Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers' focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.
Biofilms can form on living or inert surfaces and prevail in natural, industrial, and hospital environments. They are made of bacteria organized in a coordinated functional community. Biofilms do not respond to antibiotic treatment due to multiple mechanisms of tolerance and resistance. If bacteria are coordinated in a biofilm form, they are significantly less susceptible to antibiotics, thus making the therapeutic approach difficult. The possibility of using drugs aimed at inhibiting the formation of biofilms in combination with current antibiotics is a therapeutic approach with a major potential for this type of persistent bacterial infection. This bibliographic study aims to present the main compounds that act by inhibiting or destroying the bacterial biofilm.
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