Characterization of a novel RepA_N-family plasmid harbouring the phenicol-oxazolidinone resistance gene optrA in Enterococcus faecalis ST16 high-risk clone of goat origin

One hundred forty-five florfenicol-resistant enterococci, isolated from swine fecal samples collected from 76 pig farms, were investigated for the presence of optrA, cfr, and poxtA genes by PCR. Thirty florfenicol-resistant Enterococcus isolates had at least one linezolid resistance gene. optrA was found to be the most widespread linezolid resistance gene (23/30), while cfr and poxtA were detected in 6/30 and 7/30 enterococcal isolates, respectively. WGS analysis also showed the presence of the cfr(D) gene in Enterococcus faecalis (n = 2 isolates) and in Enterococcus avium (n = 1 isolate). The linezolid resistance genes hybridized both on chromosome and plasmids ranging from ~25 to ~240 kb. Twelve isolates were able to transfer linezolid resistance genes to enterococci recipient. WGS analysis displayed a great variability of optrA genetic contexts identical or related to transposons (Tn6628 and Tn6674), plasmids (pE035 and pWo27-9), and chromosomal regions. cfr environments showed identities with Tn6644-like transposon and a region from p12-2300 plasmid; cfr(D) genetic contexts were related to the corresponding region of the plasmid 4 of Enterococcus faecium E8014; poxtA was always found on Tn6657. Circular forms were obtained only for optrA- and poxtA-carrying genetic contexts. Clonality analysis revealed the presence of E. faecalis (ST16, ST27, ST476, and ST585) and E. faecium (ST21) clones previously isolated from humans. These results demonstrate a dissemination of linezolid resistance genes in enterococci of swine origin in Central Italy and confirm the spread of linezolid resistance in animal settings.

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Faculty Opinions recommendation of Clinical and molecular characterization of early T-cell precursor leukemia: a high-risk subgroup in adult T-ALL with a high frequency of FLT3 mutations.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718430963.793495871 ◽

2014 ◽

Author(s):

Jacob Rowe ◽

Tsila Zuckerman

Keyword(s):

T Cell ◽

High Risk ◽

Molecular Characterization ◽

High Frequency ◽

Cell Precursor ◽

Flt3 Mutations

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Characterization of Fusarium Head Blight Resistance Gene Fhb1 and Its Putative Ancestor in Chinese Wheat Germplasm

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2018.00473 ◽

2018 ◽

Vol 44 (4) ◽

pp. 473 ◽

Cited By ~ 10

Author(s):

Zhan-Wang ZHU ◽

Deng-An XU ◽

Shun-He CHENG ◽

Chun-Bao GAO ◽

Xian-Chun XIA ◽

...

Keyword(s):

Fusarium Head Blight ◽

Resistance Gene ◽

Fusarium Head Blight Resistance ◽

Blight Resistance ◽

Head Blight ◽

Wheat Germplasm ◽

Chinese Wheat

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Identification and Characterization of a Shuttle Plasmid with Antibiotic Resistance Gene from Staphylococcus aureus

Current Microbiology ◽

10.1007/s00284-002-3841-3 ◽

2003 ◽

Vol 46 (6) ◽

pp. 413-417 ◽

Cited By ~ 2

Author(s):

Mausumi Bhakta ◽

Manjusri Bal

Keyword(s):

Staphylococcus Aureus ◽

Antibiotic Resistance ◽

Resistance Gene ◽

Antibiotic Resistance Gene ◽

Shuttle Plasmid ◽

Identification And Characterization

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Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

Scientific Reports ◽

10.1038/s41598-021-91297-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

José Castela Forte ◽

Galiya Yeshmagambetova ◽

Maureen L. van der Grinten ◽

Bart Hiemstra ◽

Thomas Kaufmann ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Mortality Risk ◽

Clustering Algorithms ◽

Kidney Injury ◽

Risk Groups ◽

Admission Diagnosis ◽

Dutch Hospital ◽

Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

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The Role of [18F]Fluciclovine PET/CT in the Characterization of High-Risk Primary Prostate Cancer: Comparison with [11C]Choline PET/CT and Histopathological Analysis

Cancers ◽

10.3390/cancers13071575 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1575

Author(s):

Lucia Zanoni ◽

Riccardo Mei ◽

Lorenzo Bianchi ◽

Francesca Giunchi ◽

Lorenzo Maltoni ◽

...

Keyword(s):

Bone Marrow ◽

High Risk ◽

Malignant Lesion ◽

Lesion Detection ◽

Histopathological Analysis ◽

Choline Pet ◽

Primary Prostate Cancer ◽

Pet Ct

The primary aim of the study was to evaluate the role of [18F]Fluciclovine PET/CT in the characterization of intra-prostatic lesions in high-risk primary PCa patients eligible for radical prostatectomy, in comparison with conventional [11C]Choline PET/CT and validated by prostatectomy pathologic examination. Secondary aims were to determine the performance of PET semi-quantitative parameters (SUVmax; target-to-background ratios [TBRs], using abdominal aorta, bone marrow and liver as backgrounds) for malignant lesion detection (and best cut-off values) and to search predictive factors of malignancy. A six sextants prostate template was created and used by PET readers and pathologists for data comparison and validation. PET visual and semi-quantitative analyses were performed: for instance, patient-based, blinded to histopathology; subsequently lesion-based, un-blinded, according to the pathology reference template. Among 19 patients included (mean age 63 years, 89% high and 11% very-high-risk, mean PSA 9.15 ng/mL), 45 malignant and 31 benign lesions were found and 19 healthy areas were selected (n = 95). For both tracers, the location of the “blinded” prostate SUVmax matched with the lobe of the lesion with the highest pGS in 17/19 cases (89%). There was direct correlation between [18F]Fluciclovine uptake values and pISUP. Overall, lesion-based (n = 95), the performance of PET semiquantitative parameters, with either [18F]Fluciclovine or [11C]Choline, in detecting either malignant/ISUP2-5/ISUP4-5 PCa lesions, was moderate and similar (AUCs ≥ 0.70) but still inadequate (AUCs ≤ 0.81) as a standalone staging procedure. A [18F]Fluciclovine TBR-L3 ≥ 1.5 would depict a clinical significant lesion with a sensitivity and specificity of 85% and 68% respectively; whereas a SUVmax cut-off value of 4 would be able to identify a ISUP 4-5 lesion in all cases (sensitivity 100%), although with low specificity (52%). TBRs (especially with threshold significantly higher than aorta and slightly higher than bone marrow), may be complementary to implement malignancy targeting.

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