Bacitracin enhances ceftriaxone susceptibility of the high-level ceftriaxone-resistant gonococcal FC428 clone.

IMPORTANCE: Ceftriaxone-based antimicrobial therapies for gonorrhea are threatened by waning ceftriaxone susceptibility levels and the global dissemination of the high-level ceftriaxone-resistant gonococcal FC428 clone. Combination therapy can be an effective strategy to restrain the development of ceftriaxone resistance, and for that purpose, it is important to find an alternative antimicrobial to replace azithromycin, which has recently been removed in some countries from the recommended ceftriaxone plus azithromycin dual-antimicrobial therapy. Ideally, the second antimicrobial should display synergistic activity with ceftriaxone. We hypothesized that bacitracin might display synergistic activity with ceftriaxone because of their distinct mechanisms targeting bacterial cell wall synthesis. In this study, we showed that bacitracin indeed displays synergistic activity with ceftriaxone against Neisseria gonorrhoeae. Importantly, strains associated with the FC428 clone appeared to be particularly susceptible to the bacitracin plus ceftriaxone combination, which might therefore be an interesting dual therapy for further in vivo testing.

Antimicrobial Activity of Auranofin, Cannabidivarin, and Tolfenamic Acid against Multidrug-Resistant Neisseria gonorrhoeae.

Alternative antimicrobial therapies are urgently required for the multidrug-resistant bacterial pathogen Neisseria gonorrhoeae, for which currently ceftriaxone is the only remaining recommended first-line therapy. Repurposing of drugs that are approved for other clinical applications offers an efficient approach for development of alternative antimicrobial therapies. Auranofin, cannabidivarin, and tolfenamic acid were recently identified to display antimicrobial activity against N. gonorrhoeae. Here, we investigated their activity against a collection of 575 multidrug-resistant clinical isolates. All three compounds displayed consistent antimicrobial activity against all isolates, including against strains associated with the high-level ceftriaxone-resistant FC428 clone, with both the mode and MIC90 for auranofin of 0.5 mg/L, while both the mode and MIC90 for cannabidivarin and tolfenamic acid were 8 mg/L. Correlations between MICs of ceftriaxone and auranofin, cannabidivarin or tolfenamic acid were low, indicating that development of cross-resistance is unlikely. Furthermore, antimicrobial synergy analysis between ceftriaxone and auranofin, cannabidivarin, or tolfenamic acid by determination of the fractional inhibitory concentration index (FICI) resulted in an interpretation of indifference. Finally, time-kill analyses showed that all three compounds are bactericidal against both the N. gonorrhoeae ATCC 49226 reference strain and an FC428-associated clinical isolate, with particularly cannabidivarin displaying rapid bactericidal activity. Overall, auranofin, cannabidivarin, and tolfenamic acid displayed consistent antimicrobial activity against multidrug-resistant N. gonorrhoeae, warranting further exploration of their suitability as alternative antimicrobials for treatment of gonococcal infections. IMPORTANCE Neisseria gonorrhoeae is a major public health concern because of the high incidence of gonorrhea and the increasingly limited options for antimicrobial therapy. Strains associated with the FC428 clone are a particular concern because they have shown global dissemination and they display high-level resistance against the currently recommended ceftriaxone therapy. Therefore, development of alternative antimicrobial therapies is urgently required to ensure treatment of gonorrhea remains available in the future. Repurposing of clinically approved drugs could be a rapid approach for the development of such alternative antimicrobials. In this study, we showed that repurposing of auranofin, cannabidivarin, and tolfenamic acid for antimicrobial therapy of gonorrhea deserves further clinical explorations because these compounds displayed consistent antimicrobial activity against a large collection of contemporary multidrug-resistant gonococcal isolates that included strains associated with the FC428 clone.

Correction: Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma.

[This corrects the article DOI: 10.1371/journal.pone.0118261.].

Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis.

BACKGROUND: The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis. MATERIALS AND METHODS: We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m2 ± 6.56 kg/m2. The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level. RESULTS: The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio. CONCLUSIONS: This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 µl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.

Test-retest reproducibility analysis of lung CT image features.

Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R(2) Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046).

Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach.

A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

PURPOSE: To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org. RESULTS: High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96). CONCLUSIONS: Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.

Radiomics: the process and the challenges.

"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.

A Scalable Framework For Segmenting Magnetic Resonance Images.

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.

Autonomic nervous system mediates the cardiovascular effects of Rhodiola sacra radix in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Rhodiola sacra (Crassulaceae) exhibits cardiovascular bioactivities and is used in Tibetan medicine for promoting circulation and preventing hypertension. However, the underlying mechanisms of its cardiovascular effects are poorly understood. AIM OF THE STUDY: The aim of this study was therefore to evaluate the cardiovascular activity of water-soluble fraction (WtF) and n-butanol-soluble fraction (BtF) of Rhodiola sacra radix and to explore its mechanism of action in propofol anesthetized Sprague-Dawley rats. MATERIALS AND METHODS: The changes of blood pressure, heart rate and cardiac contractility after systemic administration of the extracts (10-75 mg/kg) were examined for at least 40 min. Different antagonists were used to evaluate the mechanisms of cardiovascular effects of the extracts. RESULTS: Intravenous injection of the WtF (10, 25, 35, 50 or 75 mg/kg) exhibited dose-dependent hypotension and increases in heart rate and cardiac contractility. In contrast, mild alterations in the same cardiovascular parameters were detected only at high dose (75 mg/kg) BtF. The WtF-induced hypotensive, positive inotropic and chronotropic effects were significantly abolished by pretreatment with hexamethonium (30 mg/kg, i.v.) or reserpine (5 mg/kg, i.v.), whereas the hypotensive, but not the positive inotropic or chronotropic effect was potentiated by captopril (2.5 mg/kg, i.v.). Pretreatment with methylatropine (1 mg/kg, i.v.), on the other hand, reversed the positive inotropic and chronotropic but not the hypotensive effects of WtF. The WtF-induced cardiovascular responses were not affected in rats pretreated with N(G)-nitro-l-arginine methyl ester (20 mg/kg, i.v.). CONCLUSIONS: We conclude that systemic administration of the WtF of Rhodiola sacra radix elicited a potent hypotensive effect that was mediated by the withdrawal of sympathetic vasomotor tone and interaction with the circulatory angiotensin system. The positive inotropic and chronotropic effects of WtF may result from a direct vagal inhibition on the heart.