Addressing erroneous scale assumptions in microbe and gene set enrichment analysis.

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

From pre-test and post-test probabilities to medical decision making.

BACKGROUND: A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive. METHODS: We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making. RESULTS: We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r ∗ = ( 1 - p ) / p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r ∗ represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians. CONCLUSIONS: Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.

Measuring and mitigating PCR bias in microbiota datasets.

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

Allogenic Fecal Microbiota Transplantation in Patients With Nonalcoholic Fatty Liver Disease Improves Abnormal Small Intestinal Permeability: A Randomized Control Trial.

INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD) is an obesity-related disorder that is rapidly increasing in incidence and is considered the hepatic manifestation of the metabolic syndrome. The gut microbiome plays a role in metabolism and maintaining gut barrier integrity. Studies have found differences in the microbiota between NAFLD and healthy patients and increased intestinal permeability in patients with NAFLD. Fecal microbiota transplantation (FMT) can be used to alter the gut microbiome. It was hypothesized that an FMT from a thin and healthy donor given to patients with NAFLD would improve insulin resistance (IR), hepatic proton density fat fraction (PDFF), and intestinal permeability. METHODS: Twenty-one patients with NAFLD were recruited and randomized in a ratio of 3:1 to either an allogenic (n = 15) or an autologous (n = 6) FMT delivered by using an endoscope to the distal duodenum. IR was calculated by HOMA-IR, hepatic PDFF was measured by MRI, and intestinal permeability was tested using the lactulose:mannitol urine test. Additional markers of metabolic syndrome and the gut microbiota were examined. Patient visits occurred at baseline, 2, 6 weeks, and 6 months post-FMT. RESULTS: There were no significant changes in HOMA-IR or hepatic PDFF in patients who received the allogenic or autologous FMT. Allogenic FMT patients with elevated small intestinal permeability (>0.025 lactulose:mannitol, n = 7) at baseline had a significant reduction 6 weeks after allogenic FMT. DISCUSSION: FMT did not improve IR as measured by HOMA-IR or hepatic PDFF but did have the potential to reduce small intestinal permeability in patients with NAFLD.

Modeling the temporal dynamics of the gut microbial community in adults and infants.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.

Background: Cholera is a public health problem worldwide, and the risk factors for infection are only partially understood. Methods: We prospectively studied household contacts of patients with cholera to compare those who were infected to those who were not. We constructed predictive machine learning models of susceptibility, using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro. Results: We found that machine learning models based on gut microbiota, as well as models based on known clinical and epidemiological risk factors, predicted V. cholerae infection. A predictive gut microbiota of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked. Conclusion: These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

A Multicenter Study of Bacterial Blood Stream Infections in Pediatric Allogeneic Hematopoietic Cell Transplantation Recipients: The Role of Acute Gastrointestinal Graft-versus-Host Disease.

Blood stream infections (BSI) caused by enteric organisms are associated with a particularly high mortality rate in allogeneic hematopoietic cell transplantation (alloHCT) recipients. We conducted a retrospective multicenter study aiming to analyze the risk factors associated with antibiotic resistance and impact of BSI on transplantation-related mortality (TRM) in children after alloHCT. During the study period from 2004 to 2014, 395 children (mean age, 9.4 years) with at least 1 BSI were included. The incidences of resistant gram-negative rods were 20.7% to piperacillin-tazobactam, 10.9% to cefepime, 21% to ceftazidime, 11.4% to levofloxacin, and 8.16% to meropenem. Thirty-eight percent of Enterococcus spp. isolates were resistant to vancomycin. More than 1 episode of BSI was associated with significant increase in the risk of resistance to piperacillin-tazobactam, cefepime, and vancomycin. On multivariate analysis of risk factors for TRM, achievement of neutrophil engraftment by day 30 was associated with lower TRM (P = .002). However, infection with an antibiotic-resistant organism was not associated with TRM. Development of enteric bacterial BSI after the onset of acute gastrointestinal graft-versus-host disease (GVHD) was the strongest predictor of TRM (hazard ratio, 4.786; 95% confidence interval, 2.833 to 8.087; P < .001). In patients with acute gastrointestinal GVHD who subsequently developed enteric bacterial BSI, the incidence of 1-year TRM was 33.4% (SE = 7%), compared with 15.3% (SE = 2%) for those without acute gastrointestinal GVHD (P = .004). Primary prevention of a first episode of BSI is arguably the most important intervention to decrease antibiotic resistance. It is also imperative that we develop strategies to maintain gastrointestinal health, especially in patients with gastrointestinal GVHD, in an effort to prevent subsequent enteric bacterial BSI and improve survival.

