Antimicrobial activity of nanoconjugated glycopeptide antibiotics and their effect on Staphylococcus Aureus biofilm

In the era of antimicrobial resistance, the use of nanoconjugated antibiotics is regarded as a promising approach for preventing and fighting infections caused by resistant bacteria, including those exacerbated by the formation of difficult-to-treat bacterial biofilms. Thanks to their biocompatibility and magnetic properties, iron oxide nanoparticles (IONPs) are particularly attractive as antibiotic carriers for the targeting therapy. IONPs can direct conjugated antibiotics to infection sites by the use of an external magnet, facilitating tissue penetration and disturbing biofilm formation. As a consequence of antibiotic localization, a decrease in its administration dosage might be possible, reducing the side effects to non-targeted organs and the risk of antibiotic resistance spread in the commensal microbiota. Here, we prepared nanoformulations of the ‘last-resort’ glycopeptides teicoplanin and vancomycin by conjugating them to IONPs via surface functionalization with (3-aminopropyl) triethoxysilane (APTES). These superparamagnetic NP-TEICO and NP-VANCO were chemically stable and NP-TEICO (better than NP-VANCO) conserved the typical spectrum of antimicrobial activity of glycopeptide antibiotics, being effective against a panel of staphylococci and enterococci, including clinical isolates and resistant strains. By a combination of different methodological approaches, we proved that NP-TEICO and, although to a lesser extent, NP-VANCO were effective in reducing biofilm formation by three methicillin-sensitive or resistant Staphylococcus aureus strains. Moreover, when attracted and concentrated by the action of an external magnet, NP-TEICO exerted a localized inhibitory effect on S. aureus biofilm formation at low antibiotic concentration. Finally, we proved that the conjugation of glycopeptide antibiotics to IONPs reduced their intrinsic cytotoxicity toward a human cell line. Copyright © 2021 Berini, Orlandi, Gamberoni, Martegani, Armenia, Gornati, Bernardini and Marinelli

Sigh in supine and prone position during acute respiratory distress syndrome

Interventions aimed at recruiting the lung of patients with acute respiratory distress syndrome (ARDS) are not uniformly effective. Because the prone position increases homogeneity of inflation of the lung, we reasoned that it might enhance its potential for recruitment. We ventilated 10 patients with early ARDS (PaO2/FIO2, 121 +/- 46 mm Hg; positive end-expiratory pressure, 14 +/- 3 cm H2O) in supine and prone, with and without the addition of three consecutive "sighs" per minute to recruit the lung. Inspired oxygen fraction, positive end-expiratory pressure, and minute ventilation were kept constant. Sighs increased PaO2 in both supine and prone (p < 0.01). The highest values of PaO2 (192 +/- 41 mm Hg) and end-expiratory lung volume (1840 +/- 790 ml) occurred with the addition of sighs in prone and remained significantly elevated 1 hour after discontinuation of the sighs. The increase in PaO2 associated with the sighs, both in supine and prone, correlated linearly with the respective increase of end-expiratory lung volume (r = 0.82, p < 0.001). We conclude that adding a recruitment maneuver such as cyclical sighs during ventilation in the prone position may provide optimal lung recruitment in the early stage of ARDS

Acqua e mulini da vecchi a nuovi territori: il caso di Molina

il contributo illustra il rapporto tra comunit\ue0 locali e acqua nell'area di Molina, un centro della montagna lessinica veronese. Si analizzano i segni della cultura materiale e immateriale riferiti alla risorsa idrica

Supervised and unsupervised learning techniques for profiling SAGE results.

Using Serial Analysis it is now possible to obtain quantita­ tive measurements of the expression of thousands of genes present in a biological sample. Serial analysis yield a global view of gene expression that can be used in a number of interesting ways. In this paper we are investigating two different approaches for analyz­ ing the analysis of data obtained from SAGE experiments. The first one is a supervised learning process: a classification of cancer tissue using decision trees and Support Vector Machines (SVM). After that, we will analyze the results achieved by a unsupervised learning method: hierar­ chical clustering. Finally, we tried to characterize the groups found by clustering, using the classification techniques cited before