Using natural language processing techniques to inform research on nanotechnology

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics

Development of a targeted and controlled nanoparticle delivery system for FoxO1 inhibitors

Background: Poly (lactic-co-glycolic acid) (PLGA) and polyethylene glycol (PEG) are polymers approved by the United States’ Food and Drug Administration. Drugs for various medical treatments have been encapsulated in PLGA-PEG nanoparticles for targeted delivery and reduction of unwanted side effects. Methods: A flow synthesis method for PLGA-PEG nanoparticles containing FoxO1 inhibitors and adipose vasculature targeting agents was developed. A set of nanoparticles including PLGA and PLGA-PEG-P3 unloaded and drug loaded were generated. The particles were characterized by DLS, fluorescence spectroscopy, TEM, and dialysis. Endotoxin levels were measured using the LAL chromogenic assay. Our approach was compared to over 270 research articles using information extraction tools. Results: Nanoparticle hydrodynamic diameters ranged from 142.4 ±0.4 d.nm to 208.7 ±3.6 d.nm while the polydispersity index was less than 0.500 for all samples (0.057 ±0.021 to 0.369 ±0.038). Zeta potentials were all negative ranging from -4.33 mV to -13.4 mV. Stability testing confirmed that size remained unchanged for up to 4 weeks. For AS1842856, loading was 0.5 mg drug/mL solution and encapsulation efficiency was ~100%. Dialysis indicated burst release of drug in the first 4 hours. Conclusion: PLGA encapsulation of AS1842856 was successful but unsuccessful for the two more hydrophilic drugs. Alternative syntheses such as water/oil/water emulsion or liposomal encapsulation are being considered. Analysis of data from published papers on PLGA nanoparticles indicated that our results were consistent with identified process-structure relationships and few groups reported endotoxin levels even though in vivo testing was performed.https://scholarscompass.vcu.edu/gradposters/1071/thumbnail.jp

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design

Creation of an Annotated Library on FDA Approved Nanomedicines

Nanomedicine is a type of nanotechnology used in the medical field to limit the dosage amount and target drug delivery to specific cells. Nanomedicines that are approved and used tend to be extremely successful; however despite over a decade of research, only a limited number of nanomedicines have advanced for clinical use. A possible reason for the numerous nanomedicine failures is lack of easily accessible information and research on previous nanomedicines. In this project, we have compiled nanomedicine labeling information from the Drugs@FDA website. We have extracted phrases/sentences from labels relating to keywords on nanomaterial properties and drug profile characteristics. In the future, we plan to incorporate discontinued nanomedicines, nanomedicines on the market, and nanomedicines in different clinical trial phases. By compiling the descriptions and contents of a set of specific nanomedicines, a machine learning program could be developed to comb through literature and automatically identify similar nanomedicine related entities. Our research works to provide an easier and quicker method to obtain specific information on approved nanomedicines.https://scholarscompass.vcu.edu/uresposters/1175/thumbnail.jp

Photoluminescent diamond nanoparticles for cell labeling: study of the uptake mechanism in mammalian cells

Diamond nanoparticles (nanodiamonds) have been recently proposed as new labels for cellular imaging. For small nanodiamonds (size <40 nm) resonant laser scattering and Raman scattering cross-sections are too small to allow single nanoparticle observation. Nanodiamonds can however be rendered photoluminescent with a perfect photostability at room temperature. Such a remarkable property allows easier single-particle tracking over long time-scales. In this work we use photoluminescent nanodiamonds of size <50 nm for intracellular labeling and investigate the mechanism of their uptake by living cells . By blocking selectively different uptake processes we show that nanodiamonds enter cells mainly by endocytosis and converging data indicate that it is clathrin mediated. We also examine nanodiamonds intracellular localization in endocytic vesicles using immunofluorescence and transmission electron microscopy. We find a high degree of colocalization between vesicles and the biggest nanoparticles or aggregates, while the smallest particles appear free in the cytosol. Our results pave the way for the use of photoluminescent nanodiamonds in targeted intracellular labeling or biomolecule deliver

Interactions between Magnetic Nanowires and Living Cells : Uptake, Toxicity and Degradation

