Multidrug resistance of the pathogenic microorganisms to the antimicrobial drugs has become a major impediment toward successful diagnosis and management of infectious diseases. Recent advancements in nanotechnology-based medicines have opened new horizons for combating multidrug resistance in microorganisms. In particular, the use of silver nanoparticles (AgNPs) as a potent antibacterial agent has received much attention. The most critical physico-chemical parameters that affect the antimicrobial potential of AgNPs include size, shape, surface charge, concentration and colloidal state. AgNPs exhibits their antimicrobial potential through multifaceted mechanisms. AgNPs adhesion to microbial cells, penetration inside the cells, ROS and free radical generation, and modulation of microbial signal transduction pathways have been recognized as the most prominent modes of antimicrobial action. On the other side, AgNPs exposure to human cells induces cytotoxicity, genotoxicity, and inflammatory response in human cells in a cell-type dependent manner. This has raised concerns regarding use of AgNPs in therapeutics and drug delivery. We have summarized the emerging endeavors that address current challenges in relation to safe use of AgNPs in therapeutics and drug delivery platforms. Based on research done so far, we believe that AgNPs can be engineered so as to increase their efficacy, stability, specificity, biosafety and biocompatibility. In this regard, three perspectives research directions have been suggested that include (1) synthesizing AgNPs with controlled physico-chemical properties, (2) examining microbial development of resistance toward AgNPs, and (3) ascertaining the susceptibility of cytoxicity, genotoxicity, and inflammatory response to human cells upon AgNPs exposure.

Background and Aim:Differences in patient characteristics due to race or ethnicity may influence the incidence of difficult airway. Our purpose was to determine the incidence of difficult laryngoscopy and intubation, as well as the anatomical features and clinical risk factors that influence them, in the Indian population.Methods:In 330 adult patients receiving general anaesthesia with tracheal intubation, airway characteristics and clinical factors were determined and their association with difficult laryngoscopy (Cormack and Lehane grade 3 and 4) was analysed. Intubation Difficulty Scale score was used to identify degree of difficult laryngoscopy.Results:The incidence of difficult laryngoscopy and intubation was 9.7% and 4.5%, respectively. Univariate analysis showed that increasing age and weight, male gender, modified Mallampati class (MMC) 3 and 4 in sitting and supine positions, inter-incisor distance (IID) ≤3.5 cm, thyromental (TMD) and sternomental distance, ratio of height and TMD, short neck, limited mandibular protrusion, decreased range of neck movement, history of snoring, receding mandible and cervical spondylosis were associated with difficult laryngoscopy. Multivariate analysis identified four variables that were independently associated with difficult laryngoscopy: MMC class 3 and 4, range of neck movement <80°, IID ≤ 3.5 cm and snoring.Conclusions:We found an incidence of 9.7% and 4.5% for difficult laryngoscopy and difficult intubation, respectively, in Indian patients with apparently normal airways. MMC class 3 and 4, range of neck movement <80°, IID ≤ 3.5 cm and snoring were independently related to difficult laryngoscopy. There was a high incidence (48.5%) of minor difficulty in intubation.

Background:Several morphometric airway measurements have been used to predict difficult laryngoscopy (DL). This study evaluated sternomental distance (SMD) and sternomental displacement (SMDD, difference between SMD measured in neutral and extended head position), as predictors of DL and difficult intubation (DI).Materials and Methods:We studied 610 adult patients scheduled to receive general anesthesia with tracheal intubation. SMD, SMDD, physical, and airway characteristics were measured. DL (Cormack-Lehane grade 3/4) and DI (assessed by Intubation Difficulty Scale) were evaluated. The optimal cut-off points for SMD and SMDD were identified by using receiver operating characteristic (ROC) analysis. Multivariate logistic regression was used to predict DL and ROC curve was used to assess accuracy on developed regression model.Results:The incidence of DL and DI was 15.4% and 8.3%, respectively. The cut-off values for SMD and SMDD were ≤14.75 cm (sensitivity 66%, specificity 60%) and ≤5.25 cm (sensitivity 70%, specificity 53%), respectively, for predicting DL. The area under the curve (AUC) with 95% confidence interval (CI) for SMD was 0.66 (0.60–0.72) and that for SMDD was 0.687 (0.63–0.74). Multivariate analysis with logistic regression identified inter-incisor distance, neck movement <80°, SMD, SMDD, short neck and history of snoring as predictors and the predictive model so obtained exhibited a higher diagnostic accuracy (AUC: 0.82; 95% CI 0.77–0.86). SMDD, but not SMD, correlated with DI.Conclusions:Both SMD and SMDD provide a rapid, simple, objective test that may help identifying patients at risk of DL. Their predictive value improves considerably when combined with the other predictors identified by logistic regression.