Effects of antimicrobial therapy duration and class on risk of antimicrobial-resistant Gram-negative bacillus bloodstream infection in patients with AML.

BACKGROUND: Antimicrobial-resistant Gram-negative bacilli (ARGNB) bloodstream infection (BSI) has been associated with prior antibiotic exposure among hematologic malignancy patients. The relationships between days of therapy (DOT), antimicrobial class, and ARGNB BSI risk are poorly understood. METHODS: This is a single-center, case-control study of acute myeloid leukemia (AML) patients including 115 cases with ARGNB BSI and 230 matched controls with non-ARGNB BSI between January 1, 2007 and December 31, 2018. Fixed- and mixed-effects logistic regression was used to examine relationships between antibiotic DOT and risk of ARGNB BSI. Admission to an intensive care unit (ICU) within 7 days, 30-day mortality, and Pitt Bacteremia Score (PBS) were secondary outcomes. RESULTS: Prior isolation of a antimicrobial-resistant organism (ARO) (OR 4.45 95% CI 1.46, 13.54), surgery within 90 days (OR 3.71, 95% CI 1.57, 8.73), aminoglycoside DOT (OR 1.14, 95% CI 1.05, 1.23), cefepime DOT (OR 1.09, 95% CI 1.05, 1.13), and carbapenem DOT (OR 1.10, 95% CI 1.05, 1.16) were associated with increased odds of ARGNB BSI. Days since last antibiotic administration (OR 0.98, 95% CI 0.97, 0.99) and inpatient days within 90 days (OR 0.95, 95% CI 0.93, 0.98) showed reduced odds of ARGNB BSI. Total antimicrobial DOT regardless of class was not associated with ARGNB BSI. ARGNB BSI was associated with increased 30-day mortality (OR 2.86, 95% CI 1.57, 5.22) CONCLUSIONS: Among AML patients with GNB BSI, greater DOT of aminoglycosides, cefepime, and carbapenems in the 90 days prior to BSI were associated with increased odds of ARGNB BSI.

Safety of Inhaled Amphotericin B Lipid Complex as Antifungal Prophylaxis in Lung Transplant Recipients.

Inhaled formulations of amphotericin B are the most widely used antifungal prophylactic agents in lung transplant recipients, yet there are limited data on their safety. We performed a single-center retrospective cohort study of 603 consecutive patients who underwent lung transplantation between 2012 and 2017 and received antifungal prophylaxis with inhaled amphotericin B lipid complex (iABLC) from the day of transplantation until hospital discharge. Of 603 patients, 600 (99.5%) received ≥1 dose of iABLC, and 544 (90.2%) completed the recommended prophylactic course. In total, 4,128 iABLC doses (median, 5; range, 1 to 48 per patient) were administered; 24 patients received >3 months of therapy. Only one (0.2%) patient discontinued therapy due to a drug-attributable adverse event. During the first posttransplant year, 80 (13.3%) patients died (median time to death, 171 days; interquartile range [IQR], 80 to 272 days), and 3,352 (median, 6 per patient) lung biopsies were performed; 414 (68.7%) patients developed biopsy-proven acute cellular rejection. One-year adverse events in our cohort of lung transplant recipients treated with iABLC during transplant hospitalization matched national outcomes for rejection, graft loss, and death. iABLC is a safe and well-tolerated antifungal prophylactic agent in lung transplant recipients.

Fast state-space methods for inferring dendritic synaptic connectivity.

We present fast methods for filtering voltage measurements and performing optimal inference of the location and strength of synaptic connections in large dendritic trees. Given noisy, subsampled voltage observations we develop fast l1-penalized regression methods for Kalman state-space models of the neuron voltage dynamics. The value of the l1-penalty parameter is chosen using cross-validation or, for low signal-to-noise ratio, a Mallows' Cp-like criterion. Using low-rank approximations, we reduce the inference runtime from cubic to linear in the number of dendritic compartments. We also present an alternative, fully Bayesian approach to the inference problem using a spike-and-slab prior. We illustrate our results with simulations on toy and real neuronal geometries. We consider observation schemes that either scan the dendritic geometry uniformly or measure linear combinations of voltages across several locations with random coefficients. For the latter, we show how to choose the coefficients to offset the correlation between successive measurements imposed by the neuron dynamics. This results in a "compressed sensing" observation scheme, with an important reduction in the number of measurements required to infer the synaptic weights.

