Identifying factors controlling cellular uptake of gold nanoparticles by machine learning.

There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.

Microfluidic-based technologies for diagnosis, prevention, and treatment of COVID-19: recent advances and future directions.

The COVID-19 pandemic has posed significant challenges to existing healthcare systems around the world. The urgent need for the development of diagnostic and therapeutic strategies for COVID-19 has boomed the demand for new technologies that can improve current healthcare approaches, moving towards more advanced, digitalized, personalized, and patient-oriented systems. Microfluidic-based technologies involve the miniaturization of large-scale devices and laboratory-based procedures, enabling complex chemical and biological operations that are conventionally performed at the macro-scale to be carried out on the microscale or less. The advantages microfluidic systems offer such as rapid, low-cost, accurate, and on-site solutions make these tools extremely useful and effective in the fight against COVID-19. In particular, microfluidic-assisted systems are of great interest in different COVID-19-related domains, varying from direct and indirect detection of COVID-19 infections to drug and vaccine discovery and their targeted delivery. Here, we review recent advances in the use of microfluidic platforms to diagnose, treat or prevent COVID-19. We start by summarizing recent microfluidic-based diagnostic solutions applicable to COVID-19. We then highlight the key roles microfluidics play in developing COVID-19 vaccines and testing how vaccine candidates perform, with a focus on RNA-delivery technologies and nano-carriers. Next, microfluidic-based efforts devoted to assessing the efficacy of potential COVID-19 drugs, either repurposed or new, and their targeted delivery to infected sites are summarized. We conclude by providing future perspectives and research directions that are critical to effectively prevent or respond to future pandemics.

Exploring the Heterogeneity of IgE-Mediated Food Allergy through Latent Class Analysis.

INTRODUCTION: Food allergy (FA) is a heterogeneous disease with multiple morbidities and a huge burden for patients and healthcare systems. Variable manifestations, comorbidities (atopic dermatitis [AD], asthma, and/or allergic rhinitis [AR]), severity (anaphylaxis), and outcomes suggest the existence of different endotypes that cluster analyses may reveal. In this study, we aimed to investigate distinct subgroups among patients with FAs using data from 524 children/adolescents. METHODS: 524 patients with IgE-mediated FA (353 male [67%]; median age 4.4 years [IQR:3.0-6.8]), 354 (68%) had multiple FA. The history of AD, asthma, AR, and anaphylaxis was recorded in 59.4%, 35.5%, 24.2%, and 51.2% of the patients, respectively. Latent class analysis was carried out to distinguish clinical FA phenotypes using five potential markers of allergy severity (single/multiple FA, never/inactive/current asthma and AD, AR, and anaphylaxis). RESULTS: Three distinct phenotypes were identified: (1) multiple FA with eczema and respiratory multimorbidity (42%), (2) multiple FA with persistent eczema (34%), and (3) single FA with respiratory multimorbidity without eczema (24%). Compared with the single FA cluster, the prevalence of AD was significantly higher in multiple FA groups. Cluster 1 had the highest frequency of AR and allergic asthma, and the lowest rate of total tolerance of FA. DISCUSSION: We put forward the hypothesis of underlying pathogenesis according to the clinical phenotypes. While skin barrier defect may play a dominant role in the pathogenesis in Cluster 2, immune dysregulation may be dominant in Cluster 3. In Cluster 1, the most severe group, a combination of both skin barrier defects and immune dysregulation may be responsible for the clinical features.

Four subtypes of childhood allergic rhinitis identified by latent class analysis.

BACKGROUND: Childhood allergic rhinitis (AR) is clinically heterogenous. We aimed to identify distinct phenotypes among children with AR using data-driven techniques and to ascertain their association with patterns of symptoms, allergic sensitization, and comorbidities. METHODS: We recruited 510 children with physician-diagnosed AR, of whom 205 (40%) had asthma. Latent class analysis (LCA) was performed to identify latent structure within the data set using 17 variables (allergic conjunctivitis, eczema, asthma, family history of asthma, family history of allergic rhinitis, skin sensitization to 8 common allergens, tonsillectomy, adenoidectomy). RESULTS: A four-class solution was selected as the optimal model based on statistical fit. We labeled latent classes as: (1) AR with grass mono-sensitization and conjunctivitis (n = 361, 70.8%); (2) AR with house dust mite sensitization and asthma (n = 75, 14.7%); (3) AR with pet and grass polysensitization and conjunctivitis (n = 35, 6.9%); and (4) AR among children with tonsils and adenoids removed (n = 39, 7.6%). Perennial AR was significantly more common among children in Class 2 (OR 5.83, 95% CI 3.42-9.94, p < .001) and Class 3 (OR 2.88, 95% CI 1.36-6.13, p = .006). Mild and intermittent AR symptoms were significantly more common in children in Class 2 compared to those in Class 1. AR was more severe in Class 1 compared to other 3 classes, indicating that upper respiratory symptoms are more severe among children with isolated seasonal rhinitis, than in those with rhinitis and coexisting asthma. CONCLUSION: We have identified 4 phenotypes in school-age children with AR, which were associated with different patterns of clinical symptoms and comorbidities.

Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs.

Advances in cutting-edge technologies such as nano- and biotechnology have created an opportunity for re-engineering existing materials and generating new nano-scale products that can function beyond the limits of conventional ones. While the step change in the properties and functionalities of these new materials opens up new possibilities for a broad range of applications, it also calls for structural modifications to existing safety assessment processes that are primarily focused on bulk material properties. Decades after the need to modify existing risk management practices to include nano-specific behaviors and exposure pathways was recognized, relevant policies for evaluating, and controlling health risks of nano-enabled materials is still lacking. This review provides an overview of current progress in the field of nanobiotechnology rather than intentions and aspirations, summarizes long-recognized but still unresolved issues surrounding materials safety at the nanoscale, and discusses key barriers preventing generation and integration of reliable data in bio/nano-safety domain. Particular attention is given to nanostructured materials that are commonly used in biomedical applications.

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity.

The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from in vitro studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components.