Post-surgical wound infections involving Enterobacteriaceae with reduced susceptibility to ß-lactams in two Portuguese hospitals

The post-surgical period is often critical for infection acquisition. The combination of patient injury and environmental exposure through breached skin add risk to pre-existing conditions such as drug or depressed immunity. Several factors such as the period of hospital staying after surgery, base disease, age, immune system condition, hygiene policies, careless prophylactic drug administration and physical conditions of the healthcare centre may contribute to the acquisition of a nosocomial infection. A purulent wound can become complicated whenever antimicrobial therapy becomes compromised. In this pilot study, we analysed Enterobacteriaceae strains, the most significant gram-negative rods that may occur in post-surgical skin and soft tissue infections (SSTI) presenting reduced β-lactam susceptibility and those presenting extended-spectrum β-lactamases (ESBL). There is little information in our country regarding the relationship between β-lactam susceptibility, ESBL and development of resistant strains of microorganisms in SSTI. Our main results indicate Escherichia coli and Klebsiella spp. are among the most frequent enterobacteria (46% and 30% respectively) with ESBL production in 72% of Enterobacteriaceae isolates from SSTI. Moreover, coinfection occurred extensively, mainly with Pseudomonas aeruginosa and Methicillin-resistant Staphylococcus aureus (18% and 13%, respectively). These results suggest future research to explore if and how these associations are involved in the development of antibiotic resistance.

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.