RNAi screen of the protein kinome identifies checkpoint kinase 1 (CHK1) as a therapeutic target in neuroblastoma.

Neuroblastoma is a childhood cancer that is often fatal despite intense multimodality therapy. In an effort to identify therapeutic targets for this disease, we performed a comprehensive loss-of-function screen of the protein kinome. Thirty kinases showed significant cellular cytotoxicity when depleted, with loss of the cell cycle checkpoint kinase 1 (CHK1/CHEK1) being the most potent. CHK1 mRNA expression was higher in MYC-Neuroblastoma-related (MYCN)-amplified (P < 0.0001) and high-risk (P = 0.03) tumors. Western blotting revealed that CHK1 was constitutively phosphorylated at the ataxia telangiectasia response kinase target site Ser345 and the autophosphorylation site Ser296 in neuroblastoma cell lines. This pattern was also seen in six of eight high-risk primary tumors but not in control nonneuroblastoma cell lines or in seven of eight low-risk primary tumors. Neuroblastoma cells were sensitive to the two CHK1 inhibitors SB21807 and TCS2312, with median IC(50) values of 564 nM and 548 nM, respectively. In contrast, the control lines had high micromolar IC(50) values, indicating a strong correlation between CHK1 phosphorylation and CHK1 inhibitor sensitivity (P = 0.0004). Furthermore, cell cycle analysis revealed that CHK1 inhibition in neuroblastoma cells caused apoptosis during S-phase, consistent with its role in replication fork progression. CHK1 inhibitor sensitivity correlated with total MYC(N) protein levels, and inducing MYCN in retinal pigmented epithelial cells resulted in CHK1 phosphorylation, which caused growth inhibition when inhibited. These data show the power of a functional RNAi screen to identify tractable therapeutical targets in neuroblastoma and support CHK1 inhibition strategies in this disease.

Normal functioning and the treatment/enhancement distinction: an opportunity based assessment.

As genome mapping technology uncovers the roots of pathologic and physiologic human functioning, important questions are brought to the fore concerning our conceptualization of ideas such as disease, treatment, and enhancement. In 1985, Norman Daniels proposed a normal-functioning model that expands John Rawls' theory of justice to obligate the provision of health care based on the constraints disease places on individual opportunity, but also limits the commitment of the medical establishment by focusing on states that represent deviations from normal human function. While some argue that the boundaries of medical institutions' commitment to provide services within a normal-functioning model are arbitrary, the degree to which these concerns truly threaten the framework is often exaggerated in special cases put forward in the literature. Furthermore, the normal-functioning model provides a comprehensive basis for agreement in discussions of medicine's commitment to the demands of social justice where resources are limited and avoids the dangerous overextension of the healthcare system and medicalization to which more expansive models are exposed.

Risk Factors for Invasive Fungal Infection in Lung Transplant Recipients on Universal Antifungal Prophylaxis.

Background: Many centers use universal antifungal prophylaxis after lung transplant, but risk factors for invasive fungal infection (IFI) in this setting are poorly described. Methods: This retrospective, single-center cohort study including 603 lung transplant recipients assessed risk factors for early (within 90 days of transplant) invasive candidiasis (IC) and invasive mold infection (IMI) and late (90-365 days after transplant) IMI using Cox proportional hazard regression. Results: In this cohort, 159 (26.4%) patients had 182 IFIs. Growth of yeast on donor culture (hazard ratio [HR], 3.30; 95% CI, 1.89-5.75) and prolonged length of stay (HR, 1.02; 95% CI, 1.01-1.03) were associated with early IC risk, whereas transplantation in 2016 or 2017 (HR, 0.21; 95% CI, 0.06-0.70; HR, 0.25; 95% CI, 0.08-0.80, respectively) and female recipient sex (HR, 0.53; 95% CI, 0.30-0.93) were associated with reduced risk. Antimold therapy (HR, 0.21; 95% CI, 0.06-0.78) was associated with lower early IMI risk, and female donor sex (HR, 0.40; 95% CI, 0.22-0.72) was associated with lower late IMI risk. Recent rejection was a risk factor for late IMI (HR, 1.73; 95% CI, 1.02-2.95), and renal replacement therapy predisposed to early IC, early IMI, and late IMI (HR, 5.67; 95% CI, 3.01-10.67; HR, 7.54; 95% CI, 1.93-29.45; HR, 5.33; 95% CI, 1.46-19.49, respectively). Conclusions: In lung transplant recipients receiving universal antifungal prophylaxis, risk factors for early IC, early IMI, and late IMI differ.

