Secondary Amine Pendant ß-Peptide Polymers Displaying Potent Antibacterial Activity and Promising Therapeutic Potential in Treating MRSA-Induced Wound Infections and Keratitis.

Interest in developing antibacterial polymers as synthetic mimics of host defense peptides (HPDs) has accelerated in recent years to combat antibiotic-resistant bacterial infections. Positively charged moieties are critical in defining the antibacterial activity and eukaryotic toxicity of HDP mimics. Most examples have utilized primary amines or guanidines as the source of positively charged moieties, inspired by the lysine and arginine residues in HDPs. Here, we explore the impact of amine group variation (primary, secondary, or tertiary amine) on the antibacterial performance of HDP-mimicking ß-peptide polymers. Our studies show that a secondary ammonium is superior to either a primary ammonium or a tertiary ammonium as the cationic moiety in antibacterial ß-peptide polymers. The optimal polymer, a homopolymer bearing secondary amino groups, displays potent antibacterial activity and the highest selectivity (low hemolysis and cytotoxicity). The optimal polymer displays potent activity against antibiotic-resistant bacteria and high therapeutic efficacy in treating MRSA-induced wound infections and keratitis as well as low acute dermal toxicity and low corneal epithelial cytotoxicity. This work suggests that secondary amines may be broadly useful in the design of antibacterial polymers.

Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae.

We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.