SummaryEscherichia coli biofilm consists of a bacterial colony embedded in a matrix of extracellular polymeric substances (EPS) which protects the microbes from adverse environmental conditions and results in infection. Besides being the major causative agent for recurrent urinary tract infections, E. coli biofilm is also responsible for indwelling medical device-related infectivity. The cell-tocell communication within the biofilm occurs due to quorum sensors that can modulate the key biochemical players enabling the bacteria to proliferate and intensify the resultant infections. The diversity in structural components of biofilm gets compounded due to the development of antibiotic resistance, hampering its eradication. Conventionally used antimicrobial agents have a restricted range of cellular targets and limited efficacy on biofilms. This emphasizes the need to explore the alternate therapeuticals like anti-adhesion compounds, phytochemicals, nanomaterials for effective drug delivery to restrict the growth of biofilm. The current review focuses on various aspects of E. coli biofilm development and the possible therapeutic approaches for prevention and treatment of biofilm-related infections.

Under the current scenario, the role of EGCG as a therapeutic agent is being utilised and new approaches are being formulated to overcome the problem of stability and bioavailability of EGCG. EGCG and its derivatives could be used for the development of drugs for the treatment of cancer, as well as various microbial, metabolic, and neurodegenerative diseases.

To compare the ability of margin classification systems to determine local recurrence (LR) risk after soft tissue sarcoma (STS) resection. MethodsTwo thousand two hundred seventeen patients with nonmetastatic extremity and truncal STS treated with surgical resection and multidisciplinary consideration of perioperative radiotherapy were retrospectively reviewed. Margins were coded by residual tumor (R) classification (in which microscopic tumor at inked margin defines R1), the R+1mm classification (in which microscopic tumor within 1 mm of ink defines R1), and the Toronto Margin Context Classification (TMCC; in which positive margins are separated into planned close but positive at critical structures, positive after whoops re-excision, and inadvertent positive margins). Multivariate competing risk regression models were created. ResultsBy R classification, LR rates at 10-year follow-up were 8%, 21%, and 44% in R0, R1, and R2, respectively. R+1mm classification resulted in increased R1 margins (726 v 278, P , .001), but led to decreased LR for R1 margins without changing R0 LR; for R0, the 10-year LR rate was 8% (range, 7% to 10%); for R1, the 10-year LR rate was 12% (10% to 15%) . The TMCC also showed various LR rates among its tiers (P , .001). LR rates for positive margins on critical structures were not different from R0 at 10 years (11% v 8%, P = .18), whereas inadvertent positive margins had high LR (5-year, 28% [95% CI, 19% to 37%]; 10-year, 35% [95% CI, 25% to 46%]; P , .001). ConclusionThe R classification identified three distinct risk levels for LR in STS. An R+1mm classification reduced LR differences between R1 and R0, suggesting that a negative but , 1-mm margin may be adequate with multidisciplinary treatment. The TMCC provides additional stratification of positive margins that may aid in surgical planning and patient education.J Clin Oncol 36.

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