Reproducible Model Selection Using Bagged Posteriors.

Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution ("BayesBag") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible.

Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime.

Due to the limitations of current voltage sensing techniques, optimal filtering of noisy, undersampled voltage signals on dendritic trees is a key problem in computational cellular neuroscience. These limitations lead to voltage data that is incomplete (in the sense of only capturing a small portion of the full spatiotemporal signal) and often highly noisy. In this paper we use a Kalman filtering framework to develop optimal experimental design methods for voltage sampling. Our approach is to use a simple greedy algorithm with lazy evaluation to minimize the expected square error of the estimated spatiotemporal voltage signal. We take advantage of some particular features of the dendritic filtering problem to efficiently calculate the Kalman estimator's covariance. We test our framework with simulations of real dendritic branching structures and compare the quality of both time-invariant and time-varying sampling schemes. While the benefit of using the experimental design methods was modest in the time-invariant case, improvements of 25-100% over more naïve methods were found when the observation locations were allowed to change with time. We also present a heuristic approximation to the greedy algorithm that is an order of magnitude faster while still providing comparable results.

Candida Infective Endocarditis: A Retrospective Study of Patient Characteristics and Risk Factors for Death in 703 United States Cases, 2015-2019.

BACKGROUND: Candida endocarditis is a rare, sometimes fatal complication of candidemia. Past investigations of this condition are limited by small sample sizes. We used the Vizient clinical database to report on characteristics of patients with Candida endocarditis and to examine risk factors for in-hospital mortality. METHODS: This was a multicenter, retrospective cohort study of 703 inpatients admitted to 179 US hospitals between October 2015 and April 2019. We reviewed demographic, diagnostic, medication administration, and procedural data from each patient's initial encounter. Univariate and multivariate logistic regression analyses were used to identify predictors of in-hospital mortality. RESULTS: Of 703 patients, 114 (16.2%) died during the index encounter. One hundred fifty-eight (22.5%) underwent an intervention on a cardiac valve. On multivariate analysis, acute and subacute liver failure was the strongest predictor of death (odds ratio [OR], 9.2; 95% confidence interval [CI], 4.8 -17.7). Female sex (OR, 1.9; 95% CI, 1.2-3.0), transfer from an outside medical facility (OR, 1.8; 95% CI, 1.1-2.8), aortic valve pathology (OR, 2.7; 95% CI, 1.5-4.9), hemodialysis (OR, 2.1; 95% CI, 1.1-4.0), cerebrovascular disease (OR, 2.2; 95% CI, 1.2-3.8), neutropenia (OR, 2.5; 95% CI, 1.3-4.8), and alcohol abuse (OR, 2.9; 95% CI, 1.3-6.7) were also associated with death on adjusted analysis, whereas opiate abuse was associated with a lower odds of death (OR, 0.5; 95% CI, 0.2-0.9). CONCLUSIONS: We found that the inpatient mortality rate was 16.2% among patients with Candida endocarditis. Acute and subacute liver failure was associated with a high risk of death, whereas opiate abuse was associated with a lower risk of death.

Bidirectional contact tracing could dramatically improve COVID-19 control.

Contact tracing is critical to controlling COVID-19, but most protocols only "forward-trace" to notify people who were recently exposed. Using a stochastic branching-process model, we find that "bidirectional" tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (Reff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.

Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, embed tumors in two dimensions with uniform manifold approximation and projection, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups, and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signatures observed in cancer cohorts. SIGNIFICANCE: The musicatk package empowers researchers to characterize mutational signatures and tumor heterogeneity with a comprehensive set of preprocessing utilities, discovery and prediction tools, and multiple functions for downstream analysis and visualization.

Breaking the mold: A case of pulmonary mucormycosis treated with isavuconazole.

Pulmonary mucormycosis is a rare opportunistic invasive fungal infection that disproportionately affects immunocompromised hosts and carries high morbidity and mortality. It is traditionally treated with combined pharmacologic and surgical modalities. Here we present a case of pulmonary mucormycosis in a patient whose disease burden precluded surgical management, and in whom acute kidney injury necessitated therapy with an alternative to the recommended pharmacologic antifungal therapy.