Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Bone marrow fibrosis is associated with non-response to CD19 CAR T-cell therapy in B-acute lymphoblastic leukemia

CD19 directed CAR T-cell therapy is used to treat relapsed/refractory B-cell acute lymphoblastic leukemia. The role of the pre-CAR bone marrow (BM) stromal microenvironment in determining response to CAR T-cell therapy has been understudied. We performed whole transcriptome analysis, reticulin fibrosis assessment and CD3 T-cell infiltration on BM core biopsies from pre- and post-CAR timepoints for 61 patients, as well as on a cohort of 54 primary B-ALL samples. Pathways of fibrosis, extracellular matrix development, and associated transcription factors AP1 and TGF-β3, were enriched and upregulated in nonresponders (NR) even prior to CAR T cell therapy. NR showed significantly higher levels of BM fibrosis compared to complete responders by both clinical reticulin assessment and AI-assisted digital image scoring. CD3+ T cells showed a trend toward lower infiltration in NR. NR had significantly higher levels of pre-CAR fibrosis compared to primary B-ALL. High levels of fibrosis were associated with lower overall survival after CAR T-cell therapy. In conclusion, BM fibrosis is a novel mechanism mediating nonresponse to CD19-directed CAR T-cell therapy in B-ALL. A widely used clinically assay for quantitating myelofibrosis can be repurposed to determine patients at high risk of non-response. Genes and pathways associated with BM fibrosis are a potential target to improve response.

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

Configurational Causal Modeling and Logic Regression

Configurational comparative methods (CCMs) and logic regression methods (LRMs) are two families of exploratory methods that employ very different techniques to analyze data generated by causal structures featuring conjunctural causation and equifinality. Aiming for the same by different means carries a substantive synergy potential, which, however, remains untapped so far because representatives of the two frameworks know little of each other. The purpose of this article is to change that. We first level the field for readers from both backgrounds by providing brief introductions to the basic ideas behind CCMs and LRMs. Then, we carve out the strengths and weaknesses of the two method families by benchmarking their performance when applied to binary data under a variety of different discovery contexts. It turns out that CCMs and LRMs have complementary strengths and weaknesses. This creates various promising avenues for cross-validation.

Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation

OBJECTIVES

To develop and test a Retina U-Net algorithm for the detection of primary lung tumors and associated metastases of all stages on FDG-PET/CT.

METHODS

A data set consisting of 364 FDG-PET/CTs of patients with histologically confirmed lung cancer was used for algorithm development and internal testing. The data set comprised tumors of all stages. All lung tumors (T), lymphatic metastases (N), and distant metastases (M) were manually segmented as 3D volumes using whole-body PET/CT series. The data set was split into a training (n = 216), validation (n = 74), and internal test data set (n = 74). Detection performance for all lesion types at multiple classifier thresholds was evaluated and false-positive-findings-per-case (FP/c) calculated. Next, detected lesions were assigned to categories T, N, or M using an automated anatomical region segmentation. Furthermore, reasons for FPs were visually assessed and analyzed. Finally, performance was tested on 20 PET/CTs from another institution.

RESULTS

Sensitivity for T lesions was 86.2% (95% CI: 77.2-92.7) at a FP/c of 2.0 on the internal test set. The anatomical correlate to most FPs was the physiological activity of bone marrow (16.8%). TNM categorization based on the anatomical region approach was correct in 94.3% of lesions. Performance on the external test set confirmed the good performance of the algorithm (overall detection rate = 88.8% (95% CI: 82.5-93.5%) and FP/c = 2.7).

CONCLUSIONS

Retina U-Nets are a valuable tool for tumor detection tasks on PET/CT and can form the backbone of reading assistance tools in this field. FPs have anatomical correlates that can lead the way to further algorithm improvements. The code is publicly available.

