Raman spectroscopy-based identification of nosocomial outbreaks of the clonal bacterium Escherichia coli

DNA-based techniques are frequently used to confirm the relatedness of putative outbreak isolates. These techniques often lack the discriminatory power when analyzing closely related microbes such as E. coli. Here the value of Raman spectroscopy as a typing tool for E. coli in a clinical setting was retrospectively evaluated

Enterococcus faecium genome dynamics during long-term asymptomatic patient gut colonization

Enterococcus faecium is a gut commensal of humans and animals. In addition, it has recently emerged as an important nosocomial pathogen through the acquisition of genetic elements that confer resistance to antibiotics and virulence. We performed a whole-genome sequencing-based study on 96 multidrug-resistant E. faecium strains that asymptomatically colonized five patients with the aim of describing the genome dynamics of this species. The patients were hospitalized on multiple occasions and isolates were collected over periods ranging from 15 months to 6.5 years. Ninety-five of the sequenced isolates belonged to E. faecium clade A1, which was previously determined to be responsible for the vast majority of clinical infections. The clade A1 strains clustered into six clonal groups of highly similar isolates, three of which consisted entirely of isolates from a single patient. We also found evidence of concurrent colonization of patients by multiple distinct lineages and transfer of strains between patients during hospitalization. We estimated the evolutionary rate of two clonal groups that each colonized single patients at 12.6 and 25.2 single-nucleotide polymorphisms (SNPs)/genome/year. A detailed analysis of the accessory genome of one of the clonal groups revealed considerable variation due to gene gain and loss events, including the chromosomal acquisition of a 37 kbp prophage and the loss of an element containing carbohydrate metabolism-related genes. We determined the presence and location of 12 different insertion sequence (IS) elements, with ISEfa5 showing a unique pattern of location in 24 of the 25 isolates, suggesting widespread ISEfa5 excision and insertion into the genome during gut colonization. Our findings show that the E. faecium genome is highly dynamic during asymptomatic colonization of the human gut. We observed considerable genomic flexibility due to frequent horizontal gene transfer and recombination, which can contribute to the generation of genetic diversity within the species and, ultimately, can contribute to its success as a nosocomial pathogen

Clostridium difficile Ribotype 027, Toxinotype III, the Netherlands

Outbreaks due to Clostridium difficile polymerase chain reaction (PCR) ribotype 027, toxinotype III, were detected in 7 hospitals in the Netherlands from April 2005 to February 2006. One hospital experienced at the same time a second outbreak due to a toxin A–negative C. difficile PCR ribotype 017 toxinotype VIII strain. The outbreaks are difficult to control

Within-host and population transmission of blaOXA-48 in K. pneumoniae and E. coli

During a large hospital outbreak of OXA-48 producing bacteria, most K. pneumoniaeOXA-48 isolates were phenotypically resistant to meropenem or imipenem, whereas most E. coliOXA-48 isolates were phenotypically susceptible to these antibiotics. In the absence of molecular gene-detection E. coliOXA-48 could remain undetected, facilitating cross-transmission and horizontal gene transfer of blaOXA-48. Based on 868 longitudinal molecular microbiological screening results from patients carrying K. pneumoniaeOXA-48 (n = 24), E. coliOXA-48 (n = 17), or both (n = 40) and mathematical modelling we determined mean durations of colonisation (278 and 225 days for K. pneumoniaeOXA-48 and E. coliOXA-48, respectively), and horizontal gene transfer rates (0.0091/day from K. pneumoniae to E. coli and 0.0015/day vice versa). Based on these findings the maximum effect of horizontal gene transfer of blaOXA-48 originating from E. coliOXA-48 on the basic reproduction number (R0) is 1.9%, and it is, therefore, unlikely that phenotypically susceptible E. coliOXA-48 will contribute significantly to the spread of blaOXA-48. Copyright

Improved CARMA Locality Estimation Model for Peer List Reordering and Its Experimental Validation

The improvement of CARMA network model by addition of locality flavor as well as IPv6 support are concerned. The direct experimental evidence of the improvement of the efficiency of the peer-to-peer networks in terms of the network throughput using previously proposed CARMA locality awareness methods is established. The possible influences of the dynamics of the number of seeding and peering nodes in a swarm (including those directly connected to the test rig) are analyzed and taken into account. Main results indicate average 2,5 % improvement in transfer speed in comparison with clean unbiased transfer modes.Розглянуто вдосконалену реалізацію моделі CARMA, до якої додано новий клас локальності та обґрунтовано включення підтримки новітнього протоколу IPv6; також отримано пряме експериментальне підтвердження підвищення ефективності однорангових мереж із використанням попередньої версії оцінки локальності за моделлю CARMA. Проаналізовано та враховано вплив динаміки кількості завершених і незавершених вузлів у рої (включаючи ті, що прямо зв’язані з експериментальним стендом). Результати експериментів свідчать про підвищення середньої швидкості передачі на 2,5 % порівняно з немодифікованим режимом передачі.Рассмотрена усовершенствованная реализация модели CARMA, в которую добавлен новый класс локальности и обосновано включение поддержки новейшего протокола IPv6; также получено прямое экспериментальное подтверждение повышения эффективности одноранговых сетей с использованием предварительной версии оценки локальности по модели CARMA. Проанализировано и учтено влияние динамики количества завершенных и незавершенных узлов в рое (включая те, которые прямо связаны с экспериментальным стендом). Результаты экспериментов свидетельствуют о повышении средней скорости передачи на 2,5 % в сравнении с немодифицированным режимом передачи

Brushland tinamou

p. 271-314 [11] p. of plates : ill., maps ; 27 cm.Includes bibliographical references (p. 314)

Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections