (aOR, 0.31; 95% CI, 0.11 to 0.89) and isolates with spa type t895 (aOR, 0.02 95% CI

The Comparative Effectiveness of Ceftolozane/Tazobactam versus Aminoglycoside- or Polymyxin-Based Regimens in Multi-Drug-Resistant Pseudomonas aeruginosa Infections

Pseudomonas aeruginosa infections are challenging to treat due to multi-drug resistance (MDR) and the complexity of the patients affected by these serious infections. As new antibiotic therapies come on the market, limited data exist about the effectiveness of such treatments in clinical practice. In this comparative effectiveness study of ceftolozane/tazobactam versus aminoglycoside- or polymyxin-based therapies among hospitalized patients with positive MDR P. aeruginosa cultures, we identified 57 patients treated with ceftolozane/tazobactam compared with 155 patients treated with aminoglycoside- or polymyxin-based regimens. Patients treated with ceftolozane/tazobactam were younger (mean age 67.5 vs. 71.1, p = 0.03) and had a higher comorbidity burden prior to hospitalization (median Charlson 5 vs. 3, p = 0.01) as well as higher rates of spinal cord injury (38.6% vs. 21.9%, p = 0.02) and P. aeruginosa-positive bone/joint cultures (12.3% vs. 0.7%, p \u3c 0.0001). Inpatient mortality was significantly lower in the ceftolozane/tazobactam group compared with aminoglycosides or polymyxins (15.8% vs. 27.7%, adjusted odds ratio 0.39, 95% confidence interval 0.16–0.93). There were no significant differences observed for the other outcomes assessed. In hospitalized patients with MDR P. aeruginosa, inpatient mortality was 61% lower among patients treated with ceftolozane/tazobactam compared to those treated with aminoglycoside- or polymyxin-based regimens

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems

Transcriptomic analysis supports similar functional roles for the two thymuses of the tammar wallaby

Background: The thymus plays a critical role in the development and maturation of T-cells. Humans have a single thoracic thymus and presence of a second thymus is considered an anomaly. However, many vertebrates have multiple thymuses. The tammar wallaby has two thymuses: a thoracic thymus (typically found in all mammals) and a dominant cervical thymus. Researchers have known about the presence of the two wallaby thymuses since the 1800s, but no genome-wide research has been carried out into possible functional differences between the two thymic tissues. Here, we used pyrosequencing to compare the transcriptomes of a cervical and thoracic thymus from a single 178 day old tammar wallaby.Results: We show that both the tammar thoracic and the cervical thymuses displayed gene expression profiles consistent with roles in T-cell development. Both thymuses expressed genes that mediate distinct phases of T-cells differentiation, including the initial commitment of blood stem cells to the T-lineage, the generation of T-cell receptor diversity and development of thymic epithelial cells. Crucial immune genes, such as chemokines were also present. Comparable patterns of expression of non-coding RNAs were seen. 67 genes differentially expressed between the two thymuses were detected, and the possible significance of these results are discussed.Conclusion: This is the first study comparing the transcriptomes of two thymuses from a single individual. Our finding supports that both thymuses are functionally equivalent and drive T-cell development. These results are an important first step in the understanding of the genetic processes that govern marsupial immunity, and also allow us to begin to trace the evolution of the mammalian immune system

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted pvalue 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74±0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51±0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

\u3cem\u3eIn Vitro\u3c/em\u3e Coagulation Effects of Ophthalmic Doses of Bevacizumab

Purpose: In vitro coagulation effects of bevacizumab, a drug with potential risks for severe hemorrhagic and arterial thromboembolic events (ATEs), are unknown. The aim of this study was to evaluate the effects of bevacizumab, including the common ophthalmic dose of 1.25 mg, on the coagulation cascade. Methods: Bevacizumab doses of 0.25, 0.5, 1.0, 1.25, 2.0, 2.5, and 4.0 mg were incubated at 37 C in the presence of pooled normal plasma (PNP) to determine its biological activity via activated partial thromboplastin time (aPTT) and prothrombin time (PT) at 30 min, 1 h, and 2 h. The control consisted of 40% normal saline and 60% PNP. All evaluations were conducted in triplet. Coagulation at each time point was compared with the control group by analysis of variance with Tukey’s post hoc test. A P value of £0.05 was considered significant. Results: Mean bevacizumab aPTT ranged from 38.4 to 43.9 s, 37.4 to 43.1 s, and 39.0 to 43.2 s at 30 min, 1 h, and 2 h, respectively. Mean bevacizumab PT ranged from 15.7 to 16.8 s at 30 min, 14.6 to 16.2 s at 1 h, and 14.0 to 15.8 s at 2 h. For the control, aPTT was similar over time (40.1, 40.0, and 40.5 s), while PT decreased from 16.5 at 30 min to 15.4 s at 2 h. Bevacizumab decreased PT significantly at 2 h, compared with the PNP control, for the following doses: 0.25 mg [difference between means 1.04 s, 95% confidence interval (CI) 0.12–1.96], 1.25 mg (1.16 s, 95% CI 0.16–2.15), 2.5 mg (0.94 s, 95% CI 0.02–1.86), and 4 mg (1.41 s, 95% CI 0.41–2.40). Significant differences were not observed in PT at 30 min and 1 h as compared with controls. For all doses of bevacizumab, aPTT did not vary from controls at the 3 measured times. Conclusions: A common ophthalmic dose of bevacizumab 1.25 mg significantly increased in vitro coagulation. Further examination of the impact of ophthalmic bevacizumab on coagulation is warranted to provide insight into any putative link between ophthalmic bevacizumab and the risk for severe ATEs

Deconvolution of cancer cell states by the XDec-SM method.

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state