KEY POINTS

• Detection of malignant lesions in PET/CT with Retina U-Net is feasible. • All false-positive findings had anatomical correlates, physiological bone marrow activity being the most prevalent. • Retina U-Nets can build the backbone for tools assisting imaging professionals in lung tumor staging.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

3D printing of resilient biogels for omnidirectional and exteroceptive soft actuators

Soft robotics greatly benefits from nature as a source of inspiration, introducing innate means of safe interaction between robotic appliances and living organisms. In contrast, the materials involved are often nonbiodegradable or stem from nonrenewable resources, contributing to an ever-growing environmental footprint. Furthermore, conventional manufacturing methods, such as mold casting, are not suitable for replicating or imitating the complexity of nature's creations. Consequently, the inclusion of sustainability concepts alongside the development of new fabrication procedures is required. We report a customized 3D-printing process based on fused deposition modeling, printing a fully biodegradable gelatin-based hydrogel (biogel) ink into dimensionally stable, complex objects. This process enables fast and cost-effective prototyping of resilient, soft robotic applications from gels that stretch to six times their original length, as well as an accessible recycling procedure with zero waste. We present printed pneumatic actuators performing omnidirectional movement at fast response times (less than a second), featuring integrated 3D-printed stretchable waveguides, capable of both proprio- and exteroception. These soft devices are endowed with dynamic real-time control capable of automated search-and-wipe routines to detect and remove obstacles. They can be reprinted several times or disposed of hazard-free at the end of their lifetime, potentially unlocking a sustainable future for soft robotics.

Incidental finding of renal cell carcinoma in an asymptomatic patient on low-dose computed tomography screening for lung cancer

INTRODUCTION In 2013, the United States Preventive Services Task Force recommended annual low-dose computed tomography (CT) screening for lung cancer in high-risk adults with a significant smoking history. These screenings result in large numbers of incidental findings, and although most of these do not warrant further investigation, there have been reported cases of incidental findings identified on CT screening that led to successful treatment of a previously undiagnosed comorbidity. CASE HISTORY Here, we report a case of papillary renal cell carcinoma that was detected incidentally on low-dose CT in an asymptomatic individual, a rare diagnosis considering that renal neoplasms account for <1% of incidental findings on these screenings. CONCLUSION This case highlights the value of investigating these incidental findings, with the goal of detecting underlying disease in some cases before it would have presented symptomatically.

Species interactions drive the spread of ampicillin resistance in human-associated gut microbiota

Background and objectives

Slowing the spread of antimicrobial resistance is urgent if we are to continue treating infectious diseases successfully. There is increasing evidence microbial interactions between and within species are significant drivers of resistance. On one hand, cross-protection by resistant genotypes can shelter susceptible microbes from the adverse effects of antibiotics, reducing the advantage of resistance. On the other hand, antibiotic-mediated killing of susceptible genotypes can alleviate competition and allow resistant strains to thrive (competitive release). Here, by observing interactions both within and between species in microbial communities sampled from humans, we investigate the potential role for cross-protection and competitive release in driving the spread of ampicillin resistance in the ubiquitous gut commensal and opportunistic pathogen Escherichia coli.

Methodology

Using anaerobic gut microcosms comprising E.coli embedded within gut microbiota sampled from humans, we tested for cross-protection and competitive release both within and between species in response to the clinically important beta-lactam antibiotic ampicillin.

Results

While cross-protection gave an advantage to antibiotic-susceptible E.coli in standard laboratory conditions (well-mixed LB medium), competitive release instead drove the spread of antibiotic-resistant E.coli in gut microcosms (ampicillin boosted growth of resistant bacteria in the presence of susceptible strains).

Conclusions and implications

Competition between resistant strains and other members of the gut microbiota can restrict the spread of ampicillin resistance. If antibiotic therapy alleviates competition with resident microbes by killing susceptible strains, as here, microbiota-based interventions that restore competition could be a key for slowing the spread of resistance.

Lay Summary

Slowing the spread of global antibiotic resistance is an urgent task. In this paper, we ask how interactions between microbial species drive the spread of resistance. We show that antibiotic killing of susceptible microbes can free up resources for resistant microbes and allow them to thrive. Therefore, we should consider microbes in light of their social interactions to understand the spread of resistance.

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis.

Evolution of honey resistance in experimental populations of bacteria depends on the type of honey and has no major side effects for antibiotic susceptibility

With rising antibiotic resistance, alternative treatments for communicable diseases are increasingly relevant. One possible alternative for some types of infections is honey, used in wound care since before 2000 BCE and more recently in licensed, medical-grade products. However, it is unclear whether medical application of honey results in the evolution of bacterial honey resistance and whether this has collateral effects on other bacterial traits such as antibiotic resistance. Here, we used single-step screening assays and serial transfer at increasing concentrations to isolate honey-resistant mutants of Escherichia coli. We only detected bacteria with consistently increased resistance to the honey they evolved in for two of the four tested honey products, and the observed increases were small (maximum twofold increase in IC90). Genomic sequencing and experiments with single-gene knockouts showed a key mechanism by which bacteria increased their honey resistance was by mutating genes involved in detoxifying methylglyoxal, which contributes to the antibacterial activity of Leptospermum honeys. Crucially, we found no evidence that honey adaptation conferred cross-resistance or collateral sensitivity against nine antibiotics from six different classes. These results reveal constraints on bacterial adaptation to different types of honey, improving our ability to predict downstream consequences of wider honey application in medicine.