Safety of hematopoietic cell infusion in children with malignant and non-malignant diseases.

HPC infusions have been associated with a variety of adverse events related to either patient or HPC product-related factors. Studies documenting infusion-related AEs in children are limited. We reviewed HPC infusion records in 354 children. Infusion-related adverse events were classified as follows: grade 0-absent, grade I-mild, grade II-moderate, grade III-severe, grade IV-life-threatening, and grade V-death. The percentage of patients with grade 0, I, and II-IV AEs was as follows: 0 = 67%, I = 23.4%, and II-V = 9.6% (one patient had fatal anaphylactic reaction to dimethyl sulfoxide). The incidence of grade II-IV hypertension was 7.1%. There was a higher incidence of AEs with infusion of allogeneic bone marrow versus allogeneic PBSCs (47.4% vs 25.3%, P = .001). Cryopreserved products had a lower incidence of infusion-associated AEs compared with fresh HPC products (24% vs 39.4%, P = .003). Allogeneic HPC infusion volume (>100 mL) was a significant risk factor for infusion-associated AEs (P < .001). Patients >10 years who received autologous HPC infusions had higher risk of AEs when compared to patients <10 years (P = .01). Our study demonstrated that despite a high incidence of infusion-associated hypertension, HPC infusion is relatively safe in children. Investigating strategies to optimize management of hypertension in the setting of HPC infusion is warranted.

CT Evaluation of Small-Diameter Coronary Artery Stents: Effect of an Integrated Circuit Detector with Iterative Reconstruction.

PURPOSE: To use suitable objective methods of analysis to assess the influence of the combination of an integrated-circuit computed tomographic (CT) detector and iterative reconstruction (IR) algorithms on the visualization of small (≤3-mm) coronary artery stents. MATERIALS AND METHODS: By using a moving heart phantom, 18 data sets obtained from three coronary artery stents with small diameters were investigated. A second-generation dual-source CT system equipped with an integrated-circuit detector was used. Images were reconstructed with filtered back-projection (FBP) and IR at a section thickness of 0.75 mm (FBP75 and IR75, respectively) and IR at a section thickness of 0.50 mm (IR50). Multirow intensity profiles in Hounsfield units were modeled by using a sum-of-Gaussians fit to analyze in-plane image characteristics. Out-of-plane image characteristics were analyzed with z upslope of multicolumn intensity profiles in Hounsfield units. Statistical analysis was conducted with one-way analysis of variance and the Student t test. RESULTS: Independent of stent diameter and heart rate, IR75 resulted in significantly increased xy sharpness, signal-to-noise ratio, and contrast-to-noise ratio, as well as decreased blurring and noise compared with FBP75 (eg, 2.25-mm stent, 0 beats per minute; xy sharpness, 278.2 vs 252.3; signal-to-noise ratio, 46.6 vs 33.5; contrast-to-noise ratio, 26.0 vs 16.8; blurring, 1.4 vs 1.5; noise, 15.4 vs 21.2; all P < .001). In the z direction, the upslopes were substantially higher in the IR50 reconstructions (2.25-mm stent: IR50, 94.0; IR75, 53.1; and FBP75, 48.1; P < .001). CONCLUSION: The implementation of an integrated-circuit CT detector provides substantially sharper out-of-plane resolution of coronary artery stents at 0.5-mm section thickness, while the use of iterative image reconstruction mostly improves in-plane stent visualization.

