Conversion of biomass into nanoparticles for meaningful
biomedical
applications is a formidable proposition with excellent prospects
but fewer patrons. A lack of general methodology for upscaled production
and limited versatility of those nanoparticles are the main drawbacks.
Herein, we report the creation of a DNA nanoparticle (DNA Dots) from
onion genomic DNA (gDNA), a plant biomass source, through controlled
hydrothermal pyrolysis in water without any chemicals. The DNA Dots
are further formulated into a stimuli-responsive hydrogel through
hybridization-mediated self-assembly with untransformed precursor
gDNA. The versatility of the DNA Dots is recognized through its crosslinking
ability with gDNA through its dangling DNA strands on the surface
resulting from incomplete carbonization during annealing without the
need for any external organic, inorganic, or polymeric crosslinkers.
The gDNA–DNA Dots hybrid hydrogel is shown to be an excellent
drug delivery vehicle for sustained release trackable through the
inherent fluorescence of the DNA Dots. Interestingly, the DNA Dots
are photoexcited with normal visible light to generate on-demand reactive
oxygen species, making them exciting candidates for combination therapeutics.
Most importantly, the ease with which the hydrogel is internalized
in fibroblast cells with minimal cytotoxicity should encourage the
nanotization of biomass as a tool for interesting sustainable biomedical
applications.
Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.
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