[200 years of the animal hospital Zurich: A cross-section through the lectures of the exam year 1864]

INTRODUCTION

The current study analyzed eleven lecture transcripts from the Zurich Veterinary School of the years -1861-1864. The work presents the staff and organizational situation of the school at that time. Lectures concerned mostly clinical subjects, especially special pathology and therapy. The texts were transcribed, summarized and analyzed according to the criteria species, diagnosis and therapy. The drugs were listed. Therapy concepts follow the principles of humoral pathology, but transition to cellular pathology was imminent. The pathogens of infectious diseases are not identified yet, but are suspected to be called «contagions».

Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples

Biological invasions can alter ecosystem stability and function, and predicting what happens when a new species or strain arrives remains a major challenge in ecology. In the mammalian gastrointestinal tract, susceptibility of the resident microbial community to invasion by pathogens has important implications for host health. However, at the community level, it is unclear whether susceptibility to invasion depends mostly on resident community composition (which microbes are present), or also on local abiotic conditions (such as nutrient status). Here, we used a gut microcosm system to disentangle some of the drivers of susceptibility to invasion in microbial communities sampled from humans. We found resident microbial communities inhibited an invading Escherichia coli strain, compared to community-free control treatments, sometimes excluding the invader completely (colonization resistance). These effects were stronger at later time points, when we also detected altered community composition and nutrient availability. By separating these two components (microbial community and abiotic environment), we found taxonomic composition played a crucial role in suppressing invasion, but this depended critically on local abiotic conditions (adapted communities were more suppressive in nutrient-depleted conditions). This helps predict when resident communities will be most susceptible to invasion, with implications for optimizing treatments based on microbiota management.

Investigation of gamma secretase gene complex mutations in German population with Hidradenitis suppurativa designate a complex polygenic heritage

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic inflammatory disease affecting apocrine gland-bearing skin in the axilla, groin and under the breasts. Mutations of the gamma secretase gene complex, which is essential in the activation of Notch signalling pathways, were shown in some families with HS and in a few sporadic cases. Although an imbalance in Notch signalling is implicated in the pathogenesis, the exact mechanism of HS development is yet unknown.

OBJECTIVES

We aim to investigate the genetic basis of HS by determining the presence of mutations of gamma secretase gene complex in a cohort of HS patients and by searching for a disease-causing pathogenic variant in a multi-generational HS family using parametric linkage analysis.

METHODS

Thirty-eight patients clinically diagnosed with HS were included in this study. All exons and exon-intron boundaries of the genes encoding gamma secretase complex consisting of six genes: APH1A, APH1B, PSENEN, NCSTN, PSEN1 and PSEN2 were sequenced by Sanger technique. Genetic mapping with parametric linkage analysis for the patients in the family was performed with eight affected and four healthy individuals. The logarithm of odds was calculated.

RESULTS

In a sporadic patient with early-onset, severe lesions in axilla and groin, a novel single-nucleotide deletion causing frameshift in exon 1 of the NCSTN gene was identified ((NM_015331.3): c.38delG, p.(Gly13Glufs*15)). The LOD score of 1.5 was never exceeded in any region of the genome, pointing towards intricate multi-genic inheritance pattern within the affected family.

CONCLUSIONS

The gamma secretase gene complex mutations were rare in our cohort (3.2%). Besides, our analysis indicates a possible complex multi-genic inheritance in a seemingly autosomal dominantly inherited large HS family. Genetics of both familial and sporadic HS may be complicated in most cases, and the role of other potential genes such as autoinflammatory and modifier genes as well as environmental factors may influence the pathogenesis.

Correction to: Coincidence analysis: a new method for causal inference in implementation science

An amendment to this paper has been published and can be accessed via the original article.

Coincidence analysis: a new method for causal inference in implementation science

BACKGROUND

Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches and can reveal new empirical findings related to implementation that might otherwise have gone undetected.

METHODS

We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings.

RESULTS

The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage.

CONCLUSIONS

CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.

Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference

A well-known approach to the optical measure of oxygen is based on the quenching of luminescence by molecular oxygen. The main challenge for this measuring method is the determination of an accurate mathematical model for the sensor response. The reason is the dependence of the sensor signal from multiple parameters (like oxygen concentration and temperature), which are cross interfering in a sensor-specific way. The common solution is to measure the different parameters separately, for example, with different sensors. Then, an approximate model is developed where these effects are parametrized ad hoc. In this work, we describe a new approach for the development of a learning sensor with parallel inference that overcomes all these difficulties. With this approach we show how to generate automatically and autonomously a very large dataset of measurements and how to use it for the training of the proposed neural-network-based signal processing. Furthermore, we demonstrate how the sensor exploits the cross-sensitivity of multiple parameters to extract them from a single set of optical measurements without any a priori mathematical model with unprecedented accuracy. Finally, we propose a completely new metric to characterize the performance of neural-network-based sensors, the Error Limited Accuracy. In general, the methods described here are not limited to oxygen and temperature sensing. They can be similarly applied for the sensing with multiple luminophores, whenever the underlying mathematical model is not known or too complex.

A Case of Lethal Abdominal Compartment Syndrome due to Rapidly Expanding Ovarian Small Cell Carcinoma Pulmonary Type

A 53-year-old woman presented with a rapidly growing pelvic mass suspected to be endometrial cancer due to endometrial biopsy showing grade 1 endometrioid adenocarcinoma. Due to severe aortic valve stenosis, she underwent a transcatheter aortic valve replacement (TAVR) for surgical optimization for a planned total abdominal hysterectomy, bilateral salpingo-oophorectomy, and tumor debulking. She was discharged on dual antiplatelet therapy with plans for future surgery, but was readmitted with abdominal distension, constipation, and urinary retention. The pelvic mass seen on prior imaging studies had increased in size. Unanticipated asystole cardiac arrest occurred two days after readmission, which on autopsy was found to be secondary to abdominal compartment syndrome from a rapidly enlarging ovarian small cell carcinoma pulmonary type.

Combining antibiotics with antivirulence compounds can have synergistic effects and reverse selection for antibiotic resistance in Pseudomonas aeruginosa

Antibiotics are losing efficacy due to the rapid evolution and spread of resistance. Treatments targeting bacterial virulence factors have been considered as alternatives because they target virulence instead of pathogen viability, and should therefore exert weaker selection for resistance than conventional antibiotics. However, antivirulence treatments rarely clear infections, which compromises their clinical applications. Here, we explore the potential of combining antivirulence drugs with antibiotics against the opportunistic human pathogen Pseudomonas aeruginosa. We combined two antivirulence compounds (gallium, a siderophore quencher, and furanone C-30, a quorum sensing [QS] inhibitor) together with four clinically relevant antibiotics (ciprofloxacin, colistin, meropenem, tobramycin) in 9×9 drug concentration matrices. We found that drug-interaction patterns were concentration dependent, with promising levels of synergies occurring at intermediate drug concentrations for certain drug pairs. We then tested whether antivirulence compounds are potent adjuvants, especially when treating antibiotic resistant (AtbR) clones. We found that the addition of antivirulence compounds to antibiotics could restore growth inhibition for most AtbR clones, and even abrogate or reverse selection for resistance in five drug combination cases. Molecular analyses suggest that selection against resistant clones occurs when resistance mechanisms involve restoration of protein synthesis, but not when efflux pumps are up-regulated. Altogether, our work provides a first systematic analysis of antivirulence-antibiotic combinatorial treatments and suggests that such combinations have the potential to be both effective in treating infections and in limiting the spread of antibiotic resistance.

