In this work, we apply a molecular imprinting strategy as a screening protocol for different influenza A subtypes, namely H5N1, H5N3, H1N1, H1N3 and H6N1. Molecularly imprinted polymers for each of these subtypes lead to appreciable sensor characteristics on a quartz crystal microbalance leading to detection limits as low as 10⁵ particles per ml. Selectivity studies indicate that each virus is preferably incorporated by its own MIP. Recognition in most cases is dominated by the neuraminidase residue rather than the hemagglutinin. Multivariate analysis shows that the sensor responses can be correlated with the differences in hemagglutinin and neuraminidase patterns from databases. This allows for virus subtype characterization and thus rapid screening.

In this report we investigate whether a molecularly imprinted polymer (MIP) of an inactivated strain of influenza A H5N1 could be used to help identify molecules capable of binding to, and inhibiting the function of the virus, via either competitive or allosteric mechanisms. Molecules which bind to the virus and induce a conformational change are expected to show reduced binding to the H5N1 specific MIP. Given the importance of molecular recognition in virus replication, such conformational change might also reduce the effectiveness of neuraminidase (N1) for cleaving the sialic groups necessary for virus replication. We show that the method can indeed differentiate between a potent neuraminidase inhibitor, H1 and H5 antibodies, and N1 specific and non-specific monosaccharide substrates. We suggest that such a method could potentially be used in conjunction with traditional biochemical assays to facilitate the identification of molecules functioning via novel modes of action.

Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 10⁵ CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R² value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL.

In Thailand, most of the bomb devices produced by terrorists use trinitrotoluene (TNT) as the main explosive material. Detecting TNT and preventing detonation could save many civilians. Due to its low vapor pressure, TNT requires an extremely high sensitivity method for detection. In this paper, we propose an application of polyacrylamide gel as an electrochemical gas sensor for explosive material detection. Cyclic voltammetry signals were collected from samples including TNT, pentaerythritol tetranitrate (PETN) and non-explosive vapors. It was found that a timederivative calculation of the signal was unique to each chemical, rather than the signal itself, demonstrating utility for the detection of explosive material. This approach for making a gel-based sensor and data analysis looks promising and requires further validation.