Monoenergetic extrapolation of cardiac dual energy CT for artifact reduction.

BACKGROUND: Monoenergetic extrapolation of cardiac dual-energy computed tomography (DECT) could be useful in artifact reduction in clinical practice. PURPOSE: To evaluate the potential of monoenergetic extrapolation of cardiac DECT data for reducing artifacts from metal and high iodine contrast concentration. MATERIAL AND METHODS: With IRB approval and in HIPAA compliance, 35 patients (22 men, 61 ± 12 years) underwent cardiac DECT with dual-source CT (100 kVp and 140 kVp). Contrast material injection protocols were adapted to the patient's weight using non-ionic low-osmolar 370 mgI/mL iopromide. Datasets were transferred to a stand-alone workstation and dedicated monoenergetic analysis software was used for postprocessing. Reconstructions with the following five photon energies were generated: 40 keV, 60 keV, 80 keV, 100 keV, and 120 keV. Artifact severity was graded on a 5-point Likert scale (0, massive artifact; 5, absence of artifact). The size of artifact and image noise (expressed as HU) in anatomic structures adjacent to the artifact were measured. Quantitative and subjective image quality was compared using Friedman and Wilcoxon tests. RESULTS: We observed artifacts arising from densely concentrated contrast material in the superior vena cava (SVC) in 18 patients, from sternal wires in 14, from bypass clips in eight, and from coronary artery stents in seven. Artifact size in monoenergetic reconstructions from 40 to 120 keV decreased from 21.3 to 19 mm for the SVC (P < 0.001), from 8.4 to 2.6 mm for sternal wires (P < 0.001), from 6.4 to 2.2 mm for bypass clips (P < 0.001), and from 5.9 to 2.7 mm for stents (P < 0.001), respectively. The quality score changed from 0.2 to 3.8 for the SVC (P < 0.001), from 0.1 to 4 for sternal wires (P < 0.001), from 0 to 3.9 for bypass clips (P < 0.001), and from 0 to 3.9 for stents (P < 0.001). Lowest noise in adjacent structures was found at 80 keV for the SVC (39.1 HU), for sternal wires (33.3), for bypass clips (26.9), and for stents (33.9). CONCLUSION: A significant reduction of high-attenuation artifacts can be achieved by use of higher monoenergetic energy levels with cardiac DECT. However, image noise in anatomic structures affected by artifacts is lowest at 80 keV, which suggests an evaluation approach that makes use of multiple energy levels for a complete diagnosis.

First-arterial-pass dual-energy CT for assessment of myocardial blood supply: do we need rest, stress, and delayed acquisition? Comparison with SPECT.

PURPOSE: To compare the relative contributions of rest, stress, and delayed acquisitions with the accuracy of dual-energy (DE) computed tomography (CT) for the assessment of myocardial blood supply. MATERIALS AND METHODS: With institutional review board approval and HIPAA compliance, 55 consecutive patients (10 women, 45 men; mean age, 62 years ± 10) clinically referred for cardiac single photon emission computed tomography (SPECT) who were known to have or were suspected of having coronary artery disease were prospectively enrolled. DE CT studies were acquired during adenosine stress, at rest, and after 6-minute delay. The DE CT iodine distribution maps were visually assessed for perfusion deficits or late iodine enhancement. Per-segment agreement between modalities was investigated with κ statistics. Test characteristics for the detection of perfusion deficits were calculated for combinations of rest, stress, and delayed DE CT acquisition, with SPECT as reference standard. RESULTS: At SPECT, 714 segments were considered normal, 192 showed fixed perfusion defects, and 29 showed reversible perfusion deficits. Sensitivity of rest-only DE CT was 92%, and specificity was 98%. Stress-only, rest-stress, stress and delayed, and the combination of all three had a sensitivity of 99% and a specificity of 97%. Of 29 segments with reversible perfusion deficits at SPECT, 13 (45%) were misclassified by using rest-stress DE CT as fixed perfusion deficits. With stress DE CT plus delayed acquisition, 13 of 192 (7%) segments with fixed perfusion deficits at SPECT were misclassified as reversible. CONCLUSION: Rest-stress acquisition should be the protocol of choice for assessment of the myocardial blood supply in DE CT. The accuracy of DE CT is not increased by the addition of a delayed DE CT acquisition, which may therefore be omitted to reduce radiation exposure. With rest-stress DE CT, almost one-half of defects that are reversible at SPECT were classified as fixed; radiologists and clinicians need to be aware of this incongruence when they interpret DE CT myocardial perfusion studies.

