Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

Disruption of phosphofructokinase activity and aerobic glycolysis in human bronchial epithelial cells by atmospheric ultrafine particulate matter

Exposure to ambient ultrafine particulate matter (UPM) causes respiratory disorders; however, the underlying molecular mechanisms remain unclear. In this study, we synthesized simulated UPM (sUPM) with controlled physicochemical properties using the spark-discharge method. Subsequently, we investigated the biological effects of sUPM using BEAS-2B human bronchial epithelial cells (HBECs) and a mouse intratracheal instillation model. High throughput RNA-sequencing and bioinformatics analyses revealed that dysregulation of the glycolytic metabolism is involved in the inhibited proliferation and survival of HBECs by sUPM treatment. Furthermore, signaling pathway and enzymatic analyses showed that the treatment of BEAS-2B cells with sUPM induces the inactivation of extracellular signal-regulated kinase (ERK) and protein kinase B (PKB, also known as AKT), resulting in the downregulation of phosphofructokinase 2 (PFK2) S483 phosphorylation, PFK enzyme activity, and aerobic glycolysis in HBECs in an oxidative stress-independent manner. Additionally, intratracheal instillation of sUPM reduced the phosphorylation of ERK, AKT, and PFK2, decreased proliferation, and increased the apoptosis of bronchial epithelial cells in mice. The findings of this study imply that UPM induces pulmonary toxicity by disrupting aerobic glycolytic metabolism in lung epithelial cells, which can provide novel insights into the toxicity mechanisms of UPM and strategies to prevent their toxic effects.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Integrating structure annotation and machine learning approaches to develop graphene toxicity models

Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.

Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment

The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drugs, food, water treatment, and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity, and (epi)genotoxicity. In addition, new technologies, such as organs-on-chips and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of nanomaterials from their production to their final disposal chain is completed using the life cycle assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review, we give an overview of all these elements of nanosafety.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

N-Acetylcysteine Alleviated the Deltamethrin-Induced Oxidative Cascade and Apoptosis in Liver and Kidney Tissues

Deltamethrin (DLM) is a synthetic pyrethroid with anti-acaricide and insecticidal properties. It is commonly used in agriculture and veterinary medicine. Humans and animals are exposed to DLM through the ingestion of polluted food and water, resulting in severe health issues. N-acetylcysteine (NAC) is a prodrug of L-cysteine, the precursor to glutathione. It can restore the oxidant-antioxidant balance. Therefore, this research aimed to examine whether NAC may protect broiler chickens against oxidative stress, at the level of biochemical and molecular alterations caused by DLM intoxication. The indicators of liver and kidney injury in the serum of DLM-intoxicated and NAC-treated groups were examined. Furthermore, lipid peroxidation, antioxidant markers, superoxide dismutase activity, and apoptotic gene expressions (caspase-3 and Bcl-2) were investigated. All parameters were significantly altered in the DLM-intoxicated group, suggesting that DLM could induce oxidative damage and apoptosis in hepato-renal tissue. The majority of the changes in the studied parameters were reversed when NAC therapy was used. In conclusion, by virtue of its antioxidant and antiapoptotic properties, NAC enabled the provision of significant protection effects against DLM-induced hepato-renal injury.

Nanoparticle-Based Drug Delivery System: The Magic Bullet for the Treatment of Chronic Pulmonary Diseases

Chronic pulmonary diseases encompass different persistent and lethal diseases, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), cystic fibrosis (CF), asthma, and lung cancers that affect millions of people globally. Traditional pharmacotherapeutic treatment approaches (i.e., bronchodilators, corticosteroids, chemotherapeutics, peptide-based agents, etc.) are not satisfactory to cure or impede diseases. With the advent of nanotechnology, drug delivery to an intended site is still difficult, but the nanoparticle's physicochemical properties can accomplish targeted therapeutic delivery. Based on their surface, size, density, and physical-chemical properties, nanoparticles have demonstrated enhanced pharmacokinetics of actives, achieving the spotlight in the drug delivery research field. In this review, the authors have highlighted different nanoparticle-based therapeutic delivery approaches to treat chronic pulmonary diseases along with the preparation techniques. The authors have remarked the nanosuspension delivery via nebulization and dry powder carrier is further effective in the lung delivery system since the particles released from these systems are innumerable to composite nanoparticles. The authors have also outlined the inhaled particle's toxicity, patented nanoparticle-based pulmonary formulations, and commercial pulmonary drug delivery devices (PDD) in other sections. Recently advanced formulations employing nanoparticles as therapeutic carriers for the efficient treatment of chronic pulmonary diseases are also canvassed.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Comparative studies on regional variations in PM2.5 in the induction of myocardial hypertrophy in mice