Associations between sensitivity to antibiotics, disinfectants and heavy metals in natural, clinical and laboratory isolates of Escherichia coli

Bacteria in nature often encounter non-antibiotic antibacterials (NAAs), such as disinfectants and heavy metals, and they can evolve resistance via mechanisms that are also involved in antibiotic resistance. Understanding whether susceptibility to different types of antibacterials is non-randomly associated across natural and clinical bacteria is therefore important for predicting the spread of resistance, yet there is no consensus about the extent of such associations or underlying mechanisms. We tested for associations between susceptibility phenotypes of 93 natural and clinical Escherichia coli isolates to various NAAs and antibiotics. Across all compound combinations, we detected a small number of non-random associations, including a trio of positive associations among chloramphenicol, triclosan and benzalkonium chloride. We investigated genetic mechanisms that can explain such associations using genomic information, genetic knockouts and experimental evolution. This revealed some mutations that are selected for by experimental exposure to one compound and confer cross-resistance to other compounds. Surprisingly, these interactions were asymmetric: selection for chloramphenicol resistance conferred cross-resistance to triclosan and benzalkonium chloride, but selection for triclosan resistance did not confer cross-resistance to other compounds. These results identify genetic changes involved in variable cross-resistance across antibiotics and NAAs, potentially contributing to associations in natural and clinical bacteria.

Resident microbial communities inhibit growth and antibiotic-resistance evolution of Escherichia coli in human gut microbiome samples

Countering the rise of antibiotic-resistant pathogens requires improved understanding of how resistance emerges and spreads in individual species, which are often embedded in complex microbial communities such as the human gut microbiome. Interactions with other microorganisms in such communities might suppress growth and resistance evolution of individual species (e.g., via resource competition) but could also potentially accelerate resistance evolution via horizontal transfer of resistance genes. It remains unclear how these different effects balance out, partly because it is difficult to observe them directly. Here, we used a gut microcosm approach to quantify the effect of three human gut microbiome communities on growth and resistance evolution of a focal strain of Escherichia coli. We found the resident microbial communities not only suppressed growth and colonisation by focal E. coli but also prevented it from evolving antibiotic resistance upon exposure to a beta-lactam antibiotic. With samples from all three human donors, our focal E. coli strain only evolved antibiotic resistance in the absence of the resident microbial community, even though we found resistance genes, including a highly effective resistance plasmid, in resident microbial communities. We identified physical constraints on plasmid transfer that can explain why our focal strain failed to acquire some of these beneficial resistance genes, and we found some chromosomal resistance mutations were only beneficial in the absence of the resident microbiota. This suggests, depending on in situ gene transfer dynamics, interactions with resident microbiota can inhibit antibiotic-resistance evolution of individual species.

Hair cortisol concentration in clinically healthy slaughter calves with and -without chronic bronchopneumonic -lesions

INTRODUCTION

The hypothesis of this study was that healthy calves undergo less stress and thus have lower hair cortisol concentrations than calves with chronic bronchopneumonic lesions. Fifty healthy calves (group 1) and 50 calves with chronic bronchopneumonic lesions (group 2) were used immediately after slaughter, at which time hair samples and both adrenal glands were collected. The hair samples and the left adrenal gland were used for cortisol measurement and the right adrenal gland was used for histological and morphometrical examinations. The median hair cortisol concentrations of calves in groups 1 and 2 were 1.6 and 1.9 pg/mg hair, respectively, and did not differ significantly. The same was true for the mean cortisol concentration of the adrenal gland (1.1 and 1.4 µg/g tissue) and for the adrenal cortisol content (3.7 and 4.6 µg). The weights of the cortex (3.3, mean, and 3.5 g, median) and medulla (1.7 and 1.8 g, both median) did not differ significantly between the groups. This study did not detect differences in hair and adrenal cortisol concentrations between clinically healthy slaughter calves with and without chronic bronchopneumonic lesions. In further studies, calves with clinical signs should be taken into account.

494. Use of a Clinical Prediction Tool to Predict Clostridium difficile Infection

Background

Clostridium difficile is a pathogen that may be a component of normal microbiota. In 2011, there were an estimated 453,000 cases of CDI in the United States and 29,300 deaths. Diagnosis of CDI is of often accomplished through nucleic acid amplification testing (NAAT) for C. difficile toxin genes, which carries a risk of false-positive results. In 1996, Katz et al. created a screen for CDI that was positive if the patient had significant diarrhea and either abdominal pain or prior antibiotic usage. Today, we believe that this tool is worth revisiting with increased incidence of CDI and improved testing methods. Our aim is to determine the current usefulness of the Katz et al. 1996 clinical decision tool for CDI.

