Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

The Effect of pH on the Size of Silver Nanoparticles Obtained in the Reduction Reaction with Citric and Malic Acids

In colloidal methods, the morphology of nanoparticles (size and shape) as well as their stability can be controlled by changing the concentration of the substrate, stabilizer, adding inorganic salts, changing the reducer/substrate molar ratio, and changing the pH and reaction time. The synthesis of silver nanoparticles was carried out according to the modified Lee and Meisel method in a wide pH range (from 2.0 to 11.0) using citric acid and malic acid, without adding any additives or stabilizers. Keeping the same reaction conditions as the concentration of acid and silver ions, temperature, and heating time, it was possible to determine the relationship between the reaction pH, the type of acid, and the size of the silver nanoparticles formed. Obtained colloids were analyzed by UV-Vis spectroscopy and investigated by means of Transmission Electron Microscope (TEM). The study showed that the colloids reduced with citric acid and malic acid are stable over time for a minimum of seven weeks. We observed that reactions occurred for citric acid from pH 6.0 to 11.0 and for malic acid from pH 7.0 to 11.0. The average size of the quasi-spherical nanoparticles changed with pH due to the increase of reaction rate.

Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties

Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

Atomic Details of Carbon-Based Nanomolecules Interacting with Proteins

Since the discovery of fullerene, carbon-based nanomolecules sparked a wealth of research across biological, medical and material sciences. Understanding the interactions of these materials with biological samples at the atomic level is crucial for improving the applications of nanomolecules and address safety aspects concerning their use in medicine. Protein crystallography provides the interface view between proteins and carbon-based nanomolecules. We review forefront structural studies of nanomolecules interacting with proteins and the mechanism underlying these interactions. We provide a systematic analysis of approaches used to select proteins interacting with carbon-based nanomolecules explored from the worldwide Protein Data Bank (wwPDB) and scientific literature. The analysis of van der Waals interactions from available data provides important aspects of interactions between proteins and nanomolecules with implications on functional consequences. Carbon-based nanomolecules modulate protein surface electrostatic and, by forming ordered clusters, could modify protein quaternary structures. Lessons learned from structural studies are exemplary and will guide new projects for bioimaging tools, tuning of intrinsically disordered proteins, and design assembly of precise hybrid materials.

Engineering the pH-Sensitivity of the Graphene and Carbon Nanotube Based Nanomedicines in Smart Cancer Therapy by Grafting Trimetyl Chitosan

PURPOSE

The aim of this study was to introduce a smart and responsive drug carrier for Doxorubicin (DOX) and Paclitaxel (PAX) for desirable therapeutic application.

METHOD

Loading and releasing of DOX and PAX from smart and pH-sensitive functionalized single-walled carbon nanotube (SWCNTs) and graphene carriers have been simulated by molecular dynamics. The influences of chitosan polymer on proposed carriers have been studied, and both carriers were functionalized with carboxyl groups to improve the loading and releasing properties of the drugs.

RESULTS

The results showed that DOX could be well adsorbed on both functionalized SWCNTs and graphene. In contrast, there was a weak electrostatic and Van der Waals interaction between both these drugs and carriers at cancerous tissues, which is highly favorable for cancer therapy. Adding trimethyl chitosan (TMC) polymer to carriers facilitated DOX release at acidic tissues. Furthermore, at blood pH, the PAX loaded on the functionalized SWCNTs carrier represented the highest dispersion of the drug while the DOX-graphene showed the highest concentration of the drug at a point. In addition, the mean-square displacement (MSD) results of PAX-graphene indicated that the PAX could be adsorbed quickly and be released slowly. Finally, functionalized graphene-TMC-PAX is a smart drug system with responsive behavior and controllable drug release, which are essential in cancer therapy.

CONCLUSION

Simultaneous application of the carboxyl group and TMC can optimize the pH sensitivity of the SWCNTs and graphene to prepare a novel and smart drug carrier for cancer therapy.