Reduced radiation dose and improved image quality at cardiovascular CT angiography by automated attenuation-based tube voltage selection: intra-individual comparison.

OBJECTIVES: To evaluate the effect of automated tube voltage selection on radiation dose and image quality at cardiovascular CT angiography (CTA). METHODS: We retrospectively analysed paired studies in 72 patients (41 male, 60.5 ± 16.5 years), who had undergone CTA acquisitions of the heart or aorta both before and after the implementation of an automated x-ray tube voltage selection algorithm (ATVS). All other parameters were kept identical between the two acquisitions. Subjective image quality (IQ) was rated and objective IQ was measured by image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and figure of merit (FOM). Image quality parameters and effective dose were compared between acquisitions. RESULTS: Overall subjective image quality improved with the percentage of cases scored as adequate or higher increasing from 79 % to 92 % after implementation of ATVS (P = 0.03). SNR (14.1 ± 5.9, 15.7 ± 6.1, P = 0.009), CNR (11.6 ± 5.3, 13.2 ± 5.6, P = 0.011), and FOM (19.9 ± 23.3, 43.8 ± 51.1, P < 0.001) were significantly higher after implementation of ATVS. Mean image noise (24.1 ± 8.4 HU, 22.7 ± 7.1 HU, P = 0.048) and mean effective dose (10.6 ± 5.9 mSv, 8.8 ± 5.0 mSv, P = 0.003) were significantly lower after implementation of ATVS. CONCLUSIONS: Automated tube voltage selection can operator-independently optimize cardiovascular CTA image acquisition parameters with improved image quality at reduced dose. KEY POINTS: • Automatic tube voltage selection optimizes tube voltage for each individual patient. • In this population, overall radiation dose decreased while image quality improved. • This tool may become valuable for improving dose/quality ratio.

Automated quantification of epicardial adipose tissue using CT angiography: evaluation of a prototype software.

OBJECTIVES: This study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA). METHODS: cCTA data sets of 70 patients (58.6 ± 12.9 years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFVM segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFVA calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman's rho, and Bland-Altman plots were used for statistical analysis. RESULTS: The final EFVA (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2 mL vs. 65.8 ± 37.0 mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6 s vs. 314.3 ± 76.3 s, P < 0.001) and showed high reliability (ICC > 0.9). CONCLUSIONS: Automated EFVA quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods. KEY POINTS: • Manual epicardial fat volume quantification correlates with risk factors but is time-consuming. • The novel software prototype automates measurement of epicardial fat volume with good accuracy. • This novel approach is less time-consuming and could be incorporated into clinical workflow.

Incremental value of pharmacological stress cardiac dual-energy CT over coronary CT angiography alone for the assessment of coronary artery disease in a high-risk population.

OBJECTIVE: The purpose of this article is to prospectively determine the value of stress dual-energy CT (DECT) myocardial perfusion imaging to coronary CT angiography (CTA) for the assessment of coronary artery disease (CAD) in a high-risk population. SUBJECTS AND METHODS: We prospectively enrolled 29 consecutive patients who were referred for cardiac SPECT examinations for known or suspected CAD to also undergo pharmacologic stress cardiac DECT. In 25 patients, cardiac catheterization was available as the reference standard for morphologically significant stenosis. The performance of coronary CTA alone, DECT myocardial perfusion alone, and the combination of both was assessed by calculating sensitivity, specificity, and AUC values. RESULTS: For morphologically significant stenosis, coronary CTA alone and myocardial DECT assessment alone had 95% sensitivity and 50% specificity. The combined approach yielded 100% sensitivity and 33% specificity if either was positive and 90% sensitivity and 67% specificity if both were positive. The AUC value was highest (0.78) if both were positive. For hemodynamically significant lesions, coronary CTA alone had 91% sensitivity and 38% specificity, and DECT alone had 95% sensitivity and 75% specificity. The combined approach yielded 100% sensitivity and 38% specificity if either was positive and 86% sensitivity and 75% specificity if both were positive. AUC values were highest for DECT alone (0.85) and the "both positive" evaluation (0.80). CONCLUSION: The combined analysis of coronary CTA and DECT myocardial perfusion reduces the number of false-positives in a high-risk population for CAD and outperforms the purely anatomic test of coronary CTA alone for the detection of morphologically and hemodynamically significant CAD.

Integrated cardiothoracic imaging with computed tomography.

The respiratory and the cardiovascular systems are intimately connected. Because of the high degree of morphological and functional interaction, pathophysiological processes in one compartment are likely to induce adaptive changes in the other. Computed tomography (CT) plays a central role in the diagnostic work up of both thoracic and cardiac disorders. Historically, these two systems have been evaluated separately; however, CT technology has evolved remarkably over recent decades. Up-to-date advanced imaging strategies allow for a combined assessment of the cardiopulmonary unit. Besides improved techniques of electrocardiogram (ECG)-synchronization for obtaining both morphological and functional information, latest advances of dual-source CT (DSCT) have shown great promise for even more comprehensive integrated cardiothoracic imaging.

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis.

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

From Pre-test and Post-test Probabilities to Medical Decision Making.

Background: A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive. Methods: We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making. Results: We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r*=(1-p)/p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r* represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians. Conclusions: Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.

Investigating antibiotic free feed additives for growth promotion in poultry: effects on performance and microbiota.

The poultry industry is evolving towards antibiotic-free production to meet market demands and decelerate the increasing spread of the antimicrobial resistance. The growing need for antibiotic free products has challenged producers to decrease or completely stop using antimicrobials as feed supplements in broiler diet to improve feed efficiency, growth rate, and intestinal health. Natural feed additives (e.g., probiotics and phytobiotics) are promising alternatives to substitute antimicrobial growth promoters. The goal of our study was to characterize the effects of a Probiotic and an Essential Oils blend on broilers' performance and perform a time-series analysis to describe their excreta microbiome. A total of 320 Cobb 500 (1-day-old) chicks were raised for 21 d in 32 randomly allocated cages. Treatments consisted of 4 experimental diets: a basal diet, and a basal diet mixed with an Antibiotic (bacitracin methylene disalicylate), an essential oils blend (oregano oil, rosemary, and red pepper), or a Probiotic (Bacillus subtilis). Body weight (on 1, 10, and 21d), and feed intake (10d and 21d) were recorded and feed conversion ratio was calculated. Droppings were collected daily (1-21d) to characterize broilers' excreta microbiota by targeted sequencing of the bacterial 16S rRNA gene. The Probiotic significantly improved feed conversion ratio for starter phase 1 to 10d (P = 0.03), grower phase 10 to 21d (P = 0.05), and total period 1 to 21d (P = 0.01) compared to the Antibiotic. Feed supplements did not affect alpha diversity but did impact microbial beta diversity (P < 0.01). Age also impacted microbiome turnover as differences in alpha and beta diversity were detected. Furthermore, when compared to the basal diet, the probiotic and antibiotic significantly impacted relative abundance of Bifidobacterium (log2 fold change -1.44, P = 0.03), Intestinimonas (log2 fold change 0.560, P < 0.01) and Ligilactobacillus (log2 fold change -1.600, P < 0.01). Overall, Probiotic supplementation but not essential oils supplementation positively impacted broilers' growth performance by directly causing directional shifts in broilers' excreta microbiota structure.