We report on the uptake, toxicity and degradation of magnetic nanowires by NIH/3T3 mouse fibroblasts. Magnetic nanowires of diameters 200 nm and lengths comprised between 1 {\mu}m and 40 {\mu}m are fabricated by controlled assembly of iron oxide ({\gamma}-Fe2O3) nanoparticles. Using optical and electron microscopy, we show that after 24 h incubation the wires are internalized by the cells and located either in membrane-bound compartments or dispersed in the cytosol. Using fluorescence microscopy, the membrane-bound compartments were identified as late endosomal/lysosomal endosomes labeled with lysosomal associated membrane protein (Lamp1). Toxicity assays evaluating the mitochondrial activity, cell proliferation and production of reactive oxygen species show that the wires do not display acute short-term (< 100 h) toxicity towards the cells. Interestingly, the cells are able to degrade the wires and to transform them into smaller aggregates, even in short time periods (days). This degradation is likely to occur as a consequence of the internal structure of the wires, which is that of a non-covalently bound aggregate. We anticipate that this degradation should prevent long-term asbestos-like toxicity effects related to high aspect ratio morphologies and that these wires represent a promising class of nanomaterials for cell manipulation and microrheology.Comment: 21 pages 12 figure

Effects of methods of descending stairs forwards versus backwards on knee joint force in patients with osteoarthritis of the knee: a clinical controlled study

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to investigate the kinetic characteristics of compensatory backward descending movement performed by patients with osteoarthritis of the knee.</p> <p>Methods</p> <p>Using a three-dimensional motion analysis system, we investigated lower extremity joint angles, joint moments, joint force of the support leg in forward and backward descending movements on stairs, and joint force of the leading leg at landing in 7 female patients with osteoarthritis of the knee.</p> <p>Results</p> <p>Compared with the forward descending movement, knee joint angle, joint moment and joint force of the support leg all decreased in the backward descending movement. Joint force of the leading leg at landing was also reduced in the backward descending movement. In addition, we confirmed that the center of body mass was mainly controlled by the knee and ankle joints in the forward descending movement, and by the hip joint in the backward descending movement.</p> <p>Conclusions</p> <p>Since it has been reported that knee flexion angle and extensor muscle strength are decreased in patients with osteoarthritis of the knee, we believe that backward descending movement is an effective method to use the hip joint to compensate forthese functional defects. In addition, due to the decreased knee joint force both in the leading and support legs in backward descending movement, the effectiveness of compensatory motion for pain control and knee joint protection was also suggested.</p

A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self- assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered beta-sheets as their size increases

Assessing Graphical Robot Aids for Interactive Co-working

The shift towards more collaborative working between humans and robots increases the need for improved interfaces. Alongside robust measures to ensure safety and task performance, humans need to gain the confidence in robot co-operators to enable true collaboration. This research investigates how graphical signage can support human–robot co-working, with the intention of increased productivity. Participants are required to co-work with a KUKA iiwa lightweight manipulator on a manufacturing task. The three conditions in the experiment differ in the signage presented to the participants – signage relevant to the task, irrelevant to the task, or no signage. A change between three conditions is expected in anxiety and negative attitudes towards robots; error rate; response time; and participants’ complacency, suggested by facial expressions. In addition to understanding how graphical languages can support human–robot co-working, this study provides a basis for further collaborative research to explore human–robot co-working in more detail

Language-free graphical signage improves human performance and reduces anxiety when working collaboratively with robots

As robots become more ubiquitous, and their capabilities extend, novice users will require intuitive instructional information related to their use. This is particularly important in the manufacturing sector, which is set to be transformed under Industry 4.0 by the deployment of collaborative robots in support of traditionally low-skilled, manual roles. In the first study of its kind, this paper reports how static graphical signage can improve performance and reduce anxiety in participants physically collaborating with a semi-autonomous robot. Three groups of 30 participants collaborated with a robot to perform a manufacturing-type process using graphical information that was relevant to the task, irrelevant, or absent. The results reveal that the group exposed to relevant signage was significantly more accurate in undertaking the task. Furthermore, their anxiety towards robots significantly decreased as a function of increasing accuracy. Finally, participants exposed to graphical signage showed positive emotional valence in response to successful trials. At a time when workers are concerned about the threat posed by robots to jobs, and with advances in technology requiring upskilling of the workforce, it is important to provide intuitive and supportive information to users. Whilst increasingly sophisticated technical solutions are being sought to improve communication and confidence in human-robot co-working, our findings demonstrate how simple signage can still be used as an effective tool to reduce user anxiety and increase task performance