Characterization and complete genome sequence of a novel N4-like bacteriophage, pSb-1 infecting Shigella boydii

Shigellosis is one of major foodborne pathogens in both developed and developing countries. Although antibiotic therapy is considered an effective treatment for shigellosis, the imprudent use of antibiotics has led to the increase of multiple-antibiotic-resistant Shigella species globally. In this study, we isolated a virulent Podoviridae bacteriophage (phage), pSb(-1), that infects Shigella boydii. One-step growth analysis revealed that this phage has a short latent period (15 min) and a large burst size (152.63 PFU/cell), indicating that pSb(-1) has good host infectivity and effective lytic activity. The double-stranded DNA genome of pSb(-1) is composed of 71,629 bp with a G + C content of 42.74%. The genome encodes 103 putative ORFs, 9 putative promoters, 21 transcriptional terminators, and one tRNA region. Genome sequence analysis of pSb(-1) and comparative analysis with the homologous phage EC1-UPM, N4-like phage revealed that there is a high degree of similarity (94%, nucleotide sequence identity) between pSb(-1) and EC1-UPM in 73 of the 103 ORFs of pSb(-1). The results of this investigation indicate that pSb(-1) is a novel virulent N4-like phage infecting S. boydii and that this phage might have potential uses against shigellosis. (C) 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.N

Eating oysters without risk of vibriosis: Application of a bacteriophage against Vibrio parahaemolyticus in oysters

Vibrio parahaemolyticus is a major cause of foodborne illness and related with the consumption of raw contaminated seafood, especially oysters. To evaluate the effectiveness of various applications of a bacteriophage (phage), pVp-1, against a multiple-antibiotic-resistant V. parahaemolyticus pandemic strain (CRS 09-17), we designed artificial contamination models that are most likely to be encountered during oyster processing. When live oysters were treated with bath immersion with pVp-1 after CRS 09-17 challenge, the growth of bacterial strain was significantly reduced. After 72 h of phage application with bath immersion, bacterial growth reduction was observed to be 8.9 x 10(6) CFU/ml (control group) to 1.4 x 10 CFU/ml (treatment group). When pVp-1 was surface-applied on the flesh of oysters after CRS 09-17 inoculation, bacterial growth was properly inhibited. After 12 h of phage application on the surface of oysters, bacterial growth inhibition was revealed to be 1.44 x 10(6) CFU/ml (control group) to 1.94 CPU/ml (treatment group). This is the first report, to the best of our knowledge, of oyster surface-application of a phage against a multiple-antibiotic-resistant V. parahaemolyticus pandemic strain, and our successful phage application to various situations emphasizes the potential use of the phage to avoid V. parahaemolyticus infection from aquaculture to consumption. (C) 2014 Elsevier B.V. All rights reserved.N

Genomic structure of the Aeromonas bacteriophage pAh6-C and its comparative genomic analysis

pAh6-C is a virulent bacteriophage (phage), isolated from a river in Korea, that infects a multiple-antibiotic-resistant A. hydrophila strain, JUNAH. The double-stranded DNA genome of pAh6-C is composed of 53,744 bp with a G + C content of 52.83 %. The genome encodes 86 putative ORFs, four putative promoters, and seven transcriptional terminator regions. Genome sequence analysis of pAh6-C and comparative analysis with the homologous Shewanella phage Spp001 revealed that there is a high degree of similarity between pAh6-C and Spp001 in 50 of the 86 ORFs of pAh6-C. The results of this investigation indicate that pAh6-C is closely related to Spp001, especially in the genes coding for proteins involved in DNA metabolism.OAIID:oai:osos.snu.ac.kr:snu2015-01/102/0000030777/3SEQ:3PERF_CD:SNU2015-01EVAL_ITEM_CD:102USER_ID:0000030777ADJUST_YN:YEMP_ID:A076079DEPT_CD:551CITE_RATE:2.282FILENAME:인쇄본.pdfDEPT_NM:수의학과SCOPUS_YN:YCONFIRM:

Isolation and Comparative Genomic Analysis of T1-Like Shigella Bacteriophage pSf-2

OAIID:oai:osos.snu.ac.kr:snu2016-01/102/0000030777/1ADJUST_YN:NEMP_ID:A076079DEPT_CD:551CITE_RATE:1.423FILENAME:art%3a10.1007%2fs00284-015-0935-2.pdfDEPT_NM:수의학과SCOPUS_YN:YCONFIRM:

Prediction of Pregnancy-Associated Hypertension Using a Scoring System: A Multicenter Cohort Study

This study aimed to develop an early pregnancy risk scoring model for pregnancy-associated hypertension (PAH) based on maternal pre-pregnancy characteristics, such as mean arterial pressure (MAP), pregnancy-associated plasma protein-A (PAPP-A) or neither. The perinatal databases of seven hospitals from January 2009 to December 2020 were randomly divided into a training set and a test set at a ratio of 70:30. The data of a total pregnant restricted population (women not taking aspirin during pregnancy) were analyzed separately. Three models (model 1, pre-pregnancy factors only; model 2, adding MAP; model 3, adding MAP and PAPP-A) and the American College of Obstetricians and Gynecologists (ACOG) risk factors model were compared. A total of 2840 (8.11%) and 1550 (3.3%) women subsequently developed PAH and preterm PAH, respectively. Performances of models 2 and 3 with areas under the curve (AUC) over 0.82 in both total population and restricted population were superior to those of model 1 (with AUCs of 0.75 and 0.748, respectively) and the ACOG risk model (with AUCs of 0.66 and 0.66) for predicting PAH and preterm PAH. The final scoring system with model 2 for predicting PAH and preterm PAH showed moderate to good performance (AUCs of 0.78 and 0.79, respectively) in the test set. “A risk scoring model for PAH and preterm PAH with pre-pregnancy factors and MAP showed moderate to high performances. Further prospective studies for validating this scoring model with biomarkers and uterine artery Doppler or without them might be required”

Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms

Abstract This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model