Differential effects of chromosome and plasmid blaCTX-M-15 genes on antibiotic susceptibilities in extended-spectrum beta-lactamase-producing Escherichia coli isolates from patients with urinary tract infection.

OBJECTIVES: To compare antibiotic susceptibilities between chromosomal and plasmid blaCTX-M-15 locations in urinary tract infection-causing extended-spectrum ß-lactamases-producing Escherichia coli blaCTX-M-15 isolated in Indonesia. METHODS: A total of 84 strains identified as extended-spectrum ß-lactamases-producing E. coli were isolated from patients with urinary tract infection in Indonesia in 2015. Antimicrobial susceptibility tests were performed on these strains using 18 antibiotics, and extended-spectrum ß-lactamase bla genes were detected by polymerase chain reaction. Gene localization of blaCTX-M-15 -positive strains was confirmed by Southern blot hybridization, and epidemiological typing was conducted using multilocus sequence typing. RESULTS: Of 54 strains harboring the blaCTX-M-15 gene, 27 showed localization on chromosome, 20 on plasmid, and seven on chromosome and plasmid. Most multilocus sequence typing sequence types of the 27 strains with chromosomal blaCTX-M-15 were ST405 (25.9%) and ST131 (22.2%) strains, whereas the 20 strains with plasmid-blaCTX-M-15 were mostly ST410 (55.0%). CONCLUSIONS: Extended-spectrum ß-lactamases-producing E. coli blaCTX-M-15 with plasmid genes show significantly higher resistant rates against piperacillin-tazobactam but lower resistant rates against chloramphenicol compared to chromosomal strains in Indonesian patients with urinary tract infection. Mechanistic investigations will be necessary to advance our knowledge of antimicrobial resistance in urinary tract infection.

Comparison of Glucose Area Under the Curve Measured Using Minimally Invasive Interstitial Fluid Extraction Technology with Continuous Glucose Monitoring System in Diabetic Patients.

BACKGROUND: Continuous glucose monitoring (CGM) is reported to be a useful technique, but difficult or inconvenient for some patients and institutions. We are developing a glucose area under the curve (AUC) monitoring system without blood sampling using a minimally invasive interstitial fluid extraction technology (MIET). Here we evaluated the accuracy of interstitial fluid glucose (IG) AUC measured by MIET in patients with diabetes for an extended time interval and the potency of detecting hyperglycemia using CGM data as a reference. METHODS: Thirty-eight inpatients with diabetes undergoing CGM were enrolled. MIET comprised a pretreatment step using a plastic microneedle array and glucose accumulation step with a hydrogel patch, which was placed on two sites from 9:00 AM to 5:00 PM or from 10:00 PM to 6:00 AM. IG AUC was calculated by accumulated glucose extracted by hydrogel patches using sodium ion as standard. RESULTS: A significant correlation was observed between the predicted AUC by MIET and CGM in daytime (r=0.76) and nighttime (r=0.82). The optimal cutoff for the IG AUC value of MIET to predict hyperglycemia over 200 mg/dL measured by CGM for 8 hours was 1,067.3 mg·hr/dL with 88.2% sensitivity and 81.5% specificity. CONCLUSION: We showed that 8-hour IG AUC levels using MIET were valuable in estimating the blood glucose AUC without blood sampling. The results also supported the concept of using this technique for evaluating glucose excursion and for screening hyperglycemia during 8 hours in patients with diabetes at any time of day.

Evaluation of postprandial glucose excursion using a novel minimally invasive glucose area-under-the-curve monitoring system.

OBJECTIVE: To develop a minimally invasive interstitial fluid extraction technology (MIET) to monitor postprandial glucose area under the curve (AUC) without blood sampling, we evaluated the accuracy of glucose AUC measured by MIET and compared with that by blood sampling after food intake. METHODS: Interstitial fluid glucose AUC (IG-AUC) following consumption of 6 different types of foods was measured by MIET. MIET consisted of stamping microneedle arrays, placing hydrogel patches on the areas, and calculating IG-AUC based on glucose levels in the hydrogels. Glycemic index (GI) was determined using IG-AUC and reference AUC measured by blood sampling. RESULTS: IG-AUC strongly correlated with reference AUC (R = 0.91), and GI determined using IG-AUC showed good correlation with that determined by reference AUC (R = 0.88). CONCLUSIONS: IG-AUC obtained by MIET can accurately predict the postprandial glucose excursion without blood sampling. In addition, feasibility of GI measurement by MIET was confirmed.