Institutional outbreak involving multiple clades of IMP-producing Enterobacter cloacae complex sequence type 78 at a cancer center in Tokyo, Japan.

BACKGROUND: Information about the clinical and microbiological characteristics of IMP-producing Enterobacterales has been limited. Here, we describe an institutional outbreak of IMP-producing Enterobacter cloacae complex (ECC) involving multiple clades of ECC sequence type (ST) 78 strains. METHODS: Antimicrobial susceptibility testing, whole-genome sequencing, and conjugation experiments of 18 IMP-producing ECC strains isolated during four-year study period were performed. Species and subspecies were determined by average nucleotide identity analysis and clonal relatedness of the isolates was analyzed with multilocus sequence typing and core-genome single nucleotide polymorphism (SNP) analysis. Relevant clinical information was extracted from medical records. RESULTS: Fourteen of 18 IMP-producing ECC isolates were determined as Enterobacter hormaechei ST78. Sixteen isolates, including 13 isolates belonging to ST78, carried blaIMP-1 in In316-like class 1 integron and also carried IncHI2 plasmids. Conjugation experiments were successful for 12 isolates carrying blaIMP-1 on IncHI2 plasmids and for an isolate carrying blaIMP-11 on an IncL/M plasmid. Although isolation of ST78 strains was clustered in a 14-months period suggesting nosocomial transmission, these strains were subdivided into three clades by SNP analysis: clade A (n = 10), clade B (n = 1), clade C (n = 3). A part of clonal relatedness was unexpected by the epidemiological information at the time of isolation of the strains. Most of the IMP-producing ECC strains were susceptible to non-ß-lactam antibiotics and had relatively low minimum inhibitory concentrations to carbapenems (≤4 µg/mL). Five of six infections caused by IMP-producing ECC were treated successfully. CONCLUSIONS: Whole-genome sequencing analysis revealed the outbreak was caused by three different clades of ST78 strains, where patients had favorable treatment outcome of the infections compared with that caused by Enterobacterales producing other carbapenemases, possibly due to their non-multidrug-resistant phenotype.

[Preparation of a brochure for patients undergoing FOLFIRI chemotherapy based on survey of adverse reactions].

We have retrospectively been collecting data on adverse reactions to FOLFIRI chemotherapy in our hospital from September 2005 to August 2006, by electronic medical records. The retrospective study was of 67 patients who received FOLFIRI for advanced colorectal cancer. Survey results showed high incidences of vomiting(58.2%), anorexia(91.4%), constipation (47.8%), diarrhea (61.2%) and alopecia(71.6%). The first cycle using FOLFIRI therapy, vomiting (20.9%), anorexia(53.7%), constipation ( 14.9%), and diarrhea (23.9%)were observed. We sought to minimize inter-individual differences in pharmaceutical care and drug consultation by clinical pharmacists and to ensure the accurate understanding of patients. We are sure that this kind of activity will help us to provide better pharmaceutical care for patients.

Classification of volcanic ash particles using a convolutional neural network and probability.

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.