Current understanding of biological interactions and processing of DNA origami nanostructures: Role of machine learning and implications in drug delivery.

DNA origami has emerged as an exciting avenue that provides a versatile two and three-dimensional DNA-based platform for nanomedicine and drug delivery applications. Their incredible programmability, custom synthesis, efficiency, biocompatibility, and physio-chemical nature make DNA origami ideal for biomedical applications. Several recent studies demonstrated the potential of DNA origami for different technological applications, especially in drug delivery. However, several challenges related to their intracellular stability, elicitation of the immune response, and cellular fate limit the in-vivo application of these nanostructures. In this review, we critically assess the molecular-level interactions of DNA nanostructures with biological systems that will be helpful to engineer and optimize DNA nanostructures for bio applications. We highlight the hurdles that impair the potential applicability of DNA origami nanostructures in the biology and medicine field. We have also expanded the details of key strategies to overcome the limitations and extend the boundaries of DNA origami closer to nanomedicine. Finally, we explore the role Artificial Intelligence and Machine Learning techniques can play to accelerate the process of their clinical applications.

Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline Raman Data Analytics.

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based "automated hyperspectral Raman analysis framework" to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen's abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.

Ultrafast synthesis of carbon quantum dots from fenugreek seeds using microwave plasma enhanced decomposition: application of C-QDs to grow fluorescent protein crystals.

Herein, we present the rapid synthesis of mono-dispersed carbon quantum dots (C-QDs) via a single-step microwave plasma-enhanced decomposition (MPED) process. Highly-crystalline C-QDs were synthesized in a matter of 5 min using the fenugreek seeds as a sustainable carbon source. It is the first report, to the best of our knowledge, where C-QDs were synthesized using MPED via natural carbon precursor. Synthesis of C-QDs requires no external temperature other than hydrogen (H2) plasma. Plasma containing the high-energy electrons and activated hydrogen ions predominantly provide the required energy directly into the reaction volume, thus maximizing the atom economy. C-QDs shows excellent Photoluminescence (PL) activity along with the dual-mode of excitation-dependent PL emission (blue and redshift). We investigate the reason behind the dual-mode of excitation-dependent PL. To prove the efficacy of the MPED process, C-QDs were also derived from fenugreek seeds using the traditional synthesis process, highlighting their respective size-distribution, crystallinity, quantum yield, and PL. Notably, C-QDs synthesis via MPED was 97.2% faster than the traditional thermal decomposition process. To the best of our knowledge, the present methodology to synthesize C-QDs via natural source employing MPED is three times faster and far more energy-efficient than reported so far. Additionally, the application of C-QDs to produce the florescent lysozyme protein crystals "hybrid bio-nano crystals" is also discussed. Such a guest-host strategy can be exploited to develop diverse and complex "bio-nano systems". The florescent lysozyme protein crystals could provide a platform for the development of novel next-generation polychrome luminescent crystals.

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes.

The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely "labelled" spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring.

Structurally distinct hybrid polymer/lipid nanoconstructs harboring a type-I ribotoxin as cellular imaging and glioblastoma-directed therapeutic vectors.

A nanoformulation composed of a ribosome inactivating protein-curcin and a hybrid solid lipid nanovector has been devised against glioblastoma. The structurally distinct nanoparticles were highly compatible to human endothelial and neuronal cells. A sturdy drug release from the particles, recorded upto 72 h, was reflected in the time-dependent toxicity. Folate-targeted nanoparticles were specifically internalized by glioma, imparting superior toxicity and curbed an aggressively proliferating in vitro 3D cancer mass in addition to suppressing the anti-apoptotic survivin and cell matrix protein vinculin. Combined with the imaging potential of the encapsulated dye, the nanovector emanates as a multifunctional anti-cancer system.

Synthesis of an ultradense forest of vertically aligned triple-walled carbon nanotubes of uniform diameter and length using hollow catalytic nanoparticles.

It still remains a crucial challenge to actively control carbon nanotube (CNT) structure such as the alignment, area density, diameter, length, chirality, and number of walls. Here, we synthesize an ultradense forest of CNTs of a uniform internal diameter by the plasma-enhanced chemical vapor deposition (PECVD) method using hollow nanoparticles (HNPs) modified with ligand as a catalyst. The diameters of the HNPs and internal cavities in the HNPs are uniform. A monolayer of densely packed HNPs is self-assembled on a silicon substrate by spin coating. HNPs shrink via the collapse of the internal cavities and phase transition from iron oxide to metallic iron in hydrogen plasma during the PECVD process. Agglomeration of catalytic NPs is avoided on account of the shrinkage of the NPs and ligand attached to the NPs. Diffusion of NPs into the substrate, which would inactivate the growth of CNTs, is also avoided on account of the ligand. As a result, an ultradense forest of triple-walled CNTs of a uniform internal diameter is successfully synthesized. The area density of the grown CNTs is as high as 0.6 × 10(12) cm(-2). Finally, the activity of the catalytic NPs and the NP/carbon interactions during the growth process of CNTs are investigated and discussed. We believe that the present approach may make a great contribution to the development of an innovative synthetic method for CNTs with selective properties.