Methods

We conducted a retrospective cross-sectional chart review at a Midwestern teaching hospital. All patients tested for CDI between June 1, 2016 and May 31, 2017 were initially eligible. Participants were excluded from data collection on the basis of missing information, a previous positive CDI test in the last 8 weeks or age <18 years. Charts were reviewed for age, sex, diarrhea, abdominal pain, antibiotic use, prior positive testing for CDI, and length of hospitalization. Data were analyzed using SAS Software.

Results

Of the initial 432 charts analyzed, 202 (46.8%) had no documented amount of diarrhea and 16 more were missing other data points, leaving 214 of 432 (49.5%) charts that included all data to be used for analysis. Of these 18 of 214 (8.4%) were positive results. The Katz screen was positive in 85 of 214 (40.2%) cases. The sensitivity, specificity, positive predictive value, and negative predictive value, respectively, were 61, 62, 13, and 95.

Conclusion

Katz et al. found a sensitivity, specificity, positive predictive value and negative predictive value of 80, 45, 18 and 94, respectively. The differences between these values and our own may be due to changes in the testing methodology and prevalence of CDI compared with a 1992 study population. The negative predictive value remains a strength. If this screening tool had been applied to our population, there may have been 128 (59.8%) fewer tests, but seven (38.9%) missed positive results.

Disclosures

All authors: No reported disclosures.

Synthesis of M-UiO-66 (M = Zr, Ce or Hf) employing 2,5-pyridinedicarboxylic acid as a linker: defect chemistry, framework hydrophilisation and sorption properties

MOFs of general composition [M₆(OH)₄(O)₄(PDC)_6−x(Cl)_2x(H₂O)_2x] with M = Zr, Ce, Hf; and 0 ≤ x ≤ 2 were obtained and characterised in detail.

Letting go: bacterial genome reduction solves the dilemma of adapting to predation mortality in a substrate-restricted environment

Resource limitation and predation mortality are major determinants of microbial population dynamics, and optimization for either aspect is considered to imply a trade-off with respect to the other. Adaptation to these selective factors may, moreover, lead to disadvantages at rich growth conditions. We present an example of a concomitant evolutionary optimization to both, substrate limitation and predation in an aggregate-forming freshwater bacterial isolate, and we elucidate an underlying genomic mechanism. Bacteria were propagated in serial batch culture in a nutrient-restricted environment either with or without a bacterivorous flagellate. Strains isolated after 26 growth cycles of the predator-prey co-cultures formed as much total biomass as the ancestor at ancestral growth conditions, albeit largely reallocated to cell aggregates. A ~273 kbp genome fragment was lost in three strains that had independently evolved with predators. These strains had significantly higher growth yield on substrate-restricted media than others that were isolated from the same treatment before the excision event. Under predation pressure, the isolates with the deletion outcompeted both, the ancestor and the strains evolved without predators even at rich growth conditions. At the same time, genome reduction led to a growth disadvantage in the presence of benzoate due to the loss of the respective degradation pathway, suggesting that niche constriction might be the price for the bidirectional optimization.

Protistan predation interferes with bacterial long-term adaptation to substrate restriction by selecting for defence morphotypes

Bacteria that are introduced into aquatic habitats face a low substrate environment interspersed with rare productive 'hotspots', as well as high protistan grazing. Whereas the former condition should select for growth performance, the latter should favour traits that reduce predation mortality, such as the formation of large cell aggregates. However, protected morphotypes often convey a growth disadvantage, and bacteria thus face a trade-off between investing in growth or defence traits. We set up an evolutionary experiment with the freshwater isolate Sphingobium sp. strain Z007 that conditionally increases aggregate formation in supernatants from a predator-prey coculture. We hypothesized that low substrate levels would favour growth performance and reduce the aggregated subpopulation, but that the concomitant presence of a flagellate predator might conserve the defence trait. After 26 (1-week) growth cycles either with (P+) or without (P-) predators, bacteria had evolved into strikingly different phenotypes. Strains from P- had low numbers of aggregates and increased growth yield, both at the original rich growth conditions and on various single carbon sources. By contrast, isolates from the P+ treatment formed elevated proportions of defence morphotypes, but exhibited lower growth yield and metabolic versatility. Moreover, the evolved strains from both treatments had lost phenotypic plasticity of aggregate formation. In summary, the (transient) residence of bacteria at oligotrophic conditions may promote a facultative oligotrophic life style, which is advantageous for survival in aquatic habitats. However, the investment in defence against predation mortality may constrain microbial adaptation to the abiotic environment.

Risk factors causing postweaning multisystemic wasting syndrome (PMWS) onset in Swiss pig farms

Postweaning multisystemic wasting syndrome (PMWS) was epizoozic between 2003 and 2008 in Switzerland. Nevertheless, infectious risk factors including porcine reproductive and respiratory syndrome virus (PRRSV) were missing at all or were seen only sporadically (enzootic pneumonia and actinobazillosis). In a case-control study, 30 farms with PMWS affected pigs were compared to 30 inconspicious farms ("matched pairs"). The case-control allocation was verified by PCV2 DNA measurements of 5 healthy weaned pigs in each control farm, 5 healthy and 5 PMWS affected weaners in each PMWS affected farm. Diseased pigs showed in average 1.8x10(8) DNA templates per ml serum significantly higher than healthy pigs from control farms with 1x10(6) DNA templates per ml serum. Virus load in healthy pigs did not differ between control- and PMWS affected farms. PMWS mainly emerged among affected pigs in the 5th to 8th week of age. In a logistic regression model risk factors were identified such as high occupancy in weaning pens (p = 0.002), large groups in gestation facilities (p = 0.03) as well as reduced birth weight < 1.3 kg (p = 0.04). We suggest these factors might have lead to chronic stress e.g. through influencing negatively social interaction in pigs or disturbances of the maturing immune system. Heavy fly and rodent infestation might not only be viewed as a vector for disease transmission, but, also as a stress factor.

Constituents of Euphorbiaceae, 13. Comm. [1] Isolation and Structure Elucidation of Five Cerebrosides from Euphorbia characias L

Five cerebrosides B-1−B-4 were isolated from the fraction B, obtained from the latex of Euphorbia characias L. On the basis of spectral evidences and chemical reactions they were characterized as (2S, 3S, 4R, 8Z)-1-O-(β-D-glucopyranosyl)-2N-[(2'R)-2'-hydroxytetracosenoyl]-8 (Z)-octadecene-1,3,4-triol-2-amino (B-1), (2S, 3S, 4R, 8Z)-1-O-(β-D-glucopyranosyl)-2 N-[(2'R)-2'-hydroxyhexacosenoyl]-8(Z)-octadecene-1,3,4-triol-2-amino(B-2), (2 S, 3 S,4 R, 8 Z)-1-O-(β-D-glucopyranosyl)-2N-[(2'R)-2'-hydroxyoctacosenoyl]-8 (Z)-octa-decene-1,3,4-triol-2-amino (B-3), (2 S, 3 S, 4R, 8Z)-1-O-(β-D-glucopyranosyl)-2N-[(2'R)-2'-hydroxyhexacosanoyl]-8 (Z)-octadecene-1,3,4-triol-2-amino (B-3a), (2S, 3S, 4R, 8Z)-1-O-(β-D-glucopyranosyl)-2 N-[(2'R)-2'-hydroxyheptacosanoyl]-8 (Z)-octadecene-1,3,4-triol-2-amino (B-4).

Reversed phase column flash chromatography was effective for the isolation of the cerebrosides. FAB-MS spectrometry, 1H NMR, 13C NMR analyses and DQF-COSY and 1H-detected HMQC (single bond and multiple bond) experiments and chemical reactions were useful in providing informations for the structure elucidation.

Improvement of anaerobic digestion performance by continuous nitrogen removal with a membrane contactor treating a substrate rich in ammonia and sulfide

The effect of reduced ammonia levels on anaerobic digestion was investigated. Two reactors were fed with slaughterhouse waste, one with a hollow fiber membrane contractor for ammonia removal and one without. Different organic loading rates (OLR) and free ammonia and sulfide concentrations were investigated. In the reactor with the membrane contactor, the NH4-N concentration was reduced threefold. At a moderate OLR (3.1 kg chemical oxygen demand - COD/m(3)/d), this reactor performed significantly better than the reference reactor. At high OLR (4.2 kg COD/m(3)/d), the reference reactor almost stopped producing methane (0.01 Nl/gCOD). The membrane reactor also showed a stable process with a methane yield of 0.23 Nl/g COD was achieved. Both reactors had predominantly a hydrogenotrophic microbial consortium, however in the membrane reactor the genus Methanosaeta (acetoclastic) was also detected. In general, all relevant parameters and the methanogenic consortium indicated improved anaerobic digestion of the reactor with the membrane.