Exposure to fine particulate matter (PM_2.5) has been indicated to be related to an increased risk of cardiovascular diseases (CVDs) in sensitive people. However, the underlying mechanisms of PM_2.5-induced CVDs are poorly understood. In the present study, PM_2.5 samples were collected during winter from four cities (Taiyuan, Beijing, Hangzhou, and Guangzhou) in China. Ten-month-old C57BL/6 female mice were exposed to PM_2.5 suspension at a dosage of 3 mg·kg^-1 (b. w.) every other day for 4 weeks by oropharyngeal aspiration. PM_2.5 from Taiyuan increased the blood pressure and the thicknesses of the left ventricular anterior and posterior walls, decreased the ratio of nucleus to cytoplasm in cardiomyocytes and reduced the systolic function of the heart in mice. Further investigation revealed that PM_2.5 from Taiyuan induced lung inflammatory cytokines with up-regulated expressions of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). The mRNA expression levels of myocardial hypertrophy markers atrial natriuretic peptide and the β isoform of myosin heavy chain (ANP and β-MHC), matrix metalloproteinase 2 (MMP2), MMP9, and inflammatory cytokines TNF-α and IL-6 in the myocardium were significantly increased after exposure to PM_2.5 of Taiyuan. Furthermore, PM_2.5 from Taiyuan activated the IL-6/JAK2/STAT3/β-MHC signaling pathway in the myocardium. The correlation between the PM_2.5 components and myocardial hypertrophy markers suggested that Zinc (Zn) and acenaphthene (AC) are related to the changes in ANP and β-MHC at the transcriptional level, respectively. The above results indicated that PM_2.5 exposure induced myocardial hypertrophy in older mice, which might be related to the critical contributions of Zn and AC in PM_2.5. The present study provides new insights into the mechanism of myocardial hypertrophy after PM_2.5 exposure.

Elucidation of the Critical Role of Core Materials in PM2.5-Induced Cytotoxicity by Interrogating Silica- and Carbon-Based Model PM2.5 Particle Libraries

An insoluble core with adsorbed pollutants constitutes the most toxic part of PM_2.5 particles. However, the toxicological difference between carbon and silica cores remains unknown. Here, we employed 32-membered carbon- and silica-based model PM_2.5 libraries that each was loaded with four toxic airborne pollutants including Cr(VI), As(III), Pb²⁺, and BaP in all possible combinations to explore their contributions to cytotoxicity in normal human bronchial cells. The following three crucial findings were revealed: (1) more adsorption of polar pollutants in a silica core (such as Cr(VI), As(III), and Pb²⁺) and nonpolar ones in a carbon core (such as BaP); (2) about 41% more cell uptake of carbon- than silica-based particles; and (3) about 59% less toxicity in silica- than carbon-based particles when pollutants other than Cr(VI) were loaded. This was reversed after Cr(VI) loading (silica particles were 56% more toxic). The difference maker is that compared to stable silica, carbon particles reduce Cr(VI) to less toxic Cr(III). Our findings highlight the different roles of carbon and silica cores in inducing health risks of PM_2.5 particles.

Exploring the liver fibrosis induced by deltamethrin exposure in quails and elucidating the protective mechanism of resveratrol

Deltamethrin (DLM) is widely used in agriculture and the prevention of human insect-borne diseases. However, the molecular mechanism of DLM induced liver injury remains unclear to date. This study investigated the potential molecular mechanism that DLM induced liver fibrosis in quails. Japanese quails received resveratrol (500 mg/kg) daily with or without DLM (45 mg/kg) exposure for 12 weeks. Histopathology, transmission electron microscopy, biochemical indexes, TUNEL, quantitative real-time PCR, and western blot analysis were performed. DLM exposure induced hepatic steatosis, oxidative stress, inflammation, and apoptosis. Most importantly, the Nrf2/TGF-β1/Smad3 signaling pathway played an important role on DLM-induced liver fibrosis in quails. Interestingly, the addition of resveratrol, an Nrf2 activator, alleviates oxidative stress and inflammation response by activating Nrf2, thereby inhibits the liver fibrosis induced by DLM in quails. Collectively, these findings demonstrate that chronic exposure to DLM induces oxidative stress via the Nrf2 expression inhibition and apoptosis, and then results in liver fibrosis in quails by the activation of NF-κB/TNF-α and TGF-β1/Smad3 signaling pathway.

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

Modern nanotechnology research has generated numerous experimental data for various nanomaterials. However, the few nanomaterial databases available are not suitable for modeling studies due to the way they are curated. Here, we report the construction of a large nanomaterial database containing annotated nanostructures suited for modeling research. The database, which is publicly available through http://www.pubvinas.com/, contains 705 unique nanomaterials covering 11 material types. Each nanomaterial has up to six physicochemical properties and/or bioactivities, resulting in more than ten endpoints in the database. All the nanostructures are annotated and transformed into protein data bank files, which are downloadable by researchers worldwide. Furthermore, the nanostructure annotation procedure generates 2142 nanodescriptors for all nanomaterials for machine learning purposes, which are also available through the portal. This database provides a public resource for data-driven nanoinformatics modeling research aimed at rational nanomaterial design and other areas of modern computational nanotechnology.