Excited-state intramolecular proton transfer (ESIPT)-associated dual-state emissive aliphatic dual-light emitting conducting polymers (DLECPs) having oxidation-reduction capacities are prepared polymerizing 2-acrylamido-2-methylpropane-1-sulfonic acid, methacrylic acid, and 2-methyl-3-(N-(2-methyl-1-sulfopropan-2-yl)acrylamido)propanoic acid monomers. Of as-synthesized DLECPs, nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopies, fluorescent enhancements (I/I), and computational investigation indicate intriguing photophysical features in DLECP3 (optimum composition). In DLECP3, ─CONH─, ─CON<, and ─COOH subluminophores are recognized by density-functional theory (DFT)/time-dependent-DFT calculations and experimental investigations.
View Article and Find Full Text PDFHerein, natural-synthetic hybrid dual-state luminescent conducting polymers (DLCPs/DLCP1-DLCP8) possessing significant optoelectrochemical properties are strategically developed by the polymerization of prop-2-enamide, cis-butenedioic acid, 2-acrylamido-2-methylpropane-1-sulfonic acid, and in situ-generated 2-(3-acrylamidopropanamido)-2-methylpropane-1-sulfonic acid alongside the grafting of gum tragacanth. The spectroscopic data of aliphatic DLCPs affirm DLCP7 as the most stable supramolecular assembly endowing optoelectronic properties. Computational calculations identified -C(═O)NH-, -C(═O)OH, -OH, and -SOH as subluminophores.
View Article and Find Full Text PDFInitially, four synthetic fluorescent polymers (SFPs) are synthesized from α-methacrylic acid and methanolacrylamide monomers carrying -C(=O)OH and -C(=O)NH subfluorophores, respectively. Among SFPs, ∼1:1 incorporation of subfluorophores in the optimum SFP3 is explored by spectroscopic analyses. Subsequently, chitosan is incorporated in SFP3 to produce five semi-synthetic fluorescent polymers (SSFPs).
View Article and Find Full Text PDFMotivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein.
Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated.