[Multicenter surveillance of Pseudomonas aeruginosa strains for antimicrobials in Aichi prefecture in 2009].

We investigated the susceptibility to antimicrobials of 204 Pseudomonas aeruginosa strains isolated from 21 hospitals in Aichi prefecture from September to November 2009. MIC distributions of various antimicrobials were analyzed in terms of geographic region of isolation, patient status (outpatient or inpatient), and type of specimens that the strain was isolated from. The results were as follows. 1. Although more than 90% of strains were susceptible to all aminoglycosides and colistin, 80-90% of them were susceptible to beta-lactams and fluoroquinolones. MIC distributions of all antimicrobials measured were not significantly different between regions. 2. Only 1 strain (0.5%) was multi-drug resistant Pseudomonas aeruginosa (MDRP). Thirteen strains (6.4%) showed imipenem MIC > or = 16 microg/mL, and 16 strains (7.8%) showed ciprofloxacin MIC > or = 4 microg/mL. These strains tended to be more isolated from urine, respiratory tract specimens, or surgical specimens. 3. The MICs of tazobactam/piperacillin, panipenem, meropenem, doripenem, biapenem, sulbactam/cefoperazone, cefepime, and aztreonam were significantly higher in strains isolated from inpatients than in those from outpatients. MIC distributions of antimicrobials other than beta-lactams were not significantly different between situations where strains were isolated. 4. MIC distributions of piperacillin, all carbapenems, cefepime, gentamicin, and all fluoroquinolones were significantly different among samples from which strains were isolated. The strains isolated from blood showed lower MICs against all antimicrobials than those from other samples. No difference was found in MIC distributions when categorized according to bacteremic origin. The MICs were apparently elevated against beta-lactams, fluoroquinolones, and gentamicin in strains isolated from respiratory tract specimens, and against beta-lactams, and fluoroquinolones in strains isolated from urine. It was suggested that in P. aeruginosa surveillance, the results should be reported by stratifying with patient status, and type of specimens that the strain was isolated from and that regional surveillance should be useful with such stratification to establish antibiograms for empirical antimicrobial choice.

Proposal for a new body sway evaluation method.

This study is aimed at examining body sway factors for 1107 healthy people aged 16 to 69 years and to propose a practical body sway evaluation method. The center of foot pressure measurement was carried out twice for 1 min. with a 1-min. rest. 30 parameters with high reliability selected from 6 domains (distance, distribution of amplitude, area, velocity, power spectrum, and body sway vector) were summarized objectively into 4 body sway factors (unit time sway, front-back sway, left-right sway, and high frequency band power). Factor scores were calculated by the estimate equation and the total standard scores of parameters with high factor loadings. Considering this result, a total standard score for each factor was classified into 4 percentile categories based on percentile rank. Using this classification criteria, body sway scores for each individual were classified into any of the above 4 domains for each factor. It was confirmed that young adults mainly belong to percentile Categories A and B and the elderly mainly belong to percentile Categories C and D. It was found that a great effect on body sway occurred when an individual's conscious condition changed (contracting a cold or after exercise). In conclusion, the body sway of healthy people can be explained by these 4 sway factors. An evaluation of the body sway pattern using the 4 sway factors may enable us to concretely understand individuals' disorders and abnormal states in addition to changes in body sway that occur with aging.