A chimeric peptide composed of a dermaseptin derivative and an RNA III-inhibiting peptide prevents graft-associated infections by antibiotic-resistant staphylococci

Staphylococcal bacteria are a prevalent cause of infections associated with foreign bodies and indwelling medical devices. Bacteria are capable of escaping antibiotic treatment through encapsulation into biofilms. RNA III-inhibiting peptide (RIP) is a heptapeptide that inhibits staphylococcal biofilm formation by obstructing quorum-sensing mechanisms. K(4)-S4(1-13)(a) is a 13-residue dermaseptin derivative (DD(13)) believed to kill bacteria via membrane disruption. We tested each of these peptides as well as a hybrid construct, DD(13)-RIP, for their ability to inhibit bacterial proliferation and suppress quorum sensing in vitro and for their efficacy in preventing staphylococcal infection in a rat graft infection model with methicillin-resistant Staphylococcus aureus (MRSA) or S. epidermidis (MRSE). In vitro, proliferation assays demonstrated that RIP had no inhibitory effect, while DD(13)-RIP and DD(13) were equally effective, and that the chimeric peptide but not DD(13) was slightly more effective than RIP in inhibiting RNA III synthesis, a regulatory RNA molecule important for staphylococcal pathogenesis. In vivo, the three peptides reduced graft-associated bacterial load in a dose-dependent manner, but the hybrid peptide was most potent in totally preventing staphylococcal infections at the lowest dose. In addition, each of the peptides acted synergistically with antibiotics. The data indicate that RIP and DD(13) act in synergy by attacking bacteria simultaneously by two different mechanisms. Such a chimeric peptide may be useful for coating medical devices to prevent drug-resistant staphylococcal infections.     

Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes

OBJECTIVES: To evaluate whether fuzzy operators can be usefully applied to the interpretation of genotypic HIV-1 drug resistance by experts, and to improve the prediction of salvage therapy outcome by adapting interpretation rules of genotypic resistance on the basis of their association with virological response data. METHODS: We used a clinical dataset of 231 patients failing highly active antiretroviral therapy (HAART) and starting salvage therapy with baseline resistance genotyping and virological outcomes after 3 and 6 months. A set of rules predicting genotypic resistance was initially derived from an expert (ADL). Rules were implemented using a fuzzy logic approach and the virological outcomes dataset used for the training phase. The resulting algorithm was validated using a separate set of 184 selected patients by correlating the resulting predicted activity with observed virological response at 3 months. For comparison, the expert systems from the drug resistance group of the Agence Nationale de Recherches sur le SIDA (ANRS-AC11) and the algorithm from the Stanford's HIV drug resistance database (Stanford HIVdb) were evaluated on the same set. RESULTS: The starting algorithm had a correlation with virological outcomes of R2=0.06 (P=0.0001). After the training phase the correlation with virological outcomes increased to R2=0.19 (P<0.000001). In the validation set of patients, the activity of the salvage regimen predicted by the fuzzy algorithm was the only variable independently predictive of the 3-month viral load change even after adjusting by the activity predicted by the two expert systems and baseline viral load (for each 10% salvage regimen's activity increase, mean HIV RNA change from baseline: -0.27 log10 copies/ml; 95% CI -0.39, -0.15). CONCLUSION: Using fuzzy operators in a virological outcomes training database to implement a rules-based algorithm for genotypic resistance interpretation, significant improvements of outcomes prediction were obtained. The resulting algorithm showed an independent predictive capability of virological outcomes over that of two rules-based interpretation algorithms made by experts. Although the system was trained and validated on a limited number of cases, the approach deserves further evaluation.     

Italian Chapter of the International Society of Cardiovascular Ultrasound expert consensus document on coronary computed tomography angiography: overview and new insights

Coronary computed tomography angiography is a noninvasive heart imaging test currently undergoing rapid development and advancement. The high resolution of the three‐dimensional pictures of the moving heart and great vessels is performed during a coronary computed tomography to identify coronary artery disease and classify patient risk for atherosclerotic cardiovascular disease. The technique provides useful information about the coronary tree and atherosclerotic plaques beyond simple luminal narrowing and plaque type defined by calcium content. This application will improve image‐guided prevention, medical therapy, and coronary interventions. The ability to interpret coronary computed tomography images is of utmost importance as we develop personalized medical care to enable therapeutic interventions stratified on the bases of plaque characteristics. This overview provides available data and expert's recommendations in the utilization of coronary computed tomography findings. We focus on the use of coronary computed tomography to detect coronary artery disease and stratify patients at risk, illustrating the implications of this test on patient management. We describe its diagnostic power in identifying patients at higher risk to develop acute coronary syndrome and its prognostic significance. Finally, we highlight the features of the vulnerable plaques imaged by coronary computed tomography angiography.