Changes in the type 2 diabetes gut mycobiome associate with metformin treatment across populations.

The human gut teems with a diverse ecosystem of microbes, yet non-bacterial portions of that community are overlooked in studies of metabolic diseases firmly linked to gut bacteria. Type 2 diabetes mellitus (T2D) is associated with compositional shifts in the gut bacterial microbiome and the mycobiome, the fungal portion of the microbiome. However, whether T2D and/or metformin treatment underpins fungal community changes is unresolved. To differentiate these effects, we curated a gut mycobiome cohort spanning 1,000 human samples across five countries and validated our findings in a murine experimental model. We use Bayesian multinomial logistic normal models to show that T2D and metformin both associate with shifts in the relative abundance of distinct gut fungi. T2D is associated with shifts in the Saccharomycetes and Sordariomycetes fungal classes, while the genera Fusarium and Tetrapisipora most consistently associate with metformin treatment. We confirmed the impact of metformin on individual gut fungi by administering metformin to healthy mice. Thus, metformin and T2D account for subtle, but significant and distinct variation in the gut mycobiome across human populations. This work highlights for the first time that metformin can confound associations of gut fungi with T2D and warrants the need to consider pharmaceutical interventions in investigations of linkages between metabolic diseases and gut microbial inhabitants. IMPORTANCE: This is the largest to-date multi-country cohort characterizing the human gut mycobiome, and the first to investigate potential perturbations in gut fungi from oral pharmaceutical treatment. We demonstrate the reproducible effects of metformin treatment on the human and murine gut mycobiome and highlight a need to consider metformin as a confounding factor in investigations between type 2 diabetes mellitus and the gut microbial ecosystem.

Dual-energy CT lung ventilation/perfusion imaging for diagnosing pulmonary embolism.

OBJECTIVES: To evaluate the feasibility and findings of combined dual-energy computed tomography (DECT) lung ventilation/perfusion imaging in patients with suspected pulmonary embolism (PE). METHODS: This study was institutional review board-approved and written informed consent was obtained from each patient. Thirty-two subjects (aged 11-61 years) underwent combined xenon-enhanced ventilation and iodine-enhanced perfusion DECT. Ventilation, perfusion and morphological information were visually interpreted. Ventilation/perfusion information was classified as mismatch (differing patterns) or match (concordant patterns). Adverse reactions and radiation doses were recorded for each subject. RESULTS: Of 32 patients undergoing xenon-enhanced DECT, six patients reported adverse reactions (shortness of breath, n = 2; mild dizziness, n = 3; limb numbness, n = 1). Twenty-eight of 32 patients could be included into the data analysis. PE was detected in 10/28 patients. PE-related ventilation/perfusion mismatch was found in 17 lung lobes in 8/10 patients and matched ventilation/perfusion was detected in 2 patients. Eighteen patients had no PE. In this group, there was no case of a ventilation/perfusion mismatch. Matched ventilation/perfusion impairment was seen in one patient. The overall radiation dose from two DECT acquisitions was 4.8 ± 1.4 mSv (range 2.7-7.5 mSv). CONCLUSIONS: DECT lung ventilation/perfusion imaging is feasible and can visualise ventilation/perfusion match or mismatch in patients with suspected PE. KEY POINTS: • Combined dual-energy CT lung ventilation/perfusion imaging is feasible. • Combined dual-energy CT ventilation/perfusion imaging provides lung morphological and functional information. • Dual-energy CT can demonstrate ventilation/perfusion mismatch in patients with